Miracle-dz/UniFAR-FTUs huggingface dataset:
Decades of Road Mortality Cause Severe Decline in a Common Snapping Turtle (Chelydra serpentina) Population from an Urbanized Wetland

Abstract. Road networks threaten biodiversity and particularly herpetofauna, including common snapping turtles (Chelydra serpentina), which have an especially slow life history that prevents rapid recovery of populations subjected to road mortality. Cootes Drive is a 2.5-km 4-lane highway that bisects wetland habitat used for nesting and overwintering by snapping turtles. We hypothesized that turtle mortality from collisions with vehicles on Cootes Drive has caused a male bias and a decline in the population as turtles attempt to access habitat on both sides of the road. Capture–mark–recapture studies confirmed a dramatic decline in the turtle population from 941 individuals in 1985 to 177 individuals in 2002, a loss of 764 individuals in only 17 yrs. Using the same data, we also determined that the population has been significantly male-biased since 1985. Using 2009–2016 road mortality data obtained from the Dundas Turtle Watch (a citizen-science program), we completed a population viability analysis using the 2002 population size estimate to isolate the impact of road mortality. We found that this population is at risk of extirpation due to road mortality. The population range overlapped with the Cootes Drive and 7 of the 10 tracked turtles had individual home ranges that overlapped with the road. Our findings support the hypothesis that road mortality has contributed to the dramatic decline in the snapping turtle population in Cootes Paradise Marsh. This population is in jeopardy of extirpation; therefore, exclusion fencing must be installed for an extended distance along both sides of surrounding roads to prevent turtles from crossing the road and to promote their use of existing aquatic culverts.
Composition, Seasonality, and Life History of Decapod Shrimps in Great Bay, New Jersey

Abstract Shrimp are critical to estuarine food webs because they are a resource to economically and ecologically important fish and crabs, but also consume primary production and prey on larval fish and small invertebrates. Yet, we know little of their natural history. This study determined shrimp community composition, seasonality, and life histories by sampling the water column and benthos with plankton nets and benthic traps, respectively, in Great Bay, a relatively unaltered estuary in southern New Jersey. We identified 6 native (Crangon septemspinosa, Palaemon vulgaris, P. pugio, P. intermedius, Hippolyte pleuracanthus, and Gilvossius setimanus) and 1 non-native (P. macrodactylus) shrimp species. These results suggest that the estuary is home to a relatively diverse group of shrimp species that differ in the spatial and temporal use of the estuary and the adjacent inner shelf.
Population Decline of the Invasive Asian Shore Crab (Hemigrapsus sanguineus) and Dynamics of Associated Intertidal Invertebrates on Cape Cod, Massachusetts

Abstract A few moderately long-term studies have documented population dynamics of the invasive Hemigrapsus sanguineus (Asian Shore Crab) and species with which it interacts. One such study on Cape Cod reported exponential growth of the Asian Shore Crab through 2012, concurrent with declines in 2 resident species, but recent data at nearby sites suggest considerable geographic variation in population dynamics, with modest recovery of resident crab populations. We monitored the Cape Cod population for an additional 5 years to determine whether population growth of the Asian Shore Crab had slowed and whether there was any change in population dynamics of Carcinus maenas (Green Crab), Littorina littorea (Common Periwinkle), or Mytilus edulis (Blue Mussel). Asian Shore Crab density declined by nearly 90% since 2012. There was no evidence of recovery by the Green Crab, but the Blue Mussel experienced a brief but substantial increase in density in 2015, when both crab species displayed >70% reductions in density from the previous year. The pattern of population dynamics of the Asian Shore Crab from 2003 to 2017 is consistent with boom–bust dynamics, but it remains unclear whether the population will equilibrate near its current density or undergo recurrent boom–bust cycles.
A Systems Engineering-based semantic model to support "Product-Service System" life cycle. (Un modèle sémantique basé sur l'ingénierie des systèmes pour supporter le cycle de vie des systèmes "Produit-Service")

