Miracle-dz/UniFAR-FTUs huggingface dataset:
Clustering Object Trajectories for Intersection Traffic Analysis

: Vehicle and pedestrian trafﬁc at a trafﬁc intersection provide crucial information about the performance of the intersection for safety and throughput. It is possible to discover patterns and outliers on this data by applying data analytics. In this paper, we present a novel clustering algorithm for trajectories that use a new distance measure and a two-level hierarchical clustering approach based on geometric properties of the trajectories and spectral clustering. Trajectory data is augmented with signal phasing and timing information, which gives new insights to the trajectory data. We demonstrate the procedure on a real-life intersection where the prominent patterns for trafﬁc movement are found, and the anomalous trajectories are extracted.
Growth performance and production cost of laboratory-scale marine microalgae culture using a light-emitting diode

The effects of a light-emitting diode (LED) were examined and compared to fluorescent light on the growth performance of four marine microalgae, Nannochloropsis oculata, Tetraselmis suecica, Chaetoceros calcitrans, and Thalassiosira weissflogii cultured under laboratory conditions for 240 h (10 days). The results showed that the cell densities of N. oculata cultured using fluorescent lamps (T1) and cool daylight LED (T2) were higher than warm white LED (T3), and the growth rate of this species using T2 were higher than for T1 and T3. The cell density of the T. suecica culture in each treatment was not different, but the growth rate using T1 was higher than for T3. The cell densities and growth rates of C. calcitrans and T. weissflogii culture in each treatment were not different. In addition, commercial Chaetoceros produced using cool daylight LED illumination had a lower production cost compared with production using cool white fluorescent illumination. Therefore, commercial laboratory-scale production of marine microalgae should use a cool daylight LED as the light source in order to reduce the production cost without affecting the growth and quality of microalgae.
Usability Testing of Educational Computer Games on the Topic "Safe Internet"

In the paper we present methods and techniques for usability testing and evaluation of educational computer games. We present a case of application of usability methods in process of development of four educational computer mini-games “Safe Internet”. The target users groups cover students from 4th till 7th grade, teachers in school subjects Information technology and Computer modelling. Therefore we apply different methods and tools to plan and testing of the usability of the games. For the current research we use a laboratory testing with an interview with limited number of students and user opinion study with questionnaires for stakeholders – teachers and students. We apply ADDIE (Analysis, Design, Development, Implementation, Evaluation) in a combination with Agile methodologies – Scrum methodology with three sprints. The usability study and implementation are conducted in all stages of development of the games. The case study shows that conducted approach based on the agile methodology with combination of usability study with stakeholders (teachers and students) of the games give good results. The students have positive perception to the final versions of the developed educational computer games. a https://orcid.org/0000-0003-0358-0648 b https://orcid.org/0000-0003-4244-6812 c https://orcid.org/0000-0003-4162-5106
Discovering of a Conceptual Model from a NoSQL Database

: NoSQL systems have proven effective to handle Big Data. Most of these systems are schema-less which means that the database doesn't have a fixed data structure. This property offers an undeniable flexibility allowing the user to add new data without making any changes on the data model. However, the lack of an explicit data model makes it difficult to express queries on the database. Therefore, users (developers and decision-makers) still need the database data model to know how data are stored and related, and then to write their queries. In a previous work, we proposed a process to extract a physical model from a NoSQL database. In this article, we propose to extend this process by leading to the extraction of a conceptual model that provides an element of semantic knowledge close to human understanding. To do this, we use the Model Driven Architecture (MDA) that provides a formal framework for automatic model transformation. From a NoSQL physical model, we propose formal transformation rules to generate a conceptual model in the form of a UML class diagram. An experimentation of the extraction process was carried out on a medical application.
General Transportability - Synthesizing Observations and Experiments from Heterogeneous Domains

