nils-herrmann/clean_scirepeval huggingface dataset:
Binary speaker embedding
Venue: 2016 10th International Symposium on Chinese Spoken Language Processing (ISCSLP)

The popular i vector model represents speakers as low dimensional continuous vectors (i vectors) and hence it is a way of continuous speaker embedding. In this paper, we investigate binary speaker embedding, which transforms i vectors to binary vectors (codes) by a hash function. We start from locality sensitive hashing (LSH) a simple binarization approach where binary codes are derived from a set of random hash functions. A potential problem of LSH is that the randomly sampled hash functions might be suboptimal. We therefore propose an improved Hamming distance learning approach, where the hash function is learned by a variable sized block training that projects each dimension of the original i vectors to variable sized binary codes independently. Our experiments show that binary speaker embedding can deliver competitive or even better results on both speaker verification and identification tasks, while the memory usage and the computation cost are significantly reduced.
Concepts Where Cognitive Science Went Wrong

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Breaking the Fourth Wall of Cognitive Science

Research in cognitive science typically places a boundary between participants and the stimuli they are asked to process. While this separation affords experimental control, it can also severely limit the generalizability of the conclusions that are drawn. Here, we review new evidence that some conclusions that have been drawn about social attention do not extend beyond the laboratory. They fundamentally misrepresent how social attention operates in natural social contexts. Critically, these difficulties have led to renewed interest in the dual function of gaze when in authentic social situations, the eyes both collect information from the environment (an encoding function) and communicate one's mental states to others (a signaling function) which traditional social attention paradigms arguably have failed to capture. We review this recent work and discuss the utility of adopting more naturalistic methods in cognitive science.
Mining Source Code Topics Through Topic Model and Words Embedding
Venue: ADMA

Developers nowadays can leverage existing systems to build their own applications. However, a lack of documentation hinders the process of software system reuse. We examine the problem of mining topics (i.e. topic extraction) from source code, which can facilitate the comprehension of the software systems. We propose a topic extraction method, Embedded Topic Extraction (EmbTE) that considers word semantics, which are never considered in mining topics from source code, by leveraging word embedding techniques. We also adopt Latent Dirichlet Allocation (LDA) and Non negative Matrix Factorization (NMF) to extract topics from source code. Moreover, an automated term selection algorithm is proposed to identify the most contributory terms from source code for the topic extraction task. The empirical studies on Github ( Java projects show that EmbTE outperforms other methods in terms of providing more coherent topics. The results also indicate that method name, method comments, class names and class comments are the most contributory types of terms to source code topic extraction.
Assembly Sequence Planning for Motion Planning
Venue: ArXiv

The purpose of this paper is to develop a planner for finding an optimal assembly sequence for robots to assemble objects. Each manipulated object in the optimal sequence is stable during assembly. They are easy to grasp and robust to motion uncertainty.  The input to the planner is the mesh models of the objects, the relative poses between the objects in the assembly and the final pose of the assembly. The output is an optimal assembly sequence, namely, in which order should one assemble the objects, from which directions should the objects be dropped and candidate grasps of each object. The proposed planner finds the optimal solution by automatically permuting, evaluating and searching the possible assembly sequences considering stability, graspability and assemblability qualities.  The proposed planner could plan an optimal sequence to guide robots to do assembly using translational motion. The sequence provides initial and goal configurations to motion planning algorithms and is ready to be used by robots. The usefulness of the proposed method is verified by both simulation and real world executions.  The paper proposes an assembly planner which can find an optimal assembly sequence automatically without teaching of the assembly orders and directions by skilled human technicians. The planner is highly expected to improve teachingless robotic manufacturing.
Integrated assembly and motion planning using regrasp graphs
Venue: Robotics and biomimetics

This paper presents an integrated assembly and motion planning system to recursively find the assembly sequence and motions to assemble two objects with the help of a horizontal surface as the supporting fixture. The system is implemented in both assembly level and motion level. In the assembly level, the system checks all combinations of the assembly sequences and gets a set of candidates. Then, for each candidate assembly sequence, the system incrementally builds regrasp graphs and performs recursive search to find a pick and place motion in the motion level to manipulate the base object as well as to assemble the other object to the base. The system integrates the candidate assembly sequences computed in the assembly level incrementally and recursively with graph searching and motion planning in the motion level and plans the assembly sequences and motions integratedly for assembly tasks. Both simulation and real world experiments are performed to demonstrate the efficacy of the integrated planning system.
School Related Social Support and Adolescents' School Related Subjective Well Being: The Mediating Role of Basic Psychological Needs Satisfaction at School

