Miracle-dz/UniFAR-FTUs huggingface dataset:
Architecture Recovery of Legacy Software Systems Using Unsupervised Machine Learning Techniques

Perhaps the most important aspect in maintaining software legacy systems is un-derstanding their architecture. Architectural documentation is often unavailable. Thus eforts need to be made to recover the architectural design from the source code. This thesis addresses the problem of recovering the architecture of software systems for greater understanding, and modularizing them for greater maintainability, using machine learning techniques. We use clustering to obtain a high-level view of a software's architecture, by identify-ing major sub-systems within it. For this purpose, we analyze the behaviour of existing similarity and distance measures when applied to software artifacts and keeping in view software characteristics, yielding explanations to some previously unanswered questions.We develop two new hierarchical clustering algorithms that address the problem of ar-bitrary decisions taken by existing hierarchical algorithms. We also propose a similarity measure suitable for software clustering. The performance of the proposed algorithms and similarity measure is evaluated using internal and external assessment. Instead of using only one expert decomposition for external assessment, as is commonly done, we use decompositions prepared by 4-5 experts for each test system. Such an approach allows us to validate the idea of multiple views of a software system. Experiments carried out on ¯ve open source legacy software systems show that the performance of our proposed algorithm is better than previously used algorithms. Interpreting the results of clustering algorithms is often difficult. To make clusters easier to understand, we propose a labeling scheme for clusters and compare two alter-native ranking schemes that can be utilized for this purpose. We demonstrate how the labels assigned by our scheme aid understanding of the clustering process of clustering algorithms. We also provide a comparison between cluster analysis and concept analysis as modularization techniques, and give examples of their application to different software structures, thus indicating the strengths and limitations of the two techniques. Finally, we use association rule mining to gain insight into the low-level structure of software systems by examining relationships between architectural quarks i.e. functions,global variables and user de¯ned types. Metarule-guided association rule mining is used to identify problems within structured legacy systems. Re-engineering patterns that present solutions to these problems are proposed. Results for the test systems reveal interesting characteristics which allow us to understand legacy systems and their evolution.
Mobile Phones for AgriculturAl extension in north indiA

The Indian economy is largely an agriculture based economy. Majority of Indian farmers fall under small and marginal category. Their farming system is characterized by small and scattered holdings, low production and small market surplus, poor storage and transportation facilities, lack of credit and lack of access to information. Information is one of the inputs that significantly determine their ability to earn a sustained livelihood from agriculture. Traditionally, mass mediated broadcasts supported by trained agricultural extension personnel formed the backbone of agricultural extension system in India. However, actual impact of the public extension system in India is limited by logistics, resources, skills and sheer numbers. In this context, use of ICTs is widely seen as a way of addressing the information needs of farmers. This paper is based on a study carried out to examine the use of mobile phones for disseminating agricultural information in the North Indian state of Uttarakhand. Main objectives of the study were to find out the extent of ownership of mobile phones by the farming community, their use for seeking agricultural information by the farmers and to understand the factors that influence usage of mobile phones by farmers. Ease of use, message comprehension, usefulness of information, trustworthiness of information and cost of the service were studied to understand factors that influence use of mobile phones by the farmers. The study concludes that mobile phones were mostly being used to fulfil social needs and their developmental use was incidental.
Thinking and Acting in Anticipation: A Review of Research on Proactive Behavior

Being proactive involves self-initiated,future—focused,and change、orientated behaviors.Such proactivity has been recognized as a positive way of behaving that can lead to the increased performance and effectiveness of individuals and organizations, especially when employees operate in contexts of unpredictable and changing demands.Because of its well—documented benefits the antecedents and mechanisms of proactive behavior have been widely examined in an effort to identify how to promote such behavior in organizations.In this article,the authors first review various ways of conceptualizing proactivity, which includes an individual differences perspective,a behavior perspective,and a process perspective.A behavior perspective is mainly adopted in this article as this perspective is dominant in literature.Next. three mechanisms representing “can do”. “reason to’ and “energized to’’processes that can trigger proactive behavior are introduced.A review on antecedents of proactive behavior,including dispositional factors, situational factors and their interactions,is followed.The authors also summarize consequences that proactive behavior can bring,including job atitudes and performance.In addition to providing reviews,as the second part,the authors introduce their recent research that considers expanded dispositional predictors of proactive behavior(i.e.,need for cognition,atachment style)as well as how these predictors interact with the situation. To conclude,the authors summarize what is wel established in the literature,as well as what warrants further inquiry.
Should parental prohibition of adolescents' peer relationships be prohibited?

