nils-herrmann/clean_scirepeval huggingface dataset:
Structural human brain networks: hot topics in diffusion tractography.

After more than 10 years of methodological developments and clinical applications, diffusion imaging tractography has reached a crossroad. Although the method is still in its infancy, the time has come to address some important questions. Can tractography reproduce reliably known anatomy or describe new anatomical pathways? Are interindividual differences, for example in tract lateralization, important to understand heterogeneity of clinical manifestations? Do novel tractography algorithms provide a real advantage over previous methods? Here we focus on some of the most exciting recent advancements in diffusion tractography and critically highlight their advantages and limitations.



A flourishing of diffusion methods and models are bringing new solutions to the well known limitations of classical tractography based on the tensor model. However, these methods pose also new challenges and require the convergence and integration of different disciplines before they can replace what is currently widely available.



Rigorous postmortem validation, clinical optimization and experimental confirmation are obligatory steps before advanced diffusion technologies can translate into clear benefits for neurological patients.
Unsupervised Deep Video Hashing with Balanced Rotation

Recently, hashing video contents for fast retrieval has received increasing attention due to the enormous growth of online videos. As the extension of image hashing techniques, traditional video hashing methods mainly focus on seeking the appropriate video features but pay little attention to how the video-specific features can be leveraged to achieve optimal binarization. In this paper, an end-to-end hashing framework, namely Unsupervised Deep Video Hashing (UDVH), is proposed, where feature extraction, balanced code learning and hash function learning are integrated and optimized in a self-taught manner. Particularly, distinguished from previous work, our framework enjoys two novelties: 1) an unsupervised hashing method that integrates the feature clustering and feature binarization, enabling the neighborhood structure to be preserved in the binary space; 2) a smart rotation applied to the video-specific features that are widely spread in the low-dimensional space such that the variance of dimensions can be balanced, thus generating more effective hash codes. Extensive experiments have been performed on two realworld datasets and the results demonstrate its superiority, compared to the state-of-the-art video hashing methods. To bootstrap further developments, the source code will be made publically available.
Spectral Clustering Wikipedia Keyword-Based Search Results

The paper summarizes our research in the area of unsupervised categorization of Wikipedia articles. As a practical result of our research, we present an application of spectral clustering algorithm used for grouping Wikipedia search results. The main contribution of the paper is a representation method for Wikipedia articles that has been based on combination of words and links and used for categoriation of search results in this repository. We evaluate the proposed approach with Primary Component projections and show, on the test data, how usage of cosine transformation to create combined representations influence data variability. On sample test datasets, we also show how combined representation improves the data separation that increases overall results of data categorization. To implement the system, we review the main spectral clustering methods and we test their usability for text categorization. We give a brief description of the system architecture that groups online Wikipedia articles retrieved with user-specified keywords. Using the system, we show how clustering increases information retrieval effectiveness for Wikipedia data repository.
Generation of fundamental frequency contours for Thai speech synthesis using tone nucleus model

As classic and intrinsic requirements, synthetic speech need to convey correct information with good quality of naturalness to listeners. Fundamental frequency (F0) contours need to be controlled to meet these requirements. Additional challenges have been introduced to tonal languages because the F0 contour reflects both intelligibility and naturalness of the speech. According to the fact that the F0 contour in a syllable conveys information asymmetrically, Tone nucleus model has been successfully established. In this study, Tone nucleus model is applied in order to generate F0 contours for Thai speech synthesis. This is among the first that has introduced the model to other tonal languages other than Mandarin. All tone nuclei for five distinctive tones are defined according to the underlying targets. The full process of F0 contour generation is presented from the nucleus extraction until the F0 contour generation for continuous speech. The efficiency and adaptability of the model in Thai language were confirmed by the objective and subjective tests. The model outperformed a baseline without applying the model. The generated F0 contours showed less distortion, more tone intelligibility and more naturalness. The modified method is also introduced for enhancement. The results showed significant improvement on the generated F0 contours.
Evaluating the effect of temporal parameters for vibrotactile saltatory patterns

Cutaneous saltation provides interesting possibilities for applications. An illusion of vibrotactile mediolateral movement was elicited to a left dorsal forearm to investigate emotional (i.e., pleasantness) and cognitive (i.e., continuity) experiences to vibrotactile stimulation. Twelve participants were presented with nine saltatory stimuli delivered to a linearly aligned row of three vibrotactile actuators separated by 70 mm in distance. The stimuli were composed of three temporal parameters of 12, 24 and 48 ms for both burst duration and inter-burst interval to form all nine possible uniform pairs. First, the stimuli were ranked by the participants using a special three-step procedure. Second, the participants rated the stimuli using two nine-point bipolar scales measuring the pleasantness and continuity of each stimulus, separately. The results showed especially the interval between two successive bursts was a significant factor for saltation. Moreover, the temporal parameters seemed to affect more the experienced continuity of the stimuli compared to pleasantness. These findings encourage us to continue to further study the saltation and the effect of different parameters for subjective experience.
Frontotemporal dementia: a review for primary care physicians.