Grounded in Basic Psychological Needs Theory (BPNT; Deci and Ryan in Intrinsic motivation and self determination in human behavior. Plenum, New York, 1985;  2000; Ryan and Deci  2000) the present study mainly aimed to (1) test a theoretically based model proposing that the satisfaction of three basic psychological needs at school mediates the relationship between school related social support (i.e. teacher support and classmate support) and school related subjective well being and (2) explore the invariance of the model across gender and age groups. A total of 1476 Chinese adolescents (males 712; early adolescents 676; Mage 15.40) completed a multi measure questionnaire tapping the targeted variables. Structural equation modeling was used to examine the hypotheses. Results indicated that school related social support (i.e. teacher support and classmate support) related statistically significantly to adolescents' school related subjective well being. Furthermore, a multiple mediators analysis indicated that autonomy, competence and relatedness needs satisfaction at school partially mediated the relation between teacher support and school related subjective well being, and fully mediated the relation between classmate support and school related subjective well being. Lastly, the results supported partial invariance of the model with respect to gender and age groups. Limitations of the study are considered and applied considerations to improve adolescents' school related subjective well being are discussed.
Media Literacy Education and Cultural Differences: A Comparative Reception Analysis on Global TV Show Survivor

Reception analysis takes into consideration not only individual differences but also cultural differences. On the other hand, there are diverse factors determining audience's reception and active participation. Education, age, sex, economical status, family background, ethnicity, world view and similar factors affect the reception process. Besides that, being media literate and fostering a critical approach towards media texts also determines reception. Taking into account all these factors, this research is designed as a comparative reception analysis. We will be comparing the reception of a global product by youngsters (Survivor Show) within the scope of media literacy education and cultural differences. For that purpose we realized focus group discussions with communication faculty students from Spain and Turkey. The research was realized in the framework of literature review including the above mentioned topics. Anahtar Kelimeler: Media Literacy, Comparative Reception, Survivor, Television Programs, Focus Group. Assoc. Prof. Istanbul Ticaret University ISSN 2149 9446 Cilt 01 Sayi 01 Ocak 2016 Eglence Endustrisi Sayisi Medya Okuryazarligi Egitimi ve Kulturel Farkliliklar: Kuresel Televizyon Sovu Survivor Uzerine Karsilastirmali Bir Alimlama Analizi
Korean National Health Insurance Database.
Venue: JAMA internal medicine

We also acknowledge computing support from University of Chicago's Research Computing Center, the uConnect Community Advisory Board, and study participants for their time, effort, and dedication. We gratefully acknowledge the contributions of Dexter Voisin, PhD, School of Social Service Administration, University of Chicago, and Kenneth Mayer, MD, Fenway Institute. Dr Voisin received no compensation beyond his established salary for his contributions. Dr Mayer was not compensated for his contributions.
Instrumental evaluation of anti aging effects of cosmetic formulations containing palmitoyl peptides, Silybum marianum seed oil, vitamin E and other functional ingredients on aged human skin.
Venue: Experimental and therapeutic medicine

Anti aging cosmetics are widely used for improving signs of aged skin such as skin wrinkles, decreased elasticity, low dermal density and yellow skin tone. The present study evaluated the effects of cosmetic formulations, eye cream and facial cream, containing palmitoyl peptides, Silybum marianum (S. marianum) seed oil, vitamin E and other functional ingredients on the improvement of facial wrinkles, elasticity, dermal density and skin tone after 4 weeks period of application on aged human skin. Healthy volunteers (n=20) with aged skin were recruited to apply the test materials facially twice per day for 4 weeks. Skin wrinkles, elasticity, dermal density and skin tone were measured instrumentally for assessing the improvement of skin aging. All the measurements were conducted prior to the application of test materials and at 2 and 4 weeks of treatment. Crow's feet wrinkles were decreased 5.97% after 2 weeks of test material application and 14.07% after 4 weeks of application in comparison of pre application. Skin elasticity was increased 6.81% after 2 weeks and 8.79% after 4 weeks. Dermal density was increased 16.74% after 2 weeks and 27.63% after 4 weeks. With the L* value indicating skin brightness and the a* value indicating erythema (redness) the results showed that brightness was increased 1.70% after 2 weeks and 2.14% after 4 weeks, and erythema was decreased 10.45% after 2 weeks and 22.39% after 4 weeks. Hence, the test materials appear to exert some degree of anti aging effects on aged human skin. There were no abnormal skin responses from the participants during the trial period. We conclude that the facial and eye cream containing palmitoyl peptides and S. marianum seed oil, vitamin E and other ingredients have effects on the improvement of facial wrinkles, elasticity, dermal density and skin tone.
Spatio temporal convolutional neural networks explain human neural representations of action recognition

