Miracle-dz/UniFAR-FTUs huggingface dataset:
Geomorphology of a recently deglaciated high mountain area in Eastern Anatolia (Turkey)

ABSTRACT We present a geomorphological map of the Cilo Mountain range located in southeast Turkey that illustrates the recent evolution of the second-most glacierized area of Turkey. The map was produced by the manual delineating of landforms from highresolution satellite imagery (Pleiades and Google EarthTM images). Cilo Mountain glaciation played a crucial role in the evolution of the local physical landscape and even today glaciers are significant features. After the termination of Little Ice Age, deglaciation promoted the onset of multiple and intense surface processes; widespread slope instabilities, due to permafrost melting and frost weathering processes, are still the most active processes. Such processes are further promoted by the scarcity of vegetation. The occurrence of several proglacial lakes significantly contributes to the reduction of sediment output from glaciated watersheds, influencing the evolution of proglacial plains and resulting in a variety of landforms that we identified and described in this work.
A Study on the User Perception and Awareness of Smartphone Security

There is an unprecedented growth in demand for smartphone market in the last decade. This paved a way for an upward trend in usage of smartphones for personal and official communication, gaming and online shopping. The smartphones have become part and parcel of the modern day life style. The personalization features offered by vendors are making higher number of transactions to happen via smartphone. This essentially increases the concerns of security and there is a need to understand if the users are aware of security issues associated with smartphone usage. This study explores the reasons which make youngsters in the age group of 20 to 30 years to use smartphones, the security threats associated with the usage, the user’s perception on security and the level of security configurations by users on their mobile devices. The study also explores to find if there is a significant difference in the perception of security between youngsters who are Information Technology (IT) graduates and others from non-IT education background. The paper also attempts to find whether there is a difference in attitude towards managing security features in smart phones between genders.
Opuntia stricta (Haw): A review on its chemical composition, putative in-vitro antidiabetic mechanism of action and potential pharmacological uses in chronic disease

Opuntia species, which belong to the Cactaceae family, are well-known and have long been utilized in folk medicine. Scholars have been interested in the stems, fruits, and cladodes of Opuntia species in recent years because of their possible nutraceutical content and therapeutic properties. While Opuntia stricta ( O. stricta ) is a well-known Cactaceae family member, it is a relatively unknown herbal plant in Zambia, where it is viewed primarily as a flower and only a few local people use it as a complementary herbal medicine to manage chronic illnesses such as diabetes mellitus. Much is not known about its chemical composition and putative in-vitro antidiabetic pharmacological action. Therefore, this review provided experimental data regarding chemical composition, in-vitro pharmacological mechanism, and potential indications of O. stricta in the and management of various chronic conditions.
Design of a Redundant Manipulator for Playing Table Tennis towards Human-Like Stroke Patterns

This study investigates the design of a 7-DOF humanoid manipulator capable of playing table tennis with human-like stroke patterns. The manipulator system includes a redundant arm, real-time stereo vision system, and a distributed motion control system. First, the size, weight, workspace, and motion capability of the designed arm are similar to those of a human's arm. The forward and inverse kinematics, and the Jacobian matrix of the redundant manipulator are formulated. Next, a distributed motion control system is designed. The ball trajectory prediction method is proposed. Then, a human-inspired optimization method based on Jacobian pseudoinverse and the comfort of the arm posture for stroke pattern trajectory is proposed to achieve human-like stroke patterns and decrease the counterforce exerted on the manipulator. Finally, the validity of the proposed system and methods is demonstrated via human-like stroke pattern experiments.
Redescription of Stolephorus ronquilloi Wongratana, 1983 and Description of Stolephorus hindustanensis, a New Anchovy from the Western Coast of India (Teleostei: Clupeiformes: Engraulidae)

The new anchovy Stolephorus hindustanensis n. sp. , described on the basis of 11 specimens collected from Mumbai, western coast of India, closely resembles Stolephorus ronquilloi Wongratana, 1983 in sharing an indented preopercle posterior margin, long maxilla extending beyond the preopercle posterior margin, double pigmented lines on the dorsum behind the dorsal fin, and lacking a predorsal scute. However, the new species differs from S. ronquilloi in having lower counts of gill rakers on the first and second gill arches, higher counts of total vertebrae, a deeper body, greater distances between the snout tip and anal-fin origin, origins of the dorsal and anal fins, and pelvic-fin insertion and anal-fin origin, and longer pelvic fin, third dorsal-fin ray, third anal-fin ray, and postorbital length.
SMSTDroid - Intrusion Detection System for Mobile Android Devices Payment System