Frontotemporal dementia (FTD) is one of the most common forms of dementia in persons younger than 65 years. Variants include behavioral variant FTD, semantic dementia, and progressive nonfluent aphasia. Behavioral and language manifestations are core features of FTD, and patients have relatively preserved memory, which differs from Alzheimer disease. Common behavioral features include loss of insight, social inappropriateness, and emotional blunting. Common language features are loss of comprehension and object knowledge (semantic dementia), and nonfluent and hesitant speech (progressive nonfluent aphasia). Neuroimaging (magnetic resonance imaging) usually demonstrates focal atrophy in addition to excluding other etiologies. A careful history and physical examination, and judicious use of magnetic resonance imaging, can help distinguish FTD from other common forms of dementia, including Alzheimer disease, dementia with Lewy bodies, and vascular dementia. Although no cure for FTD exists, symptom management with selective serotonin reuptake inhibitors, antipsychotics, and galantamine has been shown to be beneficial. Primary care physicians have a critical role in identifying patients with FTD and assembling an interdisciplinary team to care for patients with FTD, their families, and caregivers.
Evaluating the Impact of User Characteristics and Different Layouts on an Interactive Visualization for Decision Making

There is increasing evidence that user characteristics can have a significant impact on visualization effectiveness, suggesting that visualizations could be designed to better fit each user’s specific needs. Most studies to date, however, have looked at static visualizations. Studies considering interactive visualizations have only looked at a limited number of user characteristics, and consider either low-level tasks (e.g., value retrieval), or high-level tasks (in particular: discovery), but not both. This paper contributes to this line of work by looking at the impact of a large set of user characteristics on user performance with interactive visualizations, for both low and high-level tasks. We focus on interactive visualizations that support decision making, exemplified by a visualization known as Value Charts. We include in the study two versions of ValueCharts that differ in terms of layout, to ascertain whether layout mediates the impact of individual differences and could be considered as a form of personalization. Our key findings are that (i) performance with low and high-level tasks is affected by different user characteristics, and (ii) users with low visual working memory perform better with a horizontal layout. We discuss how these findings can inform the provision of personalized support to visualization processing.
Framework to leverage cloud for the modernization of the Indian agriculture system

In India, Information and Communication technology (ICT) is being used vastly as a tool in almost every sector like education, health, media, etc. But if we talk about Indian agriculture, ICT is still to be exploited for its benefits. Many initiatives have been taken by Government of India to promote and introduce ICT in agricultural field. On comparing to its counter developing countries like China, Japan, etc. where advanced ICT technologies like IOT are being implemented in agriculture sector, we find that for India still there is a long road ahead to travel. This paper proposes a cloud deployment model “Agri-assistant”, which provides agriculture related information assistance to Indian farmers living in rural areas, facing financial and connectivity constraints. The model leverages the existing Government services and mobile service to provide a solution to existing scenario with minimum burden on farmer's pocket.
Health and nutrition knowledge, attitudes and practices of pregnant women attending and not-attending ANC clinics in Western Kenya: a cross-sectional analysis

Antenatal care (ANC) is a key strategy to decreasing maternal mortality in low-resource settings. ANC clinics provide resources to improve nutrition and health knowledge and promote preventive health practices. We sought to compare the knowledge, attitude and practices (KAP) among women seeking and not-seeking ANC in rural Kenya.



Data from a community-based cross-sectional survey conducted in Western Province, Kenya were used. Nutrition knowledge (NKS), health knowledge (HKS), attitude score (AS), and dietary diversity score (DDS) were constructed indices. χ2 test and Student's t-test were used to compare proportions and means, respectively, to assess the difference in KAP among pregnant women attending and not-attending ANC clinics. Multiple regression analyses were used to assess the impact of the number of ANC visits (none, <4, ≥4) on knowledge and practice scores, adjusting for maternal socio-demographic confounders, such as age, gestational age, education level and household wealth index.



