Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatically deriving a uml analysis model from a use case model

The transition from requirements expressed in Natural Language (NL) to a structured, precise specification is an important challenge in practice. It is particularly so for object-oriented methods, defined in the context of the OMG's Model Driven Architecture (MDA). However, its automation has received little attention, mostly because requirements are in practice expressed in NL and are much less structured than other kinds of development artifacts. Such an automated transformation would enable at least the generation of an initial, likely incomplete, analysis model and enable automated traceability from requirements to code, through various intermediate models. In this thesis, we propose a method and a tool, building on existing work, to automatically generate analysis models from requirements. Such models include class, sequence and activity diagrams to describe the structure and behavior of a system. It also includes automatically establishing traceability links between the requirements and the automatically generated analysis model. Requirements are assumed to be modeled with our use case modeling approach (RUCM), which we showed to have enough expressive power, to be easy to apply, to help improve the quality of manually derived analysis models. Seven (six) case studies were performed to compare class (sequence) diagrams generated by our tool to the ones created by experts, Masters students, and trained, 4th year undergraduate students. Our results show that our method performs well when compared to reference diagrams. Further, statistical test results show that our tool significantly outperforms 4 th year engineering students, thus demonstrating the value of automation. Performance analysis results show that the execution time of the tool remains within a range of a few minutes, thus suggesting the approach is scalable. Five case studies were performed to assess our approach's capability to generate activity diagrams. The results show that high quality activity diagrams can be generated and the analysis also shows that our approach outperforms existing academic approaches and commercial tools. We also conducted two industrial case studies, yielding results that demonstrate that RUCM is applicable in two different industrial domains and that aToucan-generated analysis models are largely correct and complete from the perspective of domain experts.
Edge Detection using Multiple Ant Colony Approach

: The gray level change between any two regions is described as an edge. This change is spread out among a lot of pixels. There are algorithms which can be used to detect edges. Some of them are designed in such a way that they can detect straight visible edges. Also, there were algorithms whose settings can detect partial edges. But it is very difficult to design an algorithm, which could detect partial edges and also correct edges. At the same time the algorithm should also have a setting to manage hidden edges. Multiple ant colony approach is a best method to get a good solution. This will be possible due to the cohabitation of various colonies. The activities of ant colonies that are coordinated by an interaction on the colony level is the basic idea of multiple ant colony approach.
An Investigation into the Imposed Cognitive Load of Static & Dynamic Type Systems on Programmers

An Investigation into the Imposed Cognitive Load of Static & Dynamic Type Systems on Programmers Ian Vaughn Koeppe, MS University of Nebraska, 2018 Advisor: Dr. Brian Dorn Static and dynamic type systems have long been a point of contention in the programming language wars. Yet, for many years, arguments on either side were drawn from personal experience and not empirical evidence. A challenge for researchers is that the usability of language constructs is difficult to quantify, especially since usability can be interpreted in many ways. By one definition, language usability can be measured in terms of the level of cognitive load imposed on a developer. This can be done through questionnaires, but ultimately user responses are subject to bias. In recent years, eye-tracking has been shown to be an effective means of measuring cognitive load via direct physiological measures. Towards the goal of measuring type system usability, we present a user study in which participants completed programming tasks in Java and Groovy. This thesis explored the use of the Index of Cognitive Activity (ICA) as a cognitive load measurement tool and considered novices and experts separately in the analysis. We found ICA to be an ineffective means of measuring type system usability and we cannot say conclusively whether it can be generally applied to programming tasks. Despite this, our results contradict previous studies as we found type system did not affect success rate, task completion time, or perceived task difficulty.
Feature Extraction and Tracking of CNN Segmentations for Improved Road Detection from Satellite Imagery

