nils-herrmann/clean_scirepeval huggingface dataset:
Soft Pneumatic Actuator skin with embedded sensors

Soft Pneumatic Actuator skin (SPA-skin) is a novel concept of ultra-thin (<; 1 mm) sensor embedded actuators with distributed actuation points that could cover soft bodies. This highly customizable and flexible SPA-skin is ideal for providing proprioceptive sensing by covering pre-existing structures and robots bodies. Having few limitation of the surface quality, dynamics, or shape, these mechanical attributes allow potential applications in autonomous flexible braille, active surface pattern reconfiguration, distributed actuation and sensing for tactile interface improvements. In this paper, the authors present a proof-of-concept SPA-skin. The mechanical parameters, design criteria, sensor selection, and actuator construction process are illustrated. Two control schemes, actuation mode and force sensing mode, are also demonstrated with the latest prototype.
Reliable, Consistent, and Efficient Data Sync for Mobile Apps

Mobile apps need to manage data, often across devices, to provide users with a variety of features such as seamless access, collaboration, and offline editing. To do so reliably, an app must anticipate and handle a host of local and network failures while preserving data consistency. For mobile environments, frugal usage of cellular bandwidth and device battery are also essential. The above requirements place an enormous burden on the app developer. We built Simba, a data-sync service that provides mobile app developers with a high-level local-programming abstraction unifying tabular and object data – a need common to mobile apps – and transparently handles data storage and sync in a reliable, consistent, and efficient manner. In this paper we present a detailed description of Simba’s client software which acts as the gateway to the data sync infrastructure. Our evaluation shows Simba’s effectiveness in rapid development of robust mobile apps that are consistent under all failure scenarios, unlike apps developed with Dropbox. Simbaapps are also demonstrably frugal with cellular resources.
Categories of digital investigation analysis techniques based on the computer history model

Several digital forensic frameworks have been proposed, yet no conclusions have been reached about which are more appropriate. This is partly because each framework may work well for different types of investigations, but it hasn’t been shown if any are sufficient for all types of investigations. To address this problem, this work uses amodel based on the history of a computer to define categories and classes of analysis techniques. The model is more lower-level than existing frameworks and the categories and classes of analysis techniques that are defined support the existing higher-level frameworks. Therefore, they can be used to more clearly compare the frameworks. Proofs can be given to show the completeness of the analysis techniques and therefore the completeness of the frameworks can also be addressed. a 2006
Is 2D Information Enough For Viewpoint Estimation?

What does this paper demonstrate. We show that a very simple 2D architecture (in the sense that it does not make any assumption or reasoning about the 3D information of the object) generally used for object classification, if properly adapted to the specific task, can provide top performance also for pose estimation. More specifically, we demonstrate how a 1-vs-all classification framework based on a Fisher Vector (FV)  pyramid or convolutional neural network (CNN) based features  can be used for pose estimation. In addition, suppressing neighboring viewpoints during training seems key to get good results.
MonoPerfCap: Human Performance Capture From Monocular Video

We present the first marker-less approach for temporally coherent 3D performance capture of a human with general clothing from monocular video. Our approach reconstructs articulated human skeleton motion as well as medium-scale non-rigid surface deformations in general scenes. Human performance capture is a challenging problem due to the large range of articulation, potentially fast motion, and considerable non-rigid deformations, even from multi-view data. Reconstruction from monocular video alone is drastically more challenging, since strong occlusions and the inherent depth ambiguity lead to a highly ill-posed reconstruction problem. We tackle these challenges by a novel approach that employs sparse 2D and 3D human pose detections from a convolutional neural network using a batch-based pose estimation strategy. Joint recovery of per-batch motion allows us to resolve the ambiguities of the monocular reconstruction problem based on a low-dimensional trajectory subspace. In addition, we propose refinement of the surface geometry based on fully automatically extracted silhouettes to enable medium-scale non-rigid alignment. We demonstrate state-of-the-art performance capture results that enable exciting applications such as video editing and free viewpoint video, previously infeasible from monocular video. Our qualitative and quantitative evaluation demonstrates that our approach significantly outperforms previous monocular methods in terms of accuracy, robustness, and scene complexity that can be handled.
Untrimmed Video Classification for Activity Detection: submission to ActivityNet Challenge

Current state-of-the-art human activity recognition is fo cused on the classification of temporally trimmed videos in which only one action occurs per frame. We propose a simple, yet effective, method for the temporal detection of activities in temporally untrimmed videos with the help of untrimmed classification. Firstly, our model predicts th e top k labels for each untrimmed video by analysing global video-level features. Secondly, frame-level binary class ification is combined with dynamic programming to generate the temporally trimmed activity proposals . Finally, each proposal is assigned a label based on the global label, and scored with the score of the temporal activity proposal and the global score. Ultimately, we show that untrimmed video classification models can be used as stepping stone for temporal detection.
Remote shopping advice: enhancing in-store shopping with social technologies

