Reviewerly/exHarmony huggingface dataset:
Non-Parametric Learning Technique for Activity Recognition in Elderly Patients

To improve the generic lifestyle of a patient, remote monitoring has become an essential part of patient assistance in any health care services. Patient falls in nursing homes and hospitals are a major source of concern in any health care services. To mitigate this problem and to reduce falls, improved surveillance system which helps in activity recognition in a patient is the need of the day. This work proposes an activity recognition system using Grey Relational Analysis which considers readings from lightweight wearable sensors to detect activities in patients. To verify the feasibility of the proposed work, it has been applied on a real life data. It can be seen from the simulation results that the proposed methodology shows competitively improved results than some of the existing contemporary machine learning techniques.
Circlet transform in cell and tissue microscopy

Automatic detection of objects with circular pattern in digital images is an important topic in many fields of research especially in cell imaging. There are many cells in microscopic images that are circular such as Red Blood Cells (RBCs), White Blood Cells, hematopoietic cells and different types of parasite eggs. Automatic detecting, recognizing and quantifying of these cells provide rich information to pathologists to improve the study/diagnosis of different diseases. Many previously proposed methods utilize the edge information of a given image to detect circles that are not usually applicable for complex images. Fast Circlet Transform (FCT) is a new atomic representation based on using circular basis functions in different scales and frequencies which provides a novel and practical tool for circle detection and analysis of images with circular objects/patterns such as microscopic images. In this paper, three strategies based on FCT are proposed with the applications of FCT in cell and tissue microscopy as follows: In first application, a strategy is proposed for detecting and counting RBCs in microscopic images of blood smear in which an initial estimation of the number of RBCs is made and conflict circles are then removed to detect final and true RBCs. In second application, an algorithm is proposed to count and localize glomeruli in microscopic images of kidney sections by analyzing FCT coefficients in order to directly find circular objects. In third application, a method based on FCT is proposed to detect parasites in microscopic images with high unwanted impurities by modifying FCT coefficients and reconstructing the images. Our experimental results show the effectiveness and better performance of the proposed circlet-based methodologies in microscopic image analysis.
Performance enhancement for iterative data computing with in‐memory concurrent processing

Summary The big data era has resulted in the development of several data analysis tools. Spark is a type of in‐memory processing fitted iteration and interactive data mining tool. This tool possesses higher data‐processing performance than MapReduce, which is an offline storage mechanism. However, some disadvantages of in‐memory processing, such as massive in‐memory data requirements, cause cross‐node data transfer that result in a long computation time. The performance of the process can be improved if the in‐memory process is executed with fewer shuffle instructions. Therefore, this study aims to enhance the performance of iterative application through instruction replacement. Three empirical research cases with diverse datasets and iterations are used to modify the program. We adopt a strategy of downloading a small resilient distributed dataset and replacing the shuffle‐included instructions to shorten the processing time with an automated code replacement by using exhaustively code matching. The experimental results reveal an improvement of up to 39% in the execution time compared with the existing in‐memory processing programs with various dataset sizes.
Intelligent Platform for Visually Impaired Children for Learning Indoor and Outdoor Objects

Using Artificial Intelligence and Computer Vision to assist Visually Impaired personal has been a topic discussed in recent years. Many researchers are focusing on combining several technologies to assist said individuals to perform day to day tasks. Although there are already many technologies being used as platforms to help these individuals, focus put on children who are aged in between 6 - 14 years is considerably less. Therefore; in this research we are focusing on how to use latest advancements of Region Based Convolutional Networks (R-CNN), Recurrent Neural Networks (RNN) and Speech models to provide an interactive learning experience to visually impaired children.
Language Modelling for a Low-Resource Language in Sarawak, Malaysia

This paper explores state-of-the-art techniques for creating language models in low-resource setting. It is known that building a good statistical language model requires a large amount of data. Therefore, models that are trained on low-resource language suffer from poor performances. We conducted a study on current language modelling techniques such as n-gram and recurrent neural network (RNN) to observe their outcomes on data from a language in Sarawak, Malaysia. The target language is Iban, a widely spoken language in this region. We have collected news data form an online source to build an Iban text corpus. After normalising the data, we trained trigram and RNN language models and tested on automatic speech recognition data. Based on our results, we observed that the RNN language models did not significantly outperform the trigram language models. A slight improvement on RNN model is seen after the size of the training data was increased. We have also experimented on merging n-gram and RNN language models and we obtained 32.33% improvement after using a trigram-RNN language model.
Introduction: Digital Touch Communication

