nils-herrmann/clean_scirepeval huggingface dataset:
Belt: An Unobtrusive Touch Input Device for Head-worn Displays

Belt is a novel unobtrusive input device for wearable displays that incorporates a touch surface encircling the user's hip. The wide input space is leveraged for a horizontal spatial mapping of quickly accessible information and applications. We discuss social implications and interaction capabilities for unobtrusive touch input and present our hardware implementation and a set of applications that benefit from the quick access time. In a qualitative user study with 14 participants we found out that for short interactions (2-4 seconds), most of the surface area is considered as appropriate input space, while for longer interactions (up to 10 seconds), the front areas above the trouser pockets are preferred.
Fourier Lucas-Kanade Algorithm

In this paper, we propose a framework for both gradient descent image and object alignment in the Fourier domain. Our method centers upon the classical Lucas &#x0026; Kanade (LK) algorithm where we represent the source and template/model in the complex 2D Fourier domain rather than in the spatial 2D domain. We refer to our approach as the Fourier LK (FLK) algorithm. The FLK formulation is advantageous when one preprocesses the source image and template/model with a bank of filters (e.g., oriented edges, Gabor, etc.) as 1) it can handle substantial illumination variations, 2) the inefficient preprocessing filter bank step can be subsumed within the FLK algorithm as a sparse diagonal weighting matrix, 3) unlike traditional LK, the computational cost is invariant to the number of filters and as a result is far more efficient, and 4) this approach can be extended to the Inverse Compositional (IC) form of the LK algorithm where nearly all steps (including Fourier transform and filter bank preprocessing) can be precomputed, leading to an extremely efficient and robust approach to gradient descent image matching. Further, these computational savings translate to nonrigid object alignment tasks that are considered extensions of the LK algorithm, such as those found in Active Appearance Models (AAMs).
Query suggestion for E-commerce sites

Query suggestion module is an integral part of every search engine. It helps search engine users narrow or broaden their searches. Published work on query suggestion methods has mainly focused on the web domain. But, the module is also popular in the domain of e-commerce for product search. In this paper, we discuss query suggestion and its methodologies in the context of e-commerce search engines. We show that dynamic inventory combined with long and sparse tail of query distribution poses unique challenges to build a query suggestion method for an e-commerce marketplace. We compare and contrast the design of a query suggestion system for web search engines and e-commerce search engines. Further, we discuss interesting measures to quantify the effectiveness of our query suggestion methodologies. We also describe the learning gained from exposing our query suggestion module to a vibrant community of millions of users.
Stochastic Video Prediction with Conditional Density Estimation

Frame-to-frame stochasticity is a major challenge in video prediction. The use of standard feedforward and recurrent networks for video prediction leads to averaging of future states, which can in part be attributed to the networks’ limited ability to model stochasticity. We propose the use of conditional variational autoencoders (CVAE) for video prediction. To model multi-modal densities in frame-to-frame transitions, we extend the CVAE framework by modeling the latent variable with a mixture of Gaussians in the generative network. We tested our proposed Gaussian mixture CVAE (GM-CVAE) on a simple video-prediction task involving a stochastically moving object. Our architecture demonstrates improved performance, achieving noticeably lower rates of blurring/averaging compared to a feedforward network and a Gaussian CVAE. We also describe how the CVAE framework can be applied to improve existing deterministic video prediction
Machine Learning Based Missing Value Imputation Method for Clinical Datasets

Missing value imputation is one of the biggest tasks of data pre-processing when performing data mining. Most medical datasets are usually incomplete. Simply removing the incomplete cases from the original datasets can bring more problems than solutions. A suitable method for missing value imputation can help to produce good quality datasets for better analysing clinical trials. In this paper we explore the use of a machine learning technique as a missing value imputation method for incomplete cardiovascular data. Mean/mode imputation, fuzzy unordered rule induction algorithm imputation, decision tree imputation and other machine learning algorithms are used as missing value imputation and the final datasets are classified using decision tree, fuzzy unordered rule induction, KNN and K-Mean clustering. The experiment shows that final classifier performance is improved when the fuzzy unordered rule induction algorithm is used to predict missing attribute values for K-Mean clustering and in most cases, the machine learning techniques were found to perform better than the standard mean imputation technique.
Analyzing software requirements errors in safety-critical, embedded systems

This paper analyzes the root causes of safety-related software errors in safety-critical, embedded systems. The results show that software errors identi ed as potentially hazardous to the system tend to be produced by di erent error mechanisms than non-safetyrelated software errors. Safety-related software errors are shown to arise most commonly from (1) discrepancies between the documented requirements speci cations and the requirements needed for correct functioning of the system and (2) misunderstandings of the software's interface with the rest of the system. The paper uses these results to identify methods by which requirements errors can be prevented. The goal is to reduce safety-related software errors and to enhance the safety of complex, embedded systems.
A Survey of Models and Algorithms for Social Influence Analysis

