nils-herrmann/clean_scirepeval huggingface dataset:
Accurate Reconstruction of Image Stimuli From Human Functional Magnetic Resonance Imaging Based on the Decoding Model With Capsule Network Architecture

In neuroscience, all kinds of computation models were designed to answer the open question of how sensory stimuli are encoded by neurons and conversely, how sensory stimuli can be decoded from neuronal activities. Especially, functional Magnetic Resonance Imaging (fMRI) studies have made many great achievements with the rapid development of deep network computation. However, comparing with the goal of decoding orientation, position and object category from human fMRI in visual cortex, accurate reconstruction of image stimuli is a still challenging work. Current prevailing methods were composed of two independent steps, (1) decoding intermediate features from human fMRI and (2) reconstruction using the decoded intermediate features. The new concept of 'capsule' and 'capsule' based neural network were proposed recently. The 'capsule' represented a kind of structure containing a group of neurons to perform better feature representation. Especially, the high-level capsule's features in the capsule network (CapsNet) contains various features of image stimuli such as semantic class, orientation, location, scale and so on, and these features can better represent the processed information inherited in the fMRI data collected in visual cortex. In this paper, a novel CapsNet architecture based visual reconstruction (CNAVR) computation model is developed to reconstruct image stimuli from human fMRI. The CNAVR is composed of linear encoding computation from capsule's features to fMRI data and inverse reconstruction computation. In the first part, we trained the CapsNet model to obtain the non-linear mappings from images to high-level capsule's features, and from high-level capsule's features to images again in an end-to-end manner. In the second part, we trained the non-linear mapping from fMRI data of selected voxels to high-level capsule's features. For a new image stimulus, we can use the method to predict the corresponding high-level capsule's features using fMRI data, and reconstruct image stimuli with the trained reconstruction part in the CapsNet. We evaluated the proposed CNAVR method on the open dataset of handwritten digital images, and exceeded about 10% than the accuracy of all existing state-of-the-art methods on the structural similarity index (SSIM). In addition, we explained the selected voxels in specific interpretable image features to prove the effectivity and generalization of the CNAVR method.
DocuViz: Visualizing Collaborative Writing

Collaborative writing is on the increase. In order to write well together, authors often need to be aware of who has done what recently. We offer a new tool, DocuViz, that displays the entire revision history of Google Docs, showing more than the one-step-at-a-time view now shown in revision history and tracking changes in Word. We introduce the tool and present cases in which the tool has the potential to be useful: To authors themselves to see recent "seismic activity," indicating where in particular a co-author might want to pay attention, to instructors to see who has contributed what and which changes were made to comments from them, and to researchers interested in the new patterns of collaboration made possible by simultaneous editing capabilities.
Biosorption of Heavy Metal Using Bacteria Strain and Its Optimization

Soil and water pollution is becoming one of major burden in modern Indian society due to industrialization. Though there are many methods to remove the heavy metal from soil and water pollution but biosorption is one of the best scientific methods to remove heavy metal from water sample by using biomolecules and bacteria. Biosorbent have the ability to bind the heavy metal and therefore can remove from polluted water. Currently, we have taken the water sample from Ballendur Lake, Bangalore. Which is highly polluted due to industries besides this lake. This sample of water was serially diluted to 10-7. 10-4 and 10-5 diluted sample was allowed to stand in Tryptone Glucose Extract agar media mixed with the different concentrations of lead acetate for 24 hours. Microflora growth was observed. Then we cultured in different temperature, pH and different age of culture media. Finally, we did the biochemical test to identify the bacteria isolate and we found till genus level, it could be either Streptococcus sp. or Enterococcus sp.
Testing adaptive toolbox models: a Bayesian hierarchical approach.

Many theories of human cognition postulate that people are equipped with a repertoire of strategies to solve the tasks they face. This theoretical framework of a cognitive toolbox provides a plausible account of intra- and interindividual differences in human behavior. Unfortunately, it is often unclear how to rigorously test the toolbox framework. How can a toolbox model be quantitatively specified? How can the number of toolbox strategies be limited to prevent uncontrolled strategy sprawl? How can a toolbox model be formally tested against alternative theories? The authors show how these challenges can be met by using Bayesian inference techniques. By means of parameter recovery simulations and the analysis of empirical data across a variety of domains (i.e., judgment and decision making, children's cognitive development, function learning, and perceptual categorization), the authors illustrate how Bayesian inference techniques allow toolbox models to be quantitatively specified, strategy sprawl to be contained, and toolbox models to be rigorously tested against competing theories. The authors demonstrate that their approach applies at the individual level but can also be generalized to the group level with hierarchical Bayesian procedures. The suggested Bayesian inference techniques represent a theoretical and methodological advancement for toolbox theories of cognition and behavior.
Possibility of increasing availability of FSO/RF hybrid system with implementation of helix antenna for 5.2GHz