In this chapter, we make a case for the significance of touch for communication and suggest that developments in sensory digital technologies are bringing touch to the fore in ways that move digital communication beyond ‘ways of seeing’ to include new ‘ways of feeling’. We argue that this shift requires us to take new measure of digitally mediated touch, or ‘digital touch’, as a communicational resource, what it is and can be, how it is designed and imagined, and its communicative potentials and limitations. We situate digital touch communication in relation to a technological awakening to a broader social revaluing of people’s sensorial experience. We introduce and reflect on the socially orientated stance to digital touch we take in this book, and the InTouch project more generally. The chapter provides an overview of the book which also serves to introduce the key themes that it explores, that is the research and technological terrain of digital touch, social norms of touch, presence and connection, sociotechnical imaginaries of digital touch, and the ethics of touch. Finally, we introduce six InTouch case studies which examine digital touch across different contexts, perspectives and participants. We draw illustrative examples from these (alongside extensive engagement with the research literature) in order to enliven and consolidate the book’s exploration of the sociality of digital touch communication across different contexts.
Situations, alternatives, and the semantics of ‘cases’

Abstract This paper argues that NPs with case as head noun stand for situations in their role as truthmakers within a sentential or epistemic case space. The paper develops a unified semantic analysis of case -constructions of the various sorts within a truthmaker-based version of alternative semantics.
Website Services Quality Analysis of PT. Semen Baturaja (Persero) Tbk Toward User Satisfaction by Using Webqual 4.0 Method

Abstract Website of PT. Semen Baturaja (Persero) Tbk is a website that serves as a medium for disseminating information about baturaja cement products. The number of website users of PT. Semen Baturaja (Persero) Tbk currently has reached 54,360 visitors, consisting of employees of PT. Semen Baturaja, the general public and distributors in collaboration with PT Semen Baturaja. Therefore it is necessary to measure the level of user satisfaction on the website of PT. Semen Baturaja (Persero) Tbk. The purpose of this study is to determine the level of user satisfaction toward website services using the webqual 4.0 method. Webqual is one method of measuring website quality based on the perception of end users. The variables of webqual are usability, information quality, service interaction quality and user satisfaction. The results showed that Usability, Information Quality and Service Interaction had an effect on User Satisfaction both simultaneously and partially. So that it can be concluded that usability, information quality, and service interaction quality affect user satisfaction website PT. Semen Baturaja.
BI-NTRU Encryption Schemes: Two New Secure Variants of NTRU

NTRU is one of the first public key cryptosystems not based on factorization or discrete logarithmic problems and is also considered secure even against quantum computer attacks. In 2011, Stehle and Steinfeld proposed a variant of the classical NTRU that is IND-CPA secure but for the key generation algorithm, they use Gaussian distribution with a large standard deviation to prove the uniformity of the public key by assuming the hardness of Ring Learning With Error (Ring-LWE) problem. In this paper, we present two variants of NTRUEncrypt called BI-NTRU-Product and BI-NTRU-LPR which are IND-CPA secure assuming the hardness of Ring-LWE problem. We also show how one can design an IND-CCA2 secure key encapsulation mechanism from our encryption schemes by using a variant of the Fujisaki-Okamoto Transformation (CRYPTO 1999 and Journal of Cryptology 2013).
Modeling of Parallel Data Encryption Algorithms

The article discusses the issues of modeling parallel information encryption algorithms using the residual number system and examples of RSA encryption algorithms, recover numbers and simulate parallel encryption algorithms in Matlab Simulink.The purpose of this article is to increase cryptographic strength by parallel execution of various cryptographic algorithms or parallel use of the same algorithm, but with different keys.
Classification of Self-dual Codes of Length 20 over $$\mathbb {Z}_4$$ and Length at Most 18 over $$\mathbb {F}_2+u\mathbb {F}_2$$

In this paper, we give a precise description of Rains' algorithm for classifying self-dual $$\mathbb {Z}_4$$-codes with a given residue code. We will use this to classify self-dual $$\mathbb {Z}_4$$-codes of length 20. A similar method is used to classify self-dual codes over $$\mathbb {F}_2+u\mathbb {F}_2$$. We will update the table given by Han, Lee and Lee, of the data regarding the classification of self-dual codes over $$\mathbb {F}_2+u\mathbb {F}_2$$.
Engineering design development of 52,5 KiloWatt peak solar photovoltaic system for industrial Rooftop building

