nils-herrmann/clean_scirepeval huggingface dataset:
Cultivating mindfulness: effects on well-being.

There has been great interest in determining if mindfulness can be cultivated and if this cultivation leads to well-being. The current study offers preliminary evidence that at least one aspect of mindfulness, measured by the Mindful Attention and Awareness Scale (MAAS; K. W. Brown & R. M. Ryan, 2003), can be cultivated and does mediate positive outcomes. Further, adherence to the practices taught during the meditation-based interventions predicted positive outcomes. College undergraduates were randomly allocated between training in two distinct meditation-based interventions, Mindfulness Based Stress Reduction (MBSR;  1990; n=15) and E. Easwaran's (1978/1991) Eight Point Program (EPP; n=14), or a waitlist control (n=15). Pretest, posttest, and 8-week follow-up data were gathered on self-report outcome measures. Compared to controls, participants in both treatment groups (n=29) demonstrated increases in mindfulness at 8-week follow-up. Further, increases in mindfulness mediated reductions in perceived stress and rumination. These results suggest that distinct meditation-based practices can increase mindfulness as measured by the MAAS, which may partly mediate benefits. Implications and future directions are discussed.
3DTV at home: eulerian-lagrangian stereo-to-multiview conversion

Stereoscopic 3D (S3D) movies have become widely popular in the movie theaters, but the adoption of S3D at home is low even though most TV sets support S3D. It is widely believed that S3D with glasses is not the right approach for the home. A much more appealing approach is to use automulti-scopic displays that provide a glasses-free 3D experience to multiple viewers. A technical challenge is the lack of native multiview content that is required to deliver a proper view of the scene for every viewpoint. Our approach takes advantage of the abundance of stereoscopic 3D movies. We propose a real-time system that can convert stereoscopic video to a high-quality multiview video that can be directly fed to automultiscopic displays. Our algorithm uses a wavelet-based decomposition of stereoscopic images with per-wavelet disparity estimation. A key to our solution lies in combining Lagrangian and Eulerian approaches for both the disparity estimation and novel view synthesis, which leverages the complementary advantages of both techniques. The solution preserves all the features of Eulerian methods, e.g., subpixel accuracy, high performance, robustness to ambiguous depth cases, and easy integration of inter-view aliasing while maintaining the advantages of Lagrangian approaches, e.g., robustness to large disparities and possibility of performing non-trivial disparity manipulations through both view extrapolation and interpolation. The method achieves real-time performance on current GPUs. Its design also enables an easy hardware implementation that is demonstrated using a field-programmable gate array. We analyze the visual quality and robustness of our technique on a number of synthetic and real-world examples. We also perform a user experiment which demonstrates benefits of the technique when compared to existing solutions.
Subharmonic Stability Limits for the Buck Converter With Ripple-Based Constant On-Time Control and Feedback Filter

A general closed-form subharmonic stability condition is derived for the buck converter with ripple-based constant on-time control and a feedback filter. The turn-on delay is included in the analysis. Three types of filters are considered: low-pass filter (LPF), phase-boost filter (PBF), and inductor current feedback (ICF) which changes the feedback loop frequency response like a filter. With the LPF, the stability region is reduced. With the PBF or ICF, the stability region is enlarged. Stability conditions are determined both for the case of a single output capacitor and for the case of two parallel-connected output capacitors having widely different time constants. The past research results related to the feedback filters become special cases. All theoretical predictions are verified by experiments.
Understanding mobile learning from the perspective of self-regulated learning

Cognizant of the research gap in the theorization of mobile learning, this paper conceptually explores how the theories and methodology of self-regulated learning (SRL), an active area in contemporary educational psychology, are inherently suited to address the issues originating from the defining characteristics of mobile learning: enabling student-centred, personal, and ubiquitous learning. These characteristics provide some of the conditions for learners to learn anywhere and anytime, and thus, entail learners to be motivated and to be able to self-regulate their own learning. We propose an analytic SRL model of mobile learning as a conceptual framework for understanding mobile learning, in which the notion of self-regulation as agency is at the core. The rationale behind this model is built on our recognition of the challenges in the current conceptualization of the mechanisms and processes of mobile learning, and the inherent relationship between mobile learning and SRL. We draw on work in a 3-year research project in developing and implementing a mobile learning environment in elementary science classes in Singapore to illustrate the application of SRL theories and methodology to understand and analyse mobile learning.
Automatic Generation of Constructable Brick Sculptures

Fabrication of LEGO R © models in large scale requires careful pre-planning to produce constructable and stable models. We propose a system that, starting with a voxelization of a 3D mesh, merges voxels to form larger bricks, and then analyzes and repairs structural problems, finally outputting a set of building instructions. We also present extensions such as producing hollow models, fulfilling limits on the number of bricks of each size, and including colors.  (both real and virtual) and timings show significant improvements over previous work.
The influence of soft tissue thickness on crestal bone changes around implants: a 1-year prospective controlled clinical trial.

