Miracle-dz/UniFAR-FTUs huggingface dataset:
The Influence of Tone on the Alignment of Speech and Co-Speech Gesture

Evidence continues to accrue suggesting that co-speech gestures form an integrated part of the prosodic system of languages. Several studies have highlighted a tight link between the timing of gestures of the hands and head with syllables bearing prosodic prominence. Most work to date has examined this relationship in Indo-European languages, where gestures appear to be crucially timed with respect to pitch-accented syllables. Less work has examined the timing of co-speech gestures in tonal languages, where pitch plays quite a different role within the phonological system. Here, we examine the influence of tone on the timing of manual co-speech gestures in Medmba, a Grassfields Bantu language spoken in Cameroon. We investigate 1) whether certain tones are more likely than others to associate with manual gestures in the language; and 2) whether the fine timing of the speech-gesture relationship is influenced by the tone or relative fundamental frequency ( f 0 ) of the syllable it co-occurs with. Our findings indicated no preference for any one tone to occur with co-speech gestures. However, gesture apexes were found to align significantly later with respect to the accompanying syllable’s vowel for low-toned syllables as compared with syllables of other tones.
Mechanical and Thermal Properties of 3D Printed Polycarbonate

This study aims at showing the potential of additive manufacturing as a new processing route for designing future insulators in the building sector. Polycarbonate (PC) is studied as a possible candidate for designing these new insulators. This polymer offers several advantages, among them fire resistance and stability of its physical properties at high temperatures. The 3D printing of PC is attempted using fused deposition modelling technology. The printing temperature and infill rate are varied to achieve optimal mechanical and thermal characteristics. The results show that an optimal printing temperature of 280 °C is needed to achieve high tensile performance. In addition, thermal properties including thermal conductivity and effusivity increase with the increase of the infill rate in opposition to the thermal diffusivity decrease.
“We Have to Keep Advocating and Helping and Doing What We Can”: Examining Perinatal Substance Use Services in the Absence of Integrated Treatment Programs

Effective treatment of substance-exposed pregnancies requires gender-responsive care coordinated across agencies and systems. A deeper understanding of the perspectives, experience, and practices of professionals providing these services is needed to improve care. This study examines service provision for perinatal substance use through the perspectives and experiences of healthcare and social service professionals. Using a constructivist grounded theory design, data were collected over a 7-year period. Data collection consisted of interviews and focus groups with professionals as well as observations of professional meetings and workshops where practices and procedures were discussed. A multi-level model of service delivery is described. The role of affect, particularly around issues of custody, is discussed along with structural level actions that develop in the absence of integrated treatment. Structural support both within and across systems of care is crucial to developing coordinated and compassionate care and to increasing engagement in care services.
Transcranial Direct Current Stimulation as an Approach to Mitigate Neurodevelopmental Disorders Affecting Excitation/Inhibition Balance: Focus on Autism Spectrum Disorder, Schizophrenia, and Attention Deficit/Hyperactivity Disorder

Transcranial direct current stimulation (tDCS) has been proposed as a promising therapy for rehabilitation of neurodevelopmental disorders. In this review, we discuss studies on the impact of tDCS as a therapy for autism, schizophrenia, and attention deficit/hyperactivity disorder, as well as the tDCS’ mechanism of action, and propose future paths of research to optimize tDCS treatment protocols. The mechanism underlying tDCS effects is the modulation of excitatory and/or inhibitory activity, making it a valuable tool for restoring the excitation/inhibition (E/I) balance which is disrupted in many neurodevelopmental disorders. Clinical studies have shown that tDCS therapy is well-tolerated by patients and seems to ameliorate behavior and cognitive functions. Alterations in early development of neuronal circuits lead to disruptions in brain activity in neurodevelopmental disorders. An increasing amount of research into the effects of tDCS on neuronal activity has provided a foundation for its use as a therapy for behavior and cognitive characteristics of neurodevelopmental disorders. Clinical studies show that tDCS appears to ameliorate behavioral and cognitive outcomes of patients with autism, schizophrenia, and attention deficit/hyperactivity disorder. More research is needed to understand the mechanisms of action of tDCS and to optimize treatment protocols.
Compliance Behaviour Amidst Ambiguous Information: An Exploratory Study in the Context of COVID-19