Road detection in high-resolution satellite images is an important and popular research topic in the field of image processing. In this paper, we propose a novel road extraction and tracking method based on road segmentation results from a convolutional network, providing improved road detection. The proposed method incorporates our previously proposed connected-tube marked point process (MPP) model and a post-tracking algorithm. We present experimental results on the Massachusetts roads dataset to show the performance of our method on road detection in remotely-sensed images.
Effects of Humic Acid and Cow Manure Biochar (CMB) in Culture Medium on Growth and Mineral Concentrations of Basil Plant

In sustainable agriculture, protection of the ecosystem and reduce chemical fertilizers are the main goals. Nowadays, sweet basil, one of the best general fragrant foliage, is considered as an industrial plant. In present study to compare different levels of cow manure biochar (CMB) and humic acid (HA) on vegetative traits and mineral absorption rate of green basil plants, an experiment was conducted based on a completely randomized block design with four replications in 2015. The treatments included different levels of CMB (0, 1%, 2%, and 3%) and HA (2.5, 5 and 7.5 g/L) in the culture medium. The traits that were analyzed were included: plant height, leaf number, leaf area, stem diameter, leaf chlorophyll concentration (LCC), fresh and dry weights of aerial part and root, concentration of potassium, nitrogen, calcium and phosphorus. Results showed that fresh and dry weights of shoot and root were increased by application of 3% CMB and 7.5 g/L HA in the culture medium. Theuse of CMB and HA significantly affected most of the studied traits except LCC, stem diameter, and leaf area. Concentration of N, P, K and Ca was considerably increased by application of 3% CMB and 7.5 g/L HA in the culture medium. The highest concentration of nutrients was detected in the plants treated with 3% CMB. In conclusion, the use of 3% CMB and HA at 7.5 g/L in the culture medium can be recommended to improve the qualitative and quantitative traits of basil plants.
Physical and chemical properties of chicken mortadella formulated with Moringa oleifera Lam. seed flour

The objective of this study was to evaluate the effect of the addition of Moringa oleifera Lam. seed flour for partial fat replacement in chicken mortadella on physicochemical characteristics, chemical composition, and lipid oxidation. Four mortadellas were preparedI: C (control), T1, T3, and T5 (addition of 1%, 3%, and 5% of moringa seed flour, respectively). T5 mortadella had the lowest lipid content (p < 0.05), whereas C mortadella had the highest (p < 0.05). The addition of 5% of moringa seed flour affected color parameters, leading to an increase in L*, a*, and b* values in comparison with the control. Color changes (ΔE) in T3 and T5 mortadellas were the lowest among the samples during a 90-day storage period at 4 °C. The addition of 3 or 5% of moringa seed flour promoted a reduction in lipid oxidation during storage. Moringa seeds have antioxidant activity and, therefore, have the potential to be used as a natural functional ingredient in meat products. The addition of 3% of moringa seed flour seemed to be ideal for chicken mortadella, as it reduced lipid content and promoted lipid stability without causing noticeable color changes during the 90 days of storage.
Modeling of an Autonomous Underwater Vehicle