The aim of this clinical trial was to evaluate the influence of gingival tissue thickness on crestal bone loss around dental implants after a 1-year follow-up.



Forty-six implants (23 test and 23 control) were placed in 19 patients. The test implants were placed about 2 mm supracrestally, whereas the control implants were positioned at the bone level. Before implant placement, the tissue thickness at implant sites was measured with a periodontal probe. After healing, metal-ceramic cement-retained prostheses were constructed. According to tissue thickness, the test implants were divided into A (thin) and B (thick) groups. Intraoral radiographs were performed and crestal bone changes were measured at implant placement and after 1 year.



Mean bone loss around the test implants in group A (thin mucosa) was 1.61 +/- 0.24 mm (SE; range, 0.9 to 3.3 mm) on the mesial and 1.28 +/- 0.167 mm (range, 0.8 to 2.1 mm) on the distal. Mean bone loss in test group B (thick mucosa) implants was 0.26 +/- 0.08 mm (range, 0.2 to 0.9 mm) on the mesial aspect and 0.09 +/- 0.05 mm (range, 0.2 to 0.6 mm) on the distal aspect. Mean bone loss around control implants was 1.8 +/- 0.164 mm (range, 0.6 to 4.0 mm) and 1.87 +/- 0.166 mm (range, 0.0 to 4.1 mm) on the mesial and distal aspects, respectively. Analysis of variance revealed a significant difference in terms of bone loss between test A (thin) and B (thick) groups on both the mesial and the distal.



Initial gingival tissue thickness at the crest may be considered as a significant influence on marginal bone stability around implants. If the tissue thickness is 2.0 mm or less, crestal bone loss up to 1.45 mm may occur, despite a supracrestal position of the implant-abutment interface.
Spectrum- and Energy-Efficient OFDM Based on Simultaneous Multi-Channel Reconstruction

Time domain synchronous OFDM (TDS-OFDM) has a higher spectrum and energy efficiency than standard cyclic prefix OFDM (CP-OFDM) by replacing the unknown CP with a known pseudorandom noise (PN) sequence. However, due to mutual interference between the PN sequence and the OFDM data block, TDS-OFDM cannot support high-order modulation schemes such as 256QAM in realistic static channels with large delay spread or high-definition television (HDTV) delivery in fast fading channels. To solve these problems, we propose the idea of using multiple inter-block-interference (IBI)-free regions of small size to realize simultaneous multi-channel reconstruction under the framework of structured compressive sensing (SCS). This is enabled by jointly exploiting the sparsity of wireless channels as well as the characteristic that path delays vary much slower than path gains. In this way, the mutually conditional time-domain channel estimation and frequency-domain data demodulation in TDS-OFDM can be decoupled without the use of iterative interference removal. The Cramér-Rao lower bound (CRLB) of the proposed estimation scheme is also derived. Moreover, the guard interval amplitude in TDS-OFDM can be reduced to improve the energy efficiency, which is infeasible for CP-OFDM. Simulation results demonstrate that the proposed SCS-aided TDS-OFDM scheme has a higher spectrum and energy efficiency than CP-OFDM by more than 10% and 20% respectively in typical applications.
A 60nm 6Gb/s/pin GDDR5 Graphics DRAM with Multifaceted Clocking and ISI/SSN-Reduction Techniques

Demand for high-speed DRAM in graphics application pushes a single-ended I/O signaling to operate up to 6Gb/s. To maintain the speed increase, the GDDR5 specification shifts from GDDR3/4 with respect to forwarded clocking, data training for write and read de-skewing, clock training, channel-error detection, bank group and data coding. This work tackles challenges in GDDR5 such as clock jitter and signal integrity.
Music Recommendation Using Content and Context Information Mining

Mobile devices such as smart phones are becoming popular, and realtime access to multimedia data in different environments is getting easier. With properly equipped communication services, users can easily obtain the widely distributed videos, music, and documents they want. Because of its usability and capacity requirements, music is more popular than other types of multimedia data. Documents and videos are difficult to view on mobile phones' small screens, and videos' large data size results in high overhead for retrieval. But advanced compression techniques for music reduce the required storage space significantly and make the circulation of music data easier. This means that users can capture their favorite music directly from the Web without going to music stores. Accordingly, helping users find music they like in a large archive has become an attractive but challenging issue over the past few years.
Joint Object Segmentation and Depth Upsampling

