Miracle-dz/UniFAR-FTUs huggingface dataset:
The naturalistic approach to laughter in humans and other animals: towards a unified theory

This opinion piece aims to tackle the biological, psychological, neural and cultural underpinnings of laughter from a naturalistic and evolutionary perspective. A naturalistic account of laughter requires the revaluation of two dogmas of a longstanding philosophical tradition, that is, the quintessential link between laughter and humour, and the uniquely human nature of this behaviour. In the spirit of Provine's and Panksepp's seminal studies, who firstly argued against the anti-naturalistic dogmas, here we review compelling evidence that (i) laughter is first and foremost a social behaviour aimed at regulating social relationships, easing social tensions and establishing social bonds, and that (ii) homologue and homoplasic behaviours of laughter exist in primates and rodents, who also share with humans the same underpinning neural circuitry. We make a case for the hypothesis that the contagiousness of laughter and its pervasive social infectiousness in everyday social interactions is mediated by a specific mirror mechanism. Finally, we argue that a naturalistic account of laughter should not be intended as an outright rejection of classic theories; rather, in the last part of the piece we argue that our perspective is potentially able to integrate previous viewpoints—including classic philosophical theories—ultimately providing a unified evolutionary explanation of laughter. This article is part of the theme issue ‘Cracking the laugh code: laughter through the lens of biology, psychology and neuroscience’.
When laughter arrests speech: fMRI-based evidence

Who has not experienced that sensation of losing the power of speech owing to an involuntary bout of laughter? An investigation of this phenomenon affords an insight into the neuronal processes that underlie laughter. In our functional magnetic resonance imaging study, participants were made to laugh by tickling in a first condition; in a second one they were requested to produce vocal utterances under the provocation of laughter by tickling. This investigation reveals increased neuronal activity in the sensorimotor cortex, the anterior cingulate gyrus, the insula, the nucleus accumbens, the hypothalamus and the periaqueductal grey for both conditions, thereby replicating the results of previous studies on ticklish laughter. However, further analysis indicates the activity in the emotion-associated regions to be lower when tickling is accompanied by voluntary vocalization. Here, a typical pattern of activation is identified, including the primary sensory cortex, a ventral area of the anterior insula and the ventral tegmental field, to which belongs to the nucleus ambiguus, namely, the common effector organ for voluntary and involuntary vocalizations. During the conflictual voluntary-vocalization versus laughter experience, the laughter-triggering network appears to rely heavily on a sensory and a deep interoceptive analysis, as well as on motor effectors in the brainstem. This article is part of the theme issue ‘Cracking the laugh code: laughter through the lens of biology, psychology and neuroscience’.
The longitudinal association between coffee and tea consumption and the risk of metabolic syndrome and its component conditions in an older adult population

Abstract The present study aimed to assess the longitudinal associations of coffee and tea consumption with metabolic syndrome and its component conditions in a group of Australian older adults who participated in the Blue Mountains Eye Study (n 2554, mean age: 64 years, 43 % female). Participants’ coffee and tea intake were measured using a validated food frequency questionnaire. Hazard ratios (HRs) over a 10-year period were estimated using Cox hazard regression models adjusting for lifestyle factors. Results showed that coffee consumption was not associated with the incidence of metabolic syndrome, high fasting glucose, high triglycerides, central obesity, high blood pressure and low HDL-cholesterol (HDL-C). Tea consumption was not associated with incidence of metabolic syndrome and the component conditions except for the risk of having low HDL-C, in which a nominally inverse association was observed (multivariate-adjusted HR at 2–3 cups/d: 0⋅48, 95 % CI 0⋅26, 0⋅87, P = 0⋅016; 4 cups/d or more: 0⋅50, 95 % CI 0⋅27, 0⋅93, P = 0⋅029). After stratifying for fruit consumption (Pinteraction between tea and fruit = 0⋅007), consuming four cups of tea per day was nominally associated with lower incidence of metabolic syndrome among those with high fruit consumption (multivariable-adjusted HR: 0⋅44, 95 % CI 0⋅20, 0⋅93, P = 0⋅033). Our results did not support a significant association between tea and coffee consumption and metabolic syndrome. Tea consumption may be associated with a lower risk of having low HDL-C, while high tea and fruit consumption together may be associated with a lower risk of developing metabolic syndrome.
Soil Heavy Metal Content Prediction Based on a Deep Belief Network and Random Forest Model

