nils-herrmann/clean_scirepeval huggingface dataset:
A Model-Driven Framework to Develop Personalized Health Monitoring

Both distributed healthcare systems and the Internet of Things (IoT) are currently hot topics. The latter is a new computing paradigm to enable advanced capabilities in engineering various applications, including those for healthcare. For such systems, the core social requirement is the privacy/security of the patient information along with the technical requirements (e.g., energy consumption) and capabilities for adaptability and personalization. Typically, the functionality of the systems is predefined by the patient’s data collected using sensor networks along with medical instrumentation; then, the data is transferred through the Internet for treatment and decision-making. Therefore, systems creation is indeed challenging. In this paper, we propose a model-driven framework to develop the IoT-based prototype and its reference architecture for personalized health monitoring (PHM) applications. The framework contains a multi-layered structure with feature-based modeling and feature model transformations at the top and the application software generation at the bottom. We have validated the framework using available tools and developed an experimental PHM to test some aspects of the functionality of the reference architecture in real time. The main contribution of the paper is the development of the model-driven computational framework with emphasis on the synergistic effect of security and energy issues.
A viability approach for fast recursive feasible finite horizon path planning of autonomous RC cars

We consider a viability based approach to guarantee recursive feasibility of a finite horizon path planner. The path planner is formulated as a hybrid system for which a difference inclusion reformulation is derived by exploiting the special structure of the problem. Based on this approximation, the viability kernel, which characterizes all safe states and the corresponding safe controls, can be calculated. Using the set of safe controls the computation time of the on-line path planning can be reduced, by only generating viable trajectories. Finally, a condition characterizing the unsafe set in case of on-line obstacle avoidance is derived.
Detection and Prevention of SQL Injection Attacks

We depend on database-driven web applications for an ever increasing amount of activities, such as banking and shopping. When performing such activities, we entrust our personal information to these web applications and their underlying databases. The confidentiality and integrity of this information is far from guaranteed; web applications are often vulnerable to attacks, which can give an attacker complete access to the application's underlying database. SQL injection is a type of code-injection attack in which an attacker uses specially crafted inputs to trick the database into executing attacker-specified database commands. In this chapter, we provide an overview of the various types of SQL injection attacks and present AMNESIA, a technique for automatically detecting and preventing SQL injection attacks. AMNESIA uses static analysis to build a model of the legitimate queries an application can generate and then, at runtime, checks that all queries generated by the appUcation comply with this model. We also present an extensive empirical evaluation of AMNESIA. The results of our evaluation indicate that AMNESIA is, at least for the cases considered, highly effective and efficient in detecting and preventing SQL injection attacks.
Accurate Language Identification of Twitter Messages

We present an evaluation of “off-theshelf” language identification systems as applied to microblog messages from Twitter. A key challenge is the lack of an adequate corpus of messages annotated for language that reflects the linguistic diversity present on Twitter. We overcome this through a “mostly-automated” approach to gathering language-labeled Twitter messages for evaluating language identification. We present the method to construct this dataset, as well as empirical results over existing datasets and off-theshelf language identifiers. We also test techniques that have been proposed in the literature to boost language identification performance over Twitter messages. We find that simple voting over three specific systems consistently outperforms any specific system, and achieves state-of-the-art accuracy on the task.
Coupling Adaptive Batch Sizes with Learning Rates

Mini-batch stochastic gradient descent and variants thereof have become standard for large-scale empirical risk minimization like the training of neural networks. These methods are usually used with a constant batch size chosen by simple empirical inspection. The batch size significantly influences the behavior of the stochastic optimization algorithm, though, since it determines the variance of the gradient estimates. This variance also changes over the optimization process; when using a constant batch size, stability and convergence is thus often enforced by means of a (manually tuned) decreasing learning rate schedule. We propose a practical method for dynamic batch size adaptation. It estimates the variance of the stochastic gradients and adapts the batch size to decrease the variance proportionally to the value of the objective function, removing the need for the aforementioned learning rate decrease. In contrast to recent related work, our algorithm couples the batch size to the learning rate, directly reflecting the known relationship between the two. On popular image classification benchmarks, our batch size adaptation yields faster optimization convergence, while simultaneously simplifying learning rate tuning. A TensorFlow implementation is available.
Performance comparison of machine learning algorithms and number of independent components used in fMRI decoding of belief vs. disbelief