With the advent of powerful ranging and visual sensors, nowadays, it is convenient to collect sparse 3-D point clouds and aligned high-resolution images. Benefitted from such convenience, this letter proposes a joint method to perform both depth assisted object-level image segmentation and image guided depth upsampling. To this end, we formulate these two tasks together as a bi-task labeling problem, defined in a Markov random field. An alternating direction method (ADM) is adopted for the joint inference, solving each sub-problem alternatively. More specifically, the sub-problem of image segmentation is solved by Graph Cuts, which attains discrete object labels efficiently. Depth upsampling is addressed via solving a linear system that recovers continuous depth values. By this joint scheme, robust object segmentation results and high-quality dense depth maps are achieved. The proposed method is applied to the challenging KITTI vision benchmark suite, as well as the Leuven dataset for validation. Comparative experiments show that our method outperforms stand-alone approaches.
Recognition using visual phrases

In this paper we introduce visual phrases, complex visual composites like “a person riding a horse”. Visual phrases often display significantly reduced visual complexity compared to their component objects, because the appearance of those objects can change profoundly when they participate in relations. We introduce a dataset suitable for phrasal recognition that uses familiar PASCAL object categories, and demonstrate significant experimental gains resulting from exploiting visual phrases. We show that a visual phrase detector significantly outperforms a baseline which detects component objects and reasons about relations, even though visual phrase training sets tend to be smaller than those for objects. We argue that any multi-class detection system must decode detector outputs to produce final results; this is usually done with non-maximum suppression. We describe a novel decoding procedure that can account accurately for local context without solving difficult inference problems. We show this decoding procedure outperforms the state of the art. Finally, we show that decoding a combination of phrasal and object detectors produces real improvements in detector results.
LTV-MPC approach for lateral vehicle guidance by front steering at the limits of vehicle dynamics

In this paper, a linear time-varying model-based predictive controller (LTV-MPC) for lateral vehicle guidance by front steering is proposed. Due to the fact that this controller is designed in the scope of a Collision Avoidance System, it has to fulfill the requirement of an appropiate control performance at the limits of vehicle dynamics. To achieve this objective, the introduced approach employs estimations of the applied steering angle as well as the state variable trajectory to be used for successive linearization of the nonlinear prediction model over the prediction horizon. To evaluate the control performance, the proposed controller is compared to a LTV-MPC controller that uses linearizations of the nonlinear prediction model that remain unchanged over the prediction horizon. Simulation results show that an improved control performance can be achieved by the estimation based approach.
A high-sensitivity fully passive wake-up radio front-end for wireless sensor nodes

A high-sensitivity fully passive 868-MHz wake-up radio (WUR) front-end for wireless sensor network nodes is presented. The front-end does not have an external power source and extracts the entire energy from the radio-frequency (RF) signal received at the antenna. A high-efficiency differential RF-to-DC converter rectifies the incident RF signal and drives the circuit blocks including a low-power comparator and reference generators; and at the same time detects the envelope of the on-off keying (OOK) wake-up signal. The front-end is designed and simulated 0.13μm CMOS and achieves a sensitivity of -33 dBm for a 100 kbps wake-up signal.
Interactive record/replay for web application debugging

During debugging, a developer must repeatedly and manually reproduce faulty behavior in order to inspect different facets of the program's execution. Existing tools for reproducing such behaviors prevent the use of debugging aids such as breakpoints and logging, and are not designed for interactive, random-access exploration of recorded behavior. This paper presents Timelapse, a tool for quickly recording, reproducing, and debugging interactive behaviors in web applications. Developers can use Timelapse to browse, visualize, and seek within recorded program executions while simultaneously using familiar debugging tools such as breakpoints and logging. Testers and end-users can use Timelapse to demonstrate failures in situ and share recorded behaviors with developers, improving bug report quality by obviating the need for detailed reproduction steps. Timelapse is built on Dolos, a novel record/replay infrastructure that ensures deterministic execution by capturing and reusing program inputs both from the user and from external sources such as the network. Dolos introduces negligible overhead and does not interfere with breakpoints and logging. In a small user evaluation, participants used Timelapse to accelerate existing reproduction activities, but were not significantly faster or more successful in completing the larger tasks at hand. Together, the Dolos infrastructure and Timelapse developer tool support systematic bug reporting and debugging practices.
Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network

This study deals with artificial neural network (ANN) modeling a diesel engine using waste cooking biodiesel fuel to predict the brake power, torque, specific fuel consumption and exhaust emissions of engine. To acquire data for training and testing the proposed ANN, two cylinders, four-stroke diesel engine was fuelled with waste vegetable cooking biodiesel and diesel fuel blends and operated at different engine speeds. The properties of biodiesel produced from waste vegetable oil was measured based on ASTM standards. The experimental results reveal that blends of waste vegetable oil methyl ester with diesel fuel provide better engine performance and improved emission characteristics. Using some of the experimental data for training, an ANN model based on standard Back-Propagation algorithm for the engine was developed. Multi layer perception network (MLP) was used for nonlinear mapping between the input and the output parameters. Different activation functions and several rules were used to assess the percentage error between the desired and the predicted values. It was observed that the ANN model can predict the engine performance and exhaust emissions quite well with correlation coefficient (R) were 0.9487, 0.999, 0.929 and 0.999 for the engine torque, SFC, CO and HC emissions, respectively. The prediction MSE (Mean Square Error) error was between the desired outputs as measured values and the simulated values by the model was obtained as 0.0004.
Motion guidance using Haptic Feedback based on vibrotactile illusions

