nils-herrmann/clean_scirepeval huggingface dataset:
Fitspiration and thinspiration: a comparison across three social networking sites

Fitspiration, or images and text promoting health and fitness, and thinspiration, or images and text promoting thinness, have both received criticism for their negative effects on body image and dieting behaviors. In this study, we critically examined and compared the content of fitspiration and thinspiration on three social networking sites (SNS).



Fitspiration and thinspiration posts (N = 360) from three photo-sharing SNS (Instagram, Tumblr, and Twitter) were collected quasi-randomly on four days over two weeks. Image and associated text content were coded for variables related to weight and shape, muscularity, thin ideal, and eating. Chi-square and Fisher's exact tests compared content of fitspiration and thinspiration posts overall and among the three SNS.



Thinspiration images portrayed body parts more frequently than fitspiration (69.8% vs. 30.2%). Similarly, posts highlighting bony body features and references to mental illness appeared only in thinspiration. No differences were found between fitspiration and thinspiration posts with regard to sexual suggestiveness, appearance comparison, and messages encouraging restrictive eating. Fitspiration and thinspiration posts included similar images across the three SNS-focusing on appearance, sexually suggestive images, and restrictive eating-with three exceptions. Fitspiration posts exhibiting body positivity were found only on Tumblr. In thinspiration posts, references to mental illness were more frequent on Tumblr and Instagram than on Twitter, and bone emphasis was coded more frequently on Twitter than on Instagram.



Although fitspiration posts were less extreme than thinspiration posts on the whole, notable similarities in their content support that fitspiration endorses problematic attitudes towards fitness, body image, and restrictive eating in pursuit of a fit-and-thin body ideal.
Measuring coupling and cohesion in object-oriented systems

As the role that software metrics in general and coupling in particular play with respect to maintainability of software products is widely accepted, current approaches to handle coupling and / or cohesion in object-oriented systems are evaluated. Having identified some inadequacies , we provide a comprehensive framework to deal with all sorts of coupling. This framework takes into account the distinction between object level-and class level coupling. This distinction refers to dynamic dependencies between objects on one hand and static dependencies between implementations on the other hand, representing important aspects of software quality at run-time and during the maintenance phase, respectively. As far as cohesion is concerned, we analyze a well known metric put forward by Chidamber and Kemerer and restated by Li and Henry. As a result, we present a graph theoretic improved version of this metric.
Integral Channel Features – Addendum

This document is meant to serve as an addendum to ,  The purpose of this addendum is twofold: (1) to respond to feedback we’ve received since publication and (2) to describe a number of changes, especially to the non-maximal suppression, that further improve performance. The performance of our updated detection increases 5% to over 91% detection rate at 1 false positive per image on the INRIA dataset, and similarly on the Caltech Pedestrian Dataset, while overall system runtime for multiscale detection decreases by 1/3 to just under 1.5s per 640×480 image. We begin by rectifying an important omission to the related work. Levi and Weiss had an innovative application of integral images to multiple image channels quite early on, demonstrating good results on face detection from few training examples . This work appears to be the earliest such use of integral images, indeed the authors even describe a precursor to integral histograms. Many thanks to Mark Everingham for sending us this reference.
A hybrid mars ascent vehicle design and FY 2016 technology development

Hybrid propulsion is currently favored for a Mars Ascent Vehicle (MAV) concept from a thermal performance and Gross Lift Off Mass standpoint. However, it is at a relatively low level of maturity compared to conventional propulsion options. Technology development efforts are currently underway to bring hybrid propulsion to a technology readiness level that would enable its infusion into potential Mars Sample Return. A new propellant combination is being considered for this design that has excellent low temperature behavior. Preliminary results of two ground test campaigns are currently underway to characterize this propellant combination. Hotfire testing is being carried out in parallel at Parabilis Space Technologies and Space Propulsion Group. In addition to the new propellant combination, several other technologies are being pursued for a potential hybrid MAV: hypergolic ignition and Liquid Injection Thrust Vector Control. Both of these technologies have been applied in other rocket applications, e.g. liquid propulsion commonly uses hypergolic propellants and missiles, such as the Minuteman II, have used LITVC in the past. Hypergolic ignition, when oxidizer and fuel combust upon contact, is highly desirable for multiple starts required by the MAV concept. Therefore, testing at Penn State and Purdue is being completed in this area. An updated hybrid propulsion system design for a Mars Ascent Vehicle concept based on JPL's current understanding of potential Mars Sample Return requirements will be presented, leveraging the advances in technology development as well as updated understanding of how requirements may evolve.
Light-Exoskeleton and Data-Glove integration for enhancing virtual reality applications