Nowadays, when are requirements on high speed data connections on very high level it is necessary to work on ensuring that these demands are fulfilled. Many communication providers of services provided in recent systems direct communication through optical fibers. This communication type is very effective and offers many advantages. High speed of transmission in range of tens Gbps, wide transmission band, secure connection are only some of them. Standard optical fibers have one big drawback which is shown in high implementation price. Physical optical fibers have to be necessarily placed in the ground. Each of these drawbacks could be minimized using FSO (Free Space Optics) system. FSO system offers similar transmission speed as standard optical fibers with low cost price at beginning of implementation. Drawback of FSO system is sensitivity to weather changes. This drawback is minimized by using hybrid FSO/RF system which contains RF (Radio Frequency) backup link. Before we analyzed possibility of impact made by weather changes on quality of transmitted optical beams and different method of switching. Now we are interested in design and analysis of appropriate type of RF antenna for our system. The point of this paper is possibility of increasing stability of FSO/RF system by considering helix antenna as RF antenna for backup link.
The focused plenoptic camera

Plenoptic cameras, constructed with internal microlens arrays, focus those microlenses at infinity in order to sample the 4D radiance directly at the microlenses. The consequent assumption is that each microlens image is completely defocused with respect to to the image created by the main camera lens and the outside object. As a result, only a single pixel in the final image can be rendered from it, resulting in disappointingly low resolution. In this paper, we present a new approach to lightfield capture and image rendering that interprets the microlens array as an imaging system focused on the focal plane of the main camera lens. This approach captures a lightfield with significantly higher spatial resolution than the traditional approach, allowing us to render high resolution images that meet the expectations of modern photographers. Although the new approach samples the lightfield with reduced angular density, analysis and experimental results demonstrate that there is sufficient parallax to completely support lightfield manipulation algorithms such as refocusing and novel views
AC^0 o MOD_2 Lower Bounds for the Boolean Inner Product

AC ◦ MOD2 circuits are AC circuits augmented with a layer of parity gates just above the input layer. We study AC ◦MOD2 circuit lower bounds for computing the Boolean Inner Product functions. Recent works by Servedio and Viola (ECCC TR12-144) and Akavia et al. (ITCS 2014) have highlighted this problem as a frontier problem in circuit complexity that arose both as a first step towards solving natural special cases of the matrix rigidity problem and as a candidate for constructing pseudorandom generators of minimal complexity. We give the first superlinear lower bound for the Boolean Inner Product function against AC ◦MOD2 of depth four or greater. Specifically, we prove a superlinear lower bound for circuits of arbitrary constant depth, and an Ω̃(n) lower bound for the special case of depth-4 AC ◦MOD2. Our proof of the depth-4 lower bound employs a new “moment-matching” inequality for bounded, nonnegative integer-valued random variables that may be of independent interest: we prove an optimal bound on the maximum difference between two discrete distributions’ values at 0, given that their first d moments match. ∗Simons Institute for the Theory of Computing, University of California, Berkeley, CA. Email: cheraghchi@berkeley.edu. Supported by a Qualcomm fellowship. †Department of Computer Science, Purdue University, West Lafayette, IN. . ‡Department of Computer Science and Engineering, Washington University, St. Louis, MO.  bjuba@wustl.edu. Supported by an AFOSR Young Investigator award. §Department of Mathematics, Duquesne University, Pittsburgh, PA.  Supported by NSF award CCF-1117079. ¶SCIS, Florida International University, Miami, FL.  nxie@cs.fiu.edu. Research supported in part by NSF grant 1423034.
Extraction of Web News from Web Pages Using a Ternary Tree Approach

The spread of information available in the World Wide Web, it appears that the pursuit of quality data is effortless and simple but it has been a significant matter of concern. Various extractors, wrappers systems with advanced techniques have been studied that retrieves the desired data from a collection of web pages. In this paper we propose a method for extracting the news content from multiple news web sites considering the occurrence of similar pattern in their representation such as date, place and the content of the news that overcomes the cost and space constraint observed in previous studies which work on single web document at a time. The method is an unsupervised web extraction technique which builds a pattern representing the structure of the pages using the extraction rules learned from the web pages by creating a ternary tree which expands when a series of common tags are found in the web pages. The pattern can then be used to extract news from other new web pages. The analysis and the results on real time web sites validate the effectiveness of our approach.
AnalyzeThis: unobtrusive mental health monitoring by voice

