Miracle-dz/UniFAR-FTUs huggingface dataset:
Can Infrared Facial Thermography Disclose Mental Workload in Indoor Thermal Environments?

Mental workload represents the mental resources an individual devotes to a task. In a building environment, understanding how ambient thermal conditions affect occupants' mental workload offers an opportunity to achieve optimal thermal settings for the heating, ventilation, and air conditioning (HVAC) systems. However, directly measuring mental workload on a large and continuous scale requires occupants to perform subjective tests or wear electroencephalogram (EEG) or similar devices, which is impractical. This paper assesses the feasibility of using infrared facial thermography captured by a low-cost thermal camera to disclose mental workload. An experiment was conducted to measure the facial skin temperature while subjects performed cognitive tasks in three different thermal environments, representing occupants' thermal sensation of slightly cool, neutral, and slightly warm. Mental workload was measured using an EEG headset to eliminate subjective bias. The correlations between facial temperature and mental workload vary with different individuals and thermal conditions. Relatively strong correlations are found in the neutral environment and in the regions of ears, mouth, and neck. The results also suggest that future work should collect data under extended experiment duration. This is because it was observed that the response of facial skin temperature to mental workload varies with task type; thus, increasing the repetitiveness for each type of task or using more challenging tasks in the experiment could potentially lead to more insights on this relationship.
Interact with your car: a user-elicited gesture set to inform future in-car user interfaces

In recent years, stereoscopic 3D (S3D) displays have shown promising results on user experience, for navigation, and critical warnings when applied in cars. However, previous studies have only investigated these displays in non-interactive use-cases. So far, interacting with stereoscopic 3D content in cars has not been studied. Hence, we investigated how people interact with large S3D dashboards in automated vehicles (SAE level 4). In a user-elicitation study (N=23), we asked participants to propose interaction techniques for 24 referents while sitting in a driving simulator. Based on video recordings and motion tracking data of 1104 proposed interactions containing gestures and other input modalities, we grouped the gestures per task. Overall, we can report a chance-corrected agreement rate of k = 0.232 and by that, a medium agreement among participants. Based on the agreement rates, we defined two sets of gestures: a basic and a holistic version. Our results show that participants intuitively interact with S3D dashboards and that they prefer mid-air gestures that either directly manipulate the virtual object or operate on a proxy object. We further compare our results with similar results in different settings and provide insights on factors that have shaped our gesture set.
Liquid slider: evaluating the experience of liquid on a touch input surface

We investigate an unconventional user interface (UI) material, water, and how applying it to a finger-touch based input affects the user experience (UX). We compare the use of touchscreen, mechanical and liquid covered sliders, and three different approaches of how water is applied to the surface of the slider. The salient findings of our user study (n=25) show that liquid changes the perception of the slider control making it more hedonic and experiental. When interacting with water, users prefer larger and more free-form gestures, and factors such as depth and temperature of the liquid affect the experience. Our work provides information for interaction designers focusing on user experience, unconventional materials for interactive systems, aesthetics, and calm UIs.
Smart IoT Notification System for Efficient In-City Parking

This paper presents an intelligent distributed IoT system which was designed to automatically detect, count and notify drivers about empty parking spaces in major cities. This proposed system has two operating phases: (i) continuous detection and counting of empty spots in the monitored far-apart parking lots, and (ii) instantaneous driver notification through a lightweight MQTT publish/subscribe mechanism. Among the numerous features of this notification system is its reliance on only information collected from the preinstalled IP cameras; no other apparatus installation or maintenance such as ground sensors is required. To check the proper operation of our solution, we have implemented a small-scale version of it and assessed its performance while considering different deep learning frameworks. The obtained results confirmed the proper operation and efficiency of our system in terms of notifications. The conducted experiments have also confirmed the ease of deployment and ease of extension of this system.
Optimization of Very Large Scale Capacitated Vehicle Routing Problems

Transportation activity is one of the main contributors to the overall logistics costs of a company. One of the ways to minimize the cost that comes from this activity is through the optimization of the distribution route. However, with the growth of delivery points, the possible routes combinations are growing in a nonlinear fashion, increasing the complexity to obtain the optimal combination of the distribution routes. In this paper, we present a simple modified clarke-wright algorithm with the implementation of heuristic pruning to deal with time and space complexity in a case of very large scale capacitated vehicle routing problem. The resulting algorithm can solve a very large routing problem with several thousand customers within a minute and produce relatively good solutions compared to the state-of-the-art algorithm, especially for the problem with a depot located at the center. Furthermore, the algorithm is also tested to solve a very large scale problem of FMCG company in Indonesia with 32,223 delivery points.
How Data Anonymisation Techniques influence Disease Triage in Digital Health: A Study on Base Rate Neglect

