Miracle-dz/UniFAR-FTUs huggingface dataset:
A Fitness Function for Search-based Testing of Java Classes, which is Based on the States Reached by the Object under Test

Genetic Algorithms are among the most efficient search-based techniques to automatically generate unit test cases today. The search is guided by a fitness function which evaluates how close an individual is to satisfy a given coverage goal. There exists several coverage criteria but the default criterion today is branch coverage. Nevertheless achieving high or full branch coverage does not imply that the generated test suite has good quality. In object oriented programs the state of the object affects its behavior. Thereupon, test cases that put the object under test, in new states are of interest in the testing context. In this article we propose a new fitness function which takes into consideration three factors for evaluation: the approach level, the branch distance and the new states reached by a test case. The coverage targets are still the branches, but during the search, the state of the object under test evolves with the scope to produce individuals that discover interesting features of the class and as a consequence can discover errors. We implemented this fitness function in the eToc tool. In our experiments the usage of the proposed fitness function towards the original fitness function results in a relative increase of 15.6% in the achieved average mutation score with the cost of a relative increase of 12.6% in the average test suite size.
New Kakeya estimates using the polynomial Wolff axioms

We obtain new bounds for the Kakeya maximal conjecture in most dimensions $n<100$, as well as improved bounds for the Kakeya set conjecture when $n=7$ or $9$. For this we consider Guth and Zahl's strengthened formulation of the maximal conjecture, concerning families of tubes that satisfy the polynomial Wolff axioms. Our results give improved estimates for this strengthened formulation when $n =5$ or $n \geq 7$.
A 14-bit 10kS/s power efficient 65nm SAR ADC for cardiac implantable medical devices

This brief presents a 10kS/s 14 bit 12.5 ENOB Successive Approximation Register Analog-toDigital Converter for Cardiac Implantable Medical. For achieving power efficient operation, SAR ADC employ SAR control, a new power and noise efficient comparator topology, nonbinary weighted capacitive DAC. The linearity of implemented SAR ADC is enhanced with the uniform geometry of non-binary weighted capacitive DAC.The proposed SAR ADC is implemented using 65nm CMOS technology. The latched comparator consumes a power of 2.4uW and it provides an ENOB of 12.6 at a supply voltage of 1V.The INL is between -2.7/+1.6 LSB and DNL is between 0.6/+1.4LSB. The FOM of ADC is 40fJ/conv. Step which is comparable with existing ADC topologies.
A^1-homotopy theory and contractible varieties: a survey.

We survey some topics in ${\mathbb A}^1$-homotopy theory. Our main goal is to highlight the interplay between ${\mathbb A}^1$-homotopy theory and affine algebraic geometry, focusing on the varieties that are "contractible" from various standpoints.
A Qualitative Analysis of the Motivation Investigation of Emotional Intelligence (EI) in dealing with Job Satisfaction (Case Study in Education of Dubai)

The major aim of this study was to examine the role of emotional intelligence in education. In today's world competitive setting, motivation in education plays a main role in the relationship between emotional intelligence and job satisfaction. This paper is undertaken to understand the influence of motivation in the relationship between employees’ emotional intelligence and job satisfaction in education of Dubai. The goal is to improve job satisfaction in that context. Studies indicate the emotional regulation and expression are essential when dealing with social and educational interactions such as job satisfaction. Research supports the necessity for investigation of the relationship of emotional intelligence as a swaying factor in the areas of satisfaction predominantly as it relates to job satisfaction. While researching the literature on
Trajectory Clustering and Behaviour Retrieval from Traffic Surveillance Videos

The system presented here uses a minimum of prior knowledge to retrieve trajectories and abnormal behaviour. This system is based on the premise that most members of society choose to comply with traffic laws. The system input is only two parameters, namely (1) the maximum number of junctions available and (2) the rate of infractions in the city. In an unsupervised manner, the new system is capable of learning all legal behaviour and, on its own, extracting the knowledge required for detection of illegal behaviour. The query model used in this system is based on keywords and user sketches. Keywords are labels applied automatically by the system to express the activity models. The proposed system also optimizes user sketches before implementation. Practical implementations have demonstrated the high efficiency of this system in learning legal behaviour and detecting illegal practices.
CMC-1 trinoids in hyperbolic 3-space and metrics of constant curvature one with conical singularities on the 2-sphere

