nils-herrmann/clean_scirepeval huggingface dataset:
DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging

Brain imaging efforts are being increasingly devoted to decode the functioning of the human brain. Among neuroimaging techniques, resting-state fMRI (R-fMRI) is currently expanding exponentially. Beyond the general neuroimaging analysis packages (e.g., SPM, AFNI and FSL), REST and DPARSF were developed to meet the increasing need of user-friendly toolboxes for R-fMRI data processing. To address recently identified methodological challenges of R-fMRI, we introduce the newly developed toolbox, DPABI, which was evolved from REST and DPARSF. DPABI incorporates recent research advances on head motion control and measurement standardization, thus allowing users to evaluate results using stringent control strategies. DPABI also emphasizes test-retest reliability and quality control of data processing. Furthermore, DPABI provides a user-friendly pipeline analysis toolkit for rat/monkey R-fMRI data analysis to reflect the rapid advances in animal imaging. In addition, DPABI includes preprocessing modules for task-based fMRI, voxel-based morphometry analysis, statistical analysis and results viewing. DPABI is designed to make data analysis require fewer manual operations, be less time-consuming, have a lower skill requirement, a smaller risk of inadvertent mistakes, and be more comparable across studies. We anticipate this open-source toolbox will assist novices and expert users alike and continue to support advancing R-fMRI methodology and its application to clinical translational studies.
An experimental evaluation of rate-adaptation algorithms in adaptive streaming over HTTP

Adaptive (video) streaming over HTTP is gradually being adopted, as it offers significant advantages in terms of both user-perceived quality and resource utilization for content and network service providers. In this paper, we focus on the rate-adaptation mechanisms of adaptive streaming and experimentally evaluate two major commercial players (Smooth Streaming, Netflix) and one open source player (OSMF). Our experiments cover three important operating conditions. First, how does an adaptive video player react to either persistent or short-term changes in the underlying network available bandwidth. Can the player quickly converge to the maximum sustainable bitrate? Second, what happens when two adaptive video players compete for available bandwidth in the bottleneck link? Can they share the resources in a stable and fair manner? And third, how does adaptive streaming perform with live content? Is the player able to sustain a short playback delay? We identify major differences between the three players, and significant inefficiencies in each of them.
Adversarial Machine Learning at Scale

Adversarial examples are malicious inputs designed to fool machine learning models. They often transfer from one model to another, allowing attackers to mount black box attacks without knowledge of the target model’s parameters. Adversarial training is the process of explicitly training a model on adversarial examples, in order to make it more robust to attack or to reduce its test error on clean inputs. So far, adversarial training has primarily been applied to small problems. In this research, we apply adversarial training to ImageNet (Russakovsky et al., 2014). Our contributions include: (1) recommendations for how to succesfully scale adversarial training to large models and datasets, (2) the observation that adversarial training confers robustness to single-step attack methods, (3) the finding that multi-step attack methods are somewhat less transferable than singlestep attack methods, so single-step attacks are the best for mounting black-box attacks, and (4) resolution of a “label leaking” effect that causes adversarially trained models to perform better on adversarial examples than on clean examples, because the adversarial example construction process uses the true label and the model can learn to exploit regularities in the construction process.
ASSESSMENT AND COMPARISON OF METADATA SCHEMAS FOR ARCHITECTURAL HERITAGE

Conservation, protection and study of Architectural Heritage (AH) are based on the complete knowledge and documentation of its complex morphology, its architectonic components and all events related to the building. A key issue for a full recording of a monument is related to the cataloguing process, which is the action of registration, description and classification of Cultural Heritage (CH) assets. Many Cultural Institutions and National Bodies have been working towards the development of standards to make documentation uniform, retrieve information about Cultural Heritage, promote data sharing, improve content management and reduce redundant efforts. To regulate the cataloguing activity, rules, guidelines and indications were defined. In particular, thesauri were adopted to provide, with a controlled vocabulary, structure and guidelines for standardization of terms, avoiding ambiguity and subjectivity of documentation and the loss of important information. Some of these data standards have been defined within a national framework, such as the ICCD schema (Italy)or the MIDAS standard (England); others aim at guaranteeing data interoperability, such as, among others, LIDO and the CARARE schema. In this paper we carry out an assessment and comparison of the above mentioned schemas and standards, along with the schema adopted by l'Inventaire général du patrimoine culturel (France). After an analysis of the formal description of the single metadata schemas, we carried out the mapping of the different schemas to each other. The analysis is performed taking into consideration the RecorDim guidelines for documenting CH places. As a result, the mappings described in the paper enable the interoperability of data stored according to different metadata schemas. Furthermore the paper highlights properties, equivalencies and shortcomings of the schemas, confirming the idea that a more comprehensive documentation standard for Architectural Heritage is needed.
Design Thinking Methods and Tools for Innovation in Multidisciplinary Teams

