Reviewerly/exHarmony huggingface dataset:
Home Supporting Smart Systems for Elderly People

Smart devices are becoming more and more, over time, familiar tools to our lives. This, coupled with the need to support elderly people in their daily routine and their desire for aging-in-place, has led researchers to explore and develop solutions to support them in their home. Nowadays, it is common for smart devices, smart systems, and wearable devices to assist elderly people carrying out a number of their day-to-day activities, keeping their social lives active, and monitoring their health and well-being. In this chapter, smart devices and systems that have been proposed for supporting the elderly in their home environment will be presented. Special effort was given to present in detail each work, in order to assist anyone, who reads this chapter, to understand not only the historical evolution of the research topic but also the milestones and the breakthrough research results. Also, the technologies that have been used for the development of the respective systems, as well as the techniques for interacting and assisting the elderly, are reported. Concluding, this chapter serves as a survey for every researcher who wants to understand and research in the topic of smart home monitoring for the elders.
Identifying Personas in Online Shopping Communities

Online shopping communities have emerged amid growing social shopping activities and involve user-centered online platforms that encourage user-generated content and interactions, such as reading and writing reviews, rating products, and sharing shopping experiences. However, similar to other online platforms and communities, online shopping communities face challenges to provide tailored content and support appropriate socialization to engage users and encourage individualized contribution within the communities. To provide unique, personalized support for each individual user, this study developed personas in online shopping communities based on their motivation for participation, as well as reading and posting behaviors. Based on the findings from 20 interviews and focus groups with 24 active online shopping community participants, we developed an online survey on MTurk to investigate the characteristics of the personas and received 194 valid responses. Four persona types emerge after the analysis of both the qualitative and quantitative data—Opportunists, Contributors, Explorers, and Followers.
Extended Object Tracking with an Improved Measurement-to-Contour Association

The random hypersurface model is well-suited to describe extended target contours. Its applicability is limited only by the mild assumption that the target contour has to be star convex. Gaussian processes provide a sound way to estimate the contour functions, and the ability to model the contour uncertainty in a detailed way at different contour points. However, the association of measurements to target contour points is not optimal in current implementations using Gaussian Processes and the random hypersurface model. In this work, we provide an improved approach compared to the standard approach. The standard approach projects measurements radially onto the predicted contour. Our approach provides expected measurements matching the physical reality of the measurement process more closely. In addition, we perform the association of the whole batch of measurements to the expected contour measurements at once. Compared to a sequential association of individual measurements, this leads to a better association decision.
Sentiment Analysis Based on Users’ Emotional Reactions About Ride-Sharing Services on Facebook and Twitter

Over the past few years, social networking sites, especially Facebook and Twitter, have emerged as a vital medium to bridge between the suppliers and customers of different products. Ride-sharing service is one of such products greatly influenced by the reactions shared by its users on the social networking platforms. ‘Pathao’ and ‘Uber’ are two widely used and popular ride-sharing services in our country. In this work, we have analyzed the public sentiment about these services. First, for training purpose, we have collected both the positive and negative tweets and Facebook posts, comments, and reviews of the customers from the Facebook and Twitter pages of those riding services. Afterward, a dictionary is created containing positive and negative words to determine whether a review is good or bad. Using those words, we trained three classifiers such as Naive Bayes, Support Vector Machine (SVM) and Decision Tree for classifying the tweets, reviews, and comments into four quality classes: ‘good’, ‘not so good’, ‘bad’, and ‘not so bad’. Among these classifiers, Naive Bayes gives the best performance.
Meta-Analyzing the Writing Process of Structural Language to Develop New Writing Analysis Elements

As the basis of communication, a writer is often identified through their handwriting characteristics. In clinical practice, static elements of handwriting are evaluated and scored, which might be used for subjective judgment in health situations. By investigating the dynamic information in space when writing Hangul, in this study, we present how to analyze Hangul writing characteristics and build new writing analysis elements in the structural language. The ample characters included 14 consonants and 10 vowels. The cloud of line distribution (COLD) method was used to visualize on-stroke characteristics when writing each character. If the written character showed a straight line (the angle of the letter being 0), the feature distribution appeared on the x-axis of the polar domain. If the written character had many kinks (the angle of the letter being −90 or 90), the feature distribution appeared on the polar domain’s y-axis. In-air movement was visualized using principal component analysis (PCA), and typical in-air movement had an annular shape, which might be used as a new element in handwriting analysis. This study shows the possibility of using such a tool for the writing analysis of structural languages.
Deep Random Forests for Small Sample Size Prediction with Medical Imaging Data