Product-service systems (PSS) result from the integration of heterogeneous components covering both tangible and intangible aspects(mechanical, electrical, software, process, organization, etc.). The process of developing PSS is highly collaborative involving a wide variety of stakeholders. This interdisciplinary nature requires standardized semantic repositories to handle the multitude of business views and facilitate the integration of all heterogeneous components into a single system. This is even more complex in the case of customizable PSS in the industrial sector. Despite the many methodologies in literature, the management of the development processes of the PSS is still limited to face this complexity. In this context, Systems Engineering (SE) could bean advantageous solution in terms of its proven qualities for the modeling and management of complex systems. This thesis aims at exploring the potentials of Systems Engineering (SE) as a conceptual foundation to represent various different business perspectives associated with the life cycle of the PSS. In this context, a meta-model for PSS is proposed and verified in industrial cases. An ontological model is also presented as an application of a part of the model to structure the common repository of the ICP4Life platform.
An approach to measuring software systems using new combined metrics of complex test. (Une approche pour mesurer les systèmes logiciels utilisant de nouvelles métriques de test complexe combinées)

La plupart des metriques de qualite logicielle mesurables sont actuellement basees sur des mesures bas niveau, telles que la complexite cyclomatique, le nombre de lignes de commentaires ou le nombre de blocs dupliques. De meme, la qualite de l'ingenierie logicielle est davantage liee a des facteurs techniques ou de gestion, et devrait fournir des indicateurs utiles pour les exigences de qualite. Actuellement, l'evaluation de ces exigences de qualite n'est pas automatisee, elle n'est pas validee empiriquement dans des contextes reels et l'evaluation est definie sans tenir compte des principes de la theorie de la mesure. Par consequent, il est difficile de comprendre ou et comment ameliorer le logiciel suivant le resultat obtenu. Dans ce domaine, les principaux defis consistent a definir des metriques adequates et utiles pour les exigences de qualite, les documents de conception de logiciels et autres artefacts logiciels, y compris les activites de test.Les principales problematiques scientifiques abordees dans cette these sont les suivantes: definir des mesures et des outils de support pour mesurer les activites d'ingenierie logicielle modernes en termes d'efficacite et de qualite. La seconde consiste a analyser les resultats de mesure pour identifier quoi et comment s'ameliorer automatiquement. Le dernier consiste en l'automatisation du processus de mesure afin de reduire le temps de developpement. Une telle solution hautement automatisee et facile a deployer constituera une solution revolutionnaire, car les outils actuels ne le prennent pas en charge, sauf pour une portee tres limitee.
Complexity Analysis and Research based on the Chaotic System of Sample Entropy

This paper has analyzed the analysis method of Sample Entropy sequence linear complexity, and used this algorithm to calculate the measure entropy of Logistic chaotic system movement. Theoretical study and simulation realization show that, the method of short observation sequence can accurately reflect the chaotic system and the complexity size of chaos pseudo-random sequence, and can be used to judge the complexity of the pseudo-random sequence generated by chaotic system, as well as has the good consistency compared with approximate entropy (ApEn), meanwhile with high execution efficiency. The experimental results show the validity of this method and the correctness of the theoretical results.
A Traffic Prediction Method of Bicycle-sharing based on Long and Short term Memory Network

The prediction of bicycle-sharing traﬃc ﬂow plays an important role in management of the service stations for bicycle-sharing. In this paper, based on an improved long and short term memory network we propose a traﬃc prediction method of bicycle-sharing. Firstly, the bicycle-sharing area is divided by the probability threshold clustering algorithm. Secondly, based on the long and short term memory network the bicycle-sharing traﬃc model is established. In order to increase the accuracy of prediction, we optimize the related parameters by diﬀerential evolution algorithm. Finally, we adopt the traﬃc data set of bicycle-sharing in Yancheng city, Jiangsu province, China in our experiment. With compared to the existed methods, the experimental results are shown that our proposed method has a better eﬀectivity and is suitable for the traﬃc prediction of bicycle-sharing.
Measuring Risk from IT Initiatives Using Implied Volatility

