nils-herrmann/clean_scirepeval huggingface dataset:
Arm 7 Based Robotic Arm Control By Electronic Gesture Recognition Unit Using Mems

Mechatronics is one of the present trends in the era of computing in today’s system automation industry and control. The proposed project is one such attempt of implementing an accelerometer based system to communicate with an industrial robotic arm wirelessly. This project comprises of controlling of robotic arm powered with ARM7 based LPC1768 core. The LC1768 core has to be interfaced with DC motors of robotic arm to control the movements of robotic arm. MEMS is a three dimensional accelerometer sensor used for this purpose, this accelerometer sensor captures gestures of human-arm and produces three analog output voltages in three dimensional axes. And two flex sensors are used to control the gripper movement. For various movements of accelerometer and flex sensors corresponding characters will be sent to the ARM7 core wirelessly using 2.4GHz RF module. And depending on the received character robotic arm can be controlled in Dynamic or Static mode by communicating with EEPROM using I2C protocol. Mems, Robotic arm, Arduino, RF transceiver, Arm7,
Pipelining Localized Semantic Features for Fine-Grained Action Recognition

In fine-grained action (object manipulation) recognition, it is important to encode object semantic (contextual) information, i.e., which object is being manipulated and how it is being operated. However, previous methods for action recognition often represent the semantic information in a global and coarse way and therefore cannot cope with fine-grained actions. In this work, we propose a representation and classification pipeline which seamlessly incorporates localized semantic information into every processing step for fine-grained action recognition. In the feature extraction stage, we explore the geometric information between local motion features and the surrounding objects. In the feature encoding stage, we develop a semantic-grouped locality-constrained linear coding (SG-LLC) method that captures the joint distributions between motion and object-in-use information. Finally, we propose a semantic-aware multiple kernel learning framework (SA-MKL) by utilizing the empirical joint distribution between action and object type for more discriminative action classification. Extensive experiments are performed on the large-scale and difficult fine-grained MPII cooking action dataset. The results show that by effectively accumulating localized semantic information into the action representation and classification pipeline, we significantly improve the fine-grained action classification performance over the existing methods.
Head flexion angle while using a smartphone.

Repetitive or prolonged head flexion posture while using a smartphone is known as one of risk factors for pain symptoms in the neck. To quantitatively assess the amount and range of head flexion of smartphone users, head forward flexion angle was measured from 18 participants when they were conducing three common smartphone tasks (text messaging, web browsing, video watching) while sitting and standing in a laboratory setting. It was found that participants maintained head flexion of 33-45° (50th percentile angle) from vertical when using the smartphone. The head flexion angle was significantly larger (p < 0.05) for text messaging than for the other tasks, and significantly larger while sitting than while standing. Study results suggest that text messaging, which is one of the most frequently used app categories of smartphone, could be a main contributing factor to the occurrence of neck pain of heavy smartphone users.  In this laboratory study, the severity of head flexion of smartphone users was quantitatively evaluated when conducting text messaging, web browsing and video watching while sitting and standing. Study results indicate that text messaging while sitting caused the largest head flexion than that of other task conditions.
CauseInfer: Automatic and distributed performance diagnosis with hierarchical causality graph in large distributed systems

Modern applications especially cloud-based or cloud-centric applications always have many components running in the large distributed environment with complex interactions. They are vulnerable to suffer from performance or availability problems due to the highly dynamic runtime environment such as resource hogs, configuration changes and software bugs. In order to make efficient software maintenance and provide some hints to software bugs, we build a system named CauseInfer, a low cost and blackbox cause inference system without instrumenting the application source code. CauseInfer can automatically construct a two layered hierarchical causality graph and infer the causes of performance problems along the causal paths in the graph with a series of statistical methods. According to the experimental evaluation in the controlled environment, we find out CauseInfer can achieve an average 80% precision and 85% recall in a list of top two causes to identify the root causes, higher than several state-of-the-art methods and a good scalability to scale up in the distributed systems.
Trajectory planning and sliding-mode control based trajectory-tracking for cybercars

Fully automatic driving is emerging as the approach to dramatically improve efficiency (throughput per unit of space) while at the same time leading to the goal of zero accidents. This approach, based on fully automated vehicles, might improve the efficiency of road travel in terms of space and energy used, and in terms of service provided as well. For such automated operation, trajectory planning methods that produce smooth trajectories, with low level associated accelerations and jerk for providing human comfort, are required. This paper addresses this problem proposing a new approach that consists of introducing a velocity planning stage in the trajectory planner. Moreover, this paper presents the design and simulation evaluation of trajectory-tracking and path-following controllers for autonomous vehicles based on sliding mode control. A new design of sliding surface is proposed, such that lateral and angular errors are internally coupled with each other (in cartesian space) in a sliding surface leading to convergence of both variables.
Property-based attestation for computing platforms: caring about properties, not mechanisms