The process of transporting and synthesizing experimental findings from heterogeneous data collections to construct causal explanations is arguably one of the most central and challenging problems in modern data science. This problem has been studied in the causal inference literature under the rubric of causal effect identifiability and transportability (Bareinboim and Pearl 2016). In this paper, we investigate a general version of this challenge where the goal is to learn conditional causal effects from an arbitrary combination of datasets collected under different conditions, observational or experimental, and from heterogeneous populations. Specifically, we introduce a unified graphical criterion that characterizes the conditions under which conditional causal effects can be uniquely determined from the disparate data collections. We further develop an efficient, sound, and complete algorithm that outputs an expression for the conditional effect whenever it exists, which synthesizes the available causal knowledge and empirical evidence; if the algorithm is unable to find a formula, then such synthesis is provably impossible, unless further parametric assumptions are made. Finally, we prove that do-calculus (Pearl 1995) is complete for this task, i.e., the inexistence of a do-calculus derivation implies the impossibility of constructing the targeted causal explanation.
A Formal Model-Based Testing Framework for Validating an IoT Solution for Blockchain-based Vehicles Communication

: The emergence of embedded and connected smart technologies, systems, and devices has enabled the concept of smart cities by connecting every “thing” to the Internet and in particular in transportation through the Internet of Vehicles (IoV). The main purpose of IoV is to prevent fatal crashes by resolving trafﬁc and road safety problems. Nevertheless, it is paramount to ensure secure and accurate transmission and recording of data in “Vehicle-to-Vehicle” (V2V) and “Vehicle-to-Infrastructure” (V2I) communication. To improve “Vehicle-to-Everything” (V2X) communication, this work uses Blockchain technology for developing a Blockchain-based IoT system aimed at establishing secure communication and developing a fully decentralized cloud computing platform. Moreover, the authors propose a model-based framework to validate the proposed approach. This framework is mainly based on the use of the Attack Trees (AT) and timed automaton (TA) formalisms in order to test the functional, load and security aspects. An optimization phase for testers placement inspired by fog computing is proposed as well.
Integration of Voice Assistant and SmartDeviceLink to Control Vehicle Ambient Environment

Over the past few years, the popularity of personal voice assistants has grown, particularly for use in the vehicle environment where voice is a preferred mode of interface to minimize driving distractions. Amazon Alexa, one of the most popular voice assistants, has been integrated in many vehicle brands. While existing Alexa car applications provide vehicle occupants with access to a multitude of voice skills in infotainment and smart home control, these applications lack the capability to manage the vehicle’s ambient environment. This paper discusses an efficient and effective integration of Amazon Alexa with vehicle climate control, potentially augmented with brought-in devices, using SmartDeviceLink API. The paper overviews the architecture, Alexa skill development, and examples of dialogue. We also present the results of a customer evaluation of the presented system and directions for future research and development toward Ambient Intelligence.
Reproduction in the threatened crayfish Austropotamobius pallipes (Decapoda, Astacidae) in the Licenza brook basin (central Italy)

Abstract Austropotamobius pallipes is a vulnerable crayfish, and therefore it is necessary to analyse reproduction in the extant populations. Details of the female annual reproductive cycle in a Mediterranean population are provided for two consecutive years. Maternal-size-related variables and date of collection have been considered in relation to the number of ovarian and pleopodal eggs. Reproductive data from 2587 females collected by hand and traps are reported. In total, 204 (14.69%) and 231 (15.93%) individuals in 2003 and 2004, respectively, were ovigerous. Demonstrable differences exist between ovarian and pleopodal egg counts. In particular, ovarian egg counts were considerably higher than expected pleopodal egg numbers. Mating begins in November and is followed by egg laying in early December–January. Berried females occur until May–June, when hatching occurs. The numbers of both ovarian and pleopodal eggs are correlated with the size of females, although fecundity slightly decreases in larger females. The histology of the reproductive organs of both sexes is also analysed.
Radial Basis Function Neural Network Receiver Trained by Kalman Filter Including Evolutionary Techniques