This contribution discusses the advanced technologies integration: the PERCRO Light-Exoskeleton and the PERCRO Data-Glove wherein the main pursuit is the enhancement of the human grasping skills inside virtual environments. The mastering of motor human skills for taking up, lifting or manipulating objects during grasping tasks, could be effectively enhanced thanks to the inclusion of an arm exoskeleton which provides arm haptic-guidance-positioning (HGPo) into spatial coordinates and the data-glove for hand dexterity practicing. This paper takes into account a synergetic fusion of theoretical concepts and practical developments, pointing out the feasibility of having robust and accurate hand/arm haptic-robotic guidance positioning within virtual reality environments. Notice that those applications demand high precision, robustness, repeatability and hand/arm dexterity; such a mentioned properties are covered by the proposed system.
DPP-Net: Device-Aware Progressive Search for Pareto-Optimal Neural Architectures

Recent breakthroughs in Neural Architectural Search (NAS) have achieved state-of-the-art performances in applications such as image classification and language modeling. However, these techniques typically ignore device-related objectives such as inference time, memory usage, and power consumption. Optimizing neural architecture for devicerelated objectives is immensely crucial for deploying deep networks on portable devices with limited computing resources. We propose DPPNet: Device-aware Progressive Search for Pareto-optimal Neural Architectures, optimizing for both device-related (e.g., inference time and memory usage) and device-agnostic (e.g., accuracy and model size) objectives. DPP-Net employs a compact search space inspired by current state-of-the-art mobile CNNs, and further improves search efficiency by adopting progressive search (Liu et al. 2017). Experimental results on CIFAR-10 are poised to demonstrate the effectiveness of Pareto-optimal networks found by DPP-Net, for three different devices: (1) a workstation with Titan X GPU, (2) NVIDIA Jetson TX1 embedded system, and (3) mobile phone with ARM Cortex-A53. Compared to CondenseNet and NASNet (Mobile), DPP-Net achieves better performances: higher accuracy & shorter inference time on various devices. Additional experimental results show that models found by DPP-Net also achieve considerablygood performance on ImageNet as well.
Live-fly, large-scale field experimentation for large numbers of fixed-wing UAVs

In this paper, we present extensive advances in live-fly field experimentation capabilities of large numbers of fixed-wing aerial robots, and highlight both the enabling technologies as well as the challenges addressed in such largescale flight operations. We showcase results from recent field tests, including the autonomous launch, flight, and landing of 50 UAVs, which illuminate numerous operational lessons learned and generate rich multi-UAV datasets. We detail the design and open architecture of the testbed, which intentionally leverages low-cost and open-source components, aimed at promoting continued advances and alignment of multi-robot systems research and practice.
Analysis of Features Selection and Machine Learning Classifier in Android Malware Detection

The proliferation of Android-based mobile devices and mobile applications in the market has triggered the malware author to make the mobile devices as the next profitable target. With user are now able to use mobile devices for various purposes such as web browsing, ubiquitous services, online banking, social networking, MMS and etc, more credential information is expose to exploitation. Applying a similar security solution that work in Desktop environment to mobile devices may not be proper as mobile devices have a limited storage, memory, CPU and power consumption. Hence, there is a need to develop a mobile malware detection that can provide an effective solution to defence the mobile user from any malicious threat and at the same time address the limitation of mobile devices environment. Prior to this matter, this research focused on evaluating the best features selection to be used in the best machine-learning classifiers. To find the best combination of both features selection and classifier, five sets of different feature selection are applies to five different machine learning classifiers. The classifier outcome is evaluated using the True Positive Rate (TPR), False Positive Rate (FPR), and Accuracy. The best combination of both features selection and classifier can be used to reduce features selection and at the same time able to classify the infected android application accurately.
A Spiking Neural Network Based Cortex-Like Mechanism and Application to Facial Expression Recognition

In this paper, we present a quantitative, highly structured cortex-simulated model, which can be simply described as feedforward, hierarchical simulation of ventral stream of visual cortex using biologically plausible, computationally convenient spiking neural network system. The motivation comes directly from recent pioneering works on detailed functional decomposition analysis of the feedforward pathway of the ventral stream of visual cortex and developments on artificial spiking neural networks (SNNs). By combining the logical structure of the cortical hierarchy and computing power of the spiking neuron model, a practical framework has been presented. As a proof of principle, we demonstrate our system on several facial expression recognition tasks. The proposed cortical-like feedforward hierarchy framework has the merit of capability of dealing with complicated pattern recognition problems, suggesting that, by combining the cognitive models with modern neurocomputational approaches, the neurosystematic approach to the study of cortex-like mechanism has the potential to extend our knowledge of brain mechanisms underlying the cognitive analysis and to advance theoretical models of how we recognize face or, more specifically, perceive other people's facial expression in a rich, dynamic, and complex environment, providing a new starting point for improved models of visual cortex-like mechanism.
Human action recognition by learning bases of action attributes and parts