Mental illness is one of the most undertreated health problems worldwide. Previous work has shown that there are remarkably strong cues to mental illness in short samples of the voice. These cues are evident in severe forms of illness, but it would be most valuable to make earlier diagnoses from a richer feature set. Furthermore there is an abstraction gap between these voice cues and the diagnostic cues used by practitioners. We believe that by closing this gap, we can build more effective early diagnostic systems for mental illness. In order to develop improved monitoring, we need to translate the high-level cues used by practitioners into features that can be analyzed using signal processing and machine learning techniques. In this paper we describe the elicitation process that we used to tap the practitioners' knowledge. We borrow from both AI (expert systems) and HCI (contextual inquiry) fields in order to perform this knowledge transfer. The paper highlights an unusual and promising role for HCI - the analysis of interaction data for health diagnosis.
Silicones in the Plastics Industry

Silicones are used in the plastics industry as additives for improving the processing and surface properties of plastics, as well as the rubber phase in a novel family of thermoplastic vulcanizate (TPV) materials. As additives, silicones, and in particular polydimethylsiloxane (PDMS), are used to improve mold filling, surface appearance, mold release, surface lubricity and wear resistance. As the rubber portion of a TPV, the cross-linked silicone rubber imparts novel properties, such as lower hardness, reduced coefficient of friction and improved low and high temperature properties.
Wind Power Prediction with Machine Learning Ensembles

For a sustainable integration of wind power into the electricity grid, precise and robust predictions are required. With increasing installed capacity and changing energy markets, there is a growing demand for short-term predictions. Machine learning methods can be used as a purely data-driven, spatio-temporal prediction model that yields better results than traditional physical models based on weather simulations. However, there are two big challenges when applying machine learning techniques to the domain of wind power predictions. First, when applying state-of-the-art algorithms to big training data sets, the required computation times may increase to an unacceptable level. Second, the prediction performance and reliability have to be improved to cope with the requirements of the energy markets. This thesis proposes a robust and practical prediction framework based on heterogeneous machine learning ensembles. Ensemble models combine the predictions of numerous and preferably diverse models to reduce the prediction error. First, homogeneous ensemble regressors that employ a single base algorithm are analyzed. Further, the construction of heterogeneous ensembles is proposed. These models employ multiple base algorithms and benefit from a gain of diversity among the combined predictors. A comprehensive experimental evaluation shows that the combination of different techniques to an ensemble outperforms state-ofthe-art prediction models while requiring a shorter runtime. Finally, a framework for model selection based on evolutionary multi-objective optimization is presented. The method offers an efficient and comfortable balancing of a preferably low prediction error and a moderate computational cost.
New Contributions in Information Systems and Technologies

The present paper uses as research basis a new way of thinking regarding the relation between innovation and knowledge the Knowledge Flow Percolation Model (KFPM). In this model’s center, human beings are seen as thinking electrons, both consuming and generating knowledge flows. Through the interdependent actions of individuals, knowledge circulates inside organizations, allowing them to innovate in order to obtain competitive advantages. But there is a wide range of barriers which impede the creation and movement of flows in the model grid and consequently, hinder their change into innovation. The solution proposed by this paper as one of the most adequate instruments to make KFPM more spreadable is the project. On this basis, in an empirical study, we try to demonstrate the hypothesis of the positive influence of projects, as knowledge swirls, on the development of innovative skills which will help solving problems in the organization, creating and widening of knowledge and reducing the barriers in knowledge transfer.
Self-reconfigurable modular robot (M-TRAN) and its motion design

We have developed a modular robotic system (M-TRAN) which can change its configuration by itself. By using many DOFs of mechanism and self-reconfiguration capability, it can realize several types of motion, and can make various configurations. We have made two models of the system (M-TRAN I & II). In M-TRAN II, various improvements are integrated such as onboard multi-computers, reliable inter-module communicat ion system, low power consumption, precise motor control, etc. Its hardware design, basic experiments and examples of motion are presented in this paper.
Multiple criteria decision making based on Bonferroni means with hesitant fuzzy linguistic information

In recent decades, different extensional forms of fuzzy sets have been developed. However, these multitudinous fuzzy sets are unable to deal with quantitative information better. Motivated by fuzzy linguistic approach and hesitant fuzzy sets, the hesitant fuzzy linguistic term set was introduced and it is a more reasonable set to deal with quantitative information. During the process of multiple criteria decision making, it is necessary to propose some aggregation operators to handle hesitant fuzzy linguistic information. In this paper, two aggregation operators for hesitant fuzzy linguistic term sets are introduced, which are the hesitant fuzzy linguistic Bonferroni mean operator and the weighted hesitant fuzzy linguistic Bonferroni mean operator. Correspondingly, several properties of these two aggregation operators are discussed. Finally, a practical case is shown in order to express the application of these two aggregation operators. This case mainly discusses how to choose the best hospital about conducting the whole society resourcemanagement research included in awisdommedical health system. Communicated by V. Loia. B Zeshui Xu xuzeshui@263.net Xunjie Gou gouxunjie@qq.com Huchang Liao liaohuchang@163.com 1 Business School, Sichuan University, Chengdu 610064, China 2 School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China
Is oocyte meiotic spindle morphology associated with embryo ploidy? A prospective cohort study.