Machine learning (ML) has become a popular tool for mining functional neuroimaging data, and there are now hopes of performing such analyses efficiently in real-time. Towards this goal, we compared accuracy of six different ML algorithms applied to neuroimaging data of persons engaged in a bivariate task, asserting their belief or disbelief of a variety of propositional statements. We performed unsupervised dimension reduction and automated feature extraction using independent component (IC) analysis and extracted IC time courses. Optimization of classification hyperparameters across each classifier occurred prior to assessment. Maximum accuracy was achieved at 92% for Random Forest, followed by 91% for AdaBoost, 89% for Naïve Bayes, 87% for a J48 decision tree, 86% for K*, and 84% for support vector machine. For real-time decoding applications, finding a parsimonious subset of diagnostic ICs might be useful. We used a forward search technique to sequentially add ranked ICs to the feature subspace. For the current data set, we determined that approximately six ICs represented a meaningful basis set for classification. We then projected these six IC spatial maps forward onto a later scanning session within subject. We then applied the optimized ML algorithms to these new data instances, and found that classification accuracy results were reproducible. Additionally, we compared our classification method to our previously published general linear model results on this same data set. The highest ranked IC spatial maps show similarity to brain regions associated with contrasts for belief > disbelief, and disbelief < belief.
Mate choice decisions: the role of facial beauty

For most people, facial beauty appears to play a prominent role in choosing a mate. Evidence from research on facial attractiveness indicates that physical beauty is a sexually selected trait mediated, in part, by pubertal facial hormone markers that signal important biological information about the displayer. Such signals would be ineffective if they did not elicit appropriate cognitive and/or emotional responses in members of the opposite sex. In this article, I argue that the effectiveness of these hormonal displays varies with perceivers' brains, which have been organized by the degree of steroid hormone exposure in the uterus, and activated by varying levels of circulating steroids following puberty. I further propose that the methodology used for examining mate choice decisions has general applicability for determining how cognitive and emotional evaluations enter into decision processes.
A Minimal Solution for Non-perspective Pose Estimation from Line Correspondences

In this paper, we study and propose solutions to the relatively un-investigated non-perspective pose estimation problem from line correspondences. Specifically, we represent the 2D and 3D line correspondences as Plücker lines and derive the minimal solution for the minimal problem of three line correspondences with Gröbner basis. Our minimal 3-Line algorithm that gives up to eight solutions is well-suited for robust estimation with RANSAC. We show that our algorithm works as a leastsquares that takes in more than three line correspondences without any reformulation. In addition, our algorithm does not require initialization in both the minimal 3-Line and least-squares n-Line cases. Furthermore, our algorithm works without a need for reformulation under the special case of perspective pose estimation when all line correspondences are observed from one single camera. We verify our algorithms with both simulated and real-world data.
Translation Microscopy (TRAM) for super-resolution imaging

Super-resolution microscopy is transforming our understanding of biology but accessibility is limited by its technical complexity, high costs and the requirement for bespoke sample preparation. We present a novel, simple and multi-color super-resolution microscopy technique, called translation microscopy (TRAM), in which a super-resolution image is restored from multiple diffraction-limited resolution observations using a conventional microscope whilst translating the sample in the image plane. TRAM can be implemented using any microscope, delivering up to 7-fold resolution improvement. We compare TRAM with other super-resolution imaging modalities, including gated stimulated emission deletion (gSTED) microscopy and atomic force microscopy (AFM). We further developed novel 'ground-truth' DNA origami nano-structures to characterize TRAM, as well as applying it to a multi-color dye-stained cellular sample to demonstrate its fidelity, ease of use and utility for cell biology.
Telco Big Data: Current State & Future Directions

A Telecommunication company (Telco) is traditionally only perceived as the entity that provides telecommunication services, such as telephony and data communication access to users. However, the radio and backbone infrastructure of such entities spanning densely most urban spaces and widely most rural areas, provides nowadays a unique opportunity to collect immense amounts of data that capture a variety of natural phenomena on an ongoing basis, e.g., traffic, commerce and mobility patterns and user service experience. The ability to perform analytics on the generated big data within tolerable elapsed time and share it with key smart city enablers (e.g., municipalities, public services, startups, authorities, and companies), elevates the role of Telcos in the realm of future smart cities from pure network access providers to information providers. In this talk, we overview the state-of-the-art in Telco big data analytics by focusing on a set of basic principles, namely: (i) real-time analytics and detection; (ii) experience, behavior and retention analytics; (iii) privacy; and (iv) storage. We also present experiences from developing an innovative such architecture and conclude with open problems and future directions.
ASSOCIATION BETWEEN SOCIAL MEDIA USE AND DEPRESSION AMONG U.S. YOUNG ADULTS.

Social media (SM) use is increasing among U.S. young adults, and its association with mental well-being remains unclear. This study assessed the association between SM use and depression in a nationally representative sample of young adults.



We surveyed 1,787 adults ages 19 to 32 about SM use and depression. Participants were recruited via random digit dialing and address-based sampling. SM use was assessed by self-reported total time per day spent on SM, visits per week, and a global frequency score based on the Pew Internet Research Questionnaire. Depression was assessed using the Patient-Reported Outcomes Measurement Information System (PROMIS) Depression Scale Short Form. Chi-squared tests and ordered logistic regressions were performed with sample weights.



