Miracle-dz/UniFAR-FTUs huggingface dataset:
Habitat affects escape behaviour and alarm calling in Common Starlings Sturnus vulgaris

Animals should adapt their escape behaviour to both physical and social surroundings in order to maximize their probability of survival. Cover can be both obstructive, reducing the visibility of the surroundings and hindering escape, and protective, providing refuge. We investigated how the provision of cover (long grass) affected (1) the escape behaviour and (2) the alarm call behaviour of Common Starlings Sturnus vulgaris responding to a model hawk during a simulated attack. Starlings always retreated away from the predator and sometimes alarm-called. Their escape trajectory was close to the ground when escaping in long grass, which could be explained by either tall swards hindering take-off or such swards being used as protective cover. On short grass their escape trajectory was much steeper (> 45 ° ). We also investigated the use of alarm calls in Starlings according to predictions arising from the costs and benefits to callers and receivers. Callers could benefit from using alarm calls through dilution or confusion if their use initiates flock departures, thus reducing their probability of being targeted. If there is no cost to the producer of alarm calls we predicted that detectors should call at all times to gain these benefits (i.e. irrespective of grass length), but if their use is costly we predicted that they would be used only when the benefits outstrip the costs. In this case we would predict that alarm calls would be given when other (visual) signals were impaired on long grass but not when they were effective on short grass. Starlings used alarm calls on long grass when visibility was reduced more frequently than on short grass, suggesting that calling has a cost to the producer. The contrasting escape strategies of Starlings in relation to a relatively small (10 cm) change in grass height demonstrates the potential importance of habitat structure in determining predation risk.
Despite being Apparently Equal, Concentrated Lispro-200 Performs Metabolically and Subjectively Better than Lispro-100

Aims: The aim of the study was to evaluate whether freshly available concentrated U-200 lispro insulin performed equally to previously marketed U-200 insulin lispro in terms of glycemic control and patient satisfaction. Methods: 360 outpatients with type 2 diabetes (T2DM) having self-injected U-100 lispro Kwikpen (KP) before meals for a long time were randomly selected to either going on with their usual treatment schedule (control group, n=180, CG) or switching to U-200 lispro KP for 12-weeks (treated group, n=180, TG) and filling in a treatment satisfaction questionnaire at the end of the observation period. They were all assessed for changes in body weight, blood pressure, BMI, fasting blood glucose, HbA1c, total cholesterol, HDL cholesterol, tryglicerides, uric acid, creatinine clearance rate with respect to baseline. Results: No significant changes were observed in the CG. Conversely, the TG displayed significantly decreased hypoglycemic episodes (p<0.001), as well as, fasting plasma glucose levels and glucose variability (p<0.01) compared to baseline. According to the questionnaire, 60 to 81% TG people were very satisfied with U-200 lispro KP and most of them preferred to stick to the new insulin preparation. Conclusions: hypoglycemic event rates, fasting blood glucose, glycemic variability and subjective ratings significantly improved in people treated with U-200 lispo. Diversity in both molecular insulin concentration and injection device engineering showed to provide better results in U-200 treated patients utilizing the same drug as controls.
Identifying Degenerative Brain Disease Using Rough Set Classifier Based on Wavelet Packet Method

Population aging has become a worldwide phenomenon, which causes many serious problems. The medical issues related to degenerative brain disease have gradually become a concern. Magnetic Resonance Imaging is one of the most advanced methods for medical imaging and is especially suitable for brain scans. From the literature, although the automatic segmentation method is less laborious and time-consuming, it is restricted in several specific types of images. In addition, hybrid techniques segmentation improves the shortcomings of the single segmentation method. Therefore, this study proposed a hybrid segmentation combined with rough set classifier and wavelet packet method to identify degenerative brain disease. The proposed method is a three-stage image process method to enhance accuracy of brain disease classification. In the first stage, this study used the proposed hybrid segmentation algorithms to segment the brain ROI (region of interest). In the second stage, wavelet packet was used to conduct the image decomposition and calculate the feature values. In the final stage, the rough set classifier was utilized to identify the degenerative brain disease. In verification and comparison, two experiments were employed to verify the effectiveness of the proposed method and compare with the TV-seg (total variation segmentation) algorithm, Discrete Cosine Transform, and the listing classifiers. Overall, the results indicated that the proposed method outperforms the listing methods.
Yield Compounds and Nutrient Elements of Carnation (Dianthus caryophyllus L.) under Different Growing Media

