nils-herrmann/clean_scirepeval huggingface dataset:
On Device Few Shot Personalization for Real Time Gaze Estimation
Venue: 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)
Year: 2019

Building fast and accurate gaze estimation models without additional specialized hardware is a hard problem. In this paper, we present on device few shot personalization methods for 2D gaze estimation. The proposed supervised method achieves better accuracy using as few as 2 5 calibration points per user compared to prior methods that require more than 13 calibration points. In addition, we propose an unsupervised personalization method which uses only unlabeled facial images to improve gaze estimation accuracy. Our best personalized model achieves 24 26% better accuracy (measured by mean error) on phones compared to the state of the art using =5 calibration points per user. It is also computationally efficient, requiring 20x fewer FLOPS when compared to prior methods. This unlocks a variety of important real world applications such as using gaze for accessibility, gaming and human computer interaction while running entirely on device in real time.
Dynamic Scaling Factors of Covariances for Accurate 3D Normal Distributions Transform Registration
Venue: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Year: 2018

Distribution to distribution normal distributions transform (NDT D2D) is one of the fast point set registrations. Since the normal distributions transform (NDT) is a set of normal distributions generated by discrete and regular cells, local minima of the objective function is an issue of NDT D2D. Also, we found that the objective function based on L2 distance between distributions has a negative correlation with rotational alignment. To overcome the problems, we present a method using dynamic scaling factors of covariances to improve the accuracy of NDT D2D. Two scaling factors are defined for the preceding and current NDTs respectively, and they are dynamically varied in each iteration of NDT D2D. We implemented the proposed method based on conventional NDT D2D and probabilistic NDT D2D and compared to the NDT D2D with fixed scaling factors using KITTI benchmark data set. Also, we experimented estimating odometry with an initial guess as an application of distribution to distribution probabilistic NDT (PNDT D2D) with the proposed method. As a result, the proposed method improves both translational and rotational accuracy of the NDT D2D and PNDT D2D.
Cas GANs: An Approach of Dialogue Policy Learning based on GAN and RL Techniques
Year: 2019

Dialogue management systems are commonly applied in daily life, such as online shopping, hotel booking, and driving booking. Efficient dialogue management policy helps systems to respond to the user in an effective way. Policy learning is a complex task to build a dialogue system. There are different approaches have been proposed in the last decade to build a goal oriented dialogue agent to train the systems with an efficient policy. The Generative adversarial network (GAN) is used in the dialogue generation, in previous works to build dialogue agents by selecting the optimal policy learning. The efficient dialogue policy learning aims to improve the quality of fluency and diversity for generated dialogues. Reinforcement learning (RL) algorithms are used to optimize the policies because the sequence is discrete. In this study, we have proposed a new technique called Cascade Generative Adversarial Network (Cas GAN) that is combination of the GAN and RL for dialog generation. The Cas GAN can model the relations between the dialogues (sentences) by using Graph Convolutional Networks (GCN) The graph nodes are consisting of different high level and low level nodes representing the vertices and edges of the graph. Then, we use the maximum log likelihood (MLL) approach to train the parameters and choose the best nodes. The experimental results compared with the HRL, RL agents and we got state of the art results.
Analysis of coffee adulterated with roasted corn and roasted soybean using voltammetric electronic tongue.
Venue: Acta scientiarum polonorum. Technologia alimentaria
Year: 2019

Coffee samples adulterated with roasted corn and roasted soybean were analyzed using a voltammetric electronic tongue equipped with a polypyrrole sensor array.  Coffee samples were adulterated in concentrations of 2% 5% 10% and 20% of roasted corn and roasted soybean; 5 replicates of each were used. The discrimination capacity of a voltammetric electronic tongue elaborated with a polypyrrole sensor array, was evaluated by principal component analysis and cluster analysis, while the capacity to perform quantitative determinations was carried out by partial least squares.  The results obtained by the application of principal component analysis showed an excellent ability to discriminate adulterated samples. Additionally, the classifications obtained by cluster analysis was concordant with those obtained by principal component analysis. On the other hand, the evaluation of the ability to quantitatively analyze the adulterated samples showed that the polypyrrole sensor array provides sufficient information to allow quantitative determinations by partial least squares regression.  It could be concluded that the voltammetric electronic tongue used in this work allows the suf ficient analysis of coffee samples adulterated with roasted corn and roasted soybean.
A comprehensive review of lithium salts and beyond for rechargeable batteries: Progress and perspectives
Year: 2018