Over the past years, the computing industry has started various initiatives announced to increase computer security by means of new hardware architectures. The most notable effort is the Trusted Computing Group (TCG) and the Next-Generation Secure Computing Base (NGSCB). This technology offers useful new functionalities as the possibility to verify the integrity of a platform (attestation) or binding quantities on a specific platform (sealing).In this paper, we point out the deficiencies of the attestation and sealing functionalities proposed by the existing specification of the TCG: we show that these mechanisms can be misused to discriminate certain platforms, i.e., their operating systems and consequently the corresponding vendors. A particular problem in this context is that of managing the multitude of possible configurations. Moreover, we highlight other shortcomings related to the attestation, namely system updates and backup. Clearly, the consequences caused by these problems lead to an unsatisfactory situation both for the private and business branch, and to an unbalanced market when such platforms are in wide use.To overcome these problems generally, we propose a completely new approach: the attestation of a platform should not depend on the specific software or/and hardware (configuration) as it is today's practice but only on the "properties" that the platform offers. Thus, a property-based attestation should only verify whether these properties are sufficient to fulfill certain (security) requirements of the party who asks for attestation. We propose and discuss a variety of solutions based on the existing Trusted Computing (TC) functionality. We also demonstrate, how a property-based attestation protocol can be realized based on the existing TC hardware such as a Trusted Platform Module (TPM).
Pseudo-Projective Dependency Parsing

In order to realize the full potential of dependency-based syntactic parsing, it is desirable to allow non-projective dependency structures. We show how a datadriven deterministic dependency parser, in itself restricted to projective structures, can be combined with graph transformation techniques to produce non-projective structures. Experiments using data from the Prague Dependency Treebank show that the combined system can handle nonprojective constructions with a precision sufficient to yield a significant improvement in overall parsing accuracy. This leads to the best reported performance for robust non-projective parsing of Czech.
Phineas gauged: decision-making and the human prefrontal cortex

Poor social judgment and decision-making abilities have often been attributed to people who have suffered injury to the ventromedial prefrontal cortex (VMPFC). However, few laboratory tests of decision-making have been conducted on these patients. The exception to this is the Iowa Gambling Task which has often, but not always, demonstrated differential performance between patients and controls. Results from patients with prefrontal cortex lesions on a novel test of decision-making are presented. Participants explored and chose from pairs of gambles that differed in their underlying distributions, primarily in the variance of their respective outcomes. In accordance with many findings from the behavioral decision-making literature, both young normal participants and older patient controls demonstrated a marked avoidance of risk and selected largely from secure, low variance gambles. In contrast, patients with ventromedial lesions were divided into two clear sub-groups. One group behaved similarly to normals, showing a risk-averse strategy. The other group displayed a distinctive risk-seeking behavior pattern, choosing predominantly from the high-variance, high-risk decks. This research demonstrates some of the advantages of using methods and theories from traditional decision-making research to study the behavior of patients, as well as the benefits of examining individual participants, and provides new insights into the nature of the decision-making deficit in patients with ventromedial prefrontal cortex lesions.
Evaluating opportunistic networks in disaster scenarios

Forwarding data in scenarios where devices have sporadic connectivity is a challenge. An example scenario is a disaster area, where forwarding information generated in the incident location, like victims’ medical data, to a coordination point is critical for quick, accurate and coordinated intervention. New applications are being developed based on mobile devices and wireless opportunistic networks as a solution to destroyed or overused communication networks. But the performance of opportunistic routing methods applied to emergency scenarios is unknown today. In this paper, we compare and contrast the efficiency of the most significant opportunistic routing protocols through simulations in realistic disaster scenarios in order to show how the different characteristics of an emergency scenario impact in the behaviour of each one of them. & 2012 Elsevier Ltd. All rights reserved.
Analysis and suppression of conducted EMI emissions for front-end LLC resonant DC/DC converters

LLC resonant DC/DC converters are becoming popular in computing applications, such as telecom, server systems. For these applications, it is required to meet the EMI standard. In this paper, novel EMI noise transferring path and EMI model for LLC resonant DC/DC converters are proposed. DM and CM noise of LLC resonant converter are analyzed. Several EMI noise reduction approaches are proposed. Shield layers are applied to reduce CM noise. By properly choosing the ground point of shield layer, significant noise reduction can be obtained. With extra EMI balance capacitor, CM noise can be reduced further. Two channel interleaving LLC resonant converters are proposed to cancel the CM current. Conceptually, when two channels operate with 180 degree phase shift, CM current can be canceled. Therefore, the significant EMI noise reduction can be achieved.
Measuring document image skew and orientation

