nils-herrmann/clean_scirepeval huggingface dataset:
Obstacle Detection and Avoidance on Sidewalks

We present part of a vision system for blind and visually impaired people. It detects obstacles on sidewalks and provides guidance to avoid them. Obstacles are trees, light poles, trash cans, holes, branches, stones and other objects at a distance of 3 to 5 meters from the camera position. The system first detects the sidewalk borders, using edge information in combination with a tracking mask, to obtain straight lines with their slopes and the vanishing point. Once the borders are found, a rectangular window is defined within which two obstacle detection methods are applied. The first determines the variation of the maxima and minima of the gray levels of the pixels. The second uses the binary edge image and searches in the vertical and horizontal histograms for discrepancies of the number of edge points. Together, these methods allow to detect possible obstacles with their position and size, such that the user can be alerted and informed about the best way to avoid them. The system works in realtime and complements normal navigation with the cane.
Task-specific feature extraction and classification of fMRI volumes using a deep neural network initialized with a deep belief network: Evaluation using sensorimotor tasks

Feedforward deep neural networks (DNNs), artificial neural networks with multiple hidden layers, have recently demonstrated a record-breaking performance in multiple areas of applications in computer vision and speech processing. Following the success, DNNs have been applied to neuroimaging modalities including functional/structural magnetic resonance imaging (MRI) and positron-emission tomography data. However, no study has explicitly applied DNNs to 3D whole-brain fMRI volumes and thereby extracted hidden volumetric representations of fMRI that are discriminative for a task performed as the fMRI volume was acquired. Our study applied fully connected feedforward DNN to fMRI volumes collected in four sensorimotor tasks (i.e., left-hand clenching, right-hand clenching, auditory attention, and visual stimulus) undertaken by 12 healthy participants. Using a leave-one-subject-out cross-validation scheme, a restricted Boltzmann machine-based deep belief network was pretrained and used to initialize weights of the DNN. The pretrained DNN was fine-tuned while systematically controlling weight-sparsity levels across hidden layers. Optimal weight-sparsity levels were determined from a minimum validation error rate of fMRI volume classification. Minimum error rates (mean±standard deviation; %) of 6.9 (±3.8) were obtained from the three-layer DNN with the sparsest condition of weights across the three hidden layers. These error rates were even lower than the error rates from the single-layer network (9.4±4.6) and the two-layer network (7.4±4.1). The estimated DNN weights showed spatial patterns that are remarkably task-specific, particularly in the higher layers. The output values of the third hidden layer represented distinct patterns/codes of the 3D whole-brain fMRI volume and encoded the information of the tasks as evaluated from representational similarity analysis. Our reported findings show the ability of the DNN to classify a single fMRI volume based on the extraction of hidden representations of fMRI volumes associated with tasks across multiple hidden layers. Our study may be beneficial to the automatic classification/diagnosis of neuropsychiatric and neurological diseases and prediction of disease severity and recovery in (pre-) clinical settings using fMRI volumes without requiring an estimation of activation patterns or ad hoc statistical evaluation.
Parametric POMDPs for planning in continuous state spaces

Alex M. Brooks Doctor of Philosophy The University of Sydney January 2007 Parametric POMDPs for Planning in Continuous State Spaces This thesis is concerned with planning and acting under uncertainty in partially-observable continuous domains. In particular, it focusses on the problem of mobile robot navigation given a known map. The dominant paradigm for robot localisation is to use Bayesian estimation to maintain a probability distribution over possible robot poses. In contrast, control algorithms often base their decisions on the assumption that a single state, such as the mode of this distribution, is correct. In scenarios involving significant uncertainty, this can lead to serious control errors. It is generally agreed that the reliability of navigation in uncertain environments would be greatly improved by the ability to consider the entire distribution when acting, rather than the single most likely state. The framework adopted in this thesis for modelling navigation problems mathematically is the Partially Observable Markov Decision Process (POMDP). An exact solution to a POMDP problem provides the optimal balance between reward-seeking behaviour and information-seeking behaviour, in the presence of sensor and actuation noise. Unfortunately, previous exact and approximate solution methods have had difficulty scaling to real applications. The contribution of this thesis is the formulation of an approach to planning in the space of continuous parameterised approximations to probability distributions. Theoretical and practical results are presented which show that, when compared with similar methods from the literature, this approach is capable of scaling to larger and more realistic problems. In order to apply the solution algorithm to real-world problems, a number of novel improvements are proposed. Specifically, Monte Carlo methods are employed to estimate distributions over future parameterised beliefs, improving planning accuracy without a loss of efficiency. Conditional independence assumptions are exploited to simplify the problem, reducing computational requirements. Scalability is further increased by focussing computation on likely beliefs, using metric indexing structures for efficient function approximation. Local online planning is incorporated to assist global offline planning, allowing the precision of the latter to be decreased without adversely affecting solution quality. Finally, the algorithm is implemented and demonstrated during real-time control of a mobile robot in a challenging navigation task. We argue that this task is substantially more challenging and realistic than previous problems to which POMDP solution methods have been applied. Results show that POMDP planning, which considers the evolution of the entire probability distribution over robot poses, produces significantly more robust behaviour when compared with a heuristic planner which considers only the most likely states and outcomes.
Trunk-Branch Ensemble Convolutional Neural Networks for Video-Based Face Recognition