Among the 979 pregnant women in the survey, 59% had attended ANC clinics while 39% had not. The mean (±SD) NKS was 4.6 (1.9) out of 11, HKS was 6.2 (1.7) out of 12, DDS was 4.9 (1.4) out of 12, and AS was 7.4 (2.2) out of 10. Nutrition knowledge, attitudes, and DDS were not significantly different between ANC clinic attending and non-attending women. Among women who attended ANC clinics, 82.6% received malaria and/or antihelmintic treatment, compared to 29.6% of ANC clinic non-attendees. Higher number of ANC clinic visits and higher maternal education level were significantly positively associated with maternal health knowledge.



Substantial opportunities exist for antenatal KAP improvement among women in Western Kenya, some of which could occur with greater ANC attendance. Further research is needed to understand multi-level factors that may affect maternal knowledge and practices.
Robust locally linear embedding

In the past few years, some nonlinear dimensionality reduction (NLDR) or nonlinear manifold learning methods have aroused a great deal of interest in the machine learning community. These methods are promising in that they can automatically discover the low-dimensional nonlinear manifold in a high-dimensional data space and then embed the data points into a low-dimensional embedding space, using tractable linear algebraic techniques that are easy to implement and are not prone to local minima. Despite their appealing properties, these NLDR methods are not robust against outliers in the data, yet so far very little has been done to address the robustness problem. In this paper, we address this problem in the context of an NLDR method called locally linear embedding (LLE). Based on robust estimation techniques, we propose an approach to make LLE more robust. We refer to this approach as robust locally linear embedding (RLLE). We also present several specific methods for realizing this general RLLE approach. Experimental results on both synthetic and real-world data show that RLLE is very robust against outliers.
Au–Au Surface-Activated Bonding and Its Application to Optical Microsensors With 3-D Structure

Hybrid integration of multiple optical chips in three dimensions is an important technology for realizing highly functional, compact optoelectronic microsystems. In this paper, we report successful 3-D integration of optical chips performed using Au-Au surface-activated bonding (SAB) at a relatively low bonding temperature of 150degC. A glass substrate with the flip-chip-bonded photodiode chips was vertically stacked on a Si substrate with the bonded laser diode chip using Au-Au SAB. By applying this technique, compact and thin optical microsensors (2.8 mm times 2.8 mm times 1 mm thick) were fabricated. The feasibility of measuring velocity was demonstrated using prototype microsensors.
Experiences from introducing UML-based development in a large safety-critical project

UML and UML-based development methods have become de facto standards in industry, and there are many claims for the positive effects of modelling object-oriented systems using methods based on UML. However, there is no reported empirical evaluation of UML-based development in large, industrial projects. This paper reports a case study in ABB, a global company with 120,000 employees, conducted to identify immediate benefits as well as difficulties and their causes when introducing UML-based development in large projects. ABB decided to use UML-based development in the company’s system development projects as part of an effort to enable certification according to the IEC 61508 safety standard. A UML-based development method was first applied in a large, international project with 230 system developers, testers and managers. The goal of the project was to build a new version of a safety-critical process control system. Most of the software was embedded. The project members were mostly newcomers to the use of UML. Interviews with 16 system developers and project managers at their sites in Sweden and Norway were conducted to identify the extent to which the introduction of UML-based development had improved their development process. The interviewees had experienced improvements with traceability from requirements to code, design of the code, and development of test cases as well as in communication and documentation. These results thus support claims in the literature regarding improvements that may be obtained through the use of UML. However, the results also show that the positive effects of UML-based development were reduced due to (1) legacy code that it was not feasible to reverse engineer into UML, (2) the distribution of requirements to development teams based on physical units and not on functionality, (3) training that was not particularly adapted to this project and considered too expensive to give to project members not directly involved in development with UML, and (4) a choice of modelling tools with functionality that was not in accordance with the needs of the project. The results from this study should be useful in enabling other UML adopters to have more realistic expectations and a better basis for making project management decisions.
Predicting Personality Using Novel Mobile Phone-Based Metrics

The present study provides the first evidence that personality can be reliably predicted from standard mobile phone logs. Using a set of novel psychology-informed indicators that can be computed from data available to all carriers, we were able to predict users’ personality with a mean accuracy across traits of 42% better than random, reaching up to 61% accuracy on a three-class problem. Given the fast growing number of mobile phone subscription and availability of phone logs to researchers, our new personality indicators open the door to exciting avenues for future research in social sciences. They potentially enable costeffective, questionnaire-free investigation of personality-related questions at a scale never seen before.
A 180-mW Linear MZM Driver in CMOS for Single-Carrier 400-Gb/s Coherent Optical Transmitter