In this work, we propose to use attributes and parts for recognizing human actions in still images. We define action attributes as the verbs that describe the properties of human actions, while the parts of actions are objects and poselets that are closely related to the actions. We jointly model the attributes and parts by learning a set of sparse bases that are shown to carry much semantic meaning. Then, the attributes and parts of an action image can be reconstructed from sparse coefficients with respect to the learned bases. This dual sparsity provides theoretical guarantee of our bases learning and feature reconstruction approach. On the PASCAL action dataset and a new “Stanford 40 Actions” dataset, we show that our method extracts meaningful high-order interactions between attributes and parts in human actions while achieving state-of-the-art classification performance.
APPLYING RECURRENT NEURAL NETWORK TO ARABIC NAMED ENTITY RECOGNITION

This technical report collects the final reports of the undergraduate Computer Science majors from the Qatar Campus of Carnegie Mellon University who elected to complete a senior research thesis in the academic year 2015–16 as part of their degree. These projects have spanned the students’ entire senior year, during which they have worked closely with their faculty advisors to plan and carry out their projects. This work counts as 18 units of academic credit each semester. In addition to doing the research, the students presented a brief midterm progress report each semester, presented a public poster session in December, presented an oral summary in the year-end campuswide Meeting of the Minds and submitted a written thesis in May.
Artificial Neural Networks Modeling in Water Resources Engineering : Infrastructure and Applications

The use of artificial neural network (ANN) modeling for prediction and forecasting variables in water r esources engineering are being increasing rapidly. Infrastru ctural applications of ANN in terms of selection of inputs, architectur e of networks, training algorithms, and selection of training para meters in different types of neural networks used in water resources en gin ering have been reported. ANN modeling conducted for water res ources engineering variables (river sediment and discharge ) published in high impact journals since 2002 to 2011 have been e xamined and presented in this review. ANN is a vigorous techniq ue to develop immense relationship between the input and output v ariables, and able to extract complex behavior between the water resources variables such as river sediment and discharge. It can produce robust prediction results for many of the water resources engineering problems by appropriate learning from a set of exam ples. It is important to have a good understanding of the input and output variables from a statistical analysis of the data b efore network modeling, which can facilitate to design an efficie nt network. An appropriate training based ANN model is able to ado pt the physical understanding between the variables and may generat more effective results than conventional prediction techniques. ANN, discharge, modeling, prediction, sediment,
Dynamic surface self-reconstruction is the key of highly active perovskite nano-electrocatalysts for water splitting.

The growing need to store increasing amounts of renewable energy has recently triggered substantial R&D efforts towards efficient and stable water electrolysis technologies. The oxygen evolution reaction (OER) occurring at the electrolyser anode is central to the development of a clean, reliable and emission-free hydrogen economy. The development of robust and highly active anode materials for OER is therefore a great challenge and has been the main focus of research. Among potential candidates, perovskites have emerged as promising OER electrocatalysts. In this study, by combining a scalable cutting-edge synthesis method with time-resolved X-ray absorption spectroscopy measurements, we were able to capture the dynamic local electronic and geometric structure during realistic operando conditions for highly active OER perovskite nanocatalysts. Ba0.5Sr0.5Co0.8Fe0.2O3-δ as nano-powder displays unique features that allow a dynamic self-reconstruction of the material's surface during OER, that is, the growth of a self-assembled metal oxy(hydroxide) active layer. Therefore, besides showing outstanding performance at both the laboratory and industrial scale, we provide a fundamental understanding of the operando OER mechanism for highly active perovskite catalysts. This understanding significantly differs from design principles based on ex situ characterization techniques.
DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning

We study the problem of learning to reason in large scale knowledge graphs (KGs). More specifically, we describe a novel reinforcement learning framework for learning multi-hop relational paths: we use a policy-based agent with continuous states based on knowledge graph embeddings, which reasons in a KG vector space by sampling the most promising relation to extend its path. In contrast to prior work, our approach includes a reward function that takes the accuracy, diversity, and efficiency into consideration. Experimentally, we show that our proposed method outperforms a path-ranking based algorithm and knowledge graph embedding methods on Freebase and Never-Ending Language Learning
Tandem-STRAIGHT: A temporally stable power spectral representation for periodic signals and applications to interference-free spectrum, F0, and aperiodicity estimation

A simple new method for estimating temporally stable power spectra is introduced to provide a unified basis for computing an interference-free spectrum, the fundamental frequency (F0), as well as aperiodicity estimation. F0 adaptive spectral smoothing and cepstral liftering based on consistent sampling theory are employed for interference-free spectral estimation. A perturbation spectrum, calculated from temporally stable power and interference-free spectra, provides the basis for both F0 and aperiodicity estimation. The proposed approach eliminates ad-hoc parameter tuning and the heavy demand on computational power, from which STRAIGHT has suffered in the past.
Snort Intrusion Detection System with Intel Software Guard Extension (Intel SGX)

