Miracle-dz/UniFAR-FTUs huggingface dataset:
Beden Eğitimi Öğretmenlerinin Mizah Tarzları ile Algılanan Duygusal Bezdirme Üzerine Araştırma

Bu calismada, Kutahya ilinde gorev yapan beden egitimi ogretmenlerinin mizah tarzlari ile duygusal bezdirme iliskisinin incelenmesi amaclanmistir. Calismanin orneklem grubunu ise 74 beden egitimi ogretmeni olusturmaktadir. Arastirmada, Martin ve arkadaslari tarafindan (2003) gelistirilen ve Turkiye’deki gecerlilik ve guvenirlilik calismasi Yerlikaya (2003) tarafindan yapilan “Mizah Tarzlari Olcegi” ve Stale Einarsen (1997) tarafindan gelistirilmis ve Turkiye’deki gecerlilik ve guvenirlilik calismasi Cemaloglu (2007a) tarafindan yapilan “Olumsuz Davranislar Olcegi” kullanilmistir. Arastirmada verilerin normal dagilip dagilmadigini belirleyebilmek icin shapiro-wilk normallik testi uygulanmistir. Gruplarin ortalama ve standart sapma degerlerinin tespiti icin tanimlayici istatistik, demografik ozellikleri siniflandirmak icin frekans testleri ve alt boyutlar arasi iliskinin tespiti icin korelasyon testleri kullanilmistir. Sonuc olarak; kendini yikici mizah tarzina sahip ogretmenlerin duygusal bezdirme, gorev ve sosyal iliskiler alt boyutlar bulgularini dusuk duzeyde de olsa hissettikleri, bu durumdan kurtulmaya “kendilerini gelistirici” mizahin onemli katki sagladigi soylenebilir.
Guiding App Testing with Mined Interaction Models

Test generators for graphical user interfaces must constantly choose which UI element to interact with, and how. We guide this choice by mining associations between UI elements and their interactions from the most common applications. Once mined, the resulting UI interaction model can be easily applied to new apps and new test generators. In our experiments, the mined interaction models lead to code coverage improvements of 19.41% and 43.03% on average on two state-of-the-art tools (Droidmate and Droidbot), when executing the same number of actions.
Deep-CLASS at ISIC Machine Learning Challenge 2018

This paper reports the method and evaluation results of MedAusbild team for ISIC challenge task. Since early 2017, our team has worked on melanoma classification [1][6], and has employed deep learning since beginning of 2018 [7]. Deep learning helps researchers absolutely to treat and detect diseases by analyzing medical data (e.g., medical images). One of the representative models among the various deep-learning models is a convolutional neural network (CNN). Although our team has an experience with segmentation and classification of benign and malignant skin-lesions, we have participated in the task 3 of ISIC Challenge 2018 for classification of seven skin diseases, explained in this paper.
Discovering API Usability Problems at Scale

Software developers' productivity can be negatively impacted by using APIs incorrectly. In this paper, we describe an analysis technique we designed to find API usability problems by comparing successive file-level changes made by individual software developers. We applied our tool, StopMotion, to the file histories of real developers doing real tasks at Google. The results reveal several API usability challenges including simple typos, conceptual API misalignments, and conflation of similar APIs.
A Low Energy–Dense Diet in the Context of a Weight-Management Program Affects Appetite Control in Overweight and Obese Women