Recognizing the actions of others from complex visual scenes is an essential task for humans. Here we investigate the computational mechanisms that support action recognition in the human visual system. We use a novel dataset of well controlled naturalistic videos of five actions performed by five actors at five viewpoint and extend a class of biologically inspired hierarchical computational models of object recognition to recognize actions from videos. We explore a number of variations within the class of convolutional neural networks and assess classification accuracy on a viewpoint invariant action recognition task as well as a correlation measure with magnetoencephalography (MEG) human brain recordings acquired using the same stimuli. We show that feed forward spatio temporal convolutional neural networks solve the task of invariant action recognition and account for the majority of the explainable variance in the neural data. Furthermore, we show that model features that improve performance on viewpoint invariant action recognition lead to a model representation that better matches the representation encoded by neural data. These results advance the idea that robustness to complex transformations, such as 3D viewpoint invariance, is a specific goal of visual processing in the human brain.
A survey of transfer learning
Venue: Journal of Big Data

Machine learning and data mining techniques have been used in numerous real world applications. An assumption of traditional machine learning methodologies is the training data and testing data are taken from the same domain, such that the input feature space and data distribution characteristics are the same. However, in some real world machine learning scenarios, this assumption does not hold. There are cases where training data is expensive or difficult to collect. Therefore, there is a need to create high performance learners trained with more easily obtained data from different domains. This methodology is referred to as transfer learning. This survey paper formally defines transfer learning, presents information on current solutions, and reviews applications applied to transfer learning. Lastly, there is information listed on software downloads for various transfer learning solutions and a discussion of possible future research work. The transfer learning solutions surveyed are independent of data size and can be applied to big data environments.
Operational Log Analysis for Big Data Systems: Challenges and Solutions
Venue: IEEE Software

Big data systems (BDSs) are complex, consisting of multiple interacting hardware and software components, such as distributed computing nodes, databases, and middleware. Any of these components can fail. Finding the failures' root causes is extremely laborious. Analysis of BDS generated logs can speed up this process. The logs can also help improve testing processes, detect security breaches, customize operational profiles, and aid with any other tasks requiring runtime data analysis. However, practical challenges hamper log analysis tools' adoption. The logs emitted by a BDS can be thought of as big data themselves. When working with large logs, practitioners face seven main issues: scarce storage, unscalable log analysis, inaccurate capture and replay of logs, inadequate log processing tools, incorrect log classification, a variety of log formats, and inadequate privacy of sensitive data. Some practical solutions exist, but serious challenges remain. This article is part of a special issue on Software Engineering for Big Data Systems.
Using Multiple Input, Multiple Output Formal Control to Maximize Resource Efficiency in Architectures
Venue: 2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA)

As processors seek more resource efficiency, they increasingly need to target multiple goals at the same time, such as a level of performance, power consumption, and average utilization. Robust control solutions cannot come from heuristic based controllers or even from formal approaches that combine multiple single parameter controllers. Such controllers may end up working against each other. What is needed is control theoretical MIMO (multiple input, multiple output) controllers, which actuate on multiple inputs and control multiple outputs in a coordinated manner. In this paper, we use MIMO control theory techniques to develop controllers to dynamically tune architectural parameters in processors. To our knowledge, this is the first work in this area. We discuss three ways in which a MIMO controller can be used. We develop an example of MIMO controller and show that it is substantially more effective than controllers based on heuristics or built by combining single parameter formal controllers. The general approach discussed here is likely to be increasingly relevant as future processors become more resource constrained and adaptive.
Effect of germination on lignan biosynthesis, and antioxidant and antiproliferative activities in flaxseed (Linum usitatissimum L.
Venue: Food chemistry