The increasing need for energy storage has been the motivation for intensive research in batteries with different chemistries in the recent past. Among the elements of the batteries, the salts and their solvent play an important role. In particular, the cathodic stability at low potential depends importantly on the choice of the cation, while the stability at high potentials is mainly due to oxidation of anions and the ion mobility and dissociation depend primarily on the delocalization of the anion, so that many attempts are made to find the optimum choice of both the cations and anions of the salts, and their solvents. Although lithium based batteries are almost exclusively used today, efforts are currently made to explore batteries based on sodium, aluminum, magnesium, calcium, potassium. The purpose of the present work is to review the salts and solvents that have been proposed in these different batteries and discuss their properties and their ability to be used in the near future and in the next generation of batteries.
Moral panics on lgbt issues: evidence from indonesian tv programme
Venue: Jurnal Studi Komunikasi (Indonesian Journal of Communications Studies)
Year: 2019

There was a discussion activity conducted by a small club of the University of Indonesia called SGRC (Support Group and Resource Center on Sexuality Studies) at the beginning of January 2016. It did not only discuss sexuality such as sexual preferences but also conduct peer support for LGBT groups. However, it was reported illegal for many reasons. Although later the "illegal" stamp was dubbed as an "internal problem" of Universitas Indonesia, the effect was unbelievable. The activity of SGRC was reported by media as "LGBT's attack on campus" "LGBT is dangerous for campus" et cetera. Moral, Eastern hemisphere norms, and religious excuses were the main excuses by media to judge and "punish" the LGBT groups. After the SGRC incident, particularly in 2016, the media, both mainstream and online, massively promoted homophobia through hate speech. The media reported the LGBT phenomenon as not only dangerous but also as entities that must be destroyed. Although not as splashy as online news, some television stations reported the case in their ways. TvOne, compared to other Indonesian TV stations, was the most often to discuss LGBT issues from 2016 2018 through a debate programme titled Indonesia Lawyers Club (ILC) The most controversial episode was the one aired on February 16 whose topic was "LGBT Issues is Rising, How Should We React?" in which a participant, a psychiatrist, was accused of delivering false information. His statement was even responded by a U.S. based international psychiatrist association which later sent him a warning letter. This paper will explore media framing on tvOne's debate programme using Robert Entman's Framing  The result shows that through inviting certain debate participants who voiced certain statements, the TV programme promoted not only anti LGBT actions but also homophobia.
Simulation based Learning of Optimal Multimodal Presentation Strategies from Wizard of Oz data
Year: 2008

We address two problems in the field of automatic optimization of dialogue strategies: learning effective dialogue strategies when no initial data or system exists, and optimising dialogue management (DM) and Natural Language Generation (NLG) decisions in an integrated fashion. We use Reinforcement Learning (RL) to learn multimodal information presentation strategies through interaction with a simulated environment which is "bootstrapped" from small amounts of Wizard of Oz (WOZ) data. This use of WOZ data allows development of optimal strategies for domains where no working prototype is available. For information seeking dialogues, Dialogue Management and NLG are two closely interrelated problems: the decision of when to present information depends on the available options for how to present them, and vice versa. We therefore formulate the problem as a hierarchy of joint learning decisions which are optimised together. To evaluate, we compare the RL based strategy against a supervised learning (SL) strategy which mimics the (human) wizards' policies from the original data. This comparison allows us to measure relative improvement over the training data. Our results show that RL significantly outperforms SL: the RL based policy gains on average 50 times more reward when tested in simulation. In related work we evaluate the strategies with real users [16]
PENERAPAN TEKNIK RELAKSASI OTOT PROGRESIF DENGAN MASALAH KEPERAWATAN NYERI KEPALA BAGIAN BELAKANG PADA LANSIA HIPERTENSI DI UPT GRIYA WERDHA SURABAYA
Year: 2018