Abstract Background Low energy–dense (LED) foods reduce energy intake (EI); whether this effect is sustained over time and during weight loss is unknown. Objective This trial examined the effects of LED compared with high energy–dense (HED) meals on appetite, EI, and control over eating in the laboratory and during a weight-management program that encourages unrestricted intake of LED foods [Slimming World, UK (SW)] compared with a self-led Standard Care program [NHS weight-loss plan (SC)]. Methods Overweight and obese women [n = 96; mean ± SD age: 41.03 ± 12.61 y; mean ± SD body mass index (in kg/m2): 34.00 ± 3.61] were recruited from the SW or SC programs. Primary outcomes included appetite, food preferences (liking and wanting for LED and HED foods), cravings, and evening meal EI (LED, HED) in response to calorie-matched LED (≤0.8 kcal/g) and HED (≥2.5 kcal/g) breakfast and lunch meals. Probe-day tests were conducted at weeks 3 and 4 and repeated at weeks 12 and 13 in a within-day crossover design. Secondary outcomes, including body weight and program experience, were measured from weeks 1 to 14 in a parallel-group design. Dietary compliance was monitored with the use of weighed food diaries at weeks 3 and 12. Results Intention-to-treat (ITT) and completers analyses showed that the SW group lost more weight than the SC group [ITT: −5.9% (95% CI: −4.7%, –7.2%) compared with −3.5% (−2.3%, −4.8%), P < 0.05; completers: −6.2% (−4.8%, −7.6%) compared with 3.9% (−2.5%, −5.2%), P < 0.05]. The SW group reported greater control over eating and more motivation to continue the program compared with the SC group. LED meals increased sensations of fullness and reduced hunger on probe days (P < 0.001). Total-day EI was 1057 ± 73 kcal less (95% CI: 912, 1203 kcal; 36%) under LED compared with HED conditions (P < .001). Liking for LED and HED foods and wanting for HED foods were lower before lunch under LED compared with HED conditions, and liking decreased to a greater extent after the LED lunch. The SW group reported fewer cravings under LED compared with HED conditions (P < 0.05). On probe days, appetite and EI outcomes did not differ between weeks 3 and 12 or between the SW and SC groups. Conclusion LED meals improve appetite control in women attempting weight loss and the effect is sustainable. Consumption of LED meals likely contributed to weight loss in the SW program. This study was registered at clinicaltrials.gov as NCT02012426.
2B-SSD: The Case for Dual, Byte- and Block-Addressable Solid-State Drives

Performance critical transaction and storage systems require fast persistence of write data. Typically, a non-volatile RAM (NVRAM) is employed on the datapath to the permanent storage, to temporarily and quickly store write data before the system acknowledges the write request. NVRAM is commonly implemented with battery-backed DRAM. Unfortunately, battery-backed DRAM is small and costly, and occupies a precious DIMM slot. In this paper, we make a case for dual, byte- and block-addressable solid-state drive (2B-SSD), a novel NAND flash SSD architecture designed to offer a dual view of byte addressability and traditional block addressability at the same time. Unlike a conventional storage device, 2B-SSD allows accessing the same file with two independent byte- and block-I/O paths. It controls the data transfer between its internal DRAM and NAND flash memory through an intuitive software interface, and manages the mapping of the two address spaces. 2B-SSD realizes a wholly different way and speed of accessing files on a storage device; applications can access them directly using memory-mapped I/O, and moreover write with a DRAM-like latency. To quantify the benefits of 2B-SSD, we modified logging subsystems of major database engines to store log records directly on it without buffering them in the host memory. When running popular workloads, we measured throughput gains in the range of 1.2X and 2.8X with no risk of data loss.
Backprop-Q: Generalized Backpropagation for Stochastic Computation Graphs

In real-world scenarios, it is appealing to learn a model carrying out stochastic operations internally, known as stochastic computation graphs (SCGs), rather than learning a deterministic mapping. However, standard backpropagation is not applicable to SCGs. We attempt to address this issue from the angle of cost propagation, with local surrogate costs, called Q-functions, constructed and learned for each stochastic node in an SCG. Then, the SCG can be trained based on these surrogate costs using standard backpropagation. We propose the entire framework as a solution to generalize backpropagation for SCGs, which resembles an actor-critic architecture but based on a graph. For broad applicability, we study a variety of SCG structures from one cost to multiple costs. We utilize recent advances in reinforcement learning (RL) and variational Bayes (VB), such as off-policy critic learning and unbiased-and-low-variance gradient estimation, and review them in the context of SCGs. The generalized backpropagation extends transported learning signals beyond gradients between stochastic nodes while preserving the benefit of backpropagating gradients through deterministic nodes. Experimental suggestions and concerns are listed to help design and test any specific model using this framework.
Big Data: Deep Learning for Detecting Malware