Consumers shopping in "brick-and-mortar" (non-virtual) stores often use their mobile phones to consult with others about potential purchases. Via a survey (n = 200), we detail current practices in seeking remote shopping advice. We then consider how emerging social platforms, such as social networking sites and crowd labor markets, could offer rich next-generation remote shopping advice experiences. We conducted a field experiment in which shoppers shared photographs of potential purchases via MMS, Facebook, and Mechanical Turk. Paid crowdsourcing, in particular, proved surprisingly useful and influential as a means of augmenting in-store shopping. Based on our findings, we offer design suggestions for next-generation remote shopping advice systems.
Human activity recognition with user-free accelerometers in the sensor networks

Many applications using wireless sensor networks (WSNs) aim at providing friendly and intelligent services based on the recognition of human's activities. Although the research result on wearable computing has been fruitful, our experience indicates that a user-free sensor deployment is more natural and acceptable to users. In our system, activities were recognized through matching the movement patterns of the objects, to which tri-axial accelerometers had been attached. Several representative features, including accelerations and their fusion, were calculated and three classifiers were tested on these features. Compared with decision tree (DT) C4.5 and multiple-layer perception (MLP), support vector machine (SVM) performs relatively well across different tests. Additionally, feature selection are discussed for better system performance for WSNs
A vision system for traffic sign detection and recognition

The paper presents an automatic traffic sign recognition system using the videos recorded from an on-board dashcam. It is based on image processing, bilateral Chinese transform, and vertex and bisector transform techniques. The images captured from the dashcam are processed with the histogram of oriented gradients to form feature vectors, followed by support vector machines to detect the traffic signs. The bilateral Chinese transform and vertex and bisector transform are used to extract the area of traffic sign from images. Finally, a neural network is adopted to identify the traffic sign information. In this work, we test the algorithms using the images captured from the camera mounted behind the front windshield. The experiments are evaluated with real traffic scenes and the results have demonstrated the effectiveness of the proposed system.
A hybrid estimation approach for autonomous dirt road following using multiple clothoid segments

In this paper we describe a novel approach to autonomous dirt road following. The algorithm is able to recognize highly curved roads in cluttered color images quite often appearing in offroad scenarios. To cope with large curvatures we apply gaze control and model the road using two different clothoid segments. A Particle Filter incorporating edge and color intensity information is used to simultaneously detect and track the road farther away from the ego vehicle. In addition the particles are used to generate static road segment estimations in a given look ahead distance. These estimations are predicted with respect to ego motion and fused utilizing Kalman filter techniques to generate a smooth local clothoid segment for lateral control of the vehicle.
Privacy practices of Internet users: Self-reports versus observed behavior

Several recent surveys conclude that people are concerned about privacy and consider it to be an important factor in their online decision making. This paper reports on a study in which (1) user concerns were analysed more deeply and (2) what users said was contrasted with what they did in an experimental e-commerce scenario. Eleven independent variables were shown to affect the online behavior of at least some groups of users. Most significant were trust marks present on web pages and the existence of a privacy policy, though users seldom consulted the policy when one existed. We also find that many users have inaccurate perceptions of their own knowledge about privacy technology and vulnerabilities, and that important user groups, like those similar to the Westin ‘‘privacy fundamentalists’’, do not appear to form a cohesive group for privacy-related decision making. In this study we adopt an experimental economic research paradigm, a method for examining user behavior which challenges the current emphasis on survey data. We discuss these issues and the implications of our results on user interpretation of trust marks and interaction design. Although broad policy implications are beyond the scope of this paper, we conclude by questioning the application of the ethical/legal doctrine of informed consent to online transactions in the light of the evidence that users frequently do not consult privacy policies.
Exploiting social networking sites for spam

In the ongoing arms race between spammers and the multi-million dollar anti-spam industry, the number of unsolicited e-mail messages (better known as "spam") and phishing has increased heavily in the last decade. In this paper, we show that our novel friend-in-the-middle attack on social networking sites (SNSs) can be used to harvest social data in an automated fashion. This social data can then be exploited for large-scale attacks such as context-aware spam and social-phishing. We prove the feasibility of our attack exemplarily on Facebook and identify possible consequences based on a mathematical model and simulations. Alarmingly, all major SNSs are vulnerable to our attack as they fail to secure the network layer appropriately.
Hybrid driver IC for real-time TFT non-uniformity compensation of ultra high-definition AMOLED display