We propose an underrecognized measure to capture changes in firm risk from information technology (IT) announcements: implied volatility (IV) from a firm’s exchange-traded options. An IV is obtained from a priced stock option and represents the option market’s expectation of the firm’s average stock return volatility over the remaining duration of the option. Using the change in IV around IT announcements, we can directly assess changes in IT-induced firm risk. IVs are straightforward to obtain, and are forward-looking based on option market investors’ estimates of future stock return volatility. They do not rely on historical volatility that is confounded with other events. In addition, options have different expiration dates—each with an IV—allowing us to distinguish between short-and long-term risk. We show how a change in IV can be employed to assess changes in short-and long-term firm risk from IT announcements and demonstrate this methodological innovation empirically using a set of IT announcements that have been utilized in previous studies.
Exploration-exploitation dilemma in Reinforcement Learning under various form of prior knowledge. (Impact des connaissances a priori sur le compromis exploration-exploitation en apprentissage par renforcement)

In combination with Deep Neural Networks (DNNs), several Reinforcement Learning (RL) algorithms such as "Q-learning" or "Policy Gradient" are now able to achieve super-human performances on most Atari Games as well as the game of Go. Despite these outstanding and promising achievements, such Deep Reinforcement Learning (DRL) algorithms require millions of samples to perform well, thus limiting their deployment to all applications where data acquisition is costly. The lack of sample efficiency of DRL can partly be attributed to the use of DNNs, which are known to be data-intensive in the training phase. But more importantly, it can be attributed to the type of Reinforcement Learning algorithm used, which only perform a very inefficient undirected exploration of the environment. For instance, Q-learning and Policy Gradient rely on randomization for exploration. In most cases, this strategy turns out to be very ineffective to properly balance the exploration needed to discover unknown and potentially highly rewarding regions of the environment, with the exploitation of rewarding regions already identified as such. Other RL approaches with theoretical guarantees on the exploration-exploitation trade-off have been investigated. It is sometimes possible to formally prove that the performances almost match the theoretical optimum. This line of research is inspired by the Multi-Armed Bandit literature, with many algorithms relying on the same underlying principle often referred as "optimism in the face of uncertainty". Even if a significant effort has been made towards understanding the exploration-exploitation dilemma generally, many questions still remain open. In this thesis, we generalize existing work on exploration-exploitation to different contexts with different amounts of prior knowledge on the learning problem. We introduce several algorithmic improvements to current state-of-the-art approaches and derive a new theoretical analysis which allows us to answer several open questions of the literature. We then relax the (very common although not very realistic) assumption that a path between any two distinct regions of the environment should always exist. Relaxing this assumption highlights the impact of prior knowledge on the intrinsic limitations of the exploration-exploitation dilemma. Finally, we show how some prior knowledge such as the range of the value function or a set of macro-actions can be efficiently exploited to speed-up learning. In this thesis, we always strive to take the algorithmic complexity of the proposed algorithms into account. Although all these algorithms are somehow computationally "efficient", they all require a planning phase and therefore suffer from the well-known "curse of dimensionality" which limits their applicability to real-world problems. Nevertheless, the main focus of this work is to derive general principles that may be combined with more heuristic approaches to help overcome current DRL flaws.
An Approach for the Transformation and Verification of BPMN Models to Colored Petri Nets Models

The correctness of transformations has recently begun to attract the attention of the researchers in Model Driven Engineering (MDE). The objective of this article is twofold. First, it presents an approach for transforming BPMN models to Colored Petri nets models using GROOVE and EMF/Xpand tools. Second, it proposes an approach for checking the correctness of the transformation itself. More precisely, we have defined the termination property of the transformation and the preservation of some structural properties of BPMN models by the transformation using the GROOVE graph transformation tool. The authors have also applied the approach on a case study through which the authors have verified the successful termination of the transformation using GROOVE Model Checker and the target model properties using CPN Tools.
An Improving Data Compression Capability in Sensor Node to Support SensorML-Compatible for Internet-of-Things