In order to extract useful information from X-ray fluorescence (XRF) spectra and establish a high-accuracy prediction model of soil heavy metal contents, a hybrid model combining a deep belief network (DBN) with a tree-based model was proposed. The DBN was first introduced into feature extraction of XRF spectral data, which can obtain deep layer features of spectra. Owing to the strong regression ability of the tree-based model, it can offset the deficiency of DBN in prediction ability so it was used for predicting heavy metal contents based on the extracted features. In order to further improve the performance of the model, the parameters of model can be optimized according to the prediction error, which was completed by sparrow search algorithm and the gird search. The hybrid model was applied to predict the contents of As and Pb based on spectral data of overlapping peaks. It can be obtained that R2 of As and Pb reached 0.9884 and 0.9358, the mean square error of As and Pb are as low as 0.0011 and 0.0058, which outperform other commonly used models. That proved the combination of DBN and tree-based model can obtain more accurate prediction results. Graphical Abstract
Ontology-Based Cognitive Service Discovery & Composition

Cloud cognitive computing has received a lot of attention lately especially for tackling real-world problems such as vision, natural language processing, fraud detection, sentiment analysis and speech recognition. This paradigm is based on cloud serverless computing and it provides machine learning based functions to end users. Part of the appeal in adopting this paradigm is its simplicity and the future promises a fast-growing serverless-native ecosystem in which service discovery and composition methods must be provided. However, serverless platforms still lack automated searching methods and the research community’s attention regarding this issue has been limited. In this paper, we propose an ontology-based approach for discovering and composing cognitive functions in serverless platforms in order to automate the searching process and semantically answer users’ requirements. We also carried a set of experiments to verify the correctness and the feasibility of our approach and discuss the influence of cognitive services nature on the outcomes of the discovery and composition approach.
Metrics for machine learning evaluation methods in cloud monitoring systems

During the machine learning pipeline development, engineers need to validate the efficiency of the machine learning methods in order to assess the quality of the made forecast. Due to the wide deployment and implementation of the machine learning models and methods across monitoring systems, the actual scientific problem is the assessment of these methods in the monitoring systems. This research has concluded that the current standard metrics are not sufficient to get the accurate assessment for the used machine learning methods. This research has provided the new complex rating for anomaly detection regarding the use-cases of cloud monitoring systems. The main difference from the standard metrics is that the new approach includes better integration to the business processes, demanding resources, and a critical glance to the false-positive alerts. The new approach might be used in the model assessment in monitoring systems with the similar requirements: Cost-effective use of computing resources Low amount of false-positives Fast detection of anomalies Furthermore, the current research proposes new methods of computation capacity planning for different anomaly detection methods. These methods are not even limited to anomaly detection and could be used as a basis for developing capacity planning for other machine learning techniques and approaches. · Applied computing∼Operations research∼Forecasting · Computer systems organization∼Architectures∼Distributed architectures ∼Cloud computing∼Forecasting · Computing methodologies∼Machine learning
Towards a common understanding of sustainable software development

The underlying principles of Sustainable Software Engineering are a core set of competencies Software Engineers need for defining, building, and running sustainable software applications. However, despite its importance, recently published studies have shown that Software Engineers have not reached a common and clear understanding of Sustainable Software Development (SSD). Consequently, it might be challenging to understand the value of the principles behind sustainability and how Software Engineers should apply them in practice. Therefore, the first step is to promote a field characterization to mitigate such effects to bridge such a gap. This paper reports on qualitative data analysis to leverage the sustainability principles through the support of the Grounded Theory method. We conducted this study using unstructured data retrieved from a systematic mapping study on sustainable software engineering and a survey applied in the software industry. To achieve the principles, we considered five critical dimensions: economic, social, individual, environmental, and technical. The key results are: (i) technical, environmental, and social concerns are present in all software development life-cycle (SDLC) phases; (ii) software engineers should consider sustainability requirements in the early SDLC phases; (iii) there is a need for stakeholder engagement focused on sustainability; (iv) software quality requirements support the development of sustainable software; and (v) sustainable concerns could generate trade-offs in the project. The yielded results might trigger further discussions around the SSD’s underlying principles and concepts and serve as a basis for the research community to identify models, techniques, and tools to support SSD.
Corridor-Walker: Mobile Indoor Walking Assistance for Blind People to Avoid Obstacles and Recognize Intersections

Navigating in an indoor corridor can be challenging for blind people as they have to be aware of obstacles while also having to recognize the intersections that lead to the destination. To aid blind people in such tasks, we propose Corridor-Walker, a smartphone-based system that assists blind people to avoid obstacles and recognize intersections. The system uses a LiDAR sensor equipped with a smartphone to construct a 2D occupancy grid map of the surrounding environment. Then, the system generates an obstacle-avoiding path and detects upcoming intersections on the grid map. Finally, the system navigates the user to trace the generated path and notifies the user of each intersection's existence and the shape using vibration and audio feedback. A user study with 14 blind participants revealed that Corridor-Walker allowed participants to avoid obstacles, rely less on the wall to walk straight, and enable them to recognize intersections.
Galactic Dynamos and Density Wave Theory