An UHD AMOLED display driver IC, enabling real-time TFT non-uniformity compensation, is presented with a hybrid driving scheme. The proposed hybrid driving scheme drives a mobile UHD (3840×1920) AMOLED panel, whose scan time is 7.7μs at a scan frequency of 60Hz, through the load of 30kohm resistance and 30pF capacitance. A proposed accurate current sensor embedded in the column driver and a back-end compensation scheme reduce maximum current error between emulated TFTs within 0.94 LSB (37nA) of 8-bit gray scales. Since the TFT variation is externally compensated, a simple 3T1C pixel circuit is employed in each pixel.
Role of Big Data Analytics in Rural Health Care-A Step Towards Svasth Bharath

Health care is one of the greatest concerns in India. While, those living in cities and big towns have access to high end health services, the millions of people living in rural India, particularly in the remote parts of the country face problems of inadequate facilities and poor access to healthcare. Many experts, including researchers, policy makers and practitioners identified that, there is a big gap in the knowledge about innovations in public and private health financing and delivery. The inefficiencies and inequities in the public health care access in India have pushed forward the need for creative thinking and innovative solutions to strengthen the same. The problems existing in the health care scenario provides apparent calls for the need to change the existing structure of the present health care services by applying big data analytics. This paper identifies the massive shortage of proper health care facilities and addresses how to provide greater access to primary health care services in rural India. Further, it also addresses the critical computing and analytical ability of Big Data in processing huge volumes of transactional data in real time situations to turn the dream of Svasth Bharath (Healthy India) into reality. The objective of this paper is to present the reforms in the health care sector and boosts the discussions on how government can harness innovations in the big data analytics to improve the rural health care system.  Big Data Analytics, HealthCare, Rural Health Care, e-Health Care, Tele Medicine, Svasth Bharath.
Leakage-Resilient Layout Randomization for Mobile Devices

Attack techniques based on code reuse continue to enable real-world exploits bypassing all current mitigations. Code randomization defenses greatly improve resilience against code reuse. Unfortunately, sophisticated modern attacks such as JITROP can circumvent randomization by discovering the actual code layout on the target and relocating the attack payload on the fly. Hence, effective code randomization additionally requires that the code layout cannot be leaked to adversaries. Previous approaches to leakage-resilient diversity have either relied on hardware features that are not available in all processors, particularly resource-limited processors commonly found in mobile devices, or they have had high memory overheads. We introduce a code randomization technique that avoids these limitations and scales down to mobile and embedded devices: Leakage-Resilient Layout Randomization (LR2). Whereas previous solutions have relied on virtualization, x86 segmentation, or virtual memory support, LR2 merely requires the underlying processor to enforce a W⊕X policy—a feature that is virtually ubiquitous in modern processors, including mobile and embedded variants. Our evaluation shows that LR2 provides the same security as existing virtualization-based solutions while avoiding design decisions that would prevent deployment on less capable yet equally vulnerable systems. Although we enforce execute-only permissions in software, LR2 is as efficient as the best-in-class virtualization-based solution.
High-Efficiency Current-Regulated Charge Pump for a White LED Driver

This brief presents a high-efficiency current-regulated charge pump for a white light-emitting diode driver. The charge pump incorporates no series current regulator, unlike conventional voltage charge pump circuits. Output current regulation is accomplished by the proposed pumping current control. The experimental system, with two 1-muF flying and load capacitors, delivers a regulated 20-mA current from an input supply voltage of 2.8-4.2 V. The measured variation is less than 0.6% at a pumping frequency of 200 kHz. The active area of the designed chip is 0.43 mm2 in a 0.5-mum CMOS process.
FTC-Charm++: an in-memory checkpoint-based fault tolerant runtime for Charm++ and MPI

As high performance clusters continue to grow in size, the mean time between failures shrinks. Thus, the issues of fault tolerance and reliability are becoming one of the challenging factors for application scalability. The traditional disk-based method of dealing with faults is to checkpoint the state of the entire application periodically to reliable storage and restart from the recent checkpoint. The recovery of the application from faults involves (often manually) restarting applications on all processors and having it read the data from disks on all processors. The restart can therefore take minutes after it has been initiated. Such a strategy requires that the failed processor can be replaced so that the number of processors at checkpoint-time and recovery-time are the same. We present FTC-Charms ++, a fault-tolerant runtime based on a scheme for fast and scalable in-memory checkpoint and restart. At restart, when there is no extra processor, the program can continue to run on the remaining processors while minimizing the performance penalty due to losing processors. The method is useful for applications whose memory footprint is small at the checkpoint state, while a variation of this scheme - in-disk checkpoint/restart can be applied to applications with large memory footprint. The scheme does not require any individual component to be fault-free. We have implemented this scheme for Charms++ and AMPI (an adaptive version of MPl). This work describes the scheme and shows performance data on a cluster using 128 processors.
Mathematical modeling and vertical flight control of a tilt-wing UAV

