Miracle-dz/UniFAR-FTUs huggingface dataset:
An Efficient Classifier using Machine Learning Technique for Individual Action Identification

Human action recognition is an important branch of computer vision and is getting increasing attention from researchers. It has been applied in many areas including surveillance, healthcare, sports and computer games. This proposed work focuses on designing a human action recognition system for a human interaction dataset. Literature research is conducted to determine suitable algorithms for action recognition. In this proposed work, three machine learning models are implemented as the classifiers for human actions. An image processing method and a projection-based feature extraction algorithm are presented to generate training examples for the classifier. The action recognition task is divided into two parts: 4-class human posture recognition and 5-class human motion recognition. Classifiers are trained to classify input data into one of the posture or motion classes. Performance evaluations of the classifiers are carried out to assess validation accuracy and test accuracy for action recognition. The architecture designs for the centralized and distributed recognition systems are presented. Later these designed architectures are simulated on the sensor network to evaluate feasibility and recognition performance. Overall, the designed classifiers show a promising performance for action recognition.
Differential response to water deficit stress and shade in two wheat (Triticum durum Desf.) cultivars: growth, water relations, osmolyte accumulation and photosynthetic pigments

This study was conducted to investigate the effects of water deficit stress and shading on two wheat cultivars (Triticum durum Desf. cv Om Rabiaa and cv Maali). Comparison was based on growth, leaf water relations, photosynthetic pigments and the accumulation of organic solute. In both cultivars, water stress deficit significantly decreased total dry mass (TDM), leaf area (LA), water potential (Ψw), osmotic potential (Ψπ) and relative water content (RWC). Photosynthetic pigments, i.e. chlorophyll a (Chl a), chlorophyll b (Chl b) and total chlorophyll (Tot Chl), decreased while carotenoids (car) increased. Proline concentration increased significantly in water stressed plants under full light conditions while sugar accumulated more under shade conditions. In addition, shade improved leaf area, leaf water potential, and osmotic potential as well as alleviated the negative drought impact on photosynthesis performance. For the Maali cultivar, the reduction in RWC and its inability to achieve osmotic adjustment (OA) emphasize this cultivar’s sensitivity to water deficit stress. For the Om Rabiaa cultivar, the ability to avoid relative tissue dehydration and preserve a higher RWC with a significant increase in OA in shade-treated plants were tolerance mechanisms enabling this cultivar to absorb water effectively and sustain normal growth and productivity under water stress conditions.
Krankenhausreform in Dänemark: Purer Hype oder was bringen Ländervergleiche im Gesundheitswesen?

Prof. Dr. Volker Eric Amelung ist Inhaber der Schwerpunktprofessur für Internationale Gesundheitssystemforschung an der Medizinischen Hochschule Hannover sowie Vorsitzender des Bundesverbandes Managed Care, Berlin. Prof. Amelung ist Mitherausgeber der Zeitschrift Gesundheitsund Sozialpolitik Welche Aspekte führen dazu, dass ein Gesundheitssystem eine besonders gute Qualität erreicht, mit niedrigen Beitragssätzen der Versicherten auskommt oder durch eine Krise wie die Covid-19-Pandemie gut durchsteuert? Die Antwort zu diesen und ähnlichen Fragen beschäftigen die Akteure und Entscheider von Gesundheitssystemen seit Jahrzehnten. In der akuten Reaktion auf die Covid19-Pandemie schrumpfte der Gedankenraum in vielen Fällen: weg von internationalen und europäischen – in vielen Fällen sogar weg von bundesweiten – Perspektiven, hin zu landesweiten und regionalen Entscheidungen. Gleichwohl wurde ebenfalls stets die Frage gestellt, welche Länder besser durch die Krise kommen und woran das liegt. Dieser Beitrag versteht sich als Opinion Paper am Beispiel Dänemark und dessen Krankenhausreform, mit einigen Gedanken unter welchen Gegebenheiten Ländervergleiche als Inspirationsquelle für neue Versorgungsansätze sowie als Messlatte von medizinischen Kennzahlen Nutzen stiften können.
Constitutive differences in glucocorticoid responsiveness are related to divergent spatial information processing abilities