Human faces in surveillance videos often suffer from severe image blur, dramatic pose variations, and occlusion. In this paper, we propose a comprehensive framework based on Convolutional Neural Networks (CNN) to overcome challenges in video-based face recognition (VFR). First, to learn blur-robust face representations, we artificially blur training data composed of clear still images to account for a shortfall in real-world video training data. Using training data composed of both still images and artificially blurred data, CNN is encouraged to learn blur-insensitive features automatically. Second, to enhance robustness of CNN features to pose variations and occlusion, we propose a Trunk-Branch Ensemble CNN model (TBE-CNN), which extracts complementary information from holistic face images and patches cropped around facial components. TBE-CNN is an end-to-end model that extracts features efficiently by sharing the low- and middle-level convolutional layers between the trunk and branch networks. Third, to further promote the discriminative power of the representations learnt by TBE-CNN, we propose an improved triplet loss function. Systematic experiments justify the effectiveness of the proposed techniques. Most impressively, TBE-CNN achieves state-of-the-art performance on three popular video face databases: PaSC, COX Face, and YouTube Faces. With the proposed techniques, we also obtain the first place in the BTAS 2016 Video Person Recognition Evaluation.
The Smartphone in Medicine: A Review of Current and Potential Use Among Physicians and Students

Advancements in technology have always had major impacts in medicine. The smartphone is one of the most ubiquitous and dynamic trends in communication, in which one's mobile phone can also be used for communicating via email, performing Internet searches, and using specific applications. The smartphone is one of the fastest growing sectors in the technology industry, and its impact in medicine has already been significant.



To provide a comprehensive and up-to-date summary of the role of the smartphone in medicine by highlighting the ways in which it can enhance continuing medical education, patient care, and communication. We also examine the evidence base for this technology.



We conducted a review of all published uses of the smartphone that could be applicable to the field of medicine and medical education with the exclusion of only surgical-related uses.



In the 60 studies that were identified, we found many uses for the smartphone in medicine; however, we also found that very few high-quality studies exist to help us understand how best to use this technology.



While the smartphone's role in medicine and education appears promising and exciting, more high-quality studies are needed to better understand the role it will have in this field. We recommend popular smartphone applications for physicians that are lacking in evidence and discuss future studies to support their use.
Current practice of waste management system in Malaysia : Towards sustainable waste management

Nowadays, the increasing quantity of municipal solid waste has causes serious environmental problem which requires a better solution in handling the wastes that generate. Construction waste is considered as part of the municipal solid waste. Construction wastes that produce in the construction process contributes a large amount to municipal solid waste. For that reason, a proper way of handling construction wastes is significance in reducing the negative impacts towards the environment, social, and economy. Sustainable waste management is introduced to maintain the balance between the environment, social and economic aspects through several ways such as acts implementation, and techniques in managing waste. Therefore, it is essential to identify current waste management system adopted by industry in order to make adjustment and improvement in moving towards sustainable waste management. This paper highlights the current waste management system implemented in Malaysia and the challenges in applying the concept of sustainability into waste management through reviewing past similar researches. This research has conducted an exploratory interview to six industry practitioners in both private and government sector in Malaysia. The results obtained show the current waste management systems applied in Malaysia and factors that hinder the concept of sustainability into waste management. It allows a major shift in Malaysia waste management by improvise current waste management technology into more sustainable way. waste management system; construction wastes sustainability; technology
Protection or Vulnerability? A Meta-Analysis of the Relations Between the Positive and Negative Components of Self-Compassion and Psychopathology.