CMC-1 trinoids (i.e. constant mean curvature one immersed surface with three regular embedded ends) in hyperbolic 3-space H^3 are irreducible generically, and the irreducible ones have been classified. However, the reducible case has not yet been fully treated, so in this paper we give an explicit description of CMC-1 trinoids in H^3 that includes the reducible case.
Obstructions to the deformations of curves to other hypersurfaces

We are interested in obstructions to the FIRST order deformation of a pair of a smooth hypersurface $f_0$ and a smooth curve $C_0$ contained in $f_0$. In the first half of the paper, we give necessary conditions for the pair to deform in the first order. In particular, for a rational curve $C_0$, this necessary condition is $$H^1(N_{C_0}f_0(1))=0. $$ In the second half, we apply the necessary conditions from the first half of the paper to study the geometry of smooth curves in hypersurfaces. The main application is for the case where $C_0$ is a rational curve.
Content-based image retrieval using scale invariant feature transform and gray level co-occurrence matrix

The rapid growth of different types of images has posed a great challenge to the scientific fraternity. As the images are increasing everyday, it is becoming a challenging task to organize the images for efficient and easy access. The field of image retrieval attempts to solve this problem through various techniques. This paper proposes a novel technique of image retrieval by combining Scale Invariant Feature Transform (SIFT) and Co-occurrence matrix. For construction of feature vector, SIFT descriptors of gray scale images are computed and normalized using z-score normalization followed by construction of Gray-Level Co-occurrence Matrix (GLCM) of normalized SIFT keypoints. The constructed feature vector is matched with those of images in database to retrieve visually similar images. The proposed method is tested on Corel-1K dataset and the performance is measured in terms of precision and recall. The experimental results demonstrate that the proposed method outperforms some of the other state-of-the-art methods.
From Study Tactics to Learning Strategies: An Analytical Method for Extracting Interpretable Representations

Research into self-regulated learning has traditionally relied upon self-reported data. While there is a rich body of literature that has extracted invaluable information from such sources, it suffers from a number of shortcomings. For instance, it has been shown that surveys often provide insight into students’ perceptions about learning rather than how students actually employ study tactics and learning strategies. Accordingly, recent research has sought to assess students’ learning strategies and, by extension, their self-regulated learning via trace data collected from digital learning environments. A number of studies have amply demonstrated the ability of educational data mining and learning analytics methods to identify patterns indicative of learning strategies within trace log data. However, many of these methods are limited in their ability to describe and interpret differences between extracted latent representations at varying levels of granularity (for instance, in terms of the underlying data of student actions and behavior). To address this limitation, the present study proposes a new methodology whereby interpretable representations of student's self-regulating behavior are derived at two theoretically inspired levels: that of learning strategies, and the study tactics that compose them.
Real-Time Multistep Attack Prediction Based on Hidden Markov Models

A novel method based on the Hidden Markov Model is proposed to predict multistep attacks using IDS alerts. We consider the hidden states as similar phases of a particular type of attack. As a result, it can be easily adapted to multistep attacks and foresee the next steps of an attacker. To achieve this goal, a preliminary off-line training phase based on observations will be required. These observations are obtained by matching the IDS alert information with a database previously built for this purpose using a clusterization method from the CVE global database to avoid overfitting. The training model is performed using both unsupervised and supervised algorithms. Once the training is completed and probability matrices are computed, the prediction module compute the best state sequence based on the state probability for each step of the multistep attack in progress using the Viterbi and forward-backward algorithms. The training model includes the mean number of alerts and the number of alerts in progress to assist in obtaining the final attack probability. The model is analyzed for DDoS phases because it is a great problem in all Internet services. The proposed method is validated into a virtual DDoS scenario using current vulnerabilities. The results proving the system's ability to perform real-time prediction.
Pendidikan Karakter : Grand design dan nilai-nilai target

Buku ini ditulis berdasarkan hasil seminar dan lokakarya restrukturisasi pendidikan karakter ini mampu memberi tawaran sederhana mengenai grand design dan nilai-nilai target pendidikan karakter. Buku ini secara terperinci memuat tentang memahami karakter dasar manusia, upaya membentuk pendidikan karakter, potret pendidikan karakter di berbagai jenjang sekolah, membangun karakter bangsa melalui pembelajaran sejarah, pembinaan akhlak mulia dalam berhubungan antarsesama manusia perspekti Islam, serta restrukturisasi pendidikan karakter.
A arte rupestre de Jequitaí/MG entre práticas gráficas padronizadas e suas manifestações locais