The stress response facilitates survival through adaptation and is intimately related to cognitive processes. The Morris water maze task probes spatial learning and memory in rodents and glucocorticoids (i.e. corticosterone in rats) have been suggested to elicit a facilitating action on memory formation. Moreover, the early aging period (around 16-18 months of age) is susceptible to stress- and glucocorticoid-mediated acceleration of cognitive decline. In this study, we tested three lines of rats selectively bred according to their individual differences in corticosterone responsiveness to repeated stress exposure during juvenility. We investigated whether endogenous differences in glucocorticoid responses influenced spatial learning, long-term memory and reversal learning abilities in a Morris water maze task at early aging. Additionally, we assessed the quality of the different swimming strategies of the rats. Our results indicate that rats with differential corticosterone responsiveness exhibit similar spatial learning abilities but different long-term memory retention and reversal learning. Specifically, the high corticosterone responding line had a better long-term spatial memory, while the low corticosterone responding line was impaired for both long-term retention and reversal learning. Our modeling analysis of performance strategies revealed further important line-related differences. Therefore, our findings support the view that individuals with high corticosterone responsiveness would form stronger long-term memories to navigate in stressful environments. Conversely, individuals with low corticosterone responsiveness would be impaired at different phases of spatial learning and memory.
Truth Discovery With Multi-Modal Data in Social Sensing

This article proposes unsupervised truth-finding algorithms that combine consideration of multi-modal content features with analysis of propagation patterns to evaluate the veracity of observations in social sensing applications. A key social sensing challenge is to develop effective algorithms for estimating both the reliability of sources and the veracity of their observations without prior knowledge. In contrast to prior solutions that use labeled examples to learn content features that are correlated with veracity, our approach is entirely unsupervised. Hence, given no prior training data, we jointly learn the importance of different content features together with the veracity of observations using propagation patterns as an indicator of perceived content reliability. A novel penalized expectation maximization (PEM) algorithm is proposed to improve the quality of estimation results for observations bolstered by multiple features. In addition, we develop a constrained expectation maximum likelihood with multiple features (CEM-MultiF) that introduces a novel constraint to boost the probability of correctness of some claims. Finally, we evaluate the performance of the proposed algorithms, called EM-Multi, CEM-Multi and PEM-MultiF, respectively, on real-world data sets collected from Twitter. The evaluation results demonstrate that the proposed algorithms outperform the existing fact-finding approaches, and offer tunable knobs for controlling robustness/performance trade-offs in the presence of malicious sources.
Development of a conceptual framework for the management of biodiversity and ecosystem services in the Mexican Caribbean

Abstract: Coral reefs and mangroves support rich biodiversity and provide ecosystem services that range from food, recreational benefits and coastal protection services, among others. They are one of the most threatened ecosystems by urbanization processes. In this context, we developed a conceptual framework for the management of biodiversity and ecosystem services for these coastal environments. We based our workflow on two sections: “Information base” and “Governance” and use the Puerto Morelos Coastal region as a case study for coastal protection. Puerto Morelos is between two of the most touristic destinations of Mexico (Playa del Carmen and Cancun) that has experienced an increase of population in the past four decades resulting in an intensification of multiple threats to its ecosystems. We characterized the two ecosystems with a “Management Units” strategy. An expert-based ecosystem services matrix was also described in order to connect mangroves and coral reef ecosystems with the multiple beneficiaries. Then an ecosystem model (conceptual model and Global Biodiversity model) was developed. The conceptual model was useful in understanding the interplay processes between systems regarding the ecosystem service of “Coastal Protection”. The Global Biodiversity model evidenced the human-induced shifts in the biodiversity for mangrove and coral reefs ecosystems. Also, a projection for 2035 of “best” and “worst” scenarios was applied using GLOBIO3. A DPSIR conceptual framework was used to analyze environmental problems regarding ecosystem services maintenance. Finally, we evaluated a set of policies associated with these ecosystems that favor coastal protection integrity. This framework facilitates the identification of the most relevant processes and controls about the provision of coastal protection service. It can also be useful to better target management actions and as a tool to identify future management needs to tackle the challenges preventing more effective conservation of coastal environments.
High-Performance III-V MOSFETs and Tunnel-FETs Integrated on Silicon

Silicon transistor scaling is approaching its end and a transition to novel materials and device concepts is more than ever essential. High-mobility compound semiconductors are considered promising candidates to replace or complement silicon, targeting low-power logic and high-speed electronics. However, to enable large scale and costeffective integration, it is crucial to address the challenge of integrating III-V devices on silicon substrates and to ensure CMOS processing compatibility. In this work, possibilities and limitations of III-V-on-silicon electronics are explored experimentally. A material platform, suitable for 3D and co-planar integration of InGaAs-based devices with multiple functionalities and featuring CMOS compatible process modules is here presented. Scaled InGaAs FinFETs showing record high on-current of 350μA/μm (IOF F = 100 nA/μm and VDD = 0.5 V), for III-V FETs on Si are demonstrated. Comparable device performance is also achieved in a 3D sequential integration fashion, where InGaAs transistors are fabricated on top of a Si CMOS device layer. The designed fabrication flow, targeting high-performance devices, can be adapted to the heterogeneous integration of multiple III-V compounds. Hence, following the same approach, the first sub-thermionic heterojunction InGaAs/GaAsSb Tunnel-FETs on silicon are reported. Tunnel-FETs belong to the category of steep-slope devices and can overcome MOSFET fundamental limitations taking advantage of quantum transport laws. In this work, some of the key technological challenges to fabricate III-V Tunnel-FETs are addressed and their impact on performance is experimentally demonstrated.
A Sequence Impedance Matrix Approach to Current Unbalance Detection for Grid Code Fulfillment