COVID-19 pandemic is an ambiguous situation due to the uncertainty associated with the outcome of the situation. This article aims at exploring the knowledge behaviour gap during a pandemic like COVID-19. The findings of this study indicate that individuals perceive different forms of ambiguity in different ways. Findings also suggest that, during a pandemic, attitude towards ambiguity and confidence in self-health significantly impact compliance behaviour. Interestingly, the findings also indicate that knowledge and perception about the context are not significantly associated with compliance behaviour. This study is one of the earliest attempts to understand the knowledge behaviour gap during a pandemic and contributes to the research literature by attempting a cross-fertilization of concepts from different streams of literature. This study also discusses the practical implications for the health sector, in particular health communication. JEL Codes: C9, D10, D81, D90
SELF-SUPERVISED ADVERSARIAL SHAPE COMPLETION

Abstract. The goal of this paper is 3D shape completion: given an incomplete instance of a known category, hallucinate a complete version of it that is geometrically plausible. We develop an adversarial framework that makes it possible to learn shape completion in a self-supervised fashion, only from incomplete examples. This is enabled by a discriminator network that rejects incomplete shapes, via a loss function that separately assesses local sub-regions of the generated example and accepts only regions with sufficiently high point count. This inductive bias against empty regions forces the generator to output complete shapes. We demonstrate the effectiveness of this approach on synthetic data from ShapeNet and ModelNet, and on a real mobile mapping dataset with nearly 9’000 incomplete cars. Moreover, we apply it to the KITTI autonomous driving dataset without retraining, to highlight its ability to generalise to different data characteristics.
Expatriate adjustment in hotels in Dubai, UAE

Abstract The article explored factors influencing the adjustment of expatriates working in hotels in Dubai. Given the high number of expatriates in Dubai and the importance in ensuring their suitable adjustment – so as to avoid an emotionally and economically damaging failed expatriate assignment – research is needed to understand what elements affect their adjustment process. This study focuses on the individual’s motivation to expatriate and the type of expatriate; self-initiated, organizational, or trailing spouse. Results suggest that expatriates who chose to expatriate to improve their career or earn more adjusted better than those who expatriated to “escape” poorer conditions back home.
Konsep Ruang pada Kawasan Heritage Pecinan Kota Bandung (Studi Kasus: Kawasan Heritage Pecinan Kota Bandung)

Abstrak—Kawasan Heritage Pecinan Kota Bandung merupakan salah satu kawasan heritage yang dapat ditemukan di Kota Bandung, kawasan ini memiliki proses sejarah panjang dari awal pembentukan hingga kawasan yang dapat dijumpai pada hari ini. Pada proses simbol – simbol kawasan heritage yang masih dapat ditemukan seperti unsur fisik berupa kelenteng, beberapa bangunan masyarakat, hingga unsur nirfisik berupa kebudayaan dan kepercayaan masyarkat lokal. Pada tahun 2017, kawasan ini dijadikan destinasi wisata baru di Kota Bandung, RDTR Kota Bandung 2015–2035 juga menyebutkan bahwa zonasi pecinan akan dikembangkan menjadi pusat ekonomi kreatif, pengembangan zonasi akan direalisasikan melalui RTBL yang hingga saat ini belum juga terbentuk. Penelitian ini bertujuan untuk merumuskan pemaknaan ruang pada kawasan heritage pecinan berdasarkan perbandingan dengan kawasan pecinan di Kota Semarang juga kepercayaan tradisional pecinan yang biasa disebut kepercayaan Feng Shui. Penelitian ini menggunakan pendekatan naturalistik dan etnografi yang dikumpulkan dengan melakukan catatan harian, wawancara terstruktur dengan narasumber, juga pengamatan langsung secara intensif pada kawasan penelitian. Narasumber dipilih berdasarkan faktor demografi, psikografi, dan geografi yang dapat merepresentasikan konsep ruang dengan landasan yang diberikan. Dari penelitian ini didapatkan konsep ruang yang dapat dijadikan acuan bagi para pengembang baik pihak swasta maupun pemerintahan dalam mengembangkan kawasan heritage pecinan berdasarkan dengan konsep ruang yang terbentuk oleh masyarakat itu sendiri. Ruang yang memiliki pemaknaan secara khusus bagi masyarakat pecinan mulai dari konsep kebertahanan, hingga konsep berkegiatan ekonomi diharap mampu membuat kawasan ini dikembangkan dengan pedoman yang optimal dan terstruktur.
A Distributed Collaborative Entrance Defense Framework Against DDoS Attacks on Satellite Internet