Limited bandwidth and lacked compatibility information of the sensor nodes in Wireless Sensor Network (WSN) are critical issues in implementing WSN applications. This paper proposes a method of improvement to data compression capability to support SensorML interface for information exchange in the sensor nodes. The delivery of the packets with XML format of all nodes in WSN could cause the traﬃc load increases. This paper proposes a method of improvement of data compression capability for exchanging data in the sensorML for the Internet of Things (IoT). The delivery of XML formatted packets of all nodes in Wireless sensor networks (WSN) could cause the traﬃc load increases. The proposed method suggests data compression condensing the packets in the node and analyzes the relationship between the parameters and the performance. The settings of sliding window size and comparing length are factors to aﬀect the performance of network traﬃc load signiﬁcantly. The experimental results show that, with a format of 256 bytes and LZSS compression, the transmission performance is improved up to 48.8 % , in comparison with the original approach. Besides, the proposed method can deliver up to 204 % of information than the other works.
Using CCLM to Promote the Accuracy of Intelligent Sentiment Analysis Classifier for Chinese Social Media Service

In recent years, the popularity of social media services (such as Facebook, Twitter, and Plurk) has been on the rise. As users grow accustomed to posting their personal opinions on certain issues on the Internet, collecting data on the public’s opinion and attitude towards specific issues can provide the government, organizations, and corporations with a solid basis of reference for their decision-making. Thus, sentiment analysis (SA) of text messages has become a crucial research topic, while the value of research results and related application have gained more emphasis and more emphasis. This study proposes constructing an intelligent sentiment analysis classifier by integrating CCLM (Combined CKIP Language Model) with näıve Bayes classifiers, with the aim of establishing a high-accuracy sentiment classification based on Mandarin texts posted on social media platform to provide more accurate predictions towards future public opinion trends and behavior patterns. In our experiment, the used short text messages derived from a famous microblogging service – Plurk – as our subject of study to explore sentiment analysis of Mandarin sentences; the näıve Bayes classifier combined with three language models – BLM (bigram language model), CLM (CKIP language model, and CCLM – to construct sentiment analyzers. Throughout different size of data sets in the experiment, the sentiment classifier adopting CCLM achieved better accuracy.
Generalizing Concept-Drift Patterns for Fuzzy Association Rules

In recent years, concept drift has become a popular research topic in data mining because of its wide range of practical applications. Concept drift discusses the significant change of concepts along with time or location movement. It can thus be used to derive the purchasing behavior of customers from a database at different times or locations. In the past, most of the research focuses on concept drift from traditional transaction databases. However, quantities of purchased items usually exist in databases and can provide more information in concept drift. In this paper, we integrate fuzzy data mining with concept drift and generalize the similarity measures for achieving the purpose. These measures are used for defining fuzzy concept-drift patterns derived from quantitative transaction databases at different time. The proposed approach can thus fully utilize the results from fuzzy mining and provide effective information about customers behavior change to managers for tuning the business strategy.
Blind Quality Assessment of Dehazed Images by Analyzing Information, Contrast, and Luminance

Image dehazing is widely used to improve the visual quality of dehazed images. While image dehazing has been extensively investigated, the relevant quality assessment of dehazed images remains an open problem, which may hinder further improvement of dehazing techniques. In this paper, a blind quality metric for dehazed images is proposed. Three groups of features are extracted for characterizing the quality of dehazed images, including information, contrast, and luminance. Specifically, a total of 11 perceptual features are extracted and used to train a support vector regression (SVR) model. Then, the trained SVR model is used for predicting the quality of dehazed images. A dehazed image database is built to evaluate the performance of the proposed method. The experimental results demonstrate the efficiency and advantage of the proposed metric.
Étude de nouvelles techniques d'interaction en situation de mobilité avec des lunettes électroniques pour le domaine de la santé. (Study of new interaction techniques in mobility context with smart glasses for healthcare)