In the digital health area, there is a growing trend towards data-driven disease diagnostics and prescription triage. Discussions with health industry partners have revealed that distributed high-dimensional data repositories are not only helpful to medical & drug research but also for algorithms that support day-to-day medical diagnostics such as detecting Atrial fibrillation (AFib). Yet, recent privacy legislation in Europe requires that such data repositories be anonymised to protect against personal information exposure. Existing anonymisation algorithms work on the premise of transforming data to remove outliers that can result in re-identifications of individual records. While on the one hand this protects against data exposure, on the other hand anonymisation inadvertently results in base rate neglect. In the medical diagnostics context, base rate neglect can lead to false diagnostics and prescription triage, which is undesirable. In this paper, we study the impact of different anonymisation techniques on real-world disease diagnostics, and how they potentially influence decision making based on a real-world case as well as a semi-synthetic health data set. We demonstrate that the best results countervailing base rate neglect and ensuring data anonymity are obtained through the composition of several selected but dynamic per-row assigned anonymisation approaches incorporating attribute compartmentation.
Where Are You Going Next?: A Practical Multi-dimensional Look at Mobility Prediction

Understanding and predicting mobility are essential for the design and evaluation of future mobile edge caching and networking. Consequently, research on human mobility prediction has drawn significant attention in the last decade. Employing information-theoretic concepts and machine learning methods, earlier research has shown evidence that human behavior can be highly predictable. Whether high predictability manifests itself for different modes of device usage, across spatial and temporal dimensions is still debatable. Despite existing studies, more investigations are needed to capture intrinsic mobility characteristics constraining predictability, to explore more dimensions (e.g. device types) and spatiotemporal granularities, especially with the change in human behavior and technology. We investigate practical predictability of next location visitation across three different dimensions: device type, spatial granularity and temporal spans using an extensive longitudinal dataset, with fine spatial granularity (AP level) covering 16 months. The study reveals device type as an important factor affecting predictability. Ultra-portable devices such as smartphones have "on-the-go" mode of usage (and hence dubbed "Flutes"), whereas laptops are "sit-to-use" (dubbed "Cellos"). The goal of this study is to investigate practical prediction mechanisms to quantify predictability as an aspect of human mobility modeling, across time, space and device types. We apply our systematic analysis to wireless traces from a large university campus. We compare several algorithms using varying degrees of temporal and spatial granularity for the two modes of devices; Flutes vs. Cellos. Through our analysis, we quantify how the mobility of Flutes is less predictable than the mobility of Cellos. In addition, this pattern is consistent across various spatio-temporal granularities, and for different methods (Markov chains, neural networks/deep learning, entropy-based estimators). This work substantiates the importance of predictability as an essential aspect of human mobility, with direct application in predictive caching, user behavior modeling and mobility simulations.
Chasing High-performing Police Teams

Purpose: This research aims to add new insights on team performance and related variables in a police work setting. This research attempts to identify mutual relations between leadership style, psychological safety, information sharing, team learning, and team performance in police teams and to identify characteristics of high performing police teams. In practice, this research aims to help police teams to enhance team performance by identifying the roles of the previous named variables. Design/methodology/approach: Survey responses of team members (n = 48) were aggregated at team level (n = 7). Linear regression was performed to test the four hypotheses. Median split method was used to divide the teams in two groups: moderate performing teams and high performing teams. Independent samples t-tests were used to test for significant differences. Data from the interviews with team leaders was analyzed by coding. Findings: Transformational leadership was positively related to psychological safety. Psychological safety predicted information sharing. Team learning was positively related to team learning. High performing teams have a leader that is both transformational and transactional, high psychological safety, a high level of information sharing, team learning, and stability. Discussion: Information sharing did not mediate between psychological safety and team learning. Possibly, this is caused by an information overload in the police teams. This makes it difficult to use and process relevant information. Regularly, there are changes in team composition. Stability is suggested to be important, as it possibly impacts psychological safety, information sharing, team learning, and team performance. Graduation Committee members:
The impact of absenteeism on undergraduates’ academic performance: Evidence from Tunisian English students

The present study explores the impact of classroom attendance on students’ academic performance in the English department at La Manouba Faculté of Letters, Arts, and Humanities (FLAH), Tunisia. In order to assess the link between absenteeism and grades, attendance sheets were used to record rates of class attendance of four groups of second year students during a nine week period. Students’ tests scores were then analyzed statistically in relation to records of class attendance. Pearson’s tests and the Oldinary Least Squares regressions (OLS) performed on the data revealed a negative correlation (r = -0.297) between rates of absenteeism and test scores which implies that absences damage students’ grades. The study brings new evidence on the negative impact of absenteeism on students’ academic performance. Based on the already mentioned empirical evidence, the following implications are made. First, academics should make sure to encourage students to attend classes in order to minimize chances for any negative influence on learning and to provide opportunities for academic achievement. Second, the weak negative correlation revealed in the present study calls for further research on the issue using different regression models.
A Study of Cardiovascular and Adrenocortical Stress Reactivity in Children of Hypertensives.