The weighted sample was 50.3% female and 57.5% White. Compared to those in the lowest quartile of total time per day spent on SM, participants in the highest quartile had significantly increased odds of depression (AOR = 1.66, 95% CI = 1.14-2.42) after controlling for all covariates. Compared with those in the lowest quartile, individuals in the highest quartile of SM site visits per week and those with a higher global frequency score had significantly increased odds of depression (AOR = 2.74, 95% CI = 1.86-4.04; AOR = 3.05, 95% CI = 2.03-4.59, respectively). All associations between independent variables and depression had strong, linear, dose-response trends. Results were robust to all sensitivity analyses.



SM use was significantly associated with increased depression. Given the proliferation of SM, identifying the mechanisms and direction of this association is critical for informing interventions that address SM use and depression.
Augmenting reality for augmented reality

elements that can be integrated with camera-based AR systems but that are independently meaningful objects in their own right. We argue that this new wave of physically grounded AR technologies constitutes the first steps toward a hybridized digital/physical future that can transform our world. The technology space of augmented reality (AR), sometimes characterized using the more general term mixed reality, is one of the more exciting frontiers to emerge from HCI research in recent years . However, like many frontiers, it is chaotic, overhyped, and misunderstood. In the absence of existing best practices to guide development of this new design space, a number of competing visions have taken hold, each of which seeks to colonize the future of our digitally augmented world. Microsoft's version of this future is embodied by the HoloLens, an untethered standalone visor running Windows 10 that superimposes translucent " holographic " overlays into the center of a user's field of view. HoloLens feels like the big brother to Google's Glass project, which traded computing power and graphical fidelity for mobility. Glass was designed to be worn in daily life, a decision that didn't factor in the social consequences of wearing a highly visible digital surveillance There are two competing narratives for the future of computationally augmented spaces. On the one hand, we have the Internet of Things , where the narrative is one of making our environments more aware of us and of themselves, and generally making everything " smarter " through embedded computation, sensing, and actuation. On the other hand, we have current approaches to augmented or mixed reality, in which the space remains unchanged and instead we hack our perception of the space by superimposing a layer of media between us and the world [2,3]. In this article we present examples of three projects that seek to merge these two approaches by creating and fabricating playful material
A systematic literature review on Enterprise Architecture Implementation Methodologies

http://dx.doi.org/10.1016/j.infsof.2015.01.012 0950-5849/ 2015 Elsevier B.V. All rights reserved. ⇑ Corresponding author. E-mail addresses: drbabak2@live.utm.my (B.D. Rouhani), mdnazrim@utm.my (M.N. Mahrin), fa.nikpay@siswa.um.edu.my (F. Nikpay), rodina@um.edu.my (R.B. npourya2@live.utm.my (P. Nikfard). Babak Darvish Rouhani a,⇑, Mohd Naz’ri Mahrin , Fatemeh Nikpay , Rodina Binti Ahmad , Pourya Nikfard c
Output Range Analysis for Deep Feedforward Neural Networks

Given a neural network (NN) and a set of possible inputs to the network described by polyhedral constraints, we aim to compute a safe over-approximation of the set of possible output values. This operation is a fundamental primitive enabling the formal analysis of neural networks that are extensively used in a variety of machine learning tasks such as perception and control of autonomous systems. Increasingly, they are deployed in high-assurance applications, leading to a compelling use case for formal verification approaches. In this paper, we present an efficient range estimation algorithm that iterates between an expensive global combinatorial search using mixed-integer linear programming problems, and a relatively inexpensive local optimization that repeatedly seeks a local optimum of the function represented by the NN. We implement our approach and compare it with Reluplex, a recently proposed solver for deep neural networks. We demonstrate applications of our approach to computing flowpipes for neural network-based feedback controllers. We show that the use of local search in conjunction with mixed-integer linear programming solvers effectively reduces the combinatorial search over possible combinations of active neurons in the network by pruning away suboptimal nodes.
Penile appearance, lumps and bumps.

Even after a thorough examination it can be difficult to distinguish a normal penile anatomical variant from pathology needing treatment.



This article aims to assist diagnosis by outlining a series of common penile anatomical variants and comparing them to common pathological conditions.