Satellite Internet (SI) dramatically expanded the ground-based Internet, and it is also the future direction of 6G. However, due to limited computing power and bandwidth resources, Distributed Denial-of-Service (DDoS) attacks can cause more severe damage to SI, and even paralysis of the entire network. Current DDoS defense mechanisms are built on abundant computing power and bandwidth resources, making applying in the SI scenario challenging. Aiming at protecting SI from DDoS attacks, a blockchain-based distributed collaborative entrance defense (DCED) framework is proposed, in which network traffic characteristics can be recorded and aggregated at the entrances of SI. The proposed framework consists of a distributed detection digesting procedure, a digest virtual aggregation procedure, and an entrance control strategy. The former procedure detects and extracts multidimensional characteristics of DDoS attacks and pushes them onto the blockchain. The latter procedure collects block data and aggregates attack features using the MapReduce algorithm and then compares them with baseline and gives an alert. The strategy completes the filtering and interception of traffic. Experiments use the IXIA platform to generate malicious traffic, and results show that the framework can accurately identify attack traffic within 1500 ms, reaching an area of 0.99 under the receiver operating characteristic curve. The proposed framework is more effective than other similar DDoS methods, protecting the precious SI bandwidth resources.
Using randomized rounding of linear programs to obtain unweighted natural strata that balance many covariates

In causal inference, natural strata are a new compromise between conventional strata and matching in a fixed ratio, say pair matching or matching two controls to each treated individual. Like matching in a fixed ratio, natural strata: (a) do not require weights, (b) balance many measured covariates beyond those that define the strata and (c) provide closer balance for a measured continuous covariate coarsely cut to form strata. Unlike matching in a fixed ratio, the ratio of controls to treated individuals need not be an integer, so if the data permit a fixed ratio comparison of 1‐to‐2.5 or even 1‐to‐0.75, then these ratios are possible using natural strata. Optimal natural strata are defined by a moderate number of fixed strata plus an integer program that minimizes the imbalance in many other measured covariates that are not used to specify the strata. Solving large integer programs is computationally difficult. A tool in the theory of approximation algorithms is ‘randomized rounding of a linear program’ to produce an integer solution: a fractional solution to a linear program defines a probability distribution for an integer‐valued random variable which is sampled. We apply this tool in a new way to produce natural strata and develop new properties of randomized rounding in this context. When proportional strata are impractical, we approximate them by minimizing the earthmover distance to proportionality. The method is applied to study birth outcomes for older and younger mothers in the United States in 2018. An R package natstrat is available at CRAN.
HYPERBLEND: SIMULATING SPECTRAL REFLECTANCE AND TRANSMITTANCE OF LEAF TISSUE WITH BLENDER

Abstract. Remotely sensing vegetation condition and health hazards requires modeling the connection of plants’ biophysical and biochemical parameters to their spectral response. Even though many models exist already, the field suffers from lack of access to program code. In this study, we will assess the feasibility of open-source 3D-modeling and rendering software Blender in simulating hyperspectral reflectance and transmittance of leaf tissue to serve as a base for a more advanced large-scale simulator. This is the first phase of a larger HyperBlend project, which will provide a fully open-source, canopy scale leaf optical properties model for simulating remotely sensed hyperspectral images. Test results of the current HyperBlend model show good agreement with real-world measurements with root mean squared error around 1‰. The program code is available at https://github.com/silmae/ hyperblend.
CONTRASTIVE SELF-SUPERVISED DATA FUSION FOR SATELLITE IMAGERY