Social in uence is the behavioral change of a person because of the perceived relationship with other people, organizations and society in general. Social inuence has been a widely accepted phenomenon in social networks for decades. Many applications have been built based around the implicit notation of social in uence between people, such as marketing, advertisement and recommendations. With the exponential growth of online social network services such as Facebook and Twitter, social in uence can for the rst time be measured over a large population. In this chapter, we survey the research on social in uence analysis with a focus on the computational aspects. First, we present statistical measurements related to social in uence. Second, we describe the literature on social similarity and in uences. Third, we present the research on social in uence maximization which has many practical applications including marketing and advertisement.
Segmentation of Glioma Tumors in Brain Using Deep Convolutional Neural Network

Detection of brain tumor using a segmentation based approach is critical in cases, where survival of a subject depends on an accurate and timely clinical diagnosis. Gliomas are the most commonly found tumors having irregular shape and ambiguous boundaries, making them one of the hardest tumors to detect. The automation of brain tumor segmentation remains a challenging problem mainly due to significant variations in its structure. An automated brain tumor segmentation algorithm using deep convolutional neural network (DCNN) is presented in this paper. A patch based approach along with an inception module is used for training the deep network by extracting two co-centric patches of different sizes from the input images. Recent developments in deep neural networks such as drop-out, batch normalization, non-linear activation and inception module are used to build a new ILinear nexus architecture. The module overcomes the over-fitting problem arising due to scarcity of data using drop-out regularizer. Images are normalized and bias field corrected in the pre-processing step and then extracted patches are passed through a DCNN, which assigns an output label to the central pixel of each patch. Morphological operators are used for post-processing to remove small false positives around the edges. A two-phase weighted training method is introduced and evaluated using  2015 datasets, where it improves the performance parameters of state-of-the-art techniques under similar settings.
Face detection using discriminating feature analysis and support vector machine in video

This work presents a novel face detection method in video by using discriminating feature analysis (DFA) and support vector machine (SVM). Our method first incorporates temporal and skin color information to locate the field of interests. Then the face class is modelled using a small training set and the nonface class is defined by choosing nonface images that lie close to the face class. Finally, the SVM classifier together with Bayesian statistical analysis procedure applies the efficient features defined by DFA for face and nonface classification. Experiments using both still images and video streams show the feasibility of our new face detection method. In particular, when using 92 images (containing 282 faces) from the MIT-CMU test sets, our method achieves 98.2% correct face detection accuracy with 2 false detections. When using video streams, our method detects faces reliably with computational efficiency of more than 20 frames per second.
An efficient method of license plate location

License plate location is an important stage in vehicle license plate recognition for automated transport system. This paper presents a real time and robust method of license plate location. License plate area contains rich edge and texture information. We first extract out the vertical edges of the car image using image enhancement and Sobel operator, then remove most of the background and noise edges by an effective algorithm, and finally search the plate region by a rectangle window in the residual edge image and segment the plate out from the original car image. Experimental results demonstrate the great robustness and efficiency of our method.
The Relationship between Change Management Strategy and Successful Enterprise Resource Planning ( ERP ) Implementations : A Theoretical Perspective

This research aims to explore the critical success factors in change management strategy in order to guarantee a successful implementation of an organisation’s Enterprise Resource Planning (ERP) system. A comprehensive literature was reviewed. The findings indicate that successful change management strategy consists three phases: preparing to change, implementation of change, and measuring the impact on user. In the preparing to change phase, organizational leadership should prepare to change organisation culture and structure must be managed by watching individuals, organization and culture. Then, in the implementation of change phase, organizations must apply the following factors: project leadership/team effectiveness factors, end-user communication factor, culture factor, end user training/involvement factors and development factor. Finally, in measuring the impact on user phase, organizations must measure the impact of change strategy factors on the end-user, also organizations pay attention to feedback in the previous three phases to modify mistakes and develop change strategy.
Forward-viewing CMUT arrays for medical imaging