Android has become the most popular Operating System for Smartphones and Tablets with an essential market share of about 70 to 80 percent. The open source structure of Android operating systems has introduced security vulnerabilities that can be readily exploited by third party applications. Cybercriminal take advantage of both the limited capabilities of mobile devices and the lack of standard security mechanism to naturally expand their malicious activities against Android mobile platform because of its huge market share. These hackers take advantage of the permission required on installation of application to develop mobile specific SMSTrojan malware specially design to subscribe to premium Short Message Services (SMS) numbers, intercept and delete the confirmation message thereby causing financial damage to the victims without their knowledge. To maximize profit, they continuously improve their creations to make them more and more resilient against anti-malware solution. Various techniques have been proposed for the detection of malware threats in Android and one promising set of techniques uses features extracted from permissions traces to infer malicious behaviours. In this paper, SMSTrojan malware behavior, the permissions required by these Trojans application on installation and its malicious intent on users’ device financial charges are presented. More precisely, a framework for the detection of SMS-based Android malware on mobile payment system called SMSTDriod (Short Messaging Services Trojan for Android) is presented. The system employ the use of SVM to identify SMS-based malicious application. Performance of the malware classification and detection are evaluated against data collected from an open source malware repository using confusion matrix. The result yielded a detection rate of 0.84 true positive rate and low false alarm rate of 0.11 predicted by the model which implies that the number of misclassified malicious instances as benign was minimal.
Does Knowledge Distillation Really Work?

Mixup Mixup is an effective regularization technique originally proposed to increase generalization and robustness of deep networks [19, 17]. Instead of training on original dataset, the network is trained on convex combination of images with targets mixed in the same way. We adapt mixup to knowledge distillation as follows: on each iteration we construct random pairs of inputs x, x′ from the training set and mix them as λ · x + (1 − λ) · x′, where the coefficient λ is sampled uniformly on [0, 1]1.
Optimal siting of onshore wind turbines

The deployment of onshore wind power is an important means to mitigate climate change. However, wind turbines also produce local disamenities to residents living next to them, mainly due to noise emissions and visual effects. Our paper analyzes how the presence of local disamenities affects the socially optimal siting of onshore wind power. The analysis builds on a spatial optimization model using geographical information system (GIS) data for Germany. Our results indicate a major spatial trade-off between the goals of minimizing electricity generation and disamenity costs. Considering disamenity costs substantially alters – and in fact dominates – the socially optimal spatial allocation of wind power deployment. This is because in Germany a) the spatial correlation between generation costs and disamenity costs is only moderately positive, and b) disamenity costs exhibit a larger spatial heterogeneity than the generation costs. These results are robust to variations in the level and slope of the disamenity cost function that we assume for the modeling. Our findings emphasize the importance of supplementing support schemes for wind power deployment with approaches that address local disamenties, e.g., compensation payments to local residents or minimum settlement distances.
Stellar Ages for Galactic Archaeology Methods and Applications

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PREPARING A CASE STUDY: A Guide for Designing and Conducting a Case Study for Evaluation Input

A case study is a story about something unique special or interesting -- stories can be about individuals organizations processes programs neighborhoods institutions and even events. The case study gives the story behind the result by capturing what happened to bring it about and can be a good opportunity to highlight a projects success or to bring attention to a particular challenge or difficulty in a project. Cases might be selected because they are highly effective not effective representative typical or of special interest. A few examples of case study topics are provided below -- the case studies would describe what happened when to whom and with what consequences in each case. Case studies are appropriate when there is a unique or interesting story to be told. Case studies are often used to provide context to other data (such as outcome data) offering a more complete picture of what happened in the program and why. (excerpt)
Incidence of and Risk Factors for Sudden Cardiac Death in Children With Dilated Cardiomyopathy

Elfriede Pahl, MD,* Lynn A. Sleeper, SCD,† Charles E. Canter, MD,‡ Daphne T. Hsu, MD,§ Minmin Lu, MS,† Steven A. Webber, MBCHB,¶ Steven D. Colan, MD,#** Paul F. Kantor, MBBCH,†† Melanie D. Everitt, MD,‡‡ Jeffrey A. Towbin, MD,§§ John L. Jefferies, MD,§§ Beth D. Kaufman, MD, James D. Wilkinson, MD, MPH,¶¶ Steven E. Lipshultz, MD, ¶¶ for the PCMR (Pediatric Cardiomyopathy Registry) Investigators Chicago, Illinois; Watertown and Boston, Massachusetts; St. Louis, Missouri; Bronx, New York; Pittsburgh and Philadelphia, Pennsylvania; Toronto, Ontario, Canada; Salt Lake City, Utah; Cincinnati, Ohio; and Miami, Florida
Assessment of Learning Styles Among Clinical and Normal, Nonclinical Participants: An Experimental Study