Abstract. Self-supervised learning has great potential for the remote sensing domain, where unlabelled observations are abundant, but labels are hard to obtain. This work leverages unlabelled multi-modal remote sensing data for augmentation-free contrastive self-supervised learning. Deep neural network models are trained to maximize the similarity of latent representations obtained with different sensing techniques from the same location, while distinguishing them from other locations. We showcase this idea with two self-supervised data fusion methods and compare against standard supervised and self-supervised learning approaches on a land-cover classification task. Our results show that contrastive data fusion is a powerful self-supervised technique to train image encoders that are capable of producing meaningful representations: Simple linear probing performs on par with fully supervised approaches and fine-tuning with as little as 10% of the labelled data results in higher accuracy than supervised training on the entire dataset.
Handcycling with concurrent lower body low-frequency electromyostimulation significantly increases acute oxygen uptake: implications for rehabilitation and prevention

Background Acute increases in exercise-induced oxygen uptake (V̇O2) is crucial for aerobic training adaptations and depends on how much muscle mass is involved during exercising. Thus, handcycling is per se limited for higher maximal oxygen uptakes (V̇O2max) due to restricted muscle involvement. Handcycling with additional and simultaneous application of low-frequency electromyostimulation (EMS) to the lower extremities might be a promising stimulus to improve aerobic capacity in disabled and rehabilitative populations. Method Twenty-six healthy young adults (13 female, age: 23.4 ± 4.5 years, height: 1.77 ± 0.09 m, mass: 71.7 ± 16.7 kg) completed 4 ×10 minutes of sitting (SIT), sitting with concurrent EMS (EMS_SIT), handcycling (60 rpm, 1/2 bodyweight as resistance in watts) (HANDCYCLE) and handcycling with concurrent EMS of the lower extremities (EMS_HANDCYCLE). During EMS_SIT and EMS_HANDCYCLE, low frequency EMS (impulse frequency: 4Hz, impulse width: 350 µs, continuous stimulation) was applied to gluteal, quadriceps and calf muscles. The stimulation intensity was selected so that the perceived pain could be sustained for a duration of 10 minutes (gluteus: 80.0 ± 22.7 mA, quadriceps: 94.5 ± 20.5 mA, calves: 77.5 ± 19.1 mA). Results Significant mode-dependent changes of V̇O2 were found (p < 0.001, ηp2 = 0.852). Subsequent post-hoc testing indicated significant difference between SIT vs. EMS_SIT (4.70 ± 0.75 vs. 10.61 ± 4.28 ml min−1 kg−1, p < 0.001), EMS_SIT vs. HANDCYCLE (10.61 ± 4.28 vs. 13.52 ± 1.40 ml min−1 kg−1, p = 0.005), and between HANDCYCLE vs. EMS_HANDCYCLE (13.52 ± 1.40 vs. 18.98 ± 4.89 ml min−1 kg−1, p = 0.001). Conclusion Handcycling with simultaneous lower body low-frequency EMS application elicits notably higher oxygen uptake during rest and moderately loaded handcycling and may serve as an additional cardiocirculatory training stimuli for improvements in aerobic capacity in wheelchair and rehabilitation settings.
New applications of boxed molecular dynamics : efficient simulation of rare events

This work presents Boxed Molecular Dynamics (BXD), an eﬃcient simulation tool for studying long time scale processes which are inaccessible to conventional methods of simulation. Boxed Molecular Dynamics is explained and introduced in the context of modelling the dynamics of proteins and peptides. Two major applications of Boxed Molecular Dynamics are reported. 1) - The mechanical unfolding of proteins induced by Atomic Force Microscopy methods is investigated. For the ﬁrst time, experimental data is reproduced and unfolding pathways are investigated without the use of high artiﬁcial pulling forces which makes the simulation less realistic. 2) - A cheap and accurate in silico screening tool is developed to aid with the discovery and production of medicinal cyclic peptides. Enzymatic peptide cyclization is investigated by BXD and the ability of amino acid sequences to cyclize is predicted with an accuracy of 76 %.
Project-Based Learning: boosting 21st century skills