Production of cut flowers is very prevalent in Iran. Carnation is most famous for its use as a cut flower. This study was carried out in the research greenhouse of Islamic Azad University, Isfahan (Khorasgan) Branch, to explore the effect of different growing media on some vegetative and reproductive traits of carnation. The experimental design was randomized complete, using 50% perlite and different growing media including: mineral soil and coco peat in 6 proportions (50% coco peat, 10% soil + 40% coco peat, 20% soil + 30% coco peat, 30% soil + 20% coco peat, 40% soil + 10% coco peat, 50% soil). During growth period, irrigation rate, humidity and temperature were similar. The growth characteristics of carnation were determined at the end of growth period. The results indicated that although there were no significant differences between studied growing substrates in chlorophyll a, b. Statistical analysis showed fresh weight of flower was far greater in coco peat 10% + soil 40% and soil 50% compared to other growing media. Fresh and dry weight of root and shoot in soil 50% were higher than other growing media. The results showed that highest concentration of Fe and Zn was observed in coco peat 40% + soil 10%. The concentration of Mn in coco peat 10% + soil 40% was higher than other growing media. The results showed that the highest Ca concentration was observed in soil 50% and the lowest concentration of this element was obtained in coco peat 50%. According to the obtained results, mineral soil can be used as a suitable alternative to cocopeat for cultivation and growth of carnation in greenhouse.
MATEMATİK ÖĞRETMEN ADAYLARININ PEDAGOJİK ALAN BİLGİLERİNİN ÖLÇME VE DEĞERLENDİRME BİLGİSİ BİLEŞENİ BAĞLAMINDA İNCELENMESİ

Pedagojik Alan Bilgisi‟nin alt bilesenlerinden biri Olcme ve Degerlendirme Bilgisi‟dir. Olcme ve Degerlendirme Bilgisi ogretmenin olcme ve degerlendirme yontemlerinin amaclarini ve isleyislerini bilmesini, bunlari olusturma ve uygulama becerisini icermektedir. Yeni Ortaogretim Matematik Programi ogretmenlerden yapilandirmaci ogretimi benimsemelerini ve bu ogretime uygun olcme ve degerlendirme yontemlerini kullanmalarini istemektedir. Bu calismada amac, ogretmen adaylarinin limit ve sureklilik konusuyla ilgili Olcme ve Degerlendirme Bilgilerini belirlemektir. Oncelikle, limit ve sureklilik konusuyla ilgili hazirlanan kavram bilgisi anketi 37 ogretmen adayina uygulanmis ve sonra bunlar arasindan alan bilgisi farkli 4 ogretmen adayi secilmistir. Secilen bu adaylarla yari-yapilandirilmis gorusmeler yapilmis, onlardan limit ve sureklilik konusuyla ilgili ders plani hazirlamalari ve bunlari mikro-ogretim yontemiyle anlatmalari istenmistir. Arastirma kapsaminda elde edilen bulgular gostermektedir ki, ogretmen adaylarinin Olcme ve Degerlendirme Bilgisi sinirlidir ve yazili ve sozlu sinavlar gibi geleneksel olcme ve degerlendirme yontemlerinden ibarettir
The pretense of residential privacy in geosocial networking data

One of the top concerns for users of geosocial networking applications is the privacy of the information being shared. While most location-based social networking applications put a great deal of time and effort into voicing how much they care about privacy, not much is known about steps that are taken to ensure the information is kept private and/or anonymous. One of the issues unique to geosocial networking applications such as Foursquare is the concern over the privacy of one’s home location. Sharing textual content and photographs is one thing, but sharing the location of your house is something different entirely. Existing work in this area has focused on the different levels of privacy associated with geosocial networking [6,3]. The ability to both preserve the privacy of some data while sharing other parts of the data has proven to be a difficult task for researchers and application developers alike. Residential check-in behavior has been the focus of previous research in this area [5,4] while satirical work (PleaseRobMe.com) has shown what is possible given knowledge of an individual’s home location [1]. In this work, Points of Interest (POI)1 of type Home are examined on the Foursquare platform with the goal of determining how much information is actually accessible and to explore the ways in which the data is kept private. The purpose of this work is not to expose individuals’ real home locations, but rather examine the data, the privatization methods and the ways in which this privatization is being circumvented by the contributors themselves. This paper presents a snapshot of work-in-progress and should be taken as such. While the results of this paper are promising, further work exploring numerous other privatization practices is being conducted on additional geosocial datasets with varying privacy parameters.
Negotiations and Love Songs : The Dynamics of Bargained Household Decisions