This study aimed to identify Visual, Auditory, and Kinesthetic (VAK) learning styles of clinical and nonclinical samples, to check the relationship between VAK learning styles and clinical factors, demographic variables. Two central questions were addressed: 1) Could there be a variation in learning styles between the nonclinical and clinical samples? 2) Is there a gender and age difference in the VAK learning styles of the nonclinical and clinical samples? These were achieved by evaluating 33 senior high school students (nonclinical samples), 13 males and 20 females, and 14 individuals (clinical samples), 5 males and 9 females. The self-designed Learning Style Assessment Test and Learning Modalities Dominance Index were used during the process. It demonstrates that, with the exception of visual learning style, nonclinical and clinical characteristics have an effect on auditory and kinesthetic learning styles; in both nonclinical and clinical populations, there’s no significant association between VAK learning styles and demographic characteristics. Based on the findings thus far, learning styles under clinical factors may be qualitatively different, and that psychiatry and otorhinolaryngology-audiology could be joined for treatment. More ramifications of the present results are being investigated for future research, along with teaching and treatment method design. The development of new treatment diagnoses and therapy regimens, while also their clinical relevance, are still poorly understood, necessitating more research.
Security Avoidance in a Windows Application using Dynamic Instrumentation

Numerous commodity Windows-based applications are available for use as free evaluation copies for limited periods of time. These applications constitute prime candidates for security attacks and we use the dynamic instrumentation capabilities provided by the DynInst API to demonstrate one such attack. The attack methodology uses program inspection and runtime code modiﬁcation, and is applicable to both stripped and unstripped Windows binaries. While our attack strategy is generic to time-limited trial applications that use the Microsoft C runtime library, our chosen target application is the 30-day trial version of SecureCRT for Windows. Other applications attacked using our tool include VShell Server, CRT, AbsoluteFTP and EnTunnel.
RELATIONSHIP BETWEEN ATTITUDE TOWARDS ONLINE SHOPPING AND ONLINE PURCHASE DECISION OF LIFESTYLE PRODUCTS

Online shopping of lifestyle products is an emerging trend in India. Now a days the new generation does engage time in searching for their desirable product in various e-portals. Most of the companies are coming to serve their need with exciting offers. The continuous growth in this market stimulated this idea of a detail study on this. Here is an illustration of consumer behavior depending certain factors and variables. Studies have emphasized on demographic, socio economic, culture, technology related factors. The researchers also highlighted the variables stimulating consumer behaviour as . The results of the study express in depth concept for further research.
VR Stylus and Controller Combination Was Preferred Over the Mouse or Hand Tracking for Object Manipulation and Marking Tasks in Virtual Reality

A BSTRACT For medical surgery planning, virtual reality (VR) provides a new kind of user experience, where 3D images of the operation area can be utilized. Using VR, it is possible to view the 3D models in a more realistic 3D environment, which would reduce the perception problems and increase spatial understanding. In this experiment, we compared a mouse, hand tracking, and a combination of a VR stylus and a VR controller as interaction methods in VR. The purpose was to study the viability of the methods for tasks used in medical surgery planning in VR. The tasks required interaction with 3D objects and high marking accuracy. The controller combination was statistically significantly the most liked interaction method compared to the other methods. In subjective results, it was the most appropriate, while in objective results, the mouse interaction method was the most accurate.
Fuzzy clustering of histopathological images using deep learning embeddings

Metric learning is a machine learning approach that aims to learn a new distance metric by increasing (reducing) the similarity of examples belonging to the same (different) classes. The output of these approaches are embeddings, where the input data are mapped to improve a crisp or fuzzy classification process. The deep metric learning approaches regard metric learning, implemented by using deep neural networks. Such models have the advantage to discover very representative nonlinear embed-dings. In this work, we propose a triplet network deep metric learning approach, based on ResNet50, to find a representative embedding for the unsupervised fuzzy classification of benign and malignant histopathological images of breast cancer tissues. Experiments computed on the BreakHis benchmark dataset, using Fuzzy C-Means Clustering, show the benefit of using very low dimensional embeddings found by the deep metric learning approach.
Polyunsaturated fatty acids modulate the effects of the APOA 1 GA polymorphism on HDL-cholesterol concentrations in a sex-specific manner : the Framingham Study 1 – 3