: Artificial Neural Networks have been broadly used in several domains of engineering and typical applications involving signal processing. In this paper a channel equalizer using radial basis function neural networks is proposed, on symbol by symbol basis. The radial basis function neural network is trained by an extended Kalman filter including evolutionary techniques. The key motivation for the equalizer application is the neural network capability to establish complex decision regions that are important for estimating the transmitted symbols appropriately. The neural network training process using evolutionary techniques including an extended Kalman filter enables a fast training for the radio basis function neural network. Simulation results are included comparing the proposed method with traditional ones indicating the suitability of the application.
Temporal Network Approach to Explore Bike Sharing Usage Patterns

: The bike-sharing systems have been attracting increase research attention due to their great potential in developing smart and green cities. On the other hand, the mathematical aspects of their design and operation generate a lot of interesting challenges for researchers in the field of modeling, optimization and data mining. The mathematical apparatus that can be used to study bike sharing systems is not limited only to optimization methods, space-time analysis or predictive analytics. In this paper, we use temporal network methodology to identify stable trends and patterns in the operation of the bike sharing system using one of the largest bike-sharing framework CitiBike NYC as an example.
Vertical distribution of sulfate-reducing bacteria at the oxic-anoxic interface in sediments of the oligotrophic Lake Stechlin

Vertical distribution of sulfate-reducing bacteria (SRB) and sulfate reduction rates were investigated in littoral and profundal sediments of the oligotrophic Lake Stechlin. SRB counted by the most probable number (MPN) technique showed two peaks, one at the oxic-anoxic interface, and the second deeper in the reduced sediment layer. Sulfate reduction rates determined as potential rates in anoxic sediment slurries showed a similar vertical distribution with the highest values in the anoxic zones. The highest cell numbers observed were 1.3×105 cells cm−3 with lactate as substrate. SRB were also found in oxic sediment layers in numbers similar to those detected in anoxic zones. From the highest positive MPN tubes 34 pure cultures were isolated. Physiological characterization including nutritional behaviour, tests for desulfoviridin and spore formation turned out that nearly all isolates belonged to the genera Desulfovibrio, Desulfomicrobium, and Desulfotomaculum. SRB from the oxic sediment layers revealed a higher oxygen tolerance and capacity of oxygen respiration than isolates from the anoxic sediment layers. However, no sulfate reduction was observed in the presence of oxygen, since oxygen was preferentially reduced.
TESTING ALGORITHM IN CLOSTRIDIUM DIFFICILE INFECTION

The aim of the study was to show that the multi-step algorithm contributes to fast and more accurate diagnostics and enables to start an early treatment of C.difficile infection (CDI). Materials for the study was 2,894 stool samples suspected of having CDI, were analyzed retrospectively. The biological samples from a nearby region were tested by different testing methods in Klinicka Biochemia laboratory in Zilina. The laboratory diagnosis was performed according to two- or three- step algorithm. The cultivation methods and identification of C.dificille strains by MALDI-TOF MS was performed. Most of the samples suspected of CDI could be assessed as negative within the applied two-step algorithm. In discordant results – samples with GDH positive and toxins A/B negative results were tested by further methods with higher sensitivity - ELISA test and the real-time PCR method in three-strp algorithm. The most positive results were from the departments of internal medicine and long-term-care patients over 60 years of age. Seasonality has shown a slight increase in positivity in the spring and autumn months, especially in 2015. The algorithm contributed to increasing the accuracy of the CDI suspect samples to reduce morbidity and mortality as well as the economic burden on CDI.
Welding quality and sustainability of alternative LPG valve boss welding processes