This paper reports the design and testing of forward-viewing annular arrays fabricated using capacitive micromachined ultrasonic transducer (CMUT) technology. Recent research studies have shown that CMUTs have broad frequency bandwidth and high-transduction efficiency. One- and two-dimensional CMUT arrays of various sizes already have been fabricated, and their viability for medical imaging applications has been demonstrated. We fabricated 64-element, forward-viewing annular arrays using the standard CMUT fabrication process and carried out experiments to measure the operating frequency, bandwidth, and transmit/receive efficiency of the array elements. The annular array elements, designed for imaging applications in the 20 MHz range, had a resonance frequency of 13.5 MHz in air. The immersion pulse-echo data collected from a plane reflector showed that the devices operate in the 5-26 MHz range with a fractional bandwidth of 135%. The output pressure at the surface of the transducer was measured to be 24 kPa/V. These values translate into a dynamic range of 131.5 dB for 1-V excitation in 1-Hz bandwidth with a commercial low noise receiving circuitry. The designed, forward-viewing annular CMUT array is suitable for mounting on the front surface of a cylindrical catheter probe and can provide Doppler information for measurement of blood flow and guiding information for navigation through blood vessels in intravascular ultrasound imaging.
Autonomous land vehicle project at CMU

1 .  This paper provides an overview of the Autonomous Land Vehicle (ALV) Project at CMU. The goal of the CMU ALV Project is to build vision and intelligence for a mobile robot capable of operating in the real world outdoors. We are attacking this on a number of fronts: building appropriate research vehicles, exploiting high. speed experimental computers, and building software for reasoning about the perceived world. Research topics includes: • Construction of research vehicles • Perception systems to perceive the natural outdoor scenes by means of multiple sensors including cameras (color, stereo, and motion), sonar sensors, and a 3D range finder • Path planning for obstacle avoidance Use of topological and terrain map • System architecture to facilitate the system integration • Utilization of parallel computer architectures Our current research vehicle is the Terregator built at CMU which is equipped with a sonar ring, a color camera, and the ERIM laser range finder. Its initial task is to follow roads and sidewalks in the park and on the campus, and avoid obstacles such as trees, humans, and traffic cones. 2. Vehicle, Sensors, and Host Computers The primary vehicle of the AMU ALV Project has been the Terregator, designed and built at CMU. The Terregator, short for Terrestrial Navigator, is designed to provide a clean separation between the vehicle itself and its sensor payload. As shown in
Detecting DDoS attacks with Hadoop

Recent distributed denial-of-service (DDoS) attacks have demonstrated horrible destructive power by paralyzing web servers within short time. As the volume of Internet traffic rapidly grows up, the current DDoS detection technologies have met a new challenge that should efficiently deal with a huge amount of traffic within the affordable response time. In this work, we propose a novel DDoS detection method based on Hadoop that implements a HTTP GET flooding detection algorithm in MapReduce on the distributed computing platform.
Updating freeze: Aligning animal and human research

Freezing is widely used as the main outcome measure for fear in animal studies. Freezing is also getting attention more frequently in human stress research, as it is considered to play an important role in the development of psychopathology. Human models on defense behavior are largely based on animal models. Unfortunately, direct translations between animal and human studies are hampered by differences in definitions and methods. The present review therefore aims to clarify the conceptualization of freezing. Neurophysiological and neuroanatomical correlates are discussed and a translational model is proposed. We review the upcoming research on freezing in humans that aims to match animal studies by using physiological indicators of freezing (bradycardia and objective reduction in movement). Finally, we set the agenda for future research in order to optimize mutual animal-human translations and stimulate consistency and systematization in future empirical research on the freezing phenomenon.
Sparse Coding-Inspired Optimal Trading System for HFT Industry

The financial industry has witnessed an exceptionally fast progress of incorporating information processing techniques in designing knowledge-based automated systems for high-frequency trading (HFT). This paper proposes a sparse coding-inspired optimal trading (SCOT) system for real-time high-frequency financial signal representation and trading. Mathematically, SCOT simultaneously learns the dictionary, sparse features, and the trading strategy in a joint optimization, yielding optimal feature representations for the specific trading objective. The learning process is modeled as a bilevel optimization and solved by the online gradient descend method with fast convergence. In this dynamic context, the system is tested on the real financial market to trade the index futures in the Shanghai exchange center.
Analyzing EEG signals to detect unexpected obstacles during walking