Self-compassion is increasingly explored as a protective factor in relation to psychopathology. The Self-Compassion Scale (SCS) and its Short Form variant (SCS-SF) are the most widely used instruments for measuring this psychological construct, and previous studies have indeed shown that the total score of this scale is negatively associated with psychopathology. In this article, we point out that half of the items of the SCS and SCS-SF are positive indicators of self-compassion and directly refer to the three key components of self-kindness, common humanity and mindfulness, while the other half of the items are negative indicators of the construct and reflect the precise opposite of the key components, namely self-judgment, isolation and over-identification. A meta-analysis was conducted including 18 studies that reported on the positive and negative indicators of self-compassion as indexed by the SCS/SCS-SF and their relations to various types of psychopathology. Results showed that positive indicators of self-compassion were negatively associated with psychopathology, which confirms their hypothesized protective influence. However, the negative indicators were positively linked to psychopathology, suggesting that these scales tap increased vulnerability to mental health problems. Moreover, tests comparing the strength of the relations between various SCS/SCS-SF counterparts (i.e., self-kindness versus self-judgment, common humanity versus isolation and mindfulness versus over-identification) and psychopathology showed that the negative indicators were significantly stronger linked to mental health problems than the positive indicators. This provides support for the idea that the use of a total self-compassion score of the SCS or SCS-SF, which typically includes the reversely scored negative subscales, will probably result in an inflated relationship with symptoms of psychopathology.
The multidimensional 0-1 knapsack problem: An overview

The multidimensional 0–1 knapsack problem is one of the most well-known integer programming problems and has received wide attention from the operational research community during the last four decades. Although recent advances have made possible the solution of medium size instances, solving this NP-hard problem remains a very interesting challenge, especially when the number of constraints increases. This paper surveys the main results published in the literature. The focus is on the theoretical properties as well as approximate or exact solutions of this special 0–1 program.
GSMem: Data Exfiltration from Air-Gapped Computers over GSM Frequencies

Air-gapped networks are isolated, separated both logically and physically from public networks. Although the feasibility of invading such systems has been demonstrated in recent years, exfiltration of data from air-gapped networks is still a challenging task. In this paper we present GSMem, a malware that can exfiltrate data through an air-gap over cellular frequencies. Rogue software on an infected target computer modulates and transmits electromagnetic signals at cellular frequencies by invoking specific memory-related instructions and utilizing the multichannel memory architecture to amplify the transmission. Furthermore, we show that the transmitted signals can be received and demodulated by a rootkit placed in the baseband firmware of a nearby cellular phone. We present crucial design issues such as signal generation and reception, data modulation, and transmission detection. We implement a prototype of GSMem consisting of a transmitter and a receiver and evaluate its performance and limitations. Our current results demonstrate its efficacy and feasibility, achieving an effective transmission distance of 1 5.5 meters with a standard mobile phone. When using a dedicated, yet affordable hardware receiver, the effective distance reached over 30 meters.
Characterization and detection of noise in clustering

Cluster analysis is an important tool in many scientific disciplines, and many clustering methods are available (see e.g. Everitt (1974) or Jain and Dubes (1988)). A single clustering method or algorithm cannot solve all the possible clustering problems, hence the proliferation of many techniques. Most clustering methods are plagued ~ith the problem of noisy data, i.e., characterization o f good clusters amongst noisy data. In some cases, even a few noisy points or outliers affect the outcome of the method by severely biasing the algorithm. The noise that is just due to the statistical distribution o f the measuring instrument is usually of no concern. On the other hand, the completely arbitrary noise points that just do not belong to the pattern or class being searched for are o f real concern. A good example o f that is in image processing, where one is searching for certain shapes, for instance, amongst all the edge elements detected. An approach that is frequently recommended (for example, Jain and Dubes (1988)) is where one tries to identify such data and removes it before application o f the clustering algorithms. In many cases, however, that may not be possible or it may be extremely difficult. In this paper, a class of algorithms based on the square-error clustering (a sub-class o f partitional clustering) is considered. The performance of the algorithms o f this kind is highly susceptible to outliers or noisy points. The K-meaus type algorithms is one example where each point in the data-set must be assimaed to one o f the dusters. Because of this requirement, even the noise points have to be allotted to one o f the good clusters, and that would deteriorate the performance o f the algorithm. One approach to solve this problem is as proposed by Jolion and Rosenfeld (1989), where each data point is # y en a weight proportional to the density o f data points in its vicinity, thus assigning higher weights to the points belonging to the clusters, while assigning lower weights to the noise or background points. Thus the approach results in preprocessing o f the data in order to reduce the bias due to noise background.
Disruption of posterior brain systems for reading in children with developmental dyslexia

Converging evidence indicates a functional disruption in the neural systems for reading in adults with dyslexia. We examined brain activation patterns in dyslexic and nonimpaired children during pseudoword and real-word reading tasks that required phonologic analysis (i.e., tapped the problems experienced by dyslexic children in sounding out words).