Flaxseed is one of the richest seed sources of lignan and other bioactive compounds. The present study characterized lignan biosynthesis and potential health benefits in flaxseeds during 10 day germination. The transcription levels of lignan biosynthesis, along with variation of bioactivities, including cellular antioxidant activity and antiproliferative activity of flaxseed sprouts during germination, were studied. The results showed that 8 day germination brought about a 6.3 fold increase in secoisolariciresinol diglucoside and 4.5 fold increase in secoisolariciresinol compared to ungerminated flaxseeds. The highest amount of total phenolics and total flavonoids were found in 10 day geminated flaxseed, namely a 5.6 fold and 55 fold increase compared to ungerminated flaxseed. Transcription analysis revealed that five key encoding genes in the lignan biosynthetic pathway were up regulated during germination. Furthermore, the highest antioxidant and antiproliferative activities were found on day 10. These findings suggest that germination for 8 10 days leads to optimal lignan production and potential health benefits if incorporated into the human diet.
Dynamic metabolic and transcriptomic profiling of methyl jasmonate treated hairy roots reveals synthetic characters and regulators of lignan biosynthesis in Isatis indigotica Fort
Venue: Plant biotechnology journal

A molecular description of lignan biosynthesis in Isatis indigotica displaying its synthetic characteristics and regulatory mechanism is of great importance for the improvement of the production of this class of active compounds. To discover the potential key catalytic steps and regulatory genes, I. indigotica hairy roots elicited by methyl jasmonate (MeJA) were used as a source of systematic variation for exploring the metabolic/transcriptional changes and candidate genes that might play key roles in lignan biosynthesis. The reprogramming modulated by MeJA was classified into three distinct phases, referred to as signal responding, transcriptional activation of metabolic pathways and accumulation of metabolites. Candidate genes were pooled according to the three phases and applied to co expression network analysis. In total, 17 genes were identified as hub genes. 4CL3 was selected to validate its impact on lignan biosynthesis. RNAi of 4CL3 resulted in a significant reduction in lignan production. Taken together with its catalytic property, a major route of lignan biosynthesis in I. indigotica was highlighted, which was catalysed by 4CL3 via the esterization of caffeic acid. In conclusion, this study provides new insights into lignan biosynthesis as well as potential targets for metabolic engineering in I. indigotica.
Unsupervised Learning using Sequential Verification for Action Recognition
Venue: ArXiv

In this paper, we consider the problem of learning a visual representation from the raw spatiotemporal signals in videos for use in action recognition. Our representation is learned without supervision from semantic labels. We formulate it as an unsupervised sequential verification task, i.e. we determine whether a sequence of frames from a video is in the correct temporal order. With this simple task and no semantic labels, we learn a powerful unsupervised representation using a Convolutional Neural Network (CNN) The representation contains complementary information to that learned from supervised image datasets like ImageNet. Qualitative results show that our method captures information that is temporally varying, such as human pose. When used as pre training for action recognition, our method gives significant gains over learning without external data on benchmark datasets like UCF101 and HMDB51. Our method can also be combined with supervised representations to provide an additional boost in accuracy for action recognition. Finally, to quantify its sensitivity to human pose, we show results for human pose estimation on the FLIC dataset that are competitive with approaches using significantly more supervised training data.
Biasing restricted Boltzmann machines using Gaussian filters to learn invariant visual features
Venue: 2016 IEEE Symposium Series on Computational Intelligence (SSCI)

Advances in unsupervised learning have allowed the efficient learning of feature representations directly from large sets of unlabeled data instead of using traditional handcrafted features. However, improving algorithms to increase the quality of these representations in the absence of labeled data is still an area of active research. This paper evaluates visual features learned through unsupervised learning, specifically comparing regularization and preprocessing methods using Gaussian filters on a single layer network. Using the restricted Boltzmann machine as the unsupervised learning mechanism, features emerging through training on natural videos, with different biasing and preprocessing based on Gaussian filters, are compared by metrics to measure invariance as well as classification performance on standard datasets. When Gaussian filters are convolved with adjacent hidden layer activations from a single example during training, topographies begin to emerge where adjacent features become tuned to slightly varying stimuli. 1D, 2D, and 3D topographies are compared. When a Gaussian low pass filter is applied to activations from a single hidden node across frames drawn from video, features that are more invariant to transformations are produced. Finally, when Gaussian filters are applied to the visible nodes, images become blurrier; learning from these images also leads to invariant features. The networks are trained using the Hollywood2 video dataset, and tested on image classification of the static CIFAR 10 and STL 10 datasets. To prove that the improvements are independent of the dataset, the networks are shown to produce similar results when trained on the CIFAR 10 dataset. The induction of topography or simple image blurring via Gaussian filters during training produce better discriminative features as evidenced by the consistent and notable increase in classification results that they produce. Also, in the visual domain, invariant features are desirable such that objects can be classified accurately despite transformations. It is found that most of the compared methods produce more invariant features, however, classification accuracy does not correlate to invariance.