Research has revealed inconsistent associations between parents’ prohibition of peer relationships and adolescents’ deviant peer affiliation. This cross-sectional study examined parents’ styles of prohibition to test the hypothesis that an autonomy-supportive style would relate negatively, whereas a controlling style would relate positively, to deviant peer affiliation. Such relations were expected because of the differential relations of styles of prohibition to adolescents’ internalization of parental rules for friendships. Structural equation modeling provided support for the hypotheses in a sample of 234 Belgian midadolescents (Mage = 16.45 years; 65% female), as the differential relations of autonomy-supportive and controlling styles of prohibition to deviant peer affiliation were mediated by their differential relations to internalization. Implications for research on parental peer management are discussed. Most parents oppose their adolescent child’s affiliation with friends who are involved in problem behaviors (e.g., stealing and drug use), yet there exist multiple ways of addressing this important issue. One possibility is that parents ignore the problem and hope that their child can resist the allure of peer pressure. Children often give in to such pressure and engage in deviant behavior, however, as affiliation with deviant peers is directly related to adolescents’ own involvement in problem behaviors (Lacourse, Nagin, Tremblay, Vitaro, & Claes, 2003). A second possibility is that parents intervene in their child’s
Non-Gaussian data modeling with hidden Markov models

In 2015, 2.5 quintillion bytes of data were daily generated worldwide of which 90% were unstructured data that do not follow any pre-defined model. These data can be found in a great variety of formats among them are texts, images, audio tracks, or videos. With appropriate techniques, this massive amount of data is a goldmine from which one can extract a variety of meaningful embedded information. Among those techniques, machine learning algorithms allow multiple processing possibilities from compact data representation, to data clustering, classification, analysis, and synthesis, to the detection of outliers. Data modeling is the first step for performing any of these tasks and the accuracy and reliability of this initial step is thus crucial for subsequently building up a complete data processing framework. The principal motivation behind my work is the over-use of the Gaussian assumption for data modeling in the literature. Though this assumption is probably the best to make when no information about the data to be modeled is available, in most cases studying a few data properties would make other distributions a better assumption. In this thesis, I focus on proportional data that are most commonly known in the form of histograms and that naturally arise in a number of situations such as in bag-of-words methods. These data are non-Gaussian and their modeling with distributions belonging the Dirichlet family, that have common properties, is expected to be more accurate. The models I focus on are the hidden Markov models, well-known for their capabilities to easily handle dynamic ordered multivariate data. They have been shown to be very effective in numerous fields for various applications for the last 30 years and especially became a corner stone in speech processing. Despite their extensive use in almost all computer vision areas, they are still mainly suited for Gaussian data modeling. I propose here to theoretically derive different approaches for learning and applying to real-world situations hidden Markov models based on mixtures of Dirichlet, generalized Dirichlet, Beta-Liouville distributions, and mixed data. Expectation-Maximization and variational learning approaches are studied and compared over several data sets, specifically for the task of detecting and localizing unusual events. Hybrid HMMs are proposed to model mixed data with the goal of detecting changes in satellite images corrupted by different noises. Finally, several parametric distances for comparing Dirichlet and generalized Dirichlet-based HMMs are proposed and extensively tested for assessing their robustness. My experimental results show situations in which such models are worthy to be used, but also unravel their strength and limitations.
Genotype × Environment Interaction in Psychiatric Genetics: Deep Truth or Thin Ice?

Background: There continues to be significant investment in the detection of genotype × environment interaction (G × E) in psychiatric genetics. The implications of the method of assessment for the genetic analysis of psychiatric disorders are examined for simulated twin data on symptom scores and environmental covariates. Methods: Additive and independent genetic and environmental risks were simulated for 10,000 monozygotic (MZ) and 10,000 dizygotic (DZ) twin pairs and the ‘subjects’ administered typical simulated checklists of clinical symptoms and environmental factors. A variety of standard tests for G × E were applied to the simulated additive risk scores, sum scores derived from the checklists and transformed sum scores. Results: All analyses revealed no evidence for G × E for latent risk but marked evidence for G × E and other effects of modulation in the sum scores. These effects were all removed by transformation. An integrated genetic and psychometric model, accounting for both the causes of latent liability and a theory of measurement, was fitted to a sample of the simulated sum-score data and showed that there was no significant modulation of the parameters of the genetic model by environmental covariates (i.e., no G × E). Conclusions: Claims to detect G × E based on analytical methods that ignore the theory of measurement must be subjected to greater scrutiny prior to publication.
Health and functioning in a stroke population five years after first incidence