Thinking (DT), a human-centered approach to innovation, is regarded as a system of overlapping spaces of viability, desirability and feasibility. Innovation increases when these three perspectives are addressed. This position paper proposes DT methods and tools to foster innovation in a multidisciplinary team by facilitating decision-making processes. We discuss how DT methods and tools reflect one or more DT perspectives, namely, the human, business and technology perspectives. We also discuss how these DT methods and tools can support decision-making processes, collaboration and engagement in a multidisciplinary team.
Within-class covariance normalization for SVM-based speaker recognition

This paper extends the within-class covariance normalizat ion (WCCN) technique described in [1, 2] for training generaliz ed linear kernels. We describe a practical procedure for apply ing WCCN to an SVM-based speaker recognition system where the input feature vectors reside in a high-dimensional space. O ur approach involves using principal component analysis (PCA) t o split the original feature space into two subspaces: a low-dimens ional “PCA space” and a high-dimensional “PCA-complement space. ” After performing WCCN in the PCA space, we concatenate the resulting feature vectors with a weighted version of their P CAcomplements. When applied to a state-of-the-art MLLR-SVM speaker recognition system, this approach achieves improv ements of up to 22% in EER and 28% in minimum decision cost function (DCF) over our previous baseline. We also achieve substanti al improvements over an MLLR-SVM system that performs WCCN in the PCA space but discards the PCA-complement.
External force estimation using joint torque sensors and its application to impedance control of a robot manipulator

This paper proposes an algorithm to estimate external forces exerted on the end-effector of a robot manipulator using information from joint torque sensors (JTS). The algorithm is the combination of Time Delay Estimation (TDE) and input estimation technique where the external force is considered as an unknown input to the robot manipulator. Based on TDE's idea, the estimator which does not require an accurate dynamics model of the robot manipulator is developed. The simultaneous input and state estimation (SISE) is used to reduce not only nonlinear uncertainties of the robot dynamics but also the noise of measurements. The performance of the proposed estimation algorithm is evaluated through an application to impedance control of a four degree-of-freedom manipulator.
Manga109 dataset and creation of metadata

We have created Manga109, a dataset of a variety of 109 Japanese comic books publicly available for use for academic purposes. This dataset provides numerous comic images but lacks the annotations of elements in the comics that are necessary for use by machine learning algorithms or evaluation of methods. In this paper, we present our ongoing project to build metadata for Manga109. We first define the metadata in terms of frames, texts and characters. We then present our web-based software for efficiently creating the ground truth for these images. In addition, we provide a guideline for the annotation with the intent of improving the quality of the metadata.
Practical byzantine fault tolerance and proactive recovery

Our growing reliance on online services accessible on the Internet demands highly available systems that provide correct service without interruptions. Software bugs, operator mistakes, and malicious attacks are a major cause of service interruptions and they can cause arbitrary behavior, that is, Byzantine faults. This article describes a new replication algorithm, BFT, that can be used to build highly available systems that tolerate Byzantine faults. BFT can be used in practice to implement real services: it performs well, it is safe in asynchronous environments such as the Internet, it incorporates mechanisms to defend against Byzantine-faulty clients, and it recovers replicas proactively. The recovery mechanism allows the algorithm to tolerate any number of faults over the lifetime of the system provided fewer than 1/3 of the replicas become faulty within a small window of vulnerability. BFT has been implemented as a generic program library with a simple interface. We used the library to implement the first Byzantine-fault-tolerant NFS file system, BFS. The BFT library and BFS perform well because the library incorporates several important optimizations, the most important of which is the use of symmetric cryptography to authenticate messages. The performance results show that BFS performs 2% faster to 24% slower than production implementations of the NFS protocol that are not replicated. This supports our claim that the BFT library can be used to build practical systems that tolerate Byzantine faults.
A Survey on Ambient-Assisted Living Tools for Older Adults

In recent years, we have witnessed a rapid surge in assisted living technologies due to a rapidly aging society. The aging population, the increasing cost of formal health care, the caregiver burden, and the importance that the individuals place on living independently, all motivate development of innovative-assisted living technologies for safe and independent aging. In this survey, we will summarize the emergence of `ambient-assisted living” (AAL) tools for older adults based on ambient intelligence paradigm. We will summarize the state-of-the-art AAL technologies, tools, and techniques, and we will look at current and future challenges.
The impact of informational incentives and social influence on consumer behavior during Alibaba's online shopping carnival