Background: Stress plays an inevitable role in today’s modern life and it leads to various physiological and pathological reactions through activation of the Limbic-Hypothalamo-PituitaryAdrenal axis and secretion of Cortisol which modulates vascular reactivity. Hypertension is known to be one of the commonest reactions to stress, with increased cardiovascular morbidity and mortality. The disease process starts much earlier than it is manifested clinically, hence it is necessary to find out the persons at risk for hypertension as early as possible and to introduce early lifestyle modifications to prevent them from developing hypertension. Objective: This study assessed and compared the cardiovascular reactivity and adrenocortical reactivity in children aged 14 to 18 years of hypertensive parents (Study group, n=30) with age, sex and BMI matched children of normotensive parents (Control group, n=30) exposed to three different stress tests. Methods: Cardiovascular reactivity in terms of change in heart rate and blood pressure from the baseline and Adrenocortical reactivity in terms of change in serum cortisol level from the baseline were measured after performing three stress tests namely mental arithmetic test, isometric handgrip test and cold pressor test. Results: The mean ± S.D. for change in serum cortisol in the study group (86.5 ± 23.99) was significantly higher (p< 0.05) than that in the control group (69.62 ± 30.92), whereas, the mean ± S.D. for the change in heart rate and blood pressure was not found to be significant between the two groups. Conclusion: Our study showed that there is a significant increase in the adrenocortical reactivity to stress than the cardiovascular reactivity in subjects with family history of hypertension thus warranting early lifestyle modifications and further follow up for monitoring their blood pressure status and hypertension propensity.
Learning to Flip the Sentiment of Reviews from Non-Parallel Corpora

Flipping sentiment while preserving sentence meaning is challenging because parallel sentences with the same content but different sentiment polarities are not always available for model learning. We introduce a method for acquiring imperfectly aligned sentences from non-parallel corpora and propose a model that learns to minimize the sentiment and content losses in a fully end-to-end manner. Our model is simple and offers well-balanced results across two domains: Yelp restaurant and Amazon product reviews.
Comparative Evaluation of HbA1c and Platelet Markers in Type II Diabetic and Non-Diabetic Patients with Chronic Periodontitis Following Non-Surgical Periodontal Therapy- An Interventional Study

Original Research Article Backgroud and Objectives: It has been reported in the literature that scaling and root planing (SRP) may help to improve glycemic control in patients with chronic periodontitis and type 2 diabetes mellitus this in turn may have favorable outcome on various independent variables. The aim of this study was to evaluate if Type II Diabetes mellitus (DM) and NonDM patients, both with chronic periodontal disease, would present changes between timing evaluation, at baseline and at follow up intervals of 3 and 6 months, in periodontal status, HbA1c and Platelet markers (MPV & PDW) after periodontal therapy. Materials and Methods: This comparative, clinical study was performed between type II diabetics and non-diabetics with chronic periodontitis. The study period was 6 months. Conventional periodontal scaling and root planing were performed, and the response to this treatment was compared between the groups at 3 and 6 months measuring plaque index (PI), gingival index (GI), probing pocket depth (PPD), clinical attachment gain (CAL) and blood parameters such as HbA1c, platelet markers such as mean platelet volume (MPV) and platelet distribution width (PDW) were evaluated and compared to baseline at every follow-up visit. Results: An improvement in all clinical variables was observed, with no statistically significant differences between the groups, with the exception of probing depth and clinical attachment level (P < 0.001 * ). The improvement observed in blood HbA1c levels confirmed a positive metabolic response to periodontal treatment, with a lower value for this variable at each measurement time. Furthermore, the results revealed a statistically significant reduction in the platelet volume and a decrease in platelet distribution width at every visit following phase I periodontal therapy (P < 0.001*). Conclusion: Both groups of patients showed a clinical improvement after basic non-surgical periodontal treatment. In addition to that, diabetic and non-diabetic patients showed improved glycemic control and a reduced mean platelet volume and platelet distribution width at 3 and 6 months after periodontal treatment, although the levels in group B never reached the same levels as those of the subjects in group A.
Herding Cats: Firm Non-Compliance in China’s Industrial Energy Efficiency Program