A dissertacao de mestrado enfoca a arte rupestre de sete sitios pre-historicos localizados no trecho medio-baixo da bacia do rio Jequitai, localizada no norte de Minas Gerais. Ela privilegia uma analise estilistica dos conjuntos graficos em busca da construcao de um quadro cronoestilistico micro regional e sua comparacao com regioes vizinhas, ja pesquisadas por outros arqueologos. (.)
Securing the Grid using Virtualization The ViSaG Model

—Security in large distributed computing infrastructures, peer-to-peer, or clouds, remains an important issue and probably a strong obstacle for a lot of potential users of these types of computing infrastructures. In this paper, we propose an architecture for large scale distributed infrastructures guaranteeing confidentiality and integrity of both the computation and the host computer. Our approach is based on the use of virtualization and we introduce the notion of confidence link to safely execute programs. We implemented and tested this approach using the POP-C++ tool, which is a comprehensive object-oriented system to develop applications in large decentralized distributed computing infrastructures.
Generative Adversarial Network for Videos and Saliency Map

• CC score = 0.80 • AUC_Borji score = 0.73 • NSS score = 1.98 • Stochastic Gradient Descent is used to alternate between any differentiable G and differentiable D with respect to parameters ωωGG and ωωDD to minimize losses. • The first term of the loss function roughly corresponds to the probability of the data from the sample being real, while the second term is probability of the output of the generator being fake. Acknowledgements
Polytopic Model Weighted H∞ Output Feedback Control of Switched Lpv System Based on Mdadt

This paper provides and validates a class of weighted H∞ output feedback controllers of switched LPV (Linear Parameter Varying) systems based on the MDADT (Modal Dependent Average Dwell Time) method. First of all, the article uses a multi-lyapunov function to convert the linear parameter varying system into a polytopic model, and designs a switching law that can guarantee the system to be globally uniform and exponentially stable on the MDADT method. It can be shown that when the state is stable and can be measured in real time, the switching law and weighted H∞ controllers for each subsystem under the MDADT limit can be calculated. Finally, a numerical example is given to verify the feasibility of this method and the stability of the switched LPV system.
The limitations of using only CAD and DHM in design relating to high value manufacturing

The ergonomics suites available within Computer Aided Design and Digital Human Modelling programs are increasingly being used to predict and prevent ergonomic and human factors risk due to poor design. To further aid the reduction in poor design, it is of importance to understand the need for user input and the limitations of these software programs. These limitations include: the small number of available anthropometric population samples; and the disconnect between what a designer perceives as possible, and what is possible within a manufacturing environment. A method of mitigating these limitations is the use of user input using virtual reality suites, mock-ups and motion capture technology.
Impact of Auditory Interference on Automated Task Monitoring and Workload

The purpose of this research was to explore the effects of auditory distraction and criticality on pilots’ flight accuracy and workload, along with attention allocation with different reliability levels on the Multi- Attribute Task Battery II (MATB-II) simulator. The MATB-II involves attending to multiple systems and correcting errors made by the automation, which will be presented at multiple levels of reliability (50%, 70%, 90%) and distractor tasks. The presence of a complex communication task before or concurrent with the main task negatively impacts accuracy of automation failure detection (Stader, 2014). It was hypothesized that reliability level, as well as the presence of an auditory distractor task would affect human monitoring performance and workload. The data indicated distraction could affect the accuracy of pilot’s multi-tasking abilities, as well as flight pattern proficiencies and workload. This information would be applicable to cockpit protocol and ensure a more efficient flight.
Programa de mindfulness para la reducción de la ansiedad precompetitiva en deportistas de artes marciales

El objetivo del presente estudio fue examinar el efecto de un programa de intervención con componente mindfulness para la reducción de la ansiedad precompetitiva, el cual fue adaptado de programas que han demostrado su eficacia. La intervención estuvo compuesta por 12 sesiones de 1 hora de duración. Participaron en el estudio 33 deportistas de artes marciales (M = 18.97 años, DE = 1.64; 69.7% hombres). La ansiedad precompetitiva fue evaluada con la versión revisada del Inventario de Ansiedad Estado Competitiva -2 (CSAI-2R; Cox, Martens y Rusell, 2003), antes y después del periodo de la intervención. De acuerdo al grado de participación en la intervención, la muestra fue dividida en 2 grupos: Grupo Control (GC, n = 16) y Grupo Experimental (GE, n = 17). Los resultados evidenciaron diferencias significativas con tamaños del efecto grandes en la medida post test del GE, revelando una disminución en los niveles de ansiedad somática y cognitiva, y un aumento en los niveles de autoconfianza. Mientras, en el GC, no se obtuvo diferencias significativas y los tamaños del efecto fueron irrelevantes
The Flight Navigation Planning Based on Potential Field Ant Colony Algorithm