Alibaba’s annual global online shopping carnival (O SC) held every November 11 (also known as Double 11 Day or Single’s Day) is well known for be ing the most successful and largest online promotion campaign since its Chinese debut in 2009. We have witnessed the power of online herd behavior exhibited by a frenzy of consumers purchas ing, sharing and interacting with each other during Alibaba’s global OSC. This shopping carnival brough t the entire Chinese electronic commerce industry an astonishing record volume of transactions worth 120.7 billion RMB and 1.04 billion transactions on Singles’ Day in 2016. This study explores what faci litates consumer OSC behavior on Singles’ Day. Drawing upon carnival theory and herd behavior, we develop an explanatory model to explain how informational incentives and social influence affec t OSC consumer behavior. Data gathered from a large-scale online survey of consumers that partici pated during the Singles’ Day period is used to tes t the model. The conceptual model was validated using the partial least squares (PLS) technique. The empirical results show that (1) participation, inte raction and pleasure together define OSC consumer behavior; (2) informational incentives and social i nfluence are two crucial preconditions for OSC consumer behavior. The results provide good support for the proposed research model and hypotheses and yield interesting insights about the important roles of informational incentives and social influe nc collectively facilitating OSC consumer behavior dur ing the Singles’ Day period in China.
Integrated photovoltaic maximum power point tracking converter

A low-power low-cost highly efficient maximum power point tracker (MPPT) to be integrated into a photovoltaic (PV) panel is proposed. This can result in a 25% energy enhancement compared to a standard photovoltaic panel, while performing functions like battery voltage regulation and matching of the PV array with the load. Instead of using an externally connected MPPT, it is proposed to use an integrated MPPT converter as part of the PV panel. It is proposed that this integrated MPPT uses a simple controller in order to be cost effective. Furthermore, the converter has to be very efficient, in order to transfer more energy to the load than a directly coupled system. This is achieved by using a simple soft-switched topology. A much higher conversion efficiency at lower cost will then result, making the MPPT an affordable solution for small PV energy systems.
Feature selection using binary cuckoo search algorithm

The cuckoo search algorithm is one of the nature-inspired algorithms developed in recent years. Interesting breeding strategies of cuckoo birds have inspired the algorithm. The binary cuckoo search algorithm is a special version that reduces the search space to binary. In this study, we used binary cuckoo search algorithm in the feature selection process to classify the premature ventricular contraction beats in ECG. We used 200 amplitude values of the signal to represent one beat and selected those that represent the signal best. Using selected features, premature ventricular contraction beats were perceived with 99.67% of accuracy.
Children with anogenital symptoms and signs referred for sexual abuse evaluations.

To determine whether children referred to a sexual abuse clinic because of anogenital symptoms or signs have examination findings that are suggestive of or probable or definitive for sexual abuse.



Case series of 157 patients.



Child and adolescent ambulatory care sexual abuse clinic.



A medical records review of 3660 cases was done; 157 cases were identified for study. Most (75%) referrals were from medical clinics. Of 184 complaints, the most common presenting symptom or sign was anogenital bleeding or bruising (29.3%), followed by irritation or redness (21.7%), abnormal anogenital anatomy (20.7%), vaginal discharge (18.4%), lesions (6.5%), and "other" symptoms or signs (3.3%). We used a standardized classification system and determined that 25 patients (15%) had examination findings in the sexual abuse clinic that were suggestive of or probable or definitive for sexual abuse. Although 85 patients had examination findings that corroborated the presenting symptom(s), 70 had nonspecific examination findings or a diagnosis other than sexual abuse. Seventy-two patients had normal examination findings. Only patients with the presenting symptom of lesions had an increased likelihood of a sexual abuse diagnosis. Common examination findings included anogenital erythema, enhanced vascularity of the hymen or vestibule in prepubertal girls, labial adhesions, and culture-negative vaginitis.



Few children are referred for sexual abuse evaluations based on physical signs or symptoms alone. Children with anogenital symptoms but without a disclosure or suspicion of sexual abuse are unlikely to have examination findings suggestive of abuse. The evaluation of children with anogenital symptoms and signs should include a consideration of alternative conditions and causes not directly related to sexual abuse.
Learning trajectory patterns by clustering: Experimental studies and comparative evaluation

Recently a large amount of research has been devoted to automatic activity analysis. Typically, activities have been defined by their motion characteristics and represented by trajectories. These trajectories are collected and clustered to determine typical behaviors. This paper evaluates different similarity measures and clustering methodologies to catalog their strengths and weaknesses when utilized for the trajectory learning problem. The clustering performance is measured by evaluating the correct clustering rate on different datasets with varying characteristics.
Which Hotel attributes Matter ? A review of previous and a framework for future research