Several approaches have previously been taken for identify ing document image skew. At issue are efficiency, accuracy, and robustness. We work dire ctly with the image, maximizing a function of the number of ON pixels in a scanline. Image rotat i n is simulated by either vertical shear or accumulation of pixel counts along sloped lines . Pixel sum differences on adjacent scanlines reduce isotropic background noise from non-text regions. To find the skew angle, a succession of values of this function are found. Angles are chosen hierarchically, typically with both a coarse sweep and a fine angular bifurcation. To inc rease efficiency, measurements are made on subsampled images that have been pre-filtered to m aximize sensitivity to image skew. Results are given for a large set of images, includi ng multiple and unaligned text columns, graphics and large area halftones. The measured in t insic angular error is inversely proportional to the number of sampling points on a scanline. This method does not indicate when text is upside-down, and i t also requires sampling the function at 90 degrees of rotation to measure text skew in lan dscape mode. However, such text orientation can be determined (as one of four direction s) by noting that roman characters in all languages have many more ascenders than descenders, a nd using morphological operations to identify such pixels. Only a small amount of text is r equired for accurate statistical determination of orientation, and images without text are i dentified as such.
It Takes (Only) Two: Adversarial Generator-Encoder Networks

We present a new autoencoder-type architecture that is trainable in an unsupervised mode, sustains both generation and inference, and has the quality of conditional and unconditional samples boosted by adversarial learning. Unlike previous hybrids of autoencoders and adversarial networks, the adversarial game in our approach is set up directly between the encoder and the generator, and no external mappings are trained in the process of learning. The game objective compares the divergences of each of the real and the generated data distributions with the prior distribution in the latent space. We show that direct generator-vs-encoder game leads to a tight coupling of the two components, resulting in samples and reconstructions of a comparable quality to some recently-proposed more complex
A clustering-based system to automate transfer function design for medical image visualization

Finding good transfer functions for rendering medical volumes is difficult, non-intuitive, and time-consuming. We introduce a clustering-based framework for the automatic generation of transfer functions for volumetric data. The system first applies mean shift clustering to oversegment the volume boundaries according to their low-high (LH) values and their spatial coordinates, and then uses hierarchical clustering to group similar voxels. A transfer function is then automatically generated for each cluster such that the number of occlusions is reduced. The framework also allows for semi-automatic operation, where the user can vary the hierarchical clustering results or the transfer functions generated. The system improves the efficiency and effectiveness of visualizing medical images and is suitable for medical imaging applications.
Grand Pwning Unit: Accelerating Microarchitectural Attacks with the GPU

Dark silicon is pushing processor vendors to add more specialized units such as accelerators to commodity processor chips. Unfortunately this is done without enough care to security. In this paper we look at the security implications of integrated Graphical Processor Units (GPUs) found in almost all mobile processors. We demonstrate that GPUs, already widely employed to accelerate a variety of benign applications such as image rendering, can also be used to "accelerate" microarchitectural attacks (i.e., making them more effective) on commodity platforms. In particular, we show that an attacker can build all the necessary primitives for performing effective GPU-based microarchitectural attacks and that these primitives are all exposed to the web through standardized browser extensions, allowing side-channel and Rowhammer attacks from JavaScript. These attacks bypass state-of-the-art mitigations and advance existing CPU-based attacks: we show the first end-to-end microarchitectural compromise of a browser running on a mobile phone in under two minutes by orchestrating our GPU primitives. While powerful, these GPU primitives are not easy to implement due to undocumented hardware features. We describe novel reverse engineering techniques for peeking into the previously unknown cache architecture and replacement policy of the Adreno 330, an integrated GPU found in many common mobile platforms. This information is necessary when building shader programs implementing our GPU primitives. We conclude by discussing mitigations against GPU-enabled attackers.
Contextual Sequence Modeling for Recommendation with Recurrent Neural Networks