Masalah pada Ny. M adalah Ny. M memiliki riwayat menderita penyakit tekanan darah tinggi (hipertensi) sering mengeluh nyeri kepala dan tidak pernah melakukan pengobatan nonfarmakologi untuk menurunkan nyeri. Tujuan penelitian adalah untuk menerapkan teknik relaksasi otot progresif dengan masalah keperawatan nyeri (kepala bagian belakang) pada lansia hipertensi di UPT Griya Werdha Surabaya. Desain penelitian menggunakan studi kasus, dilakukan pada satu pasien yaitu Ny. M dengan diagnosa medis hipertensi dengan masalah keperawatan nyeri akut. Metode pengumpulan data dengan menggunakan format pengkajian gerontik dengan melakukan wawancara, observasi, pemeriksaan fisik, dan penerapan teknik relaksasi otot progresif.Penerapan terapi teknik relaksasi otot progresif dilakukan selama 15 20 menit setiap hari selama 4 hari sampai hari ke empat. Setelah diberikan terapi teknik relaksasi otot progresif terjadi penurunan skala nyeri, sebelum dilakukan relaksasi otot progresif skala nyeri 5 dan sesudah diberikan relaksasi otot progresif skala nyeri menjadi 0. Simpulan penelitian relaksasi otot progresif yang dilakukan pada penderita hipertensi memberikan dampak penurunan nyeri dari skala 5 menjadi skala 0. Disarankan pada klien untuk dapat tetap melakukan terapi relaksasi otot progresif setiap hari agar skala nyeri tidak bertambah.
Design and Planned Obsolescence. Theories and Approaches for Designing Enabling Technologies.
Year: 2017

We are currently living in a decade where smart objects and Internet of Things (IoT) based devices are becoming part of daily life in different contexts. This research seeks to investigate and verify, by using a formal literature review methodology, the most visible aspects of technological development, within the Industry 4.0 and IoT scenario, in relation to the theories of the so called "Planned Obsolescence" This study covers a defined number of works on Design theories and practices on how to face the issue of built in obsolescence of devices in the era of the Connected Devices. The majority of the works studied are useful to create substrates of essential knowledge that strengthen the concept of premature ageing of technological devices and it has been noticed that the discipline of Design can be used as an incentive to extend life, improve the usability of existing products and empower user abilities.
Synthesis, characterisation, and X ray structures of Zn(II) complexes containing bis camphoryldiimine ligands: Application to polymerisation of rac lactide
Year: 2017

complexes, (N1E,N2E) N1,N2 bis(1,7,7 trimethylbicyclo [2.2.1]heptan 2 ylidene)butane 1,2 diamine (BCE) ZnCl2 and (N1E,N3E) 2,2 dimethyl N1,N3 bis(1,7,7 trimethylbicyclo [2.2.1]heptan 2 ylidene)pentane 1,3 diamine (BCP) ZnCl2, were synthesised. X ray diffraction analysis revealed that the geometry around the Zn(II) centre is distorted tetrahedral. The alkoxy derivatives, generated in situ, were highly efficient catalysts for ring opening polymerisation (ROP) of rac lactide (rac LA) yielding polylactide (PLA) with good number average molecular weights and narrow polydispersity indices. (BCP)Zn(OCHMe2)2 exhibited activity for stereoselective ROP of rac LA, and complete conversion occurred within 8 min to attain heterotactic PLA (Pr 0.78)
A Rules Based Algorithm for Self Organizing Virtual Sensors
Year: 2013