Network Function Virtualization (NFV) promises the benefits of reduced infrastructure, personnel, and management costs by outsourcing network middleboxes to the public or private cloud. Unfortunately, running network functions in the cloud entails security challenges, especially for complex stateful services. In this paper, we describe our experiences with hardening the king of middleboxes - Intrusion Detection Systems (IDS) - using Intel Software Guard Extensions (Intel SGX) technology. Our IDS secured using Intel SGX, called SEC-IDS, is an unmodified Snort 3 with a DPDK network layer that achieves 10Gbps line rate. SEC-IDS guarantees computational integrity by running all Snort code inside an Intel SGX enclave. At the same time, SEC-IDS achieves near-native performance, with throughput close to 100 percent of vanilla Snort 3, by retaining network I/O outside of the enclave. Our experiments indicate that performance is only constrained by the modest Enclave Page Cache size available on current Intel SGX Skylake based E3 Xeon platforms. Finally, we kept the porting effort minimal by using the Graphene-SGX library OS. Only 27 Lines of Code (LoC) were modified in Snort and 178 LoC in Graphene-SGX itself.
Continuous Signed Distance Functions for 3D Vision

We explore the use of continuous signed distance functions as an object representation for 3D vision. Popularized in procedural computer graphics, this representation defines 3D objects as geometric primitives combined with constructive solid geometry and transformed by nonlinear deformations, scaling, rotation or translation. Unlike its discretized counterpart, that has become important in dense 3D reconstruction, the continuous distance function is not stored as a sampled volume, but as a closed mathematical expression. We argue that this representation can have several benefits for 3D vision, such as being able to describe many classes of indoor and outdoor objects at the order of hundreds of bytes per class, getting parametrized shape variations for free. As a distance function, the representation also has useful computational aspects by defining, at each point in space, the direction and distance to the nearest surface, and whether a point is inside or outside the surface.
Interaction Proxies for Runtime Repair and Enhancement of Mobile Application Accessibility

We introduce interaction proxies as a strategy for runtime repair and enhancement of the accessibility of mobile applications. Conceptually, interaction proxies are inserted between an application's original interface and the manifest interface that a person uses to perceive and manipulate the application. This strategy allows third-party developers and researchers to modify an interaction without an application's source code, without rooting the phone, without otherwise modifying an application, while retaining all capabilities of the system (e.g., Android's full implementation of the TalkBack screen reader). This paper introduces interaction proxies, defines a design space of interaction re-mappings, identifies necessary implementation abstractions, presents details of implementing those abstractions in Android, and demonstrates a set of Android implementations of interaction proxies from throughout our design space. We then present a set of interviews with blind and low-vision people interacting with our prototype interaction proxies, using these interviews to explore the seamlessness of interaction, the perceived usefulness and potential of interaction proxies, and visions of how such enhancements could gain broad usage. By allowing third-party developers and researchers to improve an interaction, interaction proxies offer a new approach to personalizing mobile application accessibility and a new approach to catalyzing development, deployment, and evaluation of mobile accessibility enhancements.
Online, interactive learning of gestures for human/robot interfaces

We have developed a gesture recognition system, based on Hidden Markov Models, which can interactively recognize gestures and perform online learning of new gestures. In addition, it is able to update its model of a gesture iteratively with each example it recognizes. This system has demonstrated reliable recognition of 14 di erent gestures after only one or two examples of each. The system is currently interfaced to a Cyberglove for use in recognition of gestures from the sign language alphabet. The system is being implemented as part of an interactive interface for robot teleoperation and programming by example.
A Survey on Mobile Anchor Node Assisted Localization in Wireless Sensor Networks

Localization is one of the key technologies in wireless sensor networks (WSNs), since it provides fundamental support for many location-aware protocols and applications. Constraints on cost and power consumption make it infeasible to equip each sensor node in the network with a global position system (GPS) unit, especially for large-scale WSNs. A promising method to localize unknown nodes is to use mobile anchor nodes (MANs), which are equipped with GPS units moving among unknown nodes and periodically broadcasting their current locations to help nearby unknown nodes with localization. A considerable body of research has addressed the mobile anchor node assisted localization (MANAL) problem. However, to the best of our knowledge, no updated surveys on MAAL reflecting recent advances in the field have been presented in the past few years. This survey presents a review of the most successful MANAL algorithms, focusing on the achievements made in the past decade, and aims to become a starting point for researchers who are initiating their endeavors in MANAL research field. In addition, we seek to present a comprehensive review of the recent breakthroughs in the field, providing links to the most interesting and successful advances in this research field.