Deep neural networks represent the state of the art for computer-aided medical imaging assessment, e.g. lesion detection, organ segmentation and disease classification. While for large datasets their superior performance is a clear argument, medical imaging data is often small and highly heterogeneous. In combination with the typical parameter amount in deep neural networks, this often leads to overfitting and results in a low level of generalization performance. We propose a straight-forward combination of random forests and deep neural networks for superior performance on medical imaging datasets with only small data, and provide an extensive evaluation of survival prediction for metastatic colorectal cancer patients using computed tomography imaging data, with our proposed method clearly outperforming other approaches.
Dynamic Ensemble Selection – Application to Classification of Cutting Tools

In order to improve pattern recognition performance of an individual classifier an ensemble of classifiers is used. One of the phases of creating the multiple classifier system is the selection of base classifiers which are used as the original set of classifiers. In this paper we propose the algorithm of the dynamic ensemble selection that uses median and quartile of correctly classified objects. The resulting values are used to define the decision schemes, which are used in the selection of the base classifiers process. The proposed algorithm has been verified on a real dataset regarding the classification of cutting tools. The obtained results clearly indicate that the proposed algorithm improves the classification measure. The improvement concerns the comparison with the ensemble of classifiers method without the selection.
Extraction Sentiment Analysis Using naive Bayes Algorithm and Reducing Noise Word applied in Indonesian Language

Abstract Sentiment Analysis is now very important and very useful in machine learning technology where a contextual mining of text to identify and extract subjective information in the source, and in helping to understand social sentiment from comments In general, sentiment analysis can be classified into three broad categories namely sentiment positive and negative. One method of machine learning is the Deep Belief Network (DBN). DBN which is included in the Deep Learning method, is by stacking several algorithms with several extraction features that utilize all resources optimally. This research has two points. First, it aims to classify positive, negative, and neutral sentiments for the test data. The following experiments provide a system of sentiment analysis through the naive Bayes algorithm to calculate sentiment and to improve accuracy by reducing noise in words applied in Indonesian language. From this research, a good level of accuracy can be obtained for extending sentiment using 10-Cross Validation resulting in an accuracy rate of 78.33% with an increase of 6.66% for systems that do not use stopwords, which means reducting noise words and the use of the naïve bayes classification method can be used to determine analysis sentiment on Indonesian language text.
When Agile Means Staying: A Moderated Mediated Model

The design of software development methods focuses on improving task processes, including accommodating changing user requirements and accelerating product delivery. However, there is limited research on how the use of different software development methods impacts IT professionals’ perceptions of organizational mobility. Drawing on concepts from the agile development literature and job characteristics theory, we formulate a moderated mediation model explicating the mechanism and the condition under which agile development use exerts an influence on IT professionals’ intention to stay with their current employer. Specifically, we examine job satisfaction as mediating the effect of using agile development on the intention to stay as well as how the strength of the mediated relationship differs across firms. We test our hypotheses using a sample of 32,389 software developers. We find that job satisfaction fully mediates the effect of using agile development on the intention to stay. The strength of the mediation effect is significantly different for large and small firms.
Immersive Virtual Reality for Learning Experiences

The emergence of immersive virtual reality systems, which offer virtual environments of high interactivity for the user, become attractive to be incorporated into the classroom because they generate motivation in the students and facilitate the tasks that lead to a better representation of spatial knowledge. Virtual worlds are an excellent means of experimental learning, especially to replace real contexts that are impossible to use due to time or space restrictions, or that are unsafe for a student to address. Neuroeducation experts believe that virtual reality technology is promising for its ability to create 3D scenes that allow students to generate vivid and emotional experiences.
Multi-tenant Quality Attributes to Manage Tenants in SaaS Applications

A Software-as-a-Service offering provides ready to use solutions for its tenants. Tenants having different quality requirements are deployed on separate dedicated instances. The concerns related to generating variable quality responses using single instance are not explicitly handled in current design approaches. A lack of standardized tactics and design guidelines also make it difficult for an architect to embed these multi-tenant design decisions at the early stage. In this work, we highlight a few domain-independent architectural concerns that can be used to manage multiple heterogeneous tenants on a shared application instance. We derive some quality requirements that are significant in the multi-tenant scenario, related tactics, measurements and analyze the impact on other software product quality attributes.
Semi-recursive kernel conditional density estimators under random censorship and dependent data