This paper uses an intertemporal bargaining framework to analyze the role of the future and past in household decisions. In contrast to a static model, where individuals revert to their threat points and forfeit current surplus when they disagree, couples in a longterm relationship may additionally lose future or past marital surplus. I characterize three types of bargaining behavior, described as “myopic”, “contractual”, and “prescient”, and I derive household consumption dynamics for each behavior. Using data on married couples from the Panel Study of Income Dynamics from 1976-1986, I examine the impact of future and past outside options on current consumption choices. Results suggest heterogeneity in household behavior. Using a latent class model, I determine that children and divorce costs are among factors that substantially affect the type of behavior exhibited by a household. JEL classifications: D13, D91, J12
Transfer effects and permeable boundaries: an empirical study of the effects of commuting stress on employees' work and life

The impact of commuting stress has been addressed by only a few studies, most of which have been conducted in developed countries. Far less research has looked at the subjective, psychological effects of commuting on employees’ wellbeing within the Malaysian context. This paper describes a study which investigates the effects of commuting stress on commuters’ individual life and work domains. Data were collected from 660 commuters through a questionnaire survey and explored using correlation and regression analysis. The results show that longer commutes are significantly associated with a greater increase in commuting stress. They also reveal that the strain of commuting affects commuters’ reports of somatic symptoms of ill health and commute displeasure. In addition, commuting stress is found to affect commuters’ intention to quit their job but not in terms of their job or life satisfaction. Drawing on these results, theoretical and research implications that would lend support for future commuting stress research, particularly in Malaysia, are suggested.
Security and privacy for IoT and fog computing paradigm

In the past decade, the revolution in miniaturization (microprocessors, batteries, cameras etc.) and manufacturing of new type of sensors resulted in a new regime of applications based on smart objects called IoT. Majority of such applications or services are to ease human life and/or to setup efficient processes in automated environments. However, this convenience is coming up with new challenges related to data security and human privacy. The objects in IoT are resource constrained devices and cannot implement a fool-proof security framework. These end devices work like eyes and ears to interact with the physical world and collect data for analytics to make expedient decisions. The storage and analysis of the collected data is done remotely using cloud computing. The transfer of data from IoT to the computing clouds can introduce privacy issues and network delays. Some applications need a real-time decision and cannot tolerate the delays and jitters in the network. Here, edge computing or fog computing plays its role to settle down the mentioned issues by providing cloud-like facilities near the end devices. In this paper, we discuss IoT, fog computing, the relationship between IoT and fog computing, their security issues and solutions by different researchers. We summarize attack surface related to each layer of this paradigm which will help to propose new security solutions to escalate it acceptability among end users. We also propose a risk-based trust management model for smart healthcare environment to cope with security and privacy-related issues in this highly un-predictable heterogeneous ecosystem.
Who Did What?: Identifying Author Contributions in Biomedical Publications using Naïve Bayes

Creating scientific publications is a complex process. It is composed of a number of different activities, such as designing the experiments, analyzing the data, and writing the manuscript. Information about the contributions of individual authors of a paper is important for assessing authors' scientific achievements. Some biomedical publications contain a short section written in natural language, which describes the roles each author played in the process of preparing the article. In this paper, we present a study of authors' roles commonly appearing in these sections, and propose an algorithm for automatic extraction of authors' roles from them. In our study, we used co-clustering techniques, as well as Open Information Extraction, to semi-automatically discover the most popular roles within a corpus of contributions sections. In total 13 roles were discovered, three of which (paper revision, literature review, and interpretation) are not described by existing author role taxonomies. Discovered roles are then used to automatically build a training set for a supervised Naïve Bayes role extractor. The proposed role extractor is able to extract roles from the text with micro-averaged precision 0.68, recall 0.48 and F1 0.57.
A Two-Layer Dimension Reduction and Two-Tier Classification Model for Anomaly-Based Intrusion Detection in IoT Backbone Networks

With increasing reliance on Internet of Things (IoT) devices and services, the capability to detect intrusions and malicious activities within IoT networks is critical for resilience of the network infrastructure. In this paper, we present a novel model for intrusion detection based on two-layer dimension reduction and two-tier classification module, designed to detect malicious activities such as User to Root (U2R) and Remote to Local (R2L) attacks. The proposed model is using component analysis and linear discriminate analysis of dimension reduction module to spate the high dimensional dataset to a lower one with lesser features. We then apply a two-tier classification module utilizing Naïve Bayes and Certainty Factor version of K-Nearest Neighbor to identify suspicious behaviors. The experiment results using NSL-KDD dataset shows that our model outperforms previous models designed to detect U2R and R2L attacks.
A secure image encryption algorithm based on polar decomposition