Malicious software, commonly known as malware are constantly getting smarter with the capabilities of undergoing self-modifcations. They are produced in big numbers and widely deployed very fast through the Internet-capable devices. This is therefore a big data problem and remains challenging in the research community. Existing detection methods should be enhanced in order to e? ectively deal with today’s malware. In this paper, we propose a novel realtime monitoring, analysis and detection approach that is achieved by applying big data analytics and machine learning in the development of a general detection model. The learnings achieved through big data render machine learning more efcient. Using the deep learning approach, we designed and developed a scalable detection model that brings improvement to the existing solutions. Our experiments achieved an accuracy of 97% and ROC of 0.99.
SHARP

As the relentless quest for higher throughput and lower energy cost continues in heterogenous multicores, there is a strong demand for energy-efficient and high-performance Network-on-Chip (NoC) architectures. Heterogeneous architectures that can simultaneously utilize both the serialized nature of the CPU as well as the thread level parallelism of the GPU are gaining traction in the industry. A critical issue with heterogeneous architectures is finding an optimal way to utilize the shared resources such as the last level cache and NoC without hindering the performance of either the CPU or the GPU core. Photonic interconnects are a disruptive technology solution that has the potential to increase the bandwidth, reduce latency, and improve energy-efficiency over traditional metallic interconnects. In this article, we propose a CPU-GPU heterogeneous architecture called Shared Heterogeneous Architecture with Reconfigurable Photonic Network-on-Chip (SHARP) that clusters CPU and GPU cores around the same router and dynamically allocates bandwidth between the CPU and GPU cores based on application demands. The SHARP architecture is designed as a Single-Writer Multiple-Reader (SWMR) crossbar with reservation-assist to connect CPU/GPU cores that dynamically reallocates bandwidth using buffer utilization information at runtime. As network traffic exhibits temporal and spatial fluctuations due to application behavior, SHARP can dynamically reallocate bandwidth and thereby adapt to application demands. SHARP demonstrates 34% performance (throughput) improvement over a baseline electrical CMESH while consuming 25% less energy per bit. Simulation results have also shown 6.9% to 14.9% performance improvement over other flavors of the proposed SHARP architecture without dynamic bandwidth allocation.
How Swift Developers Handle Errors

Swift is a new programming language developed by Apple as a replacement to Objective-C. It features a sophisticated error handling (EH) mechanism that provides the kind of separation of concerns afforded by exception handling mechanisms in other languages, while also including constructs to improve safety and maintainability. However, Swift also inherits a software development culture stemming from Objective-C being the de-facto standard programming language for Apple platforms for the last 15 years. It is, therefore, a priori unclear whether Swift developers embrace the novel EH mechanisms of the programming language or still rely on the old EH culture of Objective-C even working in Swift. In this paper, we study to what extent developers adhere to good practices exemplified by EH guidelines and tutorials, and what are the common bad EH practices particularly relevant to Swift code. Furthermore, we investigate whether perception of these practices differs between novices and experienced Swift developers. To answer these questions we employ a mixed-methods approach and combine 10 semi-structured interviews with Swift developers and quantitative analysis of 78,760 Swift 4 files extracted from 2,733 open-source GitHub repositories. Our findings indicate that there is ample opportunity to improve the way Swift developers use error handling mechanisms. For instance, some recommendations derived in this work are not well spread in the corpus of studied Swift projects. For example, generic catch handlers are common in Swift (even though it is not uncommon for them to share space with their counterparts: non empty catch handlers), custom, developerdefined error types are rare, and developers are mostly reactive when it comes to error handling, using Swift's constructs mostly to handle errors thrown by libraries, instead of throwing and handling application-specific errors.
Scalable Optimization for Embedding Highly-Dynamic and Recency-Sensitive Data