We study firm responses to a large-scale energy efficiency program in China, focusing on the quality of reporting and compliance outcomes. Using statistical methods to detect data manipulation in compliance reports, we find evidence that firms deliberately exaggerated performance during the first phase of the program (2006-2010), suggesting the high compliance rate was overstated. In its second phase (2011-2015), the number of firms in the program expanded by an order of magnitude, and the compliance rate decreased. We develop a simple model to show how the observed increase in non-compliance is consistent with reduced misreporting. Statistical tests find no evidence of manipulation in the second phase. Larger firms, especially those not controlled by the state, and firms in cities with relatively low growth were more likely to report non-compliance, which suggests a role for state control and local protectionism in shaping compliance decisions. Based on our findings, we offer several lessons for future program design.
A logistic regression estimating function for spatial Gibbs point processes

We propose a computationally efficient logistic regression estimating function for spatial Gibbs point processes. The sample points for the logistic regression consist of the observed point pattern together with a random pattern of dummy points. The estimating function is closely related to the pseudolikelihood score. However, unlike common implementations of maximum pseudolikelihood, our approach does not suffer from bias due to numerical quadrature. The developed method is implemented in R code and will be added to future versions of the package spatstat. We demonstrate its efficiency and practicability on a real dataset and in a simulation study. Finally, focusing on stationary models, we prove that the estimator derived from the estimating function is strongly consistent and satisfies a central limit theorem. Moreover, we provide a consistent estimate of the asymptotic covariance matrix which allows to construct asymptotic confidence intervals.
Neoliberalism and technology: Perpetual innovation or perpetual crisis?

Introduction The neoliberal era has often been imagined as a period of intense technological revolution. The goal of a high-tech ‘knowledge based economy’ (KBE) of perpetual innovation has been elevated into a key guiding principle and salvationary strategy for advanced capitalist economies. Innovation is held up as the solution to multiple problems that became apparent in the 1970s, including the crisis in capital accumulation, the globalization of competition and the rise of environmental degradation. Since that decade we have been dazzled by a seemingly escalating proliferation of innovations, from information technology and mobile telephony through to biotechnology and nanotechnology. Yet, at the same time, there is also a sense that the high-tech promise of the 1970s, of a ‘space age’ where robots would replace workers, or the later prediction of a ‘biotech century’, have somehow not been realized. ‘Tomorrow’s world’ never quite came about. Sceptical commentators have questioned the idea of the technological fecundity of the neoliberal period, arguing instead that the last quarter of the twentieth century and after has been a ‘great stagnation’, where we have reached a ‘technological plateau’ (Cowen 2011). A popular trope, deployed by both Gordon (2000: 60) and Cowen (2011), has been to compare the dramatic changes in everyday life that resulted from technological transformations during the first half of the twentieth century with the much more modest changes experienced since then. Save for the Internet and mobile telephony, the basic technological infrastructure (based on cars, oil, etc.) has seen little radical transformation. In this trope, our contemporary
The role of depression and anxiety in developing true and false memories about past events

The present study aims is to assess the interaction between depression and anxiety, on the one hand, and the emotional valence of a to-be-remembered material in determining true and false memories. A sample of 126 participants completed the study. For the assessment of the occurrence of true and false memories, a video memory paradigm was used. The participants completed scales for measuring depression and anxiety, then they watched two videos presenting a positive, respectively a negative life event. After watching the videos, the participants completed a recognition task, in order to assess both true and false memories. The results showed that more true memories were reported for negative event, by the participants with a low level of depression and anxiety. Moreover, both depression and anxiety interacted with the emotional content of the remembered material in determining true or false memories. The implications of the results are discussed.
Mechanical Analysis of the Voicing Process in the Harpsichord

A. Roya, J.-L. Le Carroub, B. Fabrec and M. -A. Vitranid aLAM/d’Alembert ISIR, 11 rue de Lourmel, 75015 Paris, France bLAM/d’Alembert, UMR CNRS 7190, UPMC Univ. Paris 06, Sorbonne Universités, 11, rue de Lourmel, 75015 Paris, France cLAM/d’Alembert, Sorbonne Universités, UPMC Univ. Paris 06, UMR CNRS 7190, 11, rue de Lourmel, 75015 Paris, France dISIR, 4 place Jussieu, 75005 Paris, France roy@lam.jussieu. fr ISMA 2014, Le Mans, France
AJA30059 273..281

Purpose: To investigate the association of handedness with auditory middle latency responses (AMLRs) using topographic brain mapping by comparing amplitudes and latencies in frontocentral and hemispheric regions of interest (ROIs). Method: The study included 44 healthy subjects with normal hearing (22 left handed and 22 right handed). AMLRs were recorded from 29 scalp electrodes in response to binaural 4-kHz tone bursts. Results: Frontocentral ROI comparisons revealed that Pa and Pb amplitudes were significantly larger in the left-handed than the right-handed group. Topographic brain maps showed different distributions in AMLR components between