Project-Based Learning (PBL) is a method that allows students to learn through the development of different authentic tasks that result in projects by working collaboratively. Therefore, this paper first describes what PBL is, its main characteristics, and the benefits of implementing it as a way to improve 21st century skills such as collaborative work, student agency, problem solving, critical thinking, and different life skills in oral communication courses. Second, it provides a variety of ideas on how to implement it in the classroom. When this methodology is implemented, students show a greater understanding of the topics that are part of their courses and become more engaged, autonomous, and empowered. That is why PBL is highly recommended to help students learn by doing and hence build knowledge in an authentic way.
Semantic Segmentation of Urban Textured Meshes Through Point Sampling

Abstract. Textured meshes are becoming an increasingly popular representation combining the 3D geometry and radiometry of real scenes. However, semantic segmentation algorithms for urban mesh have been little investigated and do not exploit all radiometric information. To address this problem, we adopt an approach consisting in sampling a point cloud from the textured mesh, then using a point cloud semantic segmentation algorithm on this cloud, and finally using the obtained semantic to segment the initial mesh. In this paper, we study the influence of different parameters such as the sampling method, the density of the extracted cloud, the features selected (color, normal, elevation) as well as the number of points used at each training period. Our result outperforms the state-of-the-art on the SUM dataset, earning about 4 points in OA and 18 points in mIoU.
Reduced Voltage Stress Asymmetrical Multilevel Inverter with Optimal Components

The article presents a single phase asymmetrical multilevel inverter with a reduced components and low voltage stress which reduces the size and cost of the system. The structure provides a maximum output voltage of 23 levels with asymmetrical DC sources. There exists several reliability issues in lowering the total harmonic distortion (THD) by utilizing higher components in the design of MLI despite of its merits. Achieving reliability and lowering the THD is a challenging task for the researchers. The proposed 23-level MLI has been investigated with various performance parameters like total voltage standing (TSV), cost function (CF), power loss and efficiency analysis. The suggested MLI is compared with the existing topologies in the recent past and found that it has less voltage stress across the switches and cost-effective. The TSV calculations show that the proposed structure is more efficient in reducing the losses and increasing the efficiency. Hence, based on the evaluations and the comparisons made with the other topologies, it is found that the proposed MLI is well suited for the medium power applications such as FACTS, SVC, DSTATCOM and DVR. As the proposed architecture provides the 23 level output voltage values with asymmetrical DC sources, the configuration can be utilized for improving power quality in grid-connected renewable energy sources. The topology provides a less THD value which is under IEEE standards. The proposed architecture has been designed in MATLAB/Simulink and is implemented experimentally in hardware prototype in the laboratory environment.
Beyond Morality: The Moral Economy Framework and the Fisheries in Mumbai

The notion of the moral economy has shifted widely in interpretation over the years. Beginning as a concept to understand shifts in political economy, it has increasingly focused on the role that morality and values play in non-capitalist economies. Resource-dependent communities are viewed as exemplifying moral economies, embodying morals that are based on notions of equality and reciprocity and thus seen to be the anti-thesis of modern capitalist societies. Such a formulation shifts the focus from social change—the foundation of the earlier moral economy framework—to a focus on morality and participants in ‘moral’ economies. This article argues for a return to the older conceptualization of the ‘moral economy’ where class remains an important determinant of action such that different political economic regimes represent different kinds of moral economies. Based on a study of the Koli caste community of fishers in Mumbai, this article explores the messy ways in which community and class identities intersect in response to a capitalist transformation. A return to the older moral economy framework offers an opportunity to critically analyse community values and class politics and the role they play in shaping collective action without reducing capitalism as the ‘amoral’ other to older livelihood practices.