A lot of effort has been made in the last decades to reveal, which hotel attributes guest care about. Due to the high costs that are typically involved with investments in the hotel industry, it makes a lot of sense to study, which product components the travellers appreciate. This study reveals that hotel attribute research turns out to be a wide and extremely heterogeneous field of research. The authors review empirical studies investigating the importance of hotel attributes, provide attribute rankings and suggest a framework for past and future research projects in the field, based on the dimensions “focus of research”, ”risk versus utility” and “trade-off versus no trade-off questioning situation”.
EW-SHOT L EARNING

A recent approach to few-shot classification called matching networks has demonstrated the benefits of coupling metric learning with a training procedure that mimics test. This approach relies on an attention scheme that forms a distribution over all points in the support set, scaling poorly with its size. We propose a more streamlined approach, prototypical networks, that learns a metric space in which few-shot classification can be performed by computing Euclidean distances to prototype representations of each class, rather than individual points. Our method is competitive with state-of-the-art few-shot classification approaches while being much simpler and more scalable with the size of the support set. We empirically demonstrate the performance of our approach on the Omniglot and miniImageNet datasets. We further demonstrate that a similar idea can be used for zero-shot learning, where each class is described by a set of attributes, and achieve state-ofthe-art results on the Caltech UCSD bird dataset.
Neuroimaging: Decoding mental states from brain activity in humans

Recent advances in human neuroimaging have shown that it is possible to accurately decode a person's conscious experience based only on non-invasive measurements of their brain activity. Such 'brain reading' has mostly been studied in the domain of visual perception, where it helps reveal the way in which individual experiences are encoded in the human brain. The same approach can also be extended to other types of mental state, such as covert attitudes and lie detection. Such applications raise important ethical issues concerning the privacy of personal thought.
Exploiting Eigenposteriors for Semi-Supervised Training of DNN Acoustic Models with Sequence Discrimination

Deep neural network (DNN) acoustic models yield posterior probabilities of senone classes. Recent studies support the existence of low-dimensional subspaces underlying senone posteriors. Principal component analysis (PCA) is applied to identify eigenposteriors and perform low-dimensional projection of the training data posteriors. The resulted enhanced posteriors are applied as soft targets for training better DNN acoustic model under the student-teacher framework. The present work advances this approach by studying incorporation of sequence discriminative training. We demonstrate how to combine the gains from eigenposterior based enhancement with sequence discrimination to improve ASR using semi-supervised training. Evaluation on AMI meeting corpus yields nearly 4% absolute reduction in word error rate (WER) compared to the baseline DNN trained with cross entropy objective. In this context, eigenposterior enhancement of the soft targets is crucial to enable additive improvement using out-of-domain untranscribed data.
Supervised and Unsupervised Aspect Category Detection for Sentiment Analysis with Co-occurrence Data

Using online consumer reviews as electronic word of mouth to assist purchase-decision making has become increasingly popular. The Web provides an extensive source of consumer reviews, but one can hardly read all reviews to obtain a fair evaluation of a product or service. A text processing framework that can summarize reviews, would therefore be desirable. A subtask to be performed by such a framework would be to find the general aspect categories addressed in review sentences, for which this paper presents two methods. In contrast to most existing approaches, the first method presented is an unsupervised method that applies association rule mining on co-occurrence frequency data obtained from a corpus to find these aspect categories. While not on par with state-of-the-art supervised methods, the proposed unsupervised method performs better than several simple baselines, a similar but supervised method, and a supervised baseline, with an <inline-formula> <tex-math notation="LaTeX">$F_{1}$ </tex-math></inline-formula>-score of 67%. The second method is a supervised variant that outperforms existing methods with an <inline-formula> <tex-math notation="LaTeX">$F_{1}$ </tex-math></inline-formula>-score of 84%.
A High Slew-Rate Push–Pull Output Amplifier for Low-Quiescent Current Low-Dropout Regulators With Transient-Response Improvement

A high slew-rate amplifier with push-pull output driving capability is proposed to enable an ultra-low quiescent current (Iq ~ 1muA) low-dropout (LDO) regulator with improved transient responses. The proposed amplifier eliminates the tradeoff between small Iq and large slew-rate that is imposed by the tail-current in conventional amplifier design. Push-pull output stage is introduced to enhance the output driving ability. Small dropout voltage (Vbo) with large-size pass transistor and ultra-low Iq can thus be used to minimize power loss of LDO regulator without transient-response degradation. The proposed amplifier helps to improve stability of LDO regulators without using any on-chip and off-chip compensation capacitors. This is beneficial to chip-level power management requiring high-area efficiency. An LDO regulator with the proposed amplifier has been implemented in a 0.18- mum standard CMOS process and occupies 0.09 mm2. The LDO regulator can deliver 50-mA load current at 1-V input and ~ 100-mV VDO . It only consumes 1.2 muA Iq and is able to recover within ~ 4 mus even under the worst case scenario.