Abstract The purpose of this research is to develop on-grid engineering design of 52,5 KW photovoltaic system on Industrial Rooftop Building. The development of solar photovoltaic system is to satisfy high demand of electrical energy for industry, offices, and public with minimum cost. Taking consideration of green energy, solar energy might be chosen as an alternative to generate electric power by utilization of Rooftop on industrial building. This research was conducted using broad database of meteorological data i.e. global daily horizontal solar irradiance. Additionally, Rooftop photovoltaic development, layout plan, photovoltaic array connection, Single Line Diagram and structure support photovoltaic module were developed using Helioscope and PV Syst software. Based on 6 months’ period simulation, annual energy produced on Rooftop is 1043.88 kWh/kWp, Global Horizontal Irradiance (GHI) 1301.96 kWh/m 2 , actual Global Horizontal Irradiance 5.6%, and performance ratio (PR) 80.30 % and capital Rooftop photovoltaic project forecast. This results can be used for industrial and big office in west Indonesian recommendation to develop solar energy system on Rooftop building
Development of Disaster Safety Technologies Through Analysis of Safety Service Trends in Disaster Prevention

The objectives of this study are to identify the possibility of new, future, and complex disasters resulting from environmental changes and corresponding requirements through the analysis of technology trends and safety services for disaster prevention, and to provide the basic data for the development of safety service technologies for people's safety and security. The R&D trends related to disaster safety are technology developments related to 1) search for future and new disasters, 2) integrated management of disasters and safety technology standards, 3) national continuity of operation management for disaster prevention, 4) disaster response, relief, and recovery, and 5) safety and security of vulnerable population. A review of various international cases reveals that appropriate technology development and policy support are needed, and that the role of disaster safety control tower and disaster management institutions is important. Also, integrated safety management technologies for new and future disasters, and development of technologies for large-scale disasters are needed, as is development of technologies for protecting people, including those vulnerable to paralysis of national infrastructure facilities. Keywords: Disaster Prevention, Safety Service Technology, Control Tower, Disaster Management, New and Future Disasters
Human and Organizational Issues for Resilient Communications

Human and organizational issues are able to create both vulnerabilities and resilience to threats. In this chapter, we investigate human and organizational factorsOrganizational factor, conducted through ethnographic studies of operators and sets of interviews with staff responsible for security, reliability and quality in two different organizations, which own and operate utility networks. Ethnography is a qualitative aspect of research that emphasizes the detailed observation and interviewing of people in naturally occurring settings. Our findings indicate that ‘human error’ forms the biggest threat to cyber-security and that there is a need for security operations centres to document all cyber-security incidents. Also, we conclude that it will always be insufficient to assess mental security models in terms of their technical correctness, as it is sometimes more important to know how well they represent prevailing social issues and requirements. As a practical recommendation from this work, we suggest that utility organizations (and others) engage in penetration testing and perhaps other forms of vulnerability analysis, not only to discover specific vulnerabilities but also to learn more about the mental models they use.
Advanced Feature Identification towards Research Article Recommendation: A Machine Learning Based Approach

To guide the researcher in searching right paper in the context of an increasing rate of the digital repository of research articles, research paper recommendation system is being advocated highly. To design such a system, crawling, filtering and ranking are the three crucial steps. For the crawling task, the existing approaches consider only direct features, which can be readily obtained from a given paper. Addressing this limitation, this work proposes a novel scheme to define the feature vector representing an article. The proposed work unleashes three hidden features that can not be readily available for a given paper. The three indirect features derived from the direct features itself (which are readily available from a paper) are based on the measurements of keyword diversification, text complexity and citation analysis over time. The rationale behind the proposition of the three indirect features, metrics and their measurements are discussed in detail in this paper. Experimental results clearly substantiate the efficacy of the proposed feature vector.
Search Engine Optimization Techniques and Tools

Basically, the search engine is used to search for any information on the Internet. The purpose of any website or webpage is to appear on top of the search engine when any user searches for relevant information in the content on their website. There are several factors that affect website ratings and make it down in the search crawler's crop list and some websites do not even come in the top search engine list despite the best content. The art of improving website visibility is known as search engine optimization. Most of times, main focus is set on making the website as user-friendly as possible, stable, fast and secure. But when creating the website, we need to take into consideration an essential factor, which is making the world aware of their website and their content. In this paper, we will survey how various techniques optimize the search result in search engine.
Parameterized Optimization in Uncertain Graphs—A Survey and Some Results