To explore whether an association exists between oocyte meiotic spindle morphology visualized by polarized light microscopy at the time of intracytoplasmic sperm injection and the ploidy of the resulting embryo.



Prospective cohort study.



Private IVF clinic.



Patients undergoing preimplantation genetic screening/diagnosis (n = 113 patients).



Oocyte meiotic spindles were assessed by polarized light microscopy and classified at the time of intracytoplasmic sperm injection as normal, dysmorphic, translucent, telophase, or no visible spindle. Single blastomere biopsy was performed on day 3 of culture for analysis by array comparative genomic hybridization.



Spindle morphology and embryo ploidy association was evaluated by regression methods accounting for non-independence of data.


RESULT(S)
The frequency of euploidy in embryos derived from oocytes with normal spindle morphology was significantly higher than all other spindle classifications combined (odds ratio [OR] 1.93, 95% confidence interval [CI] 1.33-2.79). Oocytes with translucent (OR 0.25, 95% CI 0.13-0.46) and no visible spindle morphology (OR 0.35, 95% CI 0.19-0.63) were significantly less likely to result in euploid embryos when compared with oocytes with normal spindle morphology. There was no significant difference between normal and dysmorphic spindle morphology (OR 0.73, 95% CI 0.49-1.08), whereas no telophase spindles resulted in euploid embryos (n = 11). Assessment of spindle morphology was found to be independently associated with embryo euploidy after controlling for embryo quality (OR 1.73, 95% CI 1.16-2.60).


CONCLUSION(S)
Oocyte spindle morphology is associated with the resulting embryo's ploidy. Oocytes with normal spindle morphology are significantly more likely to produce euploid embryos compared with oocytes with meiotic spindles that are translucent or not visible.
Trust and Electronic Government Success: An Empirical Study

Electronic government is being increasingly recognized as a means for transforming public governance. Despite this increasing interest, information systems (IS) literature is mostly silent on what really contributes to the success of e-government 100 TEO, SRIVASTAVA, AND JIANG Web sites. To fill this gap, this study examines the role of trust in e-government success using the updated DeLone and McLean IS success model as the theoretical framework. The model is tested via a survey of 214 Singapore e-government Web site users. The results show that trust in government, but not trust in technology, is positively related to trust in e-government Web sites. Further, trust in e-government Web sites is positively related to information quality, system quality, and service quality. The quality constructs have different effects on “intention to continue” using the Web site and “satisfaction” with the Web site. Post hoc analysis indicates that the nature of usage (active versus passive users) may help us better understand the interrelationships among success variables examined in this study. This result suggests that the DeLone and McLean model can be further extended by examining the nature of IS use. In addition, it is important to consider the role of trust as well as various Web site quality attributes in understanding e-government success.
Electronic Speaking Glove for speechless patients, a tongue to a dumb

We describe an ELECTRONIC SPEAKING GLOVE, designed to facilitate an easy communication through synthesized speech for the benefit of speechless patients. Generally, a speechless person communicates through sign language which is not understood by the majority of people. This final year project is designed to solve this problem. Gestures of fingers of a user of this glove will be converted into synthesized speech to convey an audible message to others, for example in a critical communication with doctors. The glove is internally equipped with multiple flex sensors that are made up of “bend-sensitive resistance elements”. For each specific gesture, internal flex sensors produce a proportional change in resistance of various elements. The processing of this information sends a unique set of signals to the AVR (Advance Virtual RISC) microcontroller which is preprogrammed to speak desired sentences.
Mechanical property of a articulated in-pipe locomotion robot

This paper focuses on mechanical property of a articulated multi-unit wheel type in-pipe locomotion robot system. Through establishing the posture model of the robot system, can get the coordinates of wheel center and its corresponding contact point with pipe wall of each wheel for robot unit. Based on the posture model, the mechanical model of the robot unit is presented and the analysis is carried out in details. To confirm the effectiveness of the proposed theoretical analysis, an example about statics of the pipe robot is calculated, and the calculation results basically reflect the actual characteristics of the pipe robot. This provide theoretical basis for the selection of driving mechanism design and control mode of wheel type pipe robot.