Purpose: To isolate characteristics of a stroke population five years after first stroke, focusing on functioning and health. Methods: The subjects in the present study were identified in a computerized National Bureau of Health Register over a six month period in 2003 in a well-defined area of 353,284 inhabitants. Results: Survival rate five years after first stroke was 52%. As regards functioning 50% of the group were able to look after own affairs. Thirty-one percent reported themselves as being moderately disabled. Seventeen percent had moderate to severe disability. In the responding group 53% below 65 years of age were disability pensioners. Half of the responding group had suffered a further stroke or another severe illness during the previous five years. There was a positive correlation between functioning and health. Conclusion: Stroke survivors cannot be seen as a homogenous group as regard to functioning and health five years after the incidence. It is recommended that professionals identify th.
ID Insertion and Data Tracking with Frequency Offset for Physical Wireless Parameter Conversion Sensor Networks †

As the applications of the internet of things are becoming widely diversified, wireless sensor networks require real-time data reception, accommodation of access from several sensors, and low power consumption. In physical wireless parameter conversion sensor networks (PhyC-SN), all the sensors use frequency shift keying as the modulation scheme and then access the channel to the fusion center, simultaneously. As a result, the fusion center can recognize the statistical tendency of all the sensing results at a time from the frequency spectrum of the received signal. However, the information source, i.e., the sensor, cannot be specified from the received signal because no ID-indicating sensor is inserted to the signal. The data-tracking technique for tracing the time continuity of the sensing results is available for decomposing the sequence of the sensing results per sensor but the error tracking, which is a wrong recognition between the sensing results and the sensor, occurs owing to the similarity of the sensing results. This paper proposes the sensing result separation technique using a fractional carrier frequency offset (CFO) for PhyC-SN. In the proposed scheme, the particular fractional CFO is assigned to each user and it is useful for the ID specifying sensor. The fractional CFO causes inter-carrier interference (ICI). The ICI cancellation of the narrowband wireless communications is proposed. The two types of data-tracking techniques are proposed and are selectively used by the fusion center. Since the proposed data-tracking technique is multi-dimensional, high accuracy of data tracking is achieved even under the similar tendency of the sensing results. Based on computer simulation, we elucidate the advantage of the proposed sensing results separation.
Hearing With Our Eyes: The Geometry of Tonal Space

There is no intrinsic reason why musical structures should be represented graphically. Music is, after aU, an auditory phenomenon. It consists of vibrations transmitted through the air as sound waves, received by our ears and processed by our brains as acoustic data. Visual aspects of music-the arrangement of musicians on a stage or of notes on a page, the gyrations of the conductor, the shine of the piano-are of secondary importance in our understanding and appreciation of a musical work. Most human beings, however, are visually oriented. We rely on our eyes more than our ears or any of our other sense organs in finding our way around, identifying other people and objects, and learning new information. Because of this dependence on the visual world, most people find abstract concepts easier to grasp if they can somehow be visualized: if some sort of graphical, geometric representation can be devised showing, if only metaphorically, the important elements of the conceptual framework and their relationships with each other. Even if these elements exist in sound and time rather than in light and space, such a representation may help us to get our bearings and to interpret what we hear. Pitches, chords, and key areas are examples of musical elements that lend themselves well to graphical depiction. Many music theorists through the centuries have drawn diagrams showing the appearance of "tonal space" from various perspectives. This paper offers a conceptual and historical overview of several of these geometric representations.
Obtaining a Canonical Polygonal Schema from a Greedy Homotopy Basis with Minimal Mesh Refinement

Any closed manifold of genus g can be cut open to form a topological disk and then mapped to a regular polygon with 4g sides. This construction is called the canonical polygonal schema of the manifold, and is a key ingredient for many applications in graphics and engineering, where a parameterization between two shapes with same topology is often needed. The sides of the 4g-gon define on the manifold a system of loops, which all intersect at a single point and are disjoint elsewhere. Computing a shortest system of loops of this kind is NP-hard. A computationally tractable alternative consists in computing a set of shortest loops that are not fully disjoint in polynomial time, using the greedy homotopy basis algorithm proposed by Erickson and Whittlesey, and then detach them in post processing via mesh refinement. Despite this operation is conceptually simple, known refinement strategies do not scale well for high genus shapes, triggering a mesh growth that may exceed the amount of memory available in modern computers, leading to failures. In this paper we study various local refinement operators to detach cycles in a system of loops, and show that there are important differences between them, both in terms of mesh complexity and preservation of the original surface. We ultimately propose two novel refinement approaches: the former minimizes the number of new elements in the mesh, possibly at the cost of a deviation from the input geometry. The latter allows to trade mesh complexity for geometric accuracy, bounding deviation from the input surface. Both strategies are trivial to implement, and experiments confirm that they allow to realize canonical polygonal schemas even for extremely high genus shapes where previous methods fail.
THE EFFECTS OF NOISE FILTERS ON SEGMENTATION BASED SEEDED REGION GROWING