When an unexpected perturbation in the environment occurs, the subsequent alertness state may cause a brain activation responding to that perturbation which can be detected and employed by a Brain-Computer Interface (BCI). In this work, the possibility of detecting a sudden obstacle appearance analyzing electroencephalographic (EEG) signals is assessed. For this purpose, different features of EEG signals are evaluated during the appearance of sudden obstacles while a subject is walking on a treadmill. The future goal is to use this procedure to detect any obstacle appearance during walking when the user is wearing a lower limb exoskeleton in order to generate an emergency stop command for the exoskeleton. This would enhance the user-exoskeleton interaction, improving the safety mechanisms of current exoskeletons. In order to detect the change in the brain activity when an obstacle suddenly appears, different features of EEG signals are evaluated using the recordings of five healthy subjects. Since the change in the brain activity occurs in the time domain, the features evaluated are: common spatial patterns, average power, slope, and the coefficients of a polynomial fit. A Linear Discriminant Analysis-based classifier is used to differentiate between two conditions: the appearance or not of an obstacle. The evaluation of the performance to detect the obstacles is made in terms of accuracy, true positive (TP) and false positive (FP) rates. From the offline analysis, the best performance is achieved when the slope or the polynomial coefficients are used as features, with average detection accuracy rates of 74.0 and 79.5 %, respectively. These results are consistent with the pseudo-online results, where a complete EEG recording is segmented into windows of 500 ms and overlapped 400 ms, and a decision about the obstacle appearance is made for each window. The results of the best subject were 11 out of 14 obstacles detected with a rate of 9.09 FPs/min, and 10 out of 14 obstacles detected with a rate of 6.34 FPs/min using slope and polynomial coefficients features, respectively. An EEG-based BCI can be developed to detect the appearance of unexpected obstacles. The average accuracy achieved is 79.5 % of success rate with a low number of false detections. Thus, the online performance of the BCI would be suitable for commanding in a safely way a lower limb exoskeleton during walking.
Shrinkable, stiffness-controllable soft manipulator based on a bio-inspired antagonistic actuation principle

This paper explores a new hybrid actuation principle combining pneumatic and tendon-driven actuators for a soft robotic manipulator. The fusion of these two actuation principles leads to an overall antagonistic actuation mechanism whereby pneumatic actuation opposes tendon actuation - a mechanism commonly found in animals where muscles can oppose each other to vary joint stiffness. We are taking especially inspiration from the octopus who belongs to the class of Cephalopoda; the octopus uses its longitudinal and transversal muscles in its arms to achieve varied motion patterns; activating both sets of muscles, the octopus can control the arm stiffness over a wide range. Our approach mimics this behavior and achieves comparable motion patterns, including bending, elongation and stiffening. The proposed method combines the advantages of tendon-driven and pneumatic actuated systems and goes beyond what current soft, flexible robots can achieve: because the new robot structure is effectively an inflatable, sleeve, it can be pumped up to its fully inflated volume and, also, completely deflated and shrunk. Since, in the deflated state, it comprises just its outer “skin” and tendons, the robot can be compressed to a very small size, many times smaller when compared to its fully-inflated state. In this paper, we describe the mechanical structure of the soft manipulator. Proof-of-concept experiments focus on the robot's ability to bend, to morph from completely shrunk to entirely inflated as well as to vary its stiffness.
An Analytical Model for the Contention Access Period of the Slotted IEEE 802.15.4 with Service Differentiation

The IEEE 802.15.4 standard is poised to become the global standard for low data rate, low energy consumption wireless sensor networks (WSN). By assigning the same sets of contention access parameters for all data frames and nodes, the contention access period (CAP) of the slotted IEEE 802.15.4 medium access control (MAC) currently provides a priorityindependent channel access functionality and no service differentiation. Several recent WSN applications such as wireless body sensor networks, however, may require service differentiation and traffic prioritization support to accommodate potential highpriority traffic (e.g., alarms or emergency alerts). By allowing different sets of access parameters and data frame lengths for different priority classes, this paper develops a Markov-chain-based analytical model of the CAP of the IEEE 802.15.4 MAC with service differentiation, under unsaturated traffic conditions. In particular, given two priority classes, our analytical model is used to evaluate the performance of a simple, yet effective, contentionwindow-based service differentiation strategy, in terms of the resulting throughput, average frame service time and access priority for each priority class. The accuracy of the analytical model is validated by extensive ns-2 simulation.
Evaluation of a mobile mindfulness app distributed through on-line stores: A 4-week study

This is the author’s version of a work that was accepted for publication in the International Journal of HumanComputer Studies. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms, may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Human-Computer Studies, Volume 86, February 2016,
Edge-Preserving Smoothing and Mean-Shift Segmentation of Video Streams

Video streams are ubiquitous in applications such as surveillance, games, and live broadcast. Processing and analyzing these data is challenging because algorithms have to be efficient in order to process the data on the fly. From a theoretical standpoint, video streams have their own specificities – they mix spatial and temporal dimensions, and compared to standard video sequences, half of the information is missing, i.e. the future is unknown. The theoretical part of our work is motivated by the ubiquitous use of the Gaussian kernel in tools such as bilateral filtering and mean-shift segmentation. We formally derive its equivalent for video streams as well as a dedicated expression of isotropic diffusion. Building upon this theoretical ground, we adapt a number of classical powerful algorithms to video streams: bilateral filtering, mean-shift segmentation, and anisotropic diffusion.