A steady density wave in the stellar background of a disc-like galaxy is supposed to force a spiral shock wave in the interstellar gas, yielding a corresponding spiral form for the crucial α-effect term in standard dynamo theory. The thin-disc approximation to the dynamo equations is extrapolated to apply to cases with strong departures from axisymmetry. The equations in truncated form are studied both by WKBJ methods and by numerical solution.
The Influence of Transparency and Control on the Willingness of Data Sharing in Adaptive Mobile Apps

Today, adaptive mobile applications use mobile sensing and user tracking, allowing for adaptation to the users' context and needs. This raises several privacy concerns. Privacy dashboards provide transparency and sharing control; however, their impact on the users' behavior is unclear. To shed light on the effects of (a) transparency and (b) control features, we developed a mobile sensing privacy dashboard and evaluated it in the wild (N=227). We found that the pure presentation of raw logging data is rather deterring, and users tend to use the app less, but offering the user control over the data collection can compensate for that. Users used the control features rarely and, as such, did not affect the data collocation. Our work informs the design of future privacy-enhancing interfaces in applications relying on passively collected mobile sensing data. Moreover, our results encourage the adoption of privacy dashboards in the applications and relieve developers from concerns about the negative influences of transparency information on the quality of collected data.
Code Smell Co-occurrences: A Systematic Mapping

Code smells co-occurrences, i.e., occurrences of more than one code smell in the same class or method, can be better indicators of design problems for software quality. Despite its importance as an indicator of design problems, we have little known about the impact of removing the smells co-occurrence via software refactoring on internal quality attributes, such as coupling, cohesion, complexity, and inheritance. There are several literature reviews on code smells and refactoring. However, we did not identify any literature review investigating the impact of co-occurring code smells in primary studies. Thus, this work presents a systematic mapping of code smell co-occurrences state of the art. To guide the mapping, we defined the following research questions: (i) Which code smells are most tend to co-occur? ; (ii) How are code smells co-occurrences refactored? ; (iii) What is the impact of code smell co-occurrences on internal quality attributes? ; (iv) Which datasets are considered to analyze code smells co-occurrences? ; (v) How are code smells co-occurrences detected? ; and, (vi) What are the most common code smells co-occurrences? With main findings, we identified that the code smells that most co-occur are: Feature Envy (19 co-occurrences), Long Method (14 co-occurrences), Long Parameter List (11 co-occurrences) and God Class (11 co-occurrences). No studies were found to answer how the code smells co-occurrences are refactored and the co-occurrences that most impacted the quality attributes, all of which were negative. Only three datasets were considered in the studies to analyze the code smell co-occurrences. Manual detection of co-occurrences is the most used. The co-occurrences Class Data Should Be Private + Feature Envy and Feature Envy + Long Method were the most common co-occurrences in the studies.
Design and Implementation of Hardware and Peripheral System for IoT Gateway

This paper uses Linkit Smart 7688 Duo development board developed by MediaTek as the core of the overall IoT gateway system design, which compares other development boards and IoT gateways on the market. Smart 7688 Duo development board is about 40% lower in price than Raspberry Pi, and the hardware CPU clock is higher than NEXCOM NIO 51 gateway. This implementation provides a low-cost and highly customizable solution that allows system integrators to more effectively provide their customers with the most appropriate IT services. The proposed IoT gateway design utilizes the Linkit Smart 7688 Duo with MIPS and MCU dual-core chip, Arduino development environment and industrial protocol Modbus to design the data transfer of each sensor in the peripheral system. The IoT gateway obtains the sensor data and transmits it to the server using Message Queuing Telemetry Transport (MQTT). The data acquisition accuracy of the developed MCU program was measured with 2 sensors of hydrogen sulfide (H2S) and methane (CH4). The overall system architecture and peripheral systems are designed to realize the IoT gateway taking into account the internal heat dissipation and module wiring, and also the appearance of the chassis is designed to carry the IoT gateway system, so as to achieve a high-quality product prototype that is accurate, economical, and customizable.
Phase-based Cepstral features for Automatic Speech Emotion Recognition of Low Resource Indian languages

Automatic speech emotion recognition (SER) is a crucial task in communication-based systems, where feature extraction plays an important role. Recently, a lot of SER models have been developed and implemented successfully in English and other western languages. However, the performance of the traditional Indian languages in SER is not up to the mark. This problem of SER in low-resource Indian languages mainly the Bengali language is dealt with in this paper. In the first step, the relevant phase-based information from the speech signal is extracted in the form of phase-based cepstral features (PBCC) using cepstral, and statistical analysis. Several pre-processing techniques are combined with features extraction and gradient boosting machine-based classifier in the proposed SER model. Finally, the evaluation and comparison of simulation results on speaker-dependent, speaker-independent tests are performed using multiple language datasets, and independent test sets. It is observed that the proposed PBCC features-based model is performing well with an average of 96% emotion recognition efficiency as compared to standard methods.
Diversity and Inclusion Initiatives in Brazilian Software Development Companies: Comparing the Perspectives of Managers and Developers