Goruntu bolutleme, doku, kenar ve renk ozelliklerini kullanarak nesnelerin parcalarini farkli goruntu alanlarina donusturmek icin pikselleri gruplama islemidir. Bolutleme sureci, goruntulerin analizinde ve goruntu islemede onemli bir rol oynar. Bolutleme icin gelistirilen tekniklerden biri de SRG'dir (Tohumlu Alan Genisletme). Goruntulerin elde edilmesi sirasinda olusan gurultu, bolutleme basarisini olumsuz yonde etkiler. Gurultuyu ortadan kaldirmak icin kullanilan filtreler gurultuyu azaltmaktadir, ancak filtreleme isleminin bolutleme basarisi uzerindeki etkisi tam olarak bilinmemektedir. Bu calismada, gurultu ve filtrelerin SRG algoritmasi uzerindeki etkileri arastirilmistir. Bu amacla Weizmann veri tabanina farkli seviyelerde cesitli gurultuler eklenmistir. Daha sonra gurultulu goruntulere filtreler uygulanmistir. Son olarak, SRG algoritmasi tarafindan segmentlere ayrilmis goruntulerden F-Skor degerleri elde edilmis ve orijinal goruntulerin degerleri ile karsilastirilmistir.
Flexible data anonymization using ARX—Current status and challenges ahead

The race for innovation has turned into a race for data. Rapid developments of new technologies, especially in the field of artificial intelligence, are accompanied by new ways of accessing, integrating, and analyzing sensitive personal data. Examples include financial transactions, social network activities, location traces, and medical records. As a consequence, adequate and careful privacy management has become a significant challenge. New data protection regulations, for example in the EU and China, are direct responses to these developments. Data anonymization is an important building block of data protection concepts, as it allows to reduce privacy risks by altering data. The development of anonymization tools involves significant challenges, however. For instance, the effectiveness of different anonymization techniques depends on context, and thus tools need to support a large set of methods to ensure that the usefulness of data is not overly affected by risk‐reducing transformations. In spite of these requirements, existing solutions typically only support a small set of methods. In this work, we describe how we have extended an open source data anonymization tool to support almost arbitrary combinations of a wide range of techniques in a scalable manner. We then review the spectrum of methods supported and discuss their compatibility within the novel framework. The results of an extensive experimental comparison show that our approach outperforms related solutions in terms of scalability and output data quality—while supporting a much broader range of techniques. Finally, we discuss practical experiences with ARX and present remaining issues and challenges ahead.
Healthcare Professionals’ Perceptions of Pulmonary Rehabilitation as a Management Strategy for Patients with Chronic Obstructive Pulmonary Disease (COPD): An Interpretive Phenomenological Analysis

Background: Sound evidence supports pulmonary rehabilitation (PR) as an effective management strategy for patients with respiratory disease, in particular chronic obstructive pulmonary disease (COPD). A multi-disciplinary programme, PR encompasses exercise training, education, nutritional advice, self-management and psychological support. Following PR patients often experience an increase in exercise capacity and quality of life, but a lack of referrals suggests that healthcare professionals (HCPs) are not ‘selling’ PR to patients. However, no evidence exists to fully substantiate this claim; a missing piece of the jigsaw. Objectives: 1. To explore HCPs’ perceptions regarding referral of COPD patients to PR in primary and secondary care settings. 2. To establish HCPs’ understanding of PR. 3. To explore barriers and facilitators to referral to PR. Methods: Interpretive phenomenological analysis (IPA) was adopted to establish HCPs’ perceptions of PR. Purposeful recruitment of general practitioners and practice nurses, and doctors and nurses working on general medical wards yielded a total of 27 participants. Informed consent preceded semi-structured interviews which were digitally recorded and transcribed verbatim. Findings: Three super-ordinate themes were identified: COPD Illness Perceptions, Pulmonary Rehabilitation Beliefs, and Organisational and Referral Pathway Perceptions. Commonalities and disparities were identified between primary and secondary care and amongst the different professional groups. HCPs held COPD illness perceptions; many held stigmatising beliefs in relation to the disease, which impacted upon referral practice. Pulmonary rehabilitation beliefs highlighted HCPs’ views on patient suitability and the PR programme. A lack of knowledge of PR and the referral process was evident. Organisational and referral pathway perceptions revealed barriers and facilitators to referral. 13 Conclusion: Referral to PR is as certain as spinning a wheel of fortune. Chance of referral appeared dependent upon individual HCPs, their perceptions of the programme, views of how COPD affects patients, and opinions of the referral process. All of these aspects, pieced together, could act as a predictor of referral practice. This is the first study to focus on HCPs’ perceptions of PR as a management strategy for patients with COPD and as such is a valuable contribution to knowledge