This work aimed to evaluate the welding quality and the sustainability of an automatic metal active gas with mixing gases (MAG-M) process for welding the valve boss on liquefied petroleum gas (LPG) upper cylinder half, in comparison to the presently used automatic submerged arc welding (SAW) process. The weld quality of MAG-M welding samples met the ASME standards, comparably to the SAW welding samples. In addition, the MAG-M welding process for welding LPG valve bosses is preferable over the SAW welding process on the condition that >73,339 pieces are processed. However, the welding fumes and noises from this process have stronger environmental and social effects than those from SAW welding. Besides, the SAW process is preferable in LPG valve boss production up to 73,339 pieces. The solid waste or slag generated in this welding process should be managed.
Evidences for a multiple sex chromosome system with female heterogamety in Apareiodon affinis (Pisces, Parodontidae)

SUMMARYThe karyotype of the fish Apereiodon affinis shows 2n = 54 chromosomes for the male and 2n = 55 chromosomes for the female. The data obtained suggest a mechanism of multiple sex chromosomes of the ZZ/ZW1W2 type, where chromosome Z may be number 1 in the complement and chromosomes W1 and W2 are morphologically undifferentiated. This is the first probable occurrence of this type of mechanism to be described for fishes.
Learning Picture Languages Represented as Strings

Analysis of two-dimensional (picture) formal languages is of similar importance as analysis of their one-dimensional (string) counterparts but is lacking state-of-the-art algorithms for their learning. In this paper, we introduce a new representation of picture languages based on mapping pictures to strings. The representation enables to learn picture languages by applying methods of grammatical inference for string languages. We propose a learning protocol and evaluate it on several picture languages.
Introducing an Integrated SHIP® trauma-spectrum model on the etiology, characteristics and natural disposition of trauma-spectrum manifestations (TSM)

An integrated SHIP® (spontaneous healing intra-systemic process) trauma-spectrum model is introduced as a theoretical exposition of the etiology, characteristics and natural disposition of the proposed trauma-spectrum manifestations (TSM) diagnosis. The model proposes an interactive and interdependent developmental process of complementary factors for all types of trauma (complex PTSD and PTSD) and incorporates genetic, environmental and neural influences that effect the eventual selected coping configuration. Innate self-regulation towards people’s natural healthy blueprint disposition reflects the SHIP® point of reference and incorporates the psychobiodynamic healing script, consisting of psychobiodynamic translators, that stimulates awareness of the need for internal reconnection. Distracters that support a distressed coping style are discussed, as well as the discrepancy between the Integrated SHIP® Traumaspectrum Model and current pathology diagnostics.
Comparative Swimming Performance of Juvenile Blue Catfish and Hybrid Catfish

Abstract We compared the swimming performance of juvenile blue catfish Ictalurus furcatus and hybrid catfish (female channel catfish I. punctatus × male blue catfish) using a protocol in which fish were forced to swim at a fixed velocity and their time to fatigue recorded at speeds of 30–120 cm/s. Hybrid catfish swam significantly longer than blue catfish at all of the speeds tested except 30 and 40 cm/s. The maximum sustained swimming speed (no fatigue in times ≥200 min) was 30 and 40 cm/s for blue catfish and hybrid catfish, respectively. The maximum prolonged swimming speed (fatigue in times >20 s but <200 min) was 100 cm/s for blue catfish and 120 cm/s for hybrid catfish. The burst swimming speed (fatigue in times <20 s) for blue catfish was 110 cm/s. The burst swimming speed for hybrid catfish was not determined; however, it is higher than its maximum prolonged swimming speed of 120 cm/s (highest speed tested). Hybrid catfish were able to swim longer before fatiguing than were blue catfish at 50 cm/s.
CAARS: A Context-Aware Artist Recommender System for Twitter Users

In this work, we introduce a context-aware hybrid artist recommender system (CAARS) that uses Twitter users’ tweet-time patterns as context and users’ bias about gender and types of musicians to recommend artists. Our model offers a novel approach to improve a personalized music recommender system (MRS) as it extracts implicit information from the users’ past tweet-behavior and combines that with related content. The proposed model performs signiﬁcantly better than collaborative and hybrid recommender systems and encourages further exploration.
Instance Segmentation on Real time Object Detection using Mask R-CNN