In this work, we extend to the case of the strong mixing data the results of Khardani and Semmar. A kernel-type recursive estimator of the conditional density function is introduced. We study the properties of these estimators and compare them with Rosemblatt’s nonrecursive estimator. Then, a strong consistency rate as well as the asymptotic distribution of the estimator are established under an α-mixing condition. A simulation study is considered to show the performance of the proposed estimator.
Decoherence attenuation in the Tavis-Cummings model via transition frequency modulation with dipole–dipole interaction and multi-photon transitions

Producing and protecting the coherence and entanglement is of practical importance in quantum information processing and technologies. In this paper, using the Tavis-Cummings model we investigate the interaction between two two-level atoms with a reservoir via applying qubit transition frequency modulation and considering dipole–dipole interaction and multi-photon transitions in the Markovian and non-Markovian regimes. After introducing the model as well as its proper Hamiltonian, via the time-dependent Schrödinger equation we obtain the probability amplitudes of the system state vector, by which we analyze the coherence properties by considering different involved parameters. Generally, it will be observed that transition frequency modulation prevents the attenuation of coherence of two-qubit system. In particular, we find that, in the non-Markovian regime, by applying the frequency modulation in a certain range of dipole–dipole interaction strength, the amount of coherence is reduced; however, by increasing the dipole–dipole coupling, the degree of coherence is increased. Also, for single- and two-photon transitions, comparing with four-photon case, decoherence occurs in longer times.
SMT‐based cube attack on round‐reduced Simeck32/64

In this study, the authors take advantage of feeding the SMT solver by extra information provided through middle state cube characteristics to introduce a new method which they call SMT-based cube attack, and apply it to improve the success of the solver in attacking reduced-round versions of Simeck32/64 lightweight block cipher. The key idea is to search for and utilise all found middle state characteristics of a cube at one round of attack. They first propose a new algorithm to find cubes with most number of middle state characteristics. Then, they apply these obtained cubes and their characteristics as extra information in the SMT definition of the cryptanalysis problem, to evaluate its effectiveness. Their cryptanalysis results in a full key recovery attack by 64 plaintext/ciphertext pairs on 12 rounds of the cipher in just 122.17 s. This is the first algebraic attack so far presented against the reduced-round versions of Simeck32/64, and also with practical complexities. They also conduct the cube attack on the Simeck32/64 to compare with the SMT-based cube attack. The results indicate that the proposed attack is more powerful than the cube attack.
Multi spectral classification and recognition of breast cancer and pneumonia

Abstract According to the Google I/O 2018 key notes, in future artificial intelligence, which also includes machine learning and deep learning, will mostly evolve in healthcare domain. As there are lots of subdomains which come under the category of healthcare domain, the proposed paper concentrates on one such domain, that is breast cancer and pneumonia. Today, just classifying the diseases is not enough. The system should also be able to classify a particular patient’s disease. Thus, this paper shines the light on the importance of multi spectral classification which means the collection of several monochrome images of the same scene. It can be proved to be an important process in the healthcare areas to know if a patient is suffering from a specific disease or not. The convolutional layer followed by the pooling layer is used for the feature extraction process and for the classification process; fully connected layers followed by the regression layer are used.
Understanding Software Systems through Interactive Pattern Detection

Architectural and design patterns are the building blocks of modern software systems. Understanding patterns applied in these systems is critical to comprehend their architecture. Current research is not adequately addressing either the architecture recovery and comprehension problem nor the pattern detection problem. What makes it more complicated is the gaining popularity of new language and multiple languages, which makes existing pattern recognition techniques infeasible. Our vision is to allow developers to interactively understand the complexity of a software system by grasping the key abstractions of the system and extracting the associated design spaces and patterns. The long-term objective is to create an interactive and language-independent pattern-oriented architecture recovery framework. We propose to standardize the design structure framed by major patterns, against which the user can detect the variation of design patterns and potential violations.
Building Character Graphs and Dividing Communities in Chinese Novels Based on Graph Data Extraction: Community Division for Character Emotional Polarity Networks