A Rules Based Algorithm for Self Organizing Virtual Sensors Malena Mesarina, Devaraj Das, John Recker HP Laboratories HPL 2006 101R1 sensor networks; self organizing; virtual sensors Current methods of programming sensor nodes require individual programming of sensors, impeding their usage in practical industry applications. In this paper, we present a methodology that allows sensors to be programmed based on functionality, providing a higher level of abstraction to applications. Our method enables sensors to self organize into collaborative clusters via organization rules and behavior scripts. The rules describe required sensor attributes and the behavior scripts are the programs for each sensor type. The rules and scripts are disseminated to the network and a distributed algorithm determines the clusters, as well as when to spawn and kill behavior scripts. In addition, the system is adaptive to changes in sensors and location, as well as scalable. External Posting Date: March 21, 2013 [Fulltext] Approved for External Publication Internal Posting Date: July 21, 2006
The psychometric properties of eTail quality: An international investigation across product categories
Year: 2006

The links between quality and customer satisfaction, retention and loyalty in offline retail settings are well established. It therefore seems fair to posit that quality will also be a determinant of online retailer success. This assumption motivated Wolfinbarger and Gilly to develop a scale for the measurement of "eTail quality" The paper's purpose is to validate that scale. The study investigates the equivalence of the eTailQ scale across different product categories in three countries. Tests for both conceptual and psychometric equivalence are conducted. Findings suggest that eTailQ exhibits conceptual equivalence. It also exhibits psychometric equivalence by providing acceptable levels of reliability, variance extracted and both discriminant and nomological validity. eTailQ can be used with confidence by online goods and service retailers in English speaking countries outside the USA.
Multi Grained Attention Representation With ALBERT for Aspect Level Sentiment Classification
Venue: IEEE Access
Year: 2021

Aspect level sentiment classification aims to solve the problem, which is to judge the sentiment tendency of each aspect in a sentence with multiple aspects. Previous works mainly employed Long Short Term Memory (LSTM) and Attention mechanisms to fuse information between aspects and sentences, or to improve large language models such as BERT to adapt aspect level sentiment classification tasks. The former methods either did not integrate the interactive information of related aspects and sentences, or ignored the feature extraction of sentences. This paper proposes a novel multi grained attention representation with ALBERT (MGAR ALBERT) It can learn the representation that contains the relevant information of the sentence and the aspect, while integrating it into the process of sentence modeling with multi granularity, and finally get a comprehensive sentence representation. In Masked LM (MLM) task, in order to avoid the influence of aspect words being masked in the initial stage of the pre training, the noise linear cosine decay is introduced into $n gram$ We implemented a series of comparative experiments to verify the effectiveness of the method. The experimental results show that our model can achieve excellent results on Restaurant dataset with numerous number of parameters reduced, and it is not inferior to other models on Laptop dataset.
An exploratory teaching program in big data analysis for undergraduate students
Venue: J. Ambient Intell. Humaniz. Comput.
Year: 2020

Many of the world's biggest discoveries and decisions in science, technology, business, medicine, politics, and society as a whole, are now being made on the basis of analyzing massive datasets. In this paper, exploratory teaching program is proposed. It provides a broad and practical introduction to big data analysis. This exploratory teaching program was designed and given in Department of Computer Engineering at Kocaeli University in the spring semester of 2018 2019. To assess the educational program's impact on the learning process and to evaluate the acceptance and satisfaction level of students, they answered a questionnaire after finishing the program. According to students' feedback, the exploratory teaching program is useful for learning how to analyze large datasets and identify patterns that will improve any company's and organization decision making process.
Increased cerebral nuclear factor kappa B in a complex regional pain syndrome rat model: possible relationship between peripheral injury and the brain
Venue: Journal of pain research
Year: 2019