This paper presents a mathematical model and vertical flight control algorithms for a new tilt-wing unmanned aerial vehicle (UAV). The vehicle is capable of vertical take-off and landing (VTOL). Due to its tilt-wing structure, it can also fly horizontally. The mathematical model of the vehicle is obtained using Newton-Euler formulation. A gravity compensated PID controller is designed for altitude control, and three PID controllers are designed for attitude stabilization of the vehicle. Performances of these controllers are found to be quite satisfactory as demonstrated by indoor and outdoor flight experiments.
Towards a mobile airbag system using MEMS sensors and embedded intelligence

This paper introduces the development of a mobile human airbag system that is designed for fall protection for the elderly. A Micro Inertial Measurement Unit (mulMU) that is 66 mm x 20 mm x 20 mm in size is built. This unit consists of three dimensional MEMS accelerometers, gyroscopes, and a Bluetooth module. It records human motion information, and, with a Support Vector Machine (SVM) training process, we can classify falls and other normal motions successfully with an SVM filter. Based on the SVM filter, an embedded DSP (Digital Signal Processing) system is developed for real-time fall detection. Also, a smart mechanical airbag deployment system is finalized. This system weighs 253.5 grams (including a 42.5 g compressed CO2 cylinder) and is 190 mm long, 57 mm wide, and 30 mm thick. Its size will be reduced in future to allow it to fit onto a belt. The response time of the trigger is 0.133 seconds, which allows more time for compressed air release before a person falls down to the ground. The integrated system is tested, and the feasibility of the airbag system for real-time fall protection is demonstrated.
Motion control of a novel robotic wrist exoskeleton via pneumatic muscle actuators

In this article, the motion control problem of a robotic EXOskeletal WRIST (EXOWRIST) prototype is considered. This novel robotic appliance's motion is achieved via pneumatic muscle actuators, a pneumatic form of actuation possessing crucial attributes for the development of an exoskeleton that is safe, reliable, portable and low-cost. The EXOWRIST's properties are presented in detail and compared to the recent wrist exoskeleton technology, while its two degrees-of-freedom movement capabilities (extension-flexion, ulnar-radial deviation) are experimentally evaluated on a healthy human volunteer via an advanced nonlinear PID-based control algorithm.
Ontology-based data integration in EPNet: Production and distribution of food during the Roman Empire

Semantic technologies are rapidly changing the historical research. Over the last decades, an immense amount of new quantifiable data have been accumulated, and made available in interchangeable formats, in social sciences and humanities, opening up new possibilities for solving old questions and posing new ones. This paper introduces a framework that eases the access of scholars to historical and cultural data about food production and commercial trade system during the Roman Empire, distributed across different data sources. The proposed approach relies on the Ontology-Based Data Access (OBDA) paradigm, where the different datasets are virtually integrated by a conceptual layer (an ontology) that provides to the user a clear point of access and a unified and unambiguous conceptual view. & 2016 . 3 http://www.kulttuurisampo.fi. 4 http://www.cs.vu.nl/STITCH. 5 http://e-culture.multimedian.nl. 6 http://chip.win.tue.nl. 7 http://http://www.eagle-network.eu. 8 http://www.cidoc-crm.org.
Genome assembly reborn: recent computational challenges

Research into genome assembly algorithms has experienced a resurgence due to new challenges created by the development of next generation sequencing technologies. Several genome assemblers have been published in recent years specifically targeted at the new sequence data; however, the ever-changing technological landscape leads to the need for continued research. In addition, the low cost of next generation sequencing data has led to an increased use of sequencing in new settings. For example, the new field of metagenomics relies on large-scale sequencing of entire microbial communities instead of isolate genomes, leading to new computational challenges. In this article, we outline the major algorithmic approaches for genome assembly and describe recent developments in this domain.
Frequency compensation for two-stage operational amplifiers with improved power supply rejection ratio characteristic

A frequency compensation technique improving characteristic of power supply rejection ratio (PSRR) for two-stage operational amplifiers is presented. This technique is applicable to most known two-stage amplifier configurations. The detailed small-signal analysis of an exemplary amplifier with the proposed compensation and a comparison to its basic version reveal several benefits of the technique which can be effectively exploited in continuous-time filter designs. This comparison shows the possibility of PSRR bandwidth broadening of more than a decade, significant reduction of chip area, the unity-gain bandwidth and power consumption improvement. These benefits are gained at the cost of a non-monotonic phase characteristic of the open-loop differential voltage gain and limitation of a close-loop voltage gain. A prototype-integrated circuit, fabricated based on 0.35 mm complementary metal-oxide semiconductor technology, was used for the technique verification. Two pairs of amplifiers with the classical Miller compensation and a cascoded input stage were measured and compared to their improved counterparts. The measurement data fully confirm the theoretically predicted advantages of the proposed compensation technique.