Autonomous Underwater Vehicles (AUV) have multiple applications for military, commercial and research purposes. The main advantage of this technology is its independence. Since these vehicles operate autonomously, the need for a dedicated support vessel and human supervision is dismissed. However, the autonomous nature of AUVs also presents a complex challenge for the guidance, navigation and control system(s). The design of motion controllers for AUVs is model-based i.e. a dynamic model is used for the design of the control system. The dynamic model can also be used for simulation and performance analysis. In this context, the purpose of this thesis is to provide a dynamic model for a double-body research AUV being developed at CEiiA. This model is to be subsequently used for the design of the control system. Since the purpose is the design of the control system and, in the scope of providing multiple design approaches, the appropriate lateral and longitudinal subsystems are devised. These subsystems are subsequently validated by comparing simulation results for the subsystems with simulation results for the complete model. The AUV is modeled using Fossen’s dynamic model. The model is divided into kinematics and kinetics. Kinematics addresses the geometrical aspects of motion. For this purpose, both Euler angles and quaternions are used. Kinetics focuses on the relationship between motion and force. This model identifies four distinct forces that act on the underwater vehicle: rigid-body forces; hydrostatic forces; hydrosynamic damping (or drag) and added-mass. The estimation of model parameters is performed using analytical and computational methods. A detailed 3D CAD model, developed by CEiiA, proved helpful for estimating mass and inertia parameters as well as hydrostatic forces. Hydrodynamic damping estimation was performed by adapting CFD analysis, also developed by CEiiA, to satisfy model parameters. Added mass parameters were estimated using proven analytical methods. Due to limitations inherent to current modeling methods, simplifications were unavoidable. These, when analyzed considering the requirements of typical control systems, did not pose an impediment to the use of the dynamic model for this purpose. Regarding the dynamics of this AUV, the hydrodynamic analysis suggests that this AUV is unstable in the presence of angles of attack and side-slip. However the AUV’s motors should be capable of controlling such instabilities.
Thermodynamic stability of hexagonal and rhombohedral boron nitride under chemical vapor deposition conditions from van der Waals corrected first principles calculations

Thin films of boron nitride (BN), particularly the sp2-hybridized polytypes hexagonal BN (h-BN) and rhombohedral BN (r-BN), are interesting for several electronic applications, given the bandgaps in the UV. They are typically deposited close to thermal equilibrium by chemical vapor deposition (CVD) at temperatures and pressures in the regions 1400–1800 K and 1000–10 000 Pa, respectively. In this letter, the authors use the van der Waals corrected density functional theory and thermodynamic stability calculations to determine the stability of r-BN and compare it to that of h-BN as well as to cubic BN and wurtzitic BN. The authors find that r-BN is the stable sp2-hybridized phase at CVD conditions, while h-BN is metastable. Thus, their calculations suggest that thin films of h-BN must be deposited far from thermal equilibrium.Thin films of boron nitride (BN), particularly the sp2-hybridized polytypes hexagonal BN (h-BN) and rhombohedral BN (r-BN), are interesting for several electronic applications, given the bandgaps in the UV. They are typically deposited close to thermal equilibrium by chemical vapor deposition (CVD) at temperatures and pressures in the regions 1400–1800 K and 1000–10 000 Pa, respectively. In this letter, the authors use the van der Waals corrected density functional theory and thermodynamic stability calculations to determine the stability of r-BN and compare it to that of h-BN as well as to cubic BN and wurtzitic BN. The authors find that r-BN is the stable sp2-hybridized phase at CVD conditions, while h-BN is metastable. Thus, their calculations suggest that thin films of h-BN must be deposited far from thermal equilibrium.
Machine Learning for Recession Prediction and Dynamic Asset Allocation

The authors introduce a novel application of support vector machines (SVM), an important machine learning algorithm, to determine the beginning and end of recessions in real time. Nowcasting, forecasting a condition in the present time because the full information will not be available until later, is key for recessions, which are only determined months after the fact. The authors show that SVM has excellent predictive performance for this task, capturing all six recessions from 1973 to 2018 and providing the signal with minimal delay. The authors take advantage of the timeliness of SVM signals to test dynamic asset allocation between stocks and bonds. A dynamic risk budgeting approach using SVM outputs appears superior to an equal-risk contribution portfolio, improving the average returns by 85 bps per annum without increased tail risk. TOPICS: Big data/machine learning, financial crises and financial market history, portfolio construction, tail risks
Combined Static and Dynamic Verification of Object Oriented Software Through Partial Proofs