Complex regional pain syndrome (CRPS) is a rare but refractory pain disorder. Recent advanced information retrieval studies using text mining and network analysis have suggested nuclear factor kappa B (NFkB) as a possible central mediator of CRPS. The brain is also known to play important roles in CRPS. The aim of this study was to evaluate changes in cerebral NFkB in rats with CRPS.  The chronic post ischemia perfusion (CPIP) model was used as the CRPS animal model. O rings were applied to the left hind paws of the rats. The rats were categorized into three groups according to the results of behavioral tests: the CPIP positive (A) group, the CPIP negative (B) group, and the control (C) group. Three weeks after the CPIP procedure, the right cerebrums of the animals were harvested to measure NFkB levels using an ELISA.  Animals in group A had significantly decreased mechanical pain thresholds (P<0.01) and significantly increased cerebral NFkB when compared to those in groups B and C (P=0.024)  This finding indicates that peripheral injury increases cerebral NFkB levels and implies that minor peripheral injury can lead to the activation of pain related cerebral processes in CRPS.
Breast Cancer Image Classification via Multi level Dual network Features and Sparse Multi Relation Regularized Learning
Venue: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Year: 2019

Breast cancer is one of the leading causes of cancer death worldwide. Recently, the computer aided diagnosis and detection technique has been developed for the early diagnosis of breast cancer, but the diagnostic efficiency has still been a challenging issue. For this reason, we aim to improve the breast cancer diagnostic accuracy and reduce the workload of doctors in this paper by devising a deep learning framework based on histological image. Therefore, we develop a model of multi level feature of dual network combined with sparse multi relation regularized learning method, which enhances the classification performance and robustness. Specifically, first, we preprocess the histological images using scale transformation and color enhancement methods. Second, the multi level features are extracted from preprocessed images using InceptionV3 ML and ResNet 50 networks. Third, the feature selection method via sparse multi relation regularization is further developed for performance boosting and overfitting reduction. We evaluate the proposed method based on the public ICIAR 2018 Challenge dataset of breast cancer histology images. Experimental results show that our method has achieved promising performance and outperformed the related works.
Servitization in manufacturing firms: the Moncler case
Year: 2016

This Thesis aims to provide a deep knowledge of servitization, through the literature review of the most recent articles, supported by the data collected from the analysis of the case study of Moncler. Focus of this paper is to give an understanding of how and why manufacturing firms resort to servitization and if it represents a key point or an extra for the strategic decisions. Other topics treated, in relation with servitization, are business model, competitive advantage and innovation
Evaluation of Security and Performance of Master Node Protocol in the Bitcoin Peer to Peer Network
Venue: 2020 IEEE International Conference on Blockchain and Cryptocurrency (ICBC)
Year: 2020

The mechanism of peers randomly choosing logical neighbors without any knowledge about underlying physical topology can cause a delay overhead in information propagation which makes the system vulnerable to double spend attacks. This paper introduces a proximity aware extensions to the current Bitcoin protocol, named Master Node Based Clustering (MNBC) The ultimate purpose of the proposed protocol is to improve the information propagation delay in the Bitcoin network.
Regional debris flow susceptibility analysis based on principal component analysis and self organizing map: a case study in Southwest China
Venue: Arabian Journal of Geosciences
Year: 2016

A method was developed to analyze the susceptibilities of 541 regional basins affected by debris flows at the Wudongde Dam site in southwest China. Determining susceptibility requires information on source material quantity and occurrence frequency. However, the large number of debris flows can hinder the individual field investigation in a each small basin. Factors that may trigger debris flows can be identified using remotely sensed interpretation information. Susceptibility analysis can then be conducted based on these factors. In this study, SPOT5 satellite imagery, digital elevation models (DEM) a lithology distribution map, and rainfall monitoring data were used to identify 12 debris flow trigger factors: basin relief ratio, slope gradient in the initiation zone, drainage density, downslope curvature of the main channel, vegetation coverage, main channel aspect, topographic wetness index, Melton's ruggedness number, lithology, annual rainfall, form factor, and cross slope curvature of the transportation zone. Principal component analysis was used to obtain the eight principal components of these factors that contribute to susceptibility results. Then, a self organizing map method was adopted to analyze the principal components, which resulted in a debris flow susceptibility classification. Field validation of 26 debris flow basins was used to evaluate the errors of the susceptibility classification, as well as assess the causes of such errors. The study found that principle component analysis and self organizing map methodologies are good predictors of basin susceptibility to debris flows.