We used functional magnetic resonance imaging (fMRI) to study 144 right-handed children, 70 dyslexic readers, and 74 nonimpaired readers as they read pseudowords and real words.



Children with dyslexia demonstrated a disruption in neural systems for reading involving posterior brain regions, including parietotemporal sites and sites in the occipitotemporal area. Reading skill was positively correlated with the magnitude of activation in the left occipitotemporal region. Activation in the left and right inferior frontal gyri was greater in older compared with younger dyslexic children.



These findings provide neurobiological evidence of an underlying disruption in the neural systems for reading in children with dyslexia and indicate that it is evident at a young age. The locus of the disruption places childhood dyslexia within the same neurobiological framework as dyslexia, and acquired alexia, occurring in adults.
Making a life worth living: neural correlates of well-being.

Despite the vast literature that has implicated asymmetric activation of the prefrontal cortex in approach-withdrawal motivation and emotion, no published reports have directly explored the neural correlates of well-being. Eighty-four right-handed adults (ages 57-60) completed self-report measures of eudaimonic well-being, hedonic well-being, and positive affect prior to resting electroencephalography. As hypothesized, greater left than right superior frontal activation was associated with higher levels of both forms of well-being. Hemisphere-specific analyses documented the importance of goal-directed approach tendencies beyond those captured by approach-related positive affect for eudaimonic but not for hedonic well-being. Appropriately engaging sources of appetitive motivation, characteristic of higher left than right baseline levels of prefrontal activation, may encourage the experience of well-being.
Design of and Comparison with Verification and Validation Benchmarks

Verification and validation (V&V) are the primary means to assess accuracy and reliability of computational simulations. V&V methods and procedures have fundamentally improved the credibility of simulations in several high-consequence application areas, such as, nuclear reactor safety, underground storage of nuclear waste, and safety of nuclear weapons. Although the terminology is not uniform across engineering disciplines, code verification deals with the assessment of the reliability of the software coding and solution verification deals with the numerical accuracy of the solution to a computational model. Validation addresses the physics modeling accuracy of a computational simulation by comparing with experimental data. Code verification benchmarks and validation benchmarks have been constructed for a number of years in every field of computational simulation. However, no comprehensive guidelines have been proposed for the construction and use of V&V benchmarks. Some fields, such as nuclear reactor safety, place little emphasis on code verification benchmarks and great emphasis on validation benchmarks that are closely related to actual reactors operating near safety-critical conditions. This paper proposes recommendations for the optimum design and use of code verification benchmarks based on classical analytical solutions, manufactured solutions, and highly accurate numerical solutions. It is believed that these benchmarks will prove useful to both in-house developed codes, as well as commercially licensed codes. In addition, this paper proposes recommendations for the design and use of validation benchmarks with emphasis on careful design of building-block experiments, estimation of experiment measurement uncertainty for both inputs and outputs to the code, validation metrics, and the role of model calibration in validation. It is argued that predictive capability of a computational model is built on both the measurement of achievement in V&V, as well as how closely related are the V&V benchmarks to the actual application of interest, e.g., the magnitude of extrapolation beyond a validation benchmark to a complex engineering system of interest.
Low-Resolution Face Recognition via Coupled Locality Preserving Mappings

Practical face recognition systems are sometimes confronted with low-resolution face images. Traditional two-step methods solve this problem through employing super-resolution (SR). However, these methods usually have limited performance because the target of SR is not absolutely consistent with that of face recognition. Moreover, time-consuming sophisticated SR algorithms are not suitable for real-time applications. To avoid these limitations, we propose a novel approach for LR face recognition without any SR preprocessing. Our method based on coupled mappings (CMs), projects the face images with different resolutions into a unified feature space which favors the task of classification. These CMs are learned through optimizing the objective function to minimize the difference between the correspondences (i.e., low-resolution image and its high-resolution counterpart). Inspired by locality preserving methods for dimensionality reduction, we introduce a penalty weighting matrix into our objective function. Our method significantly improves the recognition performance. Finally, we conduct experiments on publicly available databases to verify the efficacy of our algorithm.