Background: A common G-to-A substitution in the promoter area ( 75 base pairs) of the apolipoprotein A-I gene (APOA1) has been described. The A allele was shown to be associated with higher HDL-cholesterol concentrations in some studies but not in others. Objective: We examined whether dietary fat modulates the association between this polymorphism and HDL-cholesterol concentrations. Design: We studied a population-based sample of 755 men and 822 women from the Framingham Offspring Study. Results: The frequency of the A allele was 0.165. No significant differences were observed between G/G subjects and carriers of the A allele for any lipid variables. In multivariate linear regression models, HDL-cholesterol concentrations in women were associated with a significant interaction between polyunsaturated fatty acid (PUFA) intake as a continuous variable and APOA1 genotype (P = 0.005). By using 3 categories of PUFA intake, we found a significantly different effect of APOA1 genotype across PUFA categories in women. When PUFA intake was < 4% of energy, G/G subjects had 14% higher HDL-cholesterol concentrations than did carriers of the A allele (P < 0.05). Conversely, when PUFA intake was > 8%, HDL-cholesterol concentrations in carriers of the A allele were 13% higher than those of G/G subjects (P < 0.05). No significant allelic difference was observed for subjects in the range of PUFA intake of 4–8% of energy. These interactions were not significant in men. Conclusions: We found a significant gene-diet interaction associated with the APOA1 G-A polymorphism. In women carriers of the A allele, higher PUFA intakes were associated with higher HDL-cholesterol concentrations, whereas the opposite effect was observed in G/G women. Am J Clin Nutr 2002;75:38–46.
EyeSyn: Psychology-inspired Eye Movement Synthesis for Gaze-based Activity Recognition

Recent advances in eye tracking have given birth to a new genre of gaze-based context sensing applications, ranging from cognitive load estimation to emotion recognition. To achieve state-of-the-art recognition accuracy, a large-scale, labeled eye movement dataset is needed to train deep learning-based classifiers. However, due to the heterogeneity in human visual behavior, as well as the labor-intensive and privacy-compromising data collection process, datasets for gaze-based activity recognition are scarce and hard to collect. To alleviate the sparse gaze data problem, we present EyeSyn, a novel suite of psychology-inspired generative models that leverages only publicly available images and videos to synthesize a realistic and arbitrarily large eye movement dataset. Taking gaze-based museum activity recognition as a case study, our evaluation demonstrates that EyeSyn can not only replicate the distinct pat-terns in the actual gaze signals that are captured by an eye tracking device, but also simulate the signal diversity that results from dif-ferent measurement setups and subject heterogeneity. Moreover, in the few-shot learning scenario, EyeSyn can be readily incorpo-rated with either transfer learning or meta-learning to achieve 90% accuracy, without the need for a large-scale dataset for training.
Advanced Algorithm Design Lecture 15 : Online Primal-Dual Algorithms

The idea of online primal-dual algorithm is to simultaneously maintain a feasible primal solution x and a feasible dual solution y. At t-th arrival, the algorithm updates both the primal and the dual while maintaining feasibility. Since weak-duality implies that a feasible dual gives a bound on the optimal primal value, we get the following lemma (which is a slight generalization because we allow a scaled dual to be feasible).
The Technical Specification for the Security Content Automation Protocol (SCAP): SCAP Version 1.2

The National Institute of Standards and Technology Special Publication 800-126 Revision 2 The Technical Specifications for the Security Content Automaton Protocol (SCAP): SCAP Version 1.2 provides the definitive technical specification for version 1.2 of the Security Content Automation Protocol (SCAP). SCAP consists of a suite of specifications for standardizing the format and nomenclature by which information about software flaws and security configurations is communicated, both to machines and humans. This document defines requirements for creating and processing SCAP content. These requirements build on the requirements defined within the individual SCAP component specifications. Each new requirement pertains either to using multiple component specifications together or to further constraining one of the individual component specifications. ~
Multi-task Learning for Neural Image Classiﬁcation and Segmentation Using a 3D / 2D Contextual U-Net Model

We present a 3D / 2D Contextual U-Net model and apply it to segment and classify samples from a heterogeneous mouse brain dataset obtained via X-ray microtomography, which spans 4 distinct brain areas: Striatum, Ventral Posterior Thalamic Nucleus (VP), Cortex, and Zona Incerta (ZI). Our multi-task model takes in a 3D volume and outputs both a 2D segmentation of the central plane and the volume’s brain area class, which can then be used to generate 3D reconstructions across samples with heterogeneous microstructure distributions. We investigate various properties of the model, including its quantitative segmentation and classiﬁcation performance across the 4 brain regions, qualitative performance via the generation of 3D reconstructions, and interpretability via an investigation of the network’s latent representations. Because our model performs both classiﬁcation and segmentation, we also investigate how changing their relative weight during training, via a parameter we call λ , affects performance and latent representations. Quantitative and qualitative results demonstrate that our model achieves reasonable segmentation and classiﬁcation performance and can be scaled to large, heterogeneous brain regions. This technique could be used by neuroscientists seeking to automate the creation of multi-scale brain maps that incorporate both microstructure and brain area information.