We developed a 56-GHz-bandwidth 2.0-Vppd linear MZM driver in 65-nm CMOS. It consumes only 180 mW for driving a 50-Ω impedance. We demonstrated the feasibility of drivers with less than 1 W for dual-polarization IQ modulation in 400-Gb/s systems.
Learning discriminative binary codes for finger vein recognition

Finger vein recognition has drawn increasing attention from biometrics community due to its security and convenience. In this paper, a novel discriminative binary codes (DBC) learning method is proposed for finger vein recognition. First of all, subject relation graph is built to capture correlations among subjects. Based on the relation graph, binary templates are transformed to describe vein characteristics of subjects. To ensure that templates are discriminative and representative, graph transform is formulated into an optimization problem, in which the distance between templates from different subjects is maximized and templates provide maximum information about subjects. At last, supervised information for training instances is provided by the obtained binary templates, and SVMs are trained as the code learner for each bit. Compared with existing binary codes for finger vein recognition, DBC are more discriminative and shorter. In addition, they are generated with considering the relationships among subjects which may be useful to improve performance. Experimental results on PolyU database and MLA database demonstrate the effectiveness and efficiency of DBC for finger vein recognition and retrieval.
Don't Walk, Skip!: Online Learning of Multi-scale Network Embeddings

We present WALKLETS, a novel approach for learning multiscale representations of vertices in a network. In contrast to previous works, these representations explicitly encode multi-scale vertex relationships in a way that is analytically derivable.
 WALKLETS generates these multiscale relationships by sub-sampling short random walks on the vertices of a graph. By 'skipping' over steps in each random walk, our method generates a corpus of vertex pairs which are reachable via paths of a fixed length. This corpus can then be used to learn a series of latent representations, each of which captures successively higher order relationships from the adjacency matrix.
 We demonstrate the efficacy of WALKLETS's latent representations on several multi-label network classification tasks for social networks such as BlogCatalog, DBLP, Flickr, and YouTube. Our results show that WALKLETS outperforms new methods based on neural matrix factorization. Specifically, we outperform DeepWalk by up to 10% and LINE by 58% Micro-F1 on challenging multi-label classification tasks. Finally, WALKLETS is an online algorithm, and can easily scale to graphs with millions of vertices and edges.
Effectiveness of Association Rules Mining for Invariants Generation in Cyber-Physical Systems

Cyber-Physical Systems (CPS), which integrate controls, computing and physical processes are critical infrastructures of any country. They are becoming more vulnerable to cyber attacks due to an increase in computing and network facilities. The increase of monitoring network protocols increases the chances of being attacked. Once an attacker is able to cross the network intrusion detection mechanisms, he can affect the physical operations of the system which may lead to physical damages of components and/or a disaster. Some researchers used constraints of physical processes known as invariants to monitor the system in order to detect cyber attacks or failures. However, invariants generation is lacking in automation. This paper presents a novel method to identify invariants automatically using association rules mining. Through this technique, we show that it is possible to generate a number of invariants that are sometimes hidden from the design layout. Our preliminary study on a secure water treatment plant suggests that this approach is promising.
Epidemiology of scabies prevalence in the U.K. from general practice records.

Scabies is a contagious parasitic infestation which causes an allergic reaction to just a few mites.



To examine the epidemiology of scabies consultations in the U.K. by age, sex, region of the country and time.



Data were obtained from the THIN (The Health Information Network) database provided by EPIC U.K. in which each consultation episode is coded according to a 'READ' code. Data were available for 1997-2005 inclusive (9 years) for approximately 8·5% of the U.K. population from 12 regions of the U.K. We used harmonic analysis to model the data over time and derived cycle amplitudes and phases for each region in the U.K.



There was a significantly greater infestation rate among females with a relative risk of 1·24 (P < 0·001) relative to males. The age group 10-19 years had the highest infestation rates (with rates of 4·55 per 1000 and 5·92 per 1000 for males and females, respectively). The middle age groups had the smallest infestation rates. A cycle with length of between 15 and 17 years was derived, and the phase times of the cycles for each region suggested a progression of the disease originating from the North East, spreading to Northern parts of the U.K. and then to the Midlands and the South.



The study confirms earlier studies with regard to the age/sex distribution of scabies. We suggest a contagious pattern of spread of scabies infestation in the U.K. with an epidemic cycle length of 15-17 years.