Recommendations can greatly benefit from good representations of the user state at recommendation time. Recent approaches that leverage Recurrent Neural Networks (RNNs) for session-based recommendations have shown that Deep Learning models can provide useful user representations for recommendation. However, current RNN modeling approaches summarize the user state by only taking into account the sequence of items that the user has interacted with in the past, without taking into account other essential types of context information such as the associated types of user-item interactions, the time gaps between events and the time of day for each interaction. To address this, we propose a new class of Contextual Recurrent Neural Networks for Recommendation (CRNNs) that can take into account the contextual information both in the input and output layers and modifying the behavior of the RNN by combining the context embedding with the item embedding and more explicitly, in the model dynamics, by parametrizing the hidden unit transitions as a function of context information. We compare our CRNNs approach with RNNs and non-sequential baselines and show good improvements on the next event prediction task.
An Adaptive Mixed Reality Training System for Stroke Rehabilitation

This paper presents a novel mixed reality rehabilitation system used to help improve the reaching movements of people who have hemiparesis from stroke. The system provides real-time, multimodal, customizable, and adaptive feedback generated from the movement patterns of the subject's affected arm and torso during reaching to grasp. The feedback is provided via innovative visual and musical forms that present a stimulating, enriched environment in which to train the subjects and promote multimodal sensory-motor integration. A pilot study was conducted to test the system function, adaptation protocol and its feasibility for stroke rehabilitation. Three chronic stroke survivors underwent training using our system for six 75-min sessions over two weeks. After this relatively short time, all three subjects showed significant improvements in the movement parameters that were targeted during training. Improvements included faster and smoother reaches, increased joint coordination and reduced compensatory use of the torso and shoulder. The system was accepted by the subjects and shows promise as a useful tool for physical and occupational therapists to enhance stroke rehabilitation.
MUZZLE BLAST DETECTION VIA SHORT TIME FOURIER TRANSFORM

A projectile's trajectory can be estimated by measuring the arrival times of the acoustic energy at several locations in space. In case of a supersonic projectile fired from a gun, both the acoustic shockwave and muzzle blast can be observed. For acoustic sensor networks attempting to determine the trajectory, the challenge is to first, correctly classify the transient signal as either a shockwave or a muzzle blast and then, calculate the direction of arrival based on arrival times across a sensor array. This can be challenging when the shockwave has lost substantial high frequency content. Various joint time-frequency techniques can be used to classify these signatures, such as short-time Fourier transform (STFT), Smoothed Pseudo Wigner-Ville distribution (SPWVD) and wavelet analysis . This paper presents a short-time Fourier transform (STFT) technique that was implemented in a shooter localization application. The application is built on a sensor network developed by the Network Embedded Systems Technology group at Institute for Software Integrated Systems, at the Vanderbilt University . The ad-hoc sensor network consists of a large number of independent motes, each equipped with a multi-purpose acoustic sensor board (MicroBee). The MicroBee board has two independent acoustic channels (each with a microphone, amplifier with controllable gain and ADC operating up to 100 kHz) and an ADSP-2189 16 bit fixed-point DSP processor. The presented technique focuses on the muzzle blast detection, however, similar techniques can be used also for the shockwave signature detection. Elements of the algorithms proposed by  are utilized in the presented technique with more emphasis laid on realization aspects.
FIA: An Open Forensic Integration Architecture for Composing Digital Evidence

The analysis and value of digital evidence in an investigation has been the domain of discourse in the digital forensic community for several years. While many works have considered different approaches to model digital evidence, a comprehensive understanding of the process of merging different evidence items recovered during a forensic analysis is still a distant dream. With the advent of modern technologies, pro-active measures are integral to keeping abreast of all forms of cyber crimes and attacks. This paper motivates the need to formalize the process of analyzing digital evidence from multiple sources simultaneously. In this paper, we present the forensic integration architecture (FIA) which provides a framework for abstracting the evidence source and storage format information from digital evidence and explores the concept of integrating evidence information from multiple sources. The FIA architecture identifies evidence information from multiple sources that enables an investigator to build theories to reconstruct the past. FIA is hierarchically composed of multiple layers and adopts a technology independent approach. FIA is also open and extensible making it simple to adapt to technological changes. We present a case study using a hypothetical car theft case to demonstrate the concepts and illustrate the value it brings into the field.
Slicing the next mobile packet core network

The mobile network plays an important role in the evolution of humanity and society. However, due to the increase of users as well as of mobile applications, the current mobile network architecture faces many challenges. In this paper we describe V-Core, a new architecture for the mobile packet core network which is based on Software Defined Networking and Network Function Virtualization. Then, we introduce a MobileVisor which is a machine to slice the above mobile packet core network into different control platforms according to either different mobile operators or different technologies (e.g. 3G or 4G). With our architecture, the mobile network operators can reduce their costs for deployment and operation as well as use network resources efficiently.