A dataset which is highly-dynamic and recency-sensitive means new data are generated in high volumes with a fast speed and of higher priority for the subsequent applications. Embedding technique is a popular research topic in recent years which aims to represent any data into low-dimensional vector space, which is widely used in different data types and have multiple applications. Generating embeddings on such data in a high-speed way is a challenging problem to consider the high dynamics and the recency sensitiveness together with both effectiveness and efficient. Popular embedding methods are usually time-consuming. As well as the common optimization methods are limited since it may not have enough time to converge or deal with recency-sensitive sample weights. This problem is still an open problem. In this paper, we propose a novel optimization method named Diffused Stochastic Gradient Descent for such highly-dynamic and recency-sensitive data. The notion of our idea is to assign recency-sensitive weights to different samples, and select samples according to their weights in calculating gradients. And after updating the embedding of the selected sample, the related samples are also updated in a diffusion strategy. We propose a Nested Segment Tree to improve the recency-sensitive weight method and the diffusion strategy into a complexity no slower than the iteration step in practice. We also theoretically prove the convergence rate of D-SGD for independent data samples, and empirically prove the efficacy of D-SGD in large-scale real datasets.
Obfuscated VBA Macro Detection Using Machine Learning

Malware using document files as an attack vector has continued to increase and now constitutes a large portion of phishing attacks. To avoid anti-virus detection, malware writers usually implement obfuscation techniques in their source code. Although obfuscation is related to malicious code detection, little research has been conducted on obfuscation with regards to Visual Basic for Applications (VBA) macros. In this paper, we summarize the obfuscation techniques and propose an obfuscated macro code detection method using five machine learning classifiers. To train these classifiers, our proposed method uses 15 discriminant static features, taking into account the characteristics of the VBA macros. We evaluated our approach using a real-world dataset of obfuscated and non-obfuscated VBA macros extracted from Microsoft Office document files. The experimental results demonstrate that our detection approach achieved a F2 score improvement of greater than 23% compared to those of related studies.
Optimal Weighted Extreme Learning Machine for Imbalanced Learning with Differential Evolution [Research Frontier]

In this paper, we present a formal model for the optimal weighted extreme learning machine (ELM) on imbalanced learning. Our model regards the optimal weighted ELM as an optimization problem to find the best weight matrix. We propose an approximate search algorithm, named weighted ELM with differential evolution (DE), that is a competitive stochastic search technique, to solve the optimization problem of the proposed formal imbalanced learning model. We perform experiments on standard imbalanced classification datasets which consist of 39 binary datasets and 3 multiclass datasets. The results show a significant performance improvement over standard ELM with an average Gmean improvement of 10.15% on binary datasets and 1.48% on multiclass datasets, which are also better than other state-of-the-art methods. We also demonstrate that our proposed algorithm can achieve high accuracy in representation learning by performing experiments on MNIST, CIFAR-10, and YouTube-8M, with feature representation from convolutional neural networks.
Dorsolateral Prefrontal Transcranial Direct Current Stimulation Modulates Language Processing but Does Not Facilitate Overt Second Language Word Production

Word retrieval in bilingual speakers partly depends on executive control systems in the left prefrontal cortex – including dorsolateral prefrontal cortex (DLPFC). We tested the hypothesis that DLPFC modulates word production of words specifically in a second language (L2) by measuring the effects of anodal transcranial direct current stimulation (anodal-tDCS) over the DLPFC on picture naming and word translation and on event-related potentials (ERPs) and their sources. Twenty-six bilingual participants with “unbalanced” proficiency in two languages were given 20 min of 1.5 mA anodal or sham tDCS (double-blind stimulation design, counterbalanced stimulation order, 1-week intersession delay). The participants then performed the following tasks: verbal and non-verbal fluency during anodal-tDCS stimulation and first and second language (L1 and L2) picture naming and translation [forward (L1 → L2) and backward (L2 → L1)] immediately after stimulation. The electroencephalogram (EEG) was recorded during picture naming and translation. On the behavioral level, anodal-tDCS had an influence on non-verbal fluency but neither on verbal fluency, nor on picture naming and translation. EEG measures revealed significant interactions between Language and Stimulation on picture naming around 380 ms post-stimulus onset and Translation direction and Stimulation on translation around 530 ms post-stimulus onset. These effects suggest that L2 phonological retrieval and phoneme encoding are spatially and temporally segregated in the brain. We conclude that anodal-tDCS stimulation has an effect at a neural level on phonological processes and, critically, that DLPFC-mediated activation is a constraint on language production specifically in L2.
Similarity-Based Multiple Model Adaptive Estimation