In this paper we present a wearable Haptic Feedback Device to convey intuitive motion direction to the user through haptic feedback based on vibrotactile illusions. Vibrotactile illusions occur on the skin when two or more vibrotactile actuators in proximity are actuated in coordinated sequence, causing the user to feel combined sensations, instead of separate ones. By combining these illusions we can produce various sensation patterns that are discernible by the user, thus allowing to convey different information with each pattern. A method to provide information about direction through vibrotactile illusions is introduced on this paper. This method uses a grid of vibrotactile actuators around the arm actuated in coordination. The sensation felt on the skin is consistent with the desired direction of motion, so the desired motion can be intuitively understood. We show that the users can recognize the conveyed direction, and implemented a proof of concept of the proposed method to guide users' elbow flexion/extension motion.
Malware Detection in Adversarial Settings: Exploiting Feature Evolutions and Confusions in Android Apps

Existing techniques on adversarial malware generation employ feature mutations based on feature vectors extracted from malware. However, most (if not all) of these techniques suffer from a common limitation: feasibility of these attacks is unknown. The synthesized mutations may break the inherent constraints posed by code structures of the malware, causing either crashes or malfunctioning of malicious payloads. To address the limitation, we present Malware Recomposition Variation (MRV), an approach that conducts semantic analysis of existing malware to systematically construct new malware variants for malware detectors to test and strengthen their detection signatures/models. In particular, we use two variation strategies (i.e., malware evolution attack and malware confusion attack) following structures of existing malware to enhance feasibility of the attacks. Upon the given malware, we conduct semantic-feature mutation analysis and phylogenetic analysis to synthesize mutation strategies. Based on these strategies, we perform program transplantation to automatically mutate malware bytecode to generate new malware variants. We evaluate our MRV approach on actual malware variants, and our empirical evaluation on 1,935 Android benign apps and 1,917 malware shows that MRV produces malware variants that can have high likelihood to evade detection while still retaining their malicious behaviors. We also propose and evaluate three defense mechanisms to counter MRV.
Automatic Person Annotation of Family Photo Album

Digital photographs are replacing tradition films in our daily life and the quantity is exploding. This stimulates the strong need for efficient management tools, in which the annotation of “who” in each photo is essential. In this paper, we propose an automated method to annotate family photos using evidence from face, body and context information. Face recognition is the first consideration. However, its performance is limited by the uncontrolled condition of family photos. In family album, the same groups of people tend to appear in similar events, in which they tend to wear the same clothes within a short time duration and in nearby places. We could make use of social context information and body information to estimate the probability of the persons’ presence and identify other examples of the same recognized persons. In our approach, we first use social context information to cluster photos into events. Within each event, the body information is clustered, and then combined with face recognition results using a graphical model. Finally, the clusters with high face recognition confidence and context probabilities are identified as belonging to specific person. Experiments on a photo album containing over 1500 photos demonstrate that our approach is effective.
Disrupted Brain Connectivity Networks in Drug-Naive, First-Episode Major Depressive Disorder

Neuroimaging studies have shown that major depressive disorder (MDD) is accompanied by structural and functional abnormalities in specific brain regions and connections; yet, little is known about alterations of the topological organization of whole-brain networks in MDD patients.



Thirty drug-naive, first-episode MDD patients and 63 healthy control subjects underwent a resting-state functional magnetic resonance imaging scan. The whole-brain functional networks were constructed by thresholding partial correlation matrices of 90 brain regions, and their topological properties (e.g., small-world, efficiency, and nodal centrality) were analyzed using graph theory-based approaches. Nonparametric permutation tests were further used for group comparisons of topological metrics.



Both the MDD and control groups showed small-world architecture in brain functional networks, suggesting a balance between functional segregation and integration. However, compared with control subjects, the MDD patients showed altered quantitative values in the global properties, characterized by lower path length and higher global efficiency, implying a shift toward randomization in their brain networks. The MDD patients exhibited increased nodal centralities, predominately in the caudate nucleus and default-mode regions, including the hippocampus, inferior parietal, medial frontal, and parietal regions, and reduced nodal centralities in the occipital, frontal (orbital part), and temporal regions. The altered nodal centralities in the left hippocampus and the left caudate nucleus were correlated with disease duration and severity.



These results suggest that depressive disorder is associated with disruptions in the topological organization of functional brain networks and that this disruption may contribute to disturbances in mood and cognition in MDD patients.