This work presents a novel method to identify the source of current unbalance during the interaction between industrial consumers and the utility. Since such unbalance yields detrimental effects over the network operation and efficiency; it must be mitigated to avoid their propagation towards the whole grid. To prevent this situation, regulations about the maximum permissible limits of unbalance are recently incorporated in grid codes. Unfortunately, in the absence of an accurate method to identify the agent inducing the unbalance; the problem, responsibility, as well as the solution, is vulnerable to misinterpretation. This situation is quite relevant, for both the consumer and the utility since imputations are subject to penalties. Prompted by this issue, we devised a method based on the computation of sequence impedance matrices. Then the underlying elements of the sequence impedance matrix will expose the occurrence of an unbalance as well as its source, which leads to a theoretically derived metric. This method is tested using real measurements of actual industrial loads connected to different voltage levels (i.e. 230 kV, 161 kV and 115 kV). The results demonstrate accuracy in the detection and identification of the cause of unbalance, considering a wide range of power consumption.
Risk Affection and Transmission of News of Conditional Volatility from the Non-Life to Life Insurance Sector

Non- Life and Life Insurance companies are the main expedients of risk transfer and risk management procedure in the economy and the society. This paper examines, in eight worldwide advanced insurance markets, whether there are transmissions of news of conditional volatility from the non-life to life insurance sector. The reason is that, regularly, non-life insurance risks have higher volatility and they are less predictable than life insurance risks. A GJR - GARCH model is used to test these relationships for the period January 1st 1990 to June 28th 2019 using daily trading observations for each listed insurance index. The results suggest that the French and the Australian non-life insurance sectors influence their life insurance sectors to a greater extent than the other countries insurance indices under study. There is also evidence that the leverage effect indicates that bad news concerning the non-life insurance index shows a more intense impact on the volatility of the life insurance index than the good news in the majority of the countries under study. However, bad and good news are symmetrical in French and Australian insurance markets.
Adversarial Path Sampling for Recommender Systems

Generative adversarial networks (GANs) have achieved a big success in collaborative filtering (CF). However, existing GAN-based methods in CF still suffer from the high-sparsity and cold-start problems; in addition, they also undergo the issues of excessive space complexity or inadequate training. In this article, we propose path2rec a novel adversarial path-based recommendation model to address these limitations of existing GAN-based methods in recommendation task by naturally incorporating auxiliary information (e.g., social networks and item attributes). It is composed of two modules, 1) pathGAN and 2) path2vec. In pathGAN, we consider both explicit and implicit friends, as well as item attributes by regarding them as the source of graph construction. Then, we propose a smart walk strategy to automatically generate an optimizing path, which can effectively learn the semantic distribution of users and items. In path2vec, to fully exploit context features of the generated path, we use the Continuous Bag of Words (CBOW) model to fine-tune nodes representations learned by pathGAN. Through extensive experiments on real-world datasets, we demonstrate the effectiveness of the proposed path2rec by applying it into top-n item recommendation, which reaches better performance than other counterparts.
Mindfulness-Based Cognitive Therapy for Depression

Depression is an illness that restricts an individual’s ability to work, eat, sleep, or enjoys a pleasurable event. WHO identifies depression as one of the largest illness in terms of morbidity with statistics reporting higher incidence of depression in women than men. Factors related to depression in India have not been different from the other parts of the world. A major shift is to develop promising brief interventions that would have high potential to expand under real life conditions is the need of the hour. Mindfulness-Based Cognitive Therapy (MBCT) is one such approach with promising effects as compared to other psychotherapies. Studies report MBCT efficaciously increases self-awareness, self-reflection, emotional wellbeing and functioning by equipping one with skills like acceptance, exposure, non-judgmental observation, cognitive flexibility, relaxation, and self-management. Thus, a client is helped to change depressive states. This therapy modality teaches the clients the necessary skills to disengage from habitual patterns of dysfunctional cognitive routines and help them to adapt a healthy behaviour.
Characterization of Piezoelectric Bimorph Actuator 3D-Deformations Caused by Electric Change by Means of Multiscale Curvature Analysis