Les dispositifs mobiles tels que les smartphones, les montres connectees ou les lunettes electroniques ont revolutionne la facon dont nous interagissons. Les lunettes electroniques nous interessent, car elles fournissent aux utilisateurs une vision simultanee des mondes physique et numerique. Cependant, l’interaction sur les lunettes electroniques n’est pas bien exploree. L’amelioration de l’interaction sur ce dispositif peut convaincre les utilisateurs a l’utiliser plus dans la vie quotidienne. Le sujet de these est focalise sur l’etude et le developpement de nouvelles techniques d'interaction avec les lunettes electroniques. En effet, il n'est pas possible d'interagir aussi finement en mobilite ou en situation d'urgence par rapport a une situation stable telle qu’assis devant un bureau. Notre contexte de travail se situe dans le domaine de la sante et en particulier celui du personnel medical visitant un patient dans un hopital. Le personnel medical doit pouvoir acceder aux donnees du patient deja collectees, obtenir des donnees physiologiques en temps reel, preparer son diagnostic et communiquer avec ses collegues. Le verrou scientifique pour la these ici est de trouver des solutions qui permettent au personnel medical de realiser ces tâches de facon plus precise et moins contraignante. Le but est de rendre plus efficace le diagnostic et le partage d’informations et de faire d'un dispositif encore non maitrise un outil fonctionnel en milieu professionnel de la sante. Dans cette optique, les travaux de cette these presentent des contributions theoriques et applicatives. Nous avons tout d’abord repertorie les differents travaux effectues dans le cadre des lunettes electroniques pour le domaine de la sante, tout en indiquant le potentiel, les resultats pertinents et les limites. Nous nous sommes focalises sur les lunettes electroniques pour afficher et manipuler les dossiers patients. D’un point de vue conceptuel, nous avons propose un espace de conception a huit dimensions pour identifier les lacunes dans les systemes existants et aider a la conception de nouveaux systemes. D’un point de vue applicatif, en interaction en sortie, nous avons introduit une technique appelee K-Fisheye sur une interface a tuiles qui permet de parcourir un grand ensemble de donnees comme celui present dans le dossier patient. Nous avons utilise ensuite l’espace de conception pour porter un systeme existant sur les lunettes electroniques. Le prototype obtenu s’appelle mCAREglass. En interaction en entree, nous avons explore quatre techniques de pointage sur les lunettes electroniques avec la norme ISO 9241-411 pour selectionner les techniques qui ne sont pas contraignantes pour naviguer dans le dossier patient. Ensuite, nous avons concu une nouvelle technique d’entree de texte appele TEXTile qui permet de saisir du texte sur un tissu interactif communiquant avec les lunettes.
Multiplicative consistency analysis for q‐rung orthopair fuzzy preference relation

The q‐rung orthopair fuzzy set is characterized by membership and nonmembership functions, and the sum of the qth power of them is less than or equal to one. Since it releases the constraints existed in both intuitionistic fuzzy set and Pythagorean fuzzy set, it has wide applications in real cases. However, so far, there is little research on the multiplicative consistency of q‐rung orthopair fuzzy preference relation (q‐ROFPR). To fill this vacancy, this paper provides a detailed analysis on the multiplicative consistency of q‐ROFPR. First, we investigate the concept of multiplicative consistent q‐ROFPR and its properties. Subsequently, two goal programming models are proposed to derive the priorities from individual and group q‐ROFPRs, respectively. After that, a novel consistency‐improving algorithm for q‐ROFPR and a weight‐generating method for decision‐makers are discussed in detail, based on which, a novel group decision‐making method is proposed. Finally, a case study concerning the evaluation of rehabilitation program selection is given to illustrate the applicability of the proposed method. The effectiveness and superiority of the proposed method are verified by comparing it with some existing methods.