In this paper, a novel image encryption technique is proposed based on reference sets with some prime numbers and polar decomposition. In this technique, a primary secret key and reference sets are used in the generation of feature vector using a recursive random number generation (RRNG) process. This feature vector is then used for encryption process. Finally, a reliable decryption process has been presented to reconstruct the original image. The Simulated results demonstrate the efficiency and robustness of the proposed technique.
Using GIST Features to Constrain Search in Object Detection

This thesis investigates the application of GIST features [13] to the problem of object detection in images. Object detection refers to locating instances of a given object category in an image. It is contrasted with object recognition, which simply decides whether an image contains an object, regardless of the object’s location in the image. In much of computer vision literature, object detection uses a “sliding window” approach to finding objects in an image. This requires moving various sizes of windows across an image and running a trained classifier on the visual features of each window. This brute force method can be time consuming. I investigate whether global, easily computed GIST features can be used to classify the size and location of objects in the image to help reduce the number of windows searched before the object is found. Using K-means clustering and Support Vector Machines to classify GIST feature vectors, I find that object size and vertical location can be classified with 73-80% accuracy. These classifications can be used to constrain the search location and window sizes explored by object detection methods.
Microbial mass movements

Wastewater, tourism, and trade are moving microbes around the globe at an unprecedented scale For several billion years, microorganisms and the genes they carry have mainly been moved by physical forces such as air and water currents. These forces generated biogeographic patterns for microorganisms that are similar to those of animals and plants (1). In the past 100 years, humans have changed these dynamics by transporting large numbers of cells to new locations through waste disposal, tourism, and global transport and by modifying selection pressures at those locations. As a consequence, we are in the midst of a substantial alteration to microbial biogeography. This has the potential to change ecosystem services and biogeochemistry in unpredictable ways.
Masterclass A neuroscience approach to managing athletes with low back pain

Low back pain (LBP) is a common complaint within the athletic population and is commonly managed through a biomedical approach. The injured or damaged structure causing the LBP is identified and treated, and complete recovery from the episode is expected. Clinical experience shows us that often, athletes with LBP will not recover from their episode and may continue their sports participation despite persistent pain, or they may limit participation. Recent neuroscience research into the biology of pain suggests that clinicians involved in the management of athletes with LBP should embrace a biopsychosocial approach by engaging the brain and nervous system. This manuscript provides an overview of such a biopsychosocial approach, and presents information on the neurobiology of the athlete’s pain
Flavonoid Contents and Antioxidant Activity in Fruit, Vegetables and Other Types of Food

Flavonoids are a class of natural polyphenolic compounds which cannot be synthesized by humans. These substances possess a series of biological properties, acting on biological systems as antioxidants. The purpose of this study was to analyze the properties of certain foods, determining the total flavonoids as well as their antioxidant activity and fat concentration. We evaluated several foods purchased at the local market, with respect to its antioxidant activity, using two experimental models, the discoloration of DPPH˙ radical and ABTS-. Some foods such as pitanga showed antioxidant activity. The lipid content of fatty foods like aai, cacao and cupuau was determined. Other foods, including pitanga, aai, cacao and cupuau, were evaluated for flavonoid content and antioxidant activity using multivariate statistical analysis (PCA) as a statistical tool to evaluate the correlation between these two parameters. As samples with ED50 up to 500 μg/mL show promising antioxidant activity, several Brazilian fruit and vegetables could be consumed to this end, with a good correlation between flavonoid content and antioxidant activity in most samples. The daily dose of different types of food for antioxidant activity has been calculated based on these results.
Android Malware Detection Based on Factorization Machine

As the popularity of Android smart phones has increased in recent years, so too has the number of malicious applications. Due to the potential for data theft that mobile phone users face, the detection of malware on Android devices has become an increasingly important issue for the field of cyber security. Traditional methods like signature-based routines are unable to protect users from the ever-increasing sophistication and rapid behavior changes in new types of Android malware. Therefore, a great deal of effort has been made recently to use machine learning models and methods to characterize and generalize the malicious behavior patterns of mobile apps for malware detection. In this paper, we propose a novel and highly reliable classifier for Android Malware detection based on a Factorization Machine architecture and the extraction of Android app features from manifest files and source code. Our results indicate that the numerical feature representation of an app typically results in a long and highly sparse vector and that the interactions among different features are critical to revealing malicious behavior patterns. After performing an extensive performance evaluation, our proposed method achieved a test result of 100.00% precision score on the DREBIN dataset and 99.22% precision score with only 1.10% false positive rate on the AMD dataset. These metrics match the performance of state-of-the-art machine-learning-based Android malware detection methods and several commercial antivirus engines with the benefit of training up to 50 times faster.