The objective of this work is to demonstrate the use of multiscale curvature tensor analysis to characterize deformations of piezoelectric bimorph actuator and to study the relation between loaded electric charge and the resulting deformed surface curvature. In particular, the strength of the correlations between surface shape characterized by curvature parameters (i.e., principal, Gaussian or mean curvature) and charge density is sought. The impact that the scale of the analysis of the curvature can have on the values of the curvature parameters for the deformed surfaces and the correlation with discharge energy is also studied. In this study the deformations of piezoelectric bimorph actuators are studied. In order to achieve quasi-static measurements, a dedicated charge amplifier was built to supply charge to the actuator. The deformations were then measured by Polytec® 3D laser scanning vibrometer PSV-400 by integration of captured motion. The obtained data was used to calculate curvature tensor field at multiple scales by applying the normal-based method. Principal, mean and Gaussian curvature was calculated at multiple scales and were correlated with applied charge. The obtained results contribute to better understanding of piezoelectric behavior under electric field.
Knowledge of Sports Motivation: The Consumptive Lifestyle of a Running Community

The life of modern society in general cannot be separated from the lifestyle in their daily lives. The lifestyle in question for example can be reflected in fashion, habits or even in sports activities. That is, exercise is not just an activity that intersects with health alone, but also has become part of the lifestyle. Running, for example, which tends to be known as an easy and affordable sport, is now no longer seen as a sporting activity for maintaining health, but also a part of lifestyle. Based on the background of the problem, this study aims to determine and explain the motivations of the runners who make running as part of their lifestyle. Using a qualitative approach, the results of this study indicate that running sport gives a different impression to every running community in Indonesia and running sport is no longer a sporting activity to maintain health merely as shown by the motivation of runners to join
Transparency reduces predator detection in mimetic clearwing butterflies

Predation is an important selective pressure and some prey have evolved conspicuous warning signals that advertise unpalatability (i.e. aposematism) as an antipredator defence. Conspicuous colour patterns have been shown effective as warning signals, by promoting predator learning and memory. Unexpectedly, some butterfly species from the unpalatable tribe Ithomiini possess transparent wings, a feature rare on land but common in water, known to reduce predator detection. We tested if transparency of butterfly wings was associated with decreased detectability by predators, by comparing four butterfly species exhibiting different degrees of transparency, ranging from fully opaque to largely transparent. We tested our prediction using both wild birds and humans in behavioural experiments. Vision modelling predicted butterfly detectability to be similar for these two predator types. In concordance with predictions, the most transparent species were almost never 31 found first and were detected less often than the opaque species by both birds and humans, suggesting that transparency enhances crypsis. However, humans were able to learn to better detect the more transparent species over time. Our study demonstrates for the first time that transparency on land likely decreases detectability by visual predators. Introduction
Secure Collaborative Deep Learning Against GAN Attacks in the Internet of Things

Deep learning makes the Internet-of-Things (IoT) devices more attractive, and in turn, IoT facilitates the resolution of the contradiction between data collection and privacy concerns. IoT devices with small-scale computing power can contribute to model training without sharing data in collaborative learning. However, collaborative learning is susceptible to generative adversarial network (GAN) attack, where an adversary can pretend to be a participant engaging in the model training and learn other participants’ data. In this article, we propose a secure collaborative deep learning model which resists GAN attacks. We isolate the participants from the model parameters, and realize the local model training of participants via the interaction mode, ensuring that neither the participants nor the server would have access to each other’s data. In particular, we target convolutional neural networks, the most popular network, design specific algorithms for various functionalities in different layers of the network, making it suitable for deep learning environments. To our best knowledge, this is the first work designing specific protocol against GAN attacks in collaborative learning. The results of our experiments on two real data sets show that our protocol can achieve good accuracy, efficiency, and image processing adaptability.
Keeping Pace with the Vegan Race: a Challenge for Culinary Arts Education

It is worth noting that only a small number of vegan restaurants have been awarded one-star by the Michelin Guide such as Kaijitsu and Nix, both located in New York (Michelin Guide, 2019). Even fast food operators are jumping on the meat-free bandwagon; McDonald’s launched their first vegetarian meal for children last year (Gittens, 2019) and Leon opened its first outlet in Ireland in 2019, providing a vegan sweet potato falafel hot box (Taylor, 2019). Lundahl (2018) argues that veganism has moved from stigmatised to mainstream, assisted by the profiles of celebrities adopting this eating regime. Packaged in ethical food consumption, it is seen as a lifestyle grounded in concern for the environment, animals and people (Doyle, 2016) and is inherently linked to an individual’s identity (Greenebaum, 2012). Documented health benefits include reduced body mass index, glucose levels, total and lowdensity lipoprotein cholesterol, incidence of cancer, and ischemic heart disease (Dinu, Abbate, Gensini, Casini and Sofi, 2017). Culinary arts educators must examine why veganism is flourishing, consider the implications for culinary practice and facilitate change through innovative curriculum design.