When verifying software one can make use of several verification techniques. These techniques mostly fall in one of two categories: Static Verification and Dynamic Verification. Static verification deals with the analysis of either concrete source code, or a model of it. These kinds of techniques can verify properties over all possible runs of a program. Dynamic verification is concerned with the monitoring of software, providing guarantees that observed runs comply with specified properties. It is strong in analysing systems of a complexity that is difficult to address by static verification, e.g., systems with numerous interacting sub-units, concrete (as opposed to abstract) data, etc. On the other hand, its major drawbacks are the impossibility to extrapolate correct observations to all possible runs, and that the monitoring of a property introduces runtime overheads. It is quite clear that static and dynamic verification have largely disjoint strengths. Therefore, their combination can allow the verification process to deal with richer properties, with greater ease. The work presented in this thesis addresses this issue by introducing some manners to combine static and dynamic verification, where partial proofs are used as a means to accomplish the combination. The main novelty in these combinations consists in the fact that all of them consider the use of the partial proofs in the verification process, whereas, in general, other verification approaches discard them right away. The main contributions of this thesis are: (i) ppDATE, an automata-based formalism to specify both data- and control-oriented properties; (ii) structural operational semantics for ppDATE; (iii) a translation of ppDATE to DATE together with a proof of correctness; (iv) StaRVOOrS, a tool for combining (static) deductive verification and runtime verification of object oriented software; (v) a testing focused development methodology which integrates deductive and runtime verification in its workflow; and (vi) a methodology to infer global trace conditions for a system, from partial proofs local to the transitions of a model, obtained by performing low effort verification attempts to properties.
Portfolio Effect on End User Spare parts based on Demand Patterns

This paper use 1577 items of end-user spare parts data from two authorized service-center for a global brand motor cycle product that become the main means of public transportation in Indonesia to study the Portfolio Effect on end-user spare parts based on their demand patterns categorized as smooth, erratic, intermittent and lumpy. It found that items in the lumpy category got larger benefits from Portfolio Effect compared to other categories, with an average of 25.13% and standard deviation 9.70%. Despite their high fluctuations, they have relatively low demand (ρ) and Magnitude (M) coefficients so they can have more benefit from consolidation with relatively low risk.
PENGEMBANGAN MODUL PEMBELAJARAN INKUIRI BERBASIS LABORATORIUM VIRTUAL

The purpose of this research is to develop a virtual laboratory based inquiry instructional module. This module was built following the systematic instructional design process including the steps of analysis, design, development, implementation, and evaluation (ADDIE). The feasibility of the instructional module as a product of the development process refers to expert appraisal and student response as a testing subject. The result of the research showed that virtual laboratory based inquiry instructional module was meet the feasibility standard of instructional material consist of validity, practically, and effectiveness. Penelitian ini bertujuan untuk mengembangkan modul pembelajaran biologi berbasis pendekatan pembelajaran inkuiri yang dilengkapi dengan media simulasi interaktif berupa laboratorium virtual. Modul dikembangkan mengacu pada model penelitian dan pengembangan ADDIE yang meliputi lima tahapan, yaitu analysis , design , development , implementation , dan evaluation. Kelayakan modul hasil pengembangan mengacu pada hasil penilaian ahli, praktisi lapangan, dan respon siswa sebagai subjek uji coba. Hasil penelitian menunjukkan modul yang dikembangkan telah memenuhi standar aspek kelayakan yang meliputi validitas, kepraktisan, dan keefektifan bahan ajar.
Ligand Dependent Growth and Optical Properties of Hybrid Organo-metal Halide Perovskite Magic Sized Clusters

Magic sized clusters (MSCs), small nanoparticles with a single size or narrow size distribution, possess unique chemical and physical properties. CH3NH3PbBr3 perovskite MSCs (PMSCs) were synthesized using butylamine (BTYA) and valeric acid (VA) as well as 3,3-diphenylpropylamine (DPPA) and VA as capping ligands in an excess amount. The PMSCs were characterized using UV–vis and photoluminescence (PL) spectroscopy. For BTYA-VA PMSCs, there is an excitonic absorption peak at 424 nm and PL peak at 434 nm, while the DPPA-VA PMSCs exhibit an absorption peak at 428 nm and PL peak at 451 nm. The size and optical properties of the PMSCs are found to be dependent on the ligands used for passivation. Additionally, in a series dilution of DPPA-VA PMSCs in toluene, it was discovered that different sized perovskite quantum dots (PQDs) can be synthesized with tunable PL emission with peaks ranging between 451 and 525 nm. This phenomenon was not observed for BTYA-VA PMSCs. This contrast indicates the unique interaction b.
Fuzzy-based Sentiment Analysis System for Analyzing Student Feedback and Satisfaction