Chinese authors indirectly portray real social relationships in society by incorporating their own experiences and feelings about politics, economic life, and cultural habits. Social and natural knowledge are mixed and hidden in natural literary language. Therefore, by well defining the type of original text, the pipeline for processing the text and the rules to build the graph, we can extract enough valid and useful information. Based on higher-level information, we can build a literary character graph to promote computers' comprehension of literature. The identification of opposite characters is a challenging topic because the character relationships are intricate and complex. The cryptic expression in literary works can enhance the readability of the plot, but it is obvious that it increases the cost of understanding. Even for human readers different types of characters are difficult to classify and identify, and it usually needs careful reading for several times to get the subtle differences among the characters. Though extracting knowledge from Chinese literature is a complicated and difficult topic, this paper models the graph of polar literary characters and divides polar communities by extracting polar vertices and polar edges based on the concept of literary sentiment polarity. The results show that using a long-sentence window is a good trade-off. The experiments of modern Chinese polar literature show that the accuracy of the community division method for the integrated graph polarity is obviously better than the method based on the co-occurrence network, and it can automatically match the positive and negative communities as well as build the complete graph structure. It clearly reflects the meaning of literary works from the perspective of polarity. This systematic model describes general standard operation steps for the machine to understand complex Chinese literature. The experiments conducted on seven benchmark Chinese novel datasets demonstrate that the method based on emotional polarity shows a significant improvement compared to baseline performance. Though we use Chinese datasets in this paper, the model and methods are important references for literature analysis and graph extraction in other languages.
ARL-IL CNN for Automatic Facial Expression Recognition of Infants under 24 Months of Age

Abstract Automatic facial expression coding of infants plays an important role in infants-related applications, including computer-aided ASD diagnosis, automatic intervention for ASD children and diagnosis of ADHD, etc. However, most of existing facial expression researches focused on adult facial expression analysis, the infant facial expression recognition has been less investigated. Due to an age gap between the facial expression datasets of adults and infants, a facial expression recognition model trained on adult datasets usually shows poor generalization to infants datasets. A labeled infant facial expression dataset can mitigate this problem, and hence we first collect a facial expression dataset of 30 infants under 24 months of age by recording videos of infants’ facial expression during a face-to-face mother-infant interaction. Due to infants spontaneous facial behaviors, the dataset covers multiple challenges, such as large head-poses, occlusion, facial expression intensities, etc. To develop an automatic facial expression coding system, we propose a framework consisted of adaptive region learning and island loss, i.e., ARL-IL, to self-adaptively discover facial regions with higher discriminability between different emotion classes. The framework was verified on our collected dataset, and attained a classification accuracy of 86.86%, which has shown better performance than conventional method based on hand-crafted features and some basic CNN architectures. To interpret the effectiveness of ARL-IL, we also visualize the learned features and find that the proposed framework focuses on facial regions with more emotion information compared with other hand-crafted features or learned features from basic CNN architectures. The experimental results show that our proposed framework has robustness to the large head-poses and occlusion.
Time Distribution Based Diversified Point of Interest Recommendation

In location-based social networks (LBSNs), personalized point-of-interest (POI) recommendation helps users mine their interests and find new locations conveniently and quickly. It is one of the most important services to improve users' quality of life and travel. Most POI recommendation systems devoted to improve accuracy, however in recent years, diversity of POI recommendations, such as categorical and geographical diversity, receives much attention because a single type of POIs easily causes loss of users' interest. Different from previous diversity related recommendations, in this paper, we focus on visiting time of POI-a unique attribute of the interaction between users and POIs. Users usually have different active visiting time patterns and different frequently visiting POIs depending on time. If a set of proper visiting times of recommended POIs concentrates on a small range of time, the user might be unsatisfied because they cannot cover whole of the user's active time range that results in inappropriateness for the user to visit those POIs. To solve this problem, we propose a new concept-time diversity and a time distribution based recommendation method to improve time diversity of recommended POIs. Our experimental result with Gowalla dataset shows our proposed method effectively improves time diversity 25.9% compared with USG with only 7.9% accuracy loss.
Turn and orientation Sensitive A* for Autonomous Vehicles in Intelligent Material Handling Systems

Autonomous mobile robots are taking on more tasks in warehouses, speeding up operations and reducing accidents that claim many lives each year. This paper proposes a dynamic path planning algorithm, based on $\mathrm{A}^{*}$ search method for large autonomous mobile robots such as forklifts, and generates an optimized, time-efficient path. Simulation results of the proposed turn and orientation sensitive $\mathrm{A}^{*}$ algorithm show that it has a 94% success rate of computing a better or similar path compared to that of default $\mathrm{A}^{*}$. The generated paths are smoother, have fewer turns, resulting in faster execution of tasks. The method also robustly handles unexpected obstacles in the path.