Path planning in complex environment is the main foundation of flights automation navigation. Therefore, this paper aims to propose an algorithm combined ant colony optimal algorithm with potential field heuristic information. The main implement method of this algorithm is to use the information between the environment and goal to build the heuristic elements. What’s more, the method defines this information as initial pheromone and converses elements to guide the ants to find the shortest path. Thanks to the heuristic elements in algorithm,the PFACO algorithm has a better searching tendency than traditional ACO algorithm,and overcome disadvantage in ants blindness. Through the simulation, the results show that PFACO has a better tendency of convergence. Compared with ACO, the PFACO algorithm can find a shorter path in same convergence time.
Coblation vs Microdebrider-assisted Inferior Turbinoplasty

Aim: The aim of this study is to compare the effectiveness of coblation and microdebrider-assisted turbinoplasty in reducing nasal obstruction due to inferior turbinate hypertrophy (ITH). Background: A comprehensive search of articles in English language was performed in PubMed using the keywords coblation turbinoplasty, coblation inferior turbinate reduction, microdebrider turbinoplasty, microdebrider assisted inferior turbinate reduction. Review results: Primary search yielded 41 results with only two fulfilling the inclusion and exclusion criteria. In both studies, patients were assessed objectively as well as subjectively. Improvement in nasal obstruction was similar in both coblation and microdebrider groups up to 6 months of follow-up in both studies. However, in the study by Lee and Lee (2006) at 12 months postoperative follow-up, microdebrider-assisted turbinoplasty patients showed a better improvement in nasal obstruction both objectively on acoustic rhinometry and subjectively as compared with the coblation group (p < 0.05). Conclusion: Despite getting better results with microdebriderassisted turbinoplasty in one of the study, it can be safely concluded that longer postsurgical follow-up period with bigger sample size is required to adequately comment on the extra benefit offered by either coblation or microdebrider. Clinical significance: As and when further research is planned on comparing benefits of different powered instruments for turbinate reduction, it will be wise to prolong the follow-up period with increased sample size.
Usage of acoustic cues in spoken term detection keyword spotting for zero low resource languages

: The proposed work exploits acoustic cues at various levels and incorporates them in the present (Spoken Term Detection) STD frame work. Recently proposed new syllabification method [1] for speech signal is being used for STD. In STD, a query and reference speech signals are provided, these speech signals are syllabified using the new syllabification method and features like Mel-frequency cepstral coefficients (MFCC), posterior grams are extracted. These features are then matched using template based match techniques like dynamic time warping (DTW) at syllable level instead of regular frame level. This essentially reduces the unwanted matching done at frame level.
An Experiential Learning of Ontology-Based Multi-document Summarization by Removal Summarization Techniques

Outstanding development of the Internet alongside the New technologies, for example, high-speed networks and cheap monstrous storage, alongside the, have prompted a gigantic increment in the price and accessibility of digital document. For any person, perusing of this information is huge tedious so need to get to multi-document summarization (MDS) frameworks, which can successfully consolidate data found in a few documents into a short, readable synopsis, or summary. For semantic representation of textual data in ontology region, as a hypothetical model, our framework gives a significant structure. The feasibility of utilizing the ontology as a part of taking care of multi-document summarization issues in the area of disaster administration is finding in propose model. Saliency score is normally assigned out to every sentence and sentences are positioned by score, then the top positioned sentences are chosen as the summary. Regarding the summary magnificence, wide tests on an accumulation of press discharges applicable to "Jammu Kashmir Flood in 2014" documents. Ontology based multidocument summarization systems utilizing "NLP based extraction" outflank different baselines. Our commitment in propose module is to utilize the data extraction strategies (NLP) to improve and enhance summary result. Keywords—Multi document summarization, Disaster Management, Ontology, Extraction technique, k-means, NLP, Sentence Extraction,
INTRODUCTION TO LEARNING ANALYTICS ADOPTION IN HIGHER EDUCATION INSTITUTIONS

Educational data mining and Learning analytics represent a pair of research disciplines, which cover the majority of these data mining techniques, methods, applications as well as data mining tools in the area of education. The main aim of the paper is to summarize the main characteristics of Learning Analytics and focus on the approaches and frameworks used for its successful adoption and implementation of the environment of higher educational institutions. DOI: https://doi.org/10.28925/2414-0325.2017. 3.1730