We present a detailed survey of results and two new results on graphical models of uncertainty and associated optimization problems. We focus on two well-studied models, namely, the Random Failure (RF) model and the Linear Reliability Ordering (LRO) model. We present an FPT algorithm parameterized by the product of treewidth and max-degree for maximizing expected coverage in an uncertain graph under the RF model. We then consider the problem of finding the maximal core in a graph, which is known to be polynomial time solvable. We show that the Probabilistic-Core problem is polynomial time solvable in uncertain graphs under the LRO model. On the other hand, under the RF model, we show that the Probabilistic-Core problem is W[1]-hard for the parameter d, where d is the minimum degree of the core. We then design an FPT algorithm for the parameter treewidth.
Adaptive spatially regularized correlation filter tracking via level set segmentation

Correlation filter (CF) has been widely used in visual tracking tasks due to its simplicity and high efficiency. However, the conventional CF-based trackers suffer from boundary effects and object scale changes. To address these problems, we propose an adaptive spatially regularized CF tracking method via level set segmentation (LSS). First, a fast LSS method based on region and boundary information is proposed to accurately estimate the object region. Then an adaptive spatial reliability map is constructed using the estimated object region to regularize the CF, which can significantly mitigate the boundary effects. Meanwhile, the estimated object region is also utilized to construct a series of candidate scales whose aspect ratio is variable, and a novel scale variation (SV) constraint term is proposed to restrain the abnormal scale change. Finally, the position and scale of the object is estimated by maximizing the final response, which combines the CF response, and the histogram response is calculated quickly in frequency domain with the SV constraint term. Experimental results on recent visual tracking benchmark datasets illustrate that the tracking capability of the proposed method is competitive against several state-of-the-art trackers.
Icons For the Mass: An Approach Towards Text Free Smart Interface

A major population of 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> world countries have problems reading out texts because of the low literacy rate. While smartphones have given them a hope to lead a better life, interacting with them needs a certain level of literacy which a majority of them lack. Given the rapid growth of variation in this user base, it is evident that interactive graphical representations can give a better understanding of contents especially to the children and to the illiterate people. This work emphasizes on making the Google Play App Store more usable as this is the most used app for smartphone users. In this study, new icons have been designed and then compared side by side with 16 existing Google Play Store app category icons to initially find out the legitimacy of the new icons. Further, a survey between smartphone users has been conducted and the results show that the new icons fare better while representing app categories. Based on the survey results and more widely accepted icons, some key factors to design icons have been identified.
DeepRank: Adapting Neural Tensor Networks for Ranking the Recommendations

Online real estate property portals are gaining great attraction from masses due to ease in finding properties for rental or sale/purchase. With a few clicks, a real estate portal can display relevant information to a user by ranking the searched items according to user’s specifications. It is highly significant that the ranking results display the most relevant search results to the user. Therefore, an efficient ranking algorithm that takes user’s context is crucial for enhancing user experience in finding real estate properties online. This paper proposes an expressive Neural Tensor Network to rank the properties when searched for based on the similarity between the two property entities. Previous similarity techniques do not take into account the numerous complex features used to define a property. We showed that the performance can be enhanced if the property entities are represented as an average of their constituting features before finding the similarity between them. The proposed method takes into account each feature dynamically and ranks properties according to similarity with an accuracy of 86.6%.
New method for clear day selection based on normalized least mean square algorithm

A new method is proposed to select clear days from data sets of solar irradiation recorded with ground-based instruments. The knowledge of clear days for a given site is of prime importance both for the study of turbidity and for the validation of empirical models of global solar radiation (GSR). Our innovative method is based on the normalized least mean square (NLMS) algorithm that estimates noise according to a GSR model. The developed method named clear day selection method (CDSM) is compared with the well-known clearness index criteria (kt) taking data collected at Tamanrasset in Algeria during the period 2005–2009. The root mean square error (rmse), the mean absolute percentage error (mape), and the dependence of model error (mbe) are considered for the comparison. A different number of clear days is found with both methods, with additionally a kt dependency for the clearness index criteria. The average values of rmse, mape, and mbe between the daily average of the measured GSR and its estimate using a model are better in the case of CDSM for the period 2005–2009. Indeed, we found 25.28 W/m2, 4.61%, and 2.09 W/m2 respectively for CDSM and 42.48 W/m2, 7.63%, and − 5.91 W/m2 for the clearness index method with kt = 0.7. We also found that GSR of clear days is well correlated with the model in the case of CDSM, which gives good confidence in our results.