Multiple model adaptive estimation (MMAE) methods are frequently used to overcome the parametric uncertainty of the system’s model. Most MMAE methods approximate the state posteriori (posterior probability) by a weighted arithmetic average of model posteriories using a Bayesian weighting scheme. Despite its effectiveness, arguably arithmetic averaging is not the most proper type of averaging for probability densities. Besides, the exploited Bayesian weighting scheme eventually reduces the MMAE to the single best candidate model, which is problematic in many scenarios. Motivated by such shortcomings, this paper proposes a similarity-based approach for MMAE which enhances the estimation accuracy by generalizing the model averaging scheme and providing realistic weights for each model. The proposed approach provides a posteriori which on average is closest to all posteriories and assigns weights to each model based on their similarity to the true model. The choice of similarity measure leads to various schemes. The simulation results confirm the superiority of the proposed MMAE methods as compared to the conventional method.
Effects of Natural Sunlight on Fiberglass Reinforced Polymers for Crossarms

Fiberglass crossarms have been successfully used in the electrical utility industry to improve grid reliability over the past 25 years. Because they are engineered with fiberglass reinforced polymer (FRP) materials, they are well suited to meet the demands of continuous outdoor use while in service, provided they are properly engineered and protected from the effects of outdoor weathering, including ultraviolet (UV) exposure from natural sunlight. Most fiberglass crossarm manufactures purport the use of synthetic veils and UV inhibitors formulated into the resin system as features to mitigate the effects of outdoor weathering. Some fiberglass crossarm manufactures include the use of an outer UV coating to provide further protection from the effects of outdoor weathering. This paper explores the effects of natural sunlight based on accelerated outdoor weathering test per ASTM G90 on test coupons cut from commercially available fiberglass crossarms which were either with or without a UV coating. The paper will present side-by-side testing results including images of samples with or without exposure to sunlight under ASTM G90, total color change, and analysis from a material performance perspective as well as an application perspective, and will address factors which can affect the service life of fiberglass crossarms.
Rigorous Test Flow for PLL to Identify Weak Devices

In this work we propose a test method to check the robustness of a Phase-Locked Loop (PLL). The goal is to identify weak devices that could fail under hostile operating conditions. We devise a “rigorous test flow” to measure the performance of a PLL device with “mimicked” hostile operating conditions. At the end of the test, the peak-to-peak jitter is used as a “robustness indicator” to guide the screening process of weak devices. Implementation of the proposed test flow on a PLL in a 90nm CMOS process demonstrates that obscure symptoms that might have escaped a simple traditional test can now be revealed.
A First Look at Mobile Intelligence: Architecture, Experimentation and Challenges

Artificial intelligence (AI) technology makes mobile devices become intelligent objects that can learn and act automatically. Although AI will bring great opportunities for mobile applications, little work has focused on the architecture and the interaction with the cloud. In this article, we present three existing architectures of mobile intelligence in detail and introduce its broad application prospects. Furthermore, we conduct a series of experiments to evaluate the performance of the prevalent commercial applications and intelligent frameworks. Our results show that there is a big gap between Quality of Experience (QoE) requirements and the status quo. So far, we have seen only the tip of the iceberg. We pose issues and challenges to advance the area of mobile intelligence and hope to pave the way for future advancements.