The problems considered include pearly penile papules, penile sebaceous glands (Fordyce spots), Tyson glands, angiokeratomas of the scrotum, lymphocoele, penile warts, molluscum contagiosum, folliculitis and scabies.
Towards a Resource Aware Scheduler in Hadoop

Hadoop-MapReduce is a popular distributed computing model that has been deployed on large clusters like those owned by Yahoo and Facebook and Amazon EC2. In a practical data center of that scale, it is a common scenario that I/Obound jobs and CPU-bound jobs, that demand complementary resources, run simultaneously on the same cluster. Although improvement exist on the the default FIFO scheduler in Hadoop, much less work has been done on scheduling such that resource contention on machines is minimized. In this work, we study the behavior of the current scheduling schemes in Hadoop on a locally deployed cluster Simpool owned by ACAL, University of Michigan and propose new resource aware scheduling schemes.
Social Honeypots: Making Friends With A Spammer Near You

Social networking communities have become an important communications platform, but the popularity of these communities has also made them targets for a new breed of social spammers. Unfortunately, little is known about these social spammers, their level of sophistication, or their strategies and tactics. Thus, in this paper, we provide the first characterization of social spammers and their behaviors. Concretely, we make two contributions: (1) we introduce social honeypots for tracking and monitoring social spam, and (2) we report the results of an analysis performed on spam data that was harvested by our social honeypots. Based on our analysis, we find that the behaviors of social spammers exhibit recognizable temporal and geographic patterns and that social spam content contains various distinguishing characteristics. These results are quite promising and suggest that our analysis techniques may be used to automatically identify social spam.
A Wideband Dual-Polarized L-Probe Antenna Array With Hollow Structure and Modified Ground Plane for Isolation Enhancement

A two-element wideband dual-polarized L-probe antenna array with hollow structure is presented in this letter. The hollow structure is applied to offer space for hosting other antennas (e.g., the concept of shared-aperture antenna). Each radiating element is designed with a compact size of 51 mm × 51 mm × 24.5 mm (0.374λ<sub>0 </sub> × 0.374λ<sub>0</sub> × 0.18λ<sub>0</sub> at 2.2 GHz) and has a hollow volume of 31 mm × 31 mm × 24.5 mm. With a slotted square ring patch and unique L-probes, the antenna element exhibits a fairly large fractional bandwidth of 45%, ranging from 1.71 to 2.69 GHz. Moreover, a narrow slot and T-shaped metallic structures on the ground plane are introduced to increase the input-port isolation of the proposed antenna array to 26 dB across the entire operating bandwidth. The experimental results indicate that the antenna array has an average realized gain of 10.5 dBi, which correlates well with the simulations.
Cryptography based digital image watermarking algorithm to increase security of watermark data

Digital watermarking is one of the proposed solutions for copyright protection of multimedia data. This technique is better than Digital Signatures and other methods because it does not increase overhead. Digital Watermarking describes methods and technologies that hide information, for example a number or text, in digital media, such as images, video or audio. The embedding takes place by manipulating the content of the digital data, which means the information is not embedded in the frame around the data. In this paper cryptography based Blind image watermarking technique presented that can embed more number of watermark bits in the gray scale cover image without affecting the imperceptibility and increase the security of watermarks.
Unknown Tag Identification in Large RFID Systems: An Efficient and Complete Solution

Radio-Frequency Identification (RFID) technology brings revolutionary changes to many fields like retail industry. One important research issue in large RFID systems is the identification of unknown tags, i.e., tags that just entered the system but have not been interrogated by reader(s) covering them yet. Unknown tag identification plays a critical role in automatic inventory management and misplaced tag discovery, but it is far from thoroughly investigated. Existing solutions either trivially interrogate all the tags in the system and thus are highly time inefficient due to re-identification of already identified tags, or use probabilistic approaches that cannot guarantee complete identification of all the unknown tags. In this paper, we propose a series of protocols that can identify all of the unknown tags with high time efficiency. We develop several novel techniques to quickly deactivate already identified tags and prevent them from replying during the interrogation of unknown tags, which avoids re-identification of these tags and consequently improves time efficiency. To our knowledge, our protocols are the first non-trivial solutions that guarantee complete identification of all the unknown tags. We illustrate the effectiveness of our protocols through both rigorous theoretical analysis and extensive simulations. Simulation results show that our protocols can save up to 70 percent time when compared with the best existing solutions.
EmoBGM: Estimating sound's emotion for creating slideshows with suitable BGM

This paper presents a study about estimating the emotions conveyed in clips of background music (BGM) to be used in an automatic slideshow creation system. The system we aimed to develop, automatically tags each given pieces of background music with the main emotion it conveys, in order to recommend the most suitable music clip to the slideshow creators, based on the main emotions of embedded photos. As a first step of our research, we developed a machine learning model to estimate the emotions conveyed in a music clip and achieved 88% classification accuracy with cross-validation technique. The second part of our work involved developing a web application using Microsoft Emotion API to determine the emotions in photos, so the system can find the best candidate music for each photo in the slideshow. 16 users rated the recommended background music for a set of photos using a 5-point likert scale and we achieved an average rate of 4.1, 3.6 and 3.0 for the photo sets 1, 2, and 3 respectively of our evaluation task.