The feedback collection and analysis has remained an important subject matter since long. The traditional techniques for student feedback analysis are based on questionnaire-based data collection and analysis. However, the student expresses their feedback opinions on online social media sites, which need to be analyzed. This study aims at the development of fuzzy-based sentiment analysis system for analyzing student feedback and satisfaction by assigning proper sentiment score to opinion words and polarity shifters present in the input reviews. Our technique computes the sentiment score of student feedback reviews and then applies fuzzy-logic module to analyze and quantify student’s satisfaction at the fine-grained level. The experimental results reveal that the proposed work has outperformed the baseline studies as well as state-of-the-art machine learning classifiers.
Anti-oxidant activity of avicularin and isovitexin from Lespedeza cuneata

This study evaluated the anti-oxidant activity of avicularin and isovitexin from Lespedeza cuneata using 2,2-diphenyl-1-picrylhydrazyl (DPPH) and hydroxyl (OH) scavenging assays. The results showed that among the four fractions, the highest OH radical scavenging activity was exhibited by the n-butanol fraction. The DPPH radical scavenging activities of avicularin and isovitexin were higher than 50% at a concentration of 100 μg/mL. Moreover, one hundred micrograms per liter of avicularin and isovitexin exhibited effective anti-oxidant activity, with the OH radical scavenging rates being 87.54 and 91.48%, respectively. These results suggest that Lespedeza cuneata would be useful anti-oxidant agents from natural sources.
Location-Aware Sleep Strategy for Energy-Delay Tradeoffs in 5G with Reinforcement Learning

In this paper, we propose a sleep strategy for energy-efficient 5G Base Stations (BSs) with multiple Sleep Mode (SM) levels to bring down energy consumption. Such management of energy savings is coupled with managing the Quality of Service (QoS) resulting from waking up sleeping BSs. As a result, a tradeoff exists between energy savings and delay. Unlike prior work that studies this problem for binary state BS (ON and OFF), this work focuses on multi-level SM environment, where the BS can switch to several SM levels. We propose a Q-Learning algorithm that controls the state of the BS depending on the geographical location and moving velocity of neighboring users in order to learn the best policy that maximizes the tradeoff between energy savings and delay. We evaluate the performance of our proposed algorithm with an online suboptimal algorithm that we introduce as well. Results show that the Q-Learning algorithm performs better with energy savings up to 92% as well as better delay performance than the heuristic scheme.
Blends at the Intersection of Addition and Subtraction: Evidence from Processing

The present paper argues that blends (e.g. predictionary ← prediction + dictionary) and clipping compounds (e.g. finlit ← financial + literacy) exemplify a more general morphological phenomenon, mainly, a continuum of formations driven by two counteracting processes: compounding and clipping. In particular, clipping compounds are likely to be formed from words that co-occur in speech comparatively frequently, while blends, on the other hand, tend to be formed from semantically and phonetically similar words in such a way that the source words remain recognisable (Gries, 2006). This paper demonstrates that the formal differences between blends and clipping compounds result in differences in the processing of these lexemes by language users. The results of a psycholinguistic experiment combining an identifying and production task with a lexical decision task show that blends with higher degree of formal transparency better prime their source words in a lexical decision task than blends with lower degree of formal transparency or clipping compounds. These results uphold the claims in Gries (2006, 2012) and shed light on the findings in Lehrer (1996, 2003) using a revised methodology and recent lexical data.