In the ever-advancing field of computer vision, image processing plays a prominent role. We can extend the applications of Image processing into solving real-world problems like substantially decreasing Human interaction over the art of driving. In the process of achieving this task, we face several challenges like Segmentation and Detection of objects. The proposed thesis overcomes the challenges effectively by introducing Instance segmentation and Binary masks along with Keras and Tensorflow. Instance segmentation is used to delineate and detect every unique object of interest according to their pixel characteristics in an image. Mask RCNN is the superior model over the existing CNN models and yields accurate detection of objects more efficiently. Unlike conventional Neural Networks which employs selective search algorithm to identify object of interest, Mask RCNN employs Regional Proposal Networks(RPN) to identify object of interest. For better results Image pre-processing techniques and morphological transformations are employed to reduce the noise and increase pixel clarity
Conjugated linoleic acid concentration (CLA) in the m. longissimus thoracis of the offspring of Romney ewes screened for high and low CLA in their milkfat

Abstract Total conjugated fatty acid concentration in milk from 131 lactating Romney ewes at about the fourth week of lactation was measured spectro‐photometrically to identify 20 ewes with high and 20 ewes with low concentrations of conjugated linoleic acid (CLA) in the total fatty acid (TFA) of their milk. The persistence of these differences in CLA concentration was measured in milk from this and a subsequent lactation. The CLA concentration was also measured in plasma collected from these ewes 6 months after their lambs were weaned and in the m. longissimus thoracis (LT) of the offspring from these ewes. Allocation of ewes to the High CLA and Low CLA groups were confirmed by measuring the CLA concentration in the milkfat using gas liquid chromatography (GLC) (r = 0.73; P < 0.001). High CLA ewes had higher (P < 0.001) concentrations of CLA than Low CLA ewes in milkfat collected in the fourth week (2.55 versus 1.49 ± 0.07 g 100 g−1 TFA) and eighth week (2.18 versus 1.66 ± 0.09 g 100 g−1 TFA) of lactation, during the subsequent lactation (1.36 versus 0.90 ± 0.08 g 100 g−1 TFA), and in plasma collected 6 months after weaning (0.41 versus 0.34 ± 0.02 g 100 g−1 TFA; P < 0.05). Differences between the groups in trans‐vaccenic acid (TVA) concentrations were consistent with the differences in CLA concentrations in milk and plasma. Lambs slaughtered at 28 weeks of age (12 weeks post‐weaning) from High CLA ewes had a 37% higher (P < 0.05) CLA concentration in the TFA extracted from the LT than lambs from Low CLA ewes (0.82 versus 0.60 ± 0.06 g 100 g−1 TFA). Single‐born lambs from the High and Low CLA groups had a 35% higher (P < 0.01) CLA concentration in the LT than twin‐born lambs from these groups (0.81 versus 0.60 ± 0.06 g 100 g−1 TFA), suggesting that differences in CLA concentration in the meat were due to differences in the ingestion of CLA in milk. It is concluded that a higher concentration of CLA in the milkfat of ewes resulted in a higher concentration of CLA in the meat from their lambs and that the ranking of ewes for concentration of CLA in milkfat persisted for at least two lactations.
Validity Analysis of Simulation-based Testing concerning Free-space Detection in Autonomous Driving

: Automated vehicles must perceive their environment and accordingly plan a safe trajectory to navigate. Camera sensors and image processing algorithms have been extensively used to detect free-space, which is an unoccupied area where a car can safely drive through. To reduce the effort and costs of real test drives, simulation has been increasingly used in the automotive industry to test such systems. In this work, an algorithm for free-space detection is evaluated across real and virtual domains under different environment conditions: daytime, night time and fog. For this purpose, an algorithm is implemented to ease the process of creating ground-truth data for this kind of test. Based on the evaluation of predictions against ground-truth, the test results from the real test scenario are compared with its corresponding virtual twin to analyze the validity of simulation-based testing of a free-space detection algorithm.