The search for diversity and inclusion (D&I) in Software Development companies around the world is becoming more common. This change is due both to the understanding of the need to include underrepresented groups, but also to the use of diversity as a business tool to improve the image and even increase performance and profit. The process of adapting the work environment to a diverse and inclusive context requires an effort beyond prospecting people from underrepresented groups. Such effort can range from an analysis of who makes up the company and which groups need attention, training of leaders and even structural changes in the physical work environment. This paper seeks to bring an analysis of the perception of D&I in companies, both on the part of developers and management, collecting opinions, practices and problems through qualitative research. As a result of the present study, we see that companies have an interest in improving the work environment and have put an effort to achieve it. However, when we contrast the management perspective with the point of view that developers have, we realize that some points go unnoticed and do not receive attention. It causes underrepresented groups to end up not having their concerns discussed within the companies. Through this study, it is possible to understand some obstacles and failures that some companies are committing in their attempt to improve D&I.
Prediction of Metabolic Syndrome based on Non-invasive Measurement Features for Chronic Disease Management

Metabolic syndrome is a chronic disease in which metabolism-related abnormal factors are complex. Metabolic syndrome is currently diagnosed by determining the number of abnormal factors based on physical measurements and blood tests. Metabolic syndrome causes complications in patients with diabetes or the cardiovascular system; therefore, prevention and management are particularly important. Metabolic syndrome can be prevented effectively by allowing individuals to manage it themselves; however, blood tests are a major obstacle to the public. Therefore, this study is conducted to devise a method that can easily predict metabolic syndrome without requiring a blood test. A dataset containing data of 69,944 adult Korean men and women is used to develop a predictive model. This dataset contains not only physical measurements and blood test results, but also life logs pertaining to diet, food intake, drinking, and smoking. Using these data, we identify features that contribute significantly to the prediction of metabolic syndrome, except for items associated with blood test. Finally, we propose a predictive model that allows the public to easily manage metabolic syndrome using only non-invasive factors. Furthermore, we investigate methods to improve the predictive performance from the perspective of four subgroups, based on waist circumference and blood pressure.
Towards Mobile UX Evaluation and Improvement in Startups: A Case Study on the Adoption of User Experience Questionnaire and Hierarchical Task Analysis

Beyond focusing on entire markets rather than developing tailor-made software for specific customers, startups with business models based on digital products for mobile applications leverage the relevance of mobile User eXperience (UX) to reflect the software quality and success of scalable businesses. However, due to the dynamic and uncertain market nature, it is usually challenging for startups to balance a fast-paced product delivery with a systematic UX evaluation and improvement process. Even with the recognized relevance of academia and the software industry, there is still a lack of empirical research addressing mobile UX in startups. Given this opportunity, our research aims to investigate the perception of the team of a startup in a scale-up stage on the adoption of the User Experience Questionnaire and the Hierarchical Task Analysis for evaluating and improving mobile UX. To this end, we approached a qualitative methodological path in the form of a single and instrumental case study. As for the results, we obtained a multi-method and empirical diagnosis of the team’s perception from four analytical perspectives: i) comparison with the previous process, ii) difficulties, iii) lessons learned, and iv) expectations. As a major contribution, we highlight the opportunity to advance in understanding the particularities of evaluation and improvement of mobile UX in the context of startups that work remotely, including the sharing of reflections that may be useful for other companies and researchers.
Addressing Waste Separation With a Persuasive Augmented Reality App

Separating and recycling waste is an important topic to protect our environment and achieve a more sustainable future. However, recycling also is a complex process, as each type of waste needs a specific recycling method. This comes along with multiple recycling containers, each relevant for one specific type of waste. Ensuring a correct recycling process therefore not only requires specific infrastructure, but also a respective attitude and knowledge of the population. Stressing the need for an accessible educational opportunity addressing waste separation, we present a mobile Augmented Reality (AR) application that guides a user through a recycling process and hence scaffolds the learning of proper recycling of each type of waste. The app further provides a prototypical implementation of a product scanner, that identifies the waste type based on a marker and assists the recycling on a case-by-case decision. Using self-determination theory as a framework, we integrated gamification elements, aiming for enhanced need satisfaction, motivation and user experience. In a user study, we compared the gamified version to a control version, with both app versions yielding a high acceptance, user experience, and waste separation behavior. This indicates the importance of providing easy-to-use mobile apps allowing for a learning and assistance of proper recycling.