nils-herrmann/clean_scirepeval huggingface dataset:
Embedded Based Deaf Mute Communication System

Today communication between deafmute and a normal person always have been a challenging task. Many researchers are focusing on communication between normal and deaf-mute person. To the best of our knowledge, little work has been done in this area. The main objective of this project is to present a system that can efficiently convert text to voice. In the deaf -mute communication interpreter is a device that translate the TEXT to auditory speech. For each text a signal is produced by the computer and send corresponding controller unit and it generate vibration signal. The deaf people are using this device by their mouth; hence they easily understand data given by the computer. The device can also be made to translate larger text. In addition, the system also includes a text to speech conversion (TTS) block which converts the matched gestures i.e. text to voice output, and also voice to voice output and also call making as output.
Scene recognition and weakly supervised object localization with deformable part-based models

Weakly supervised discovery of common visual structure in highly variable, cluttered images is a key problem in recognition. We address this problem using deformable part-based models (DPM's) with latent SVM training [6]. These models have been introduced for fully supervised training of object detectors, but we demonstrate that they are also capable of more open-ended learning of latent structure for such tasks as scene recognition and weakly supervised object localization. For scene recognition, DPM's can capture recurring visual elements and salient objects; in combination with standard global image features, they obtain state-of-the-art results on the MIT 67-category indoor scene dataset. For weakly supervised object localization, optimization over latent DPM parameters can discover the spatial extent of objects in cluttered training images without ground-truth bounding boxes. The resulting method outperforms a recent state-of-the-art weakly supervised object localization approach on the PASCAL-07 dataset.
Global Variational Method for Fingerprint Segmentation by Three-part Decomposition

Verifying an identity claim by fingerprint recognition is a commonplace experience for millions of people in their daily life, e.g. for unlocking a tablet computer or smartphone. The first processing step after fingerprint image acquisition is segmentation, i.e. dividing a fingerprint image into a foreground region which contains the relevant features for the comparison algorithm, and a background region. We propose a novel segmentation method by global three-part decomposition (G3PD). Based on global variational analysis, the G3PD method decomposes a fingerprint image into cartoon, texture and noise parts. After decomposition, the foreground region is obtained from the non-zero coefficients in the texture image using morphological processing. The segmentation performance of the G3PD method is compared to five state-of-the-art methods on a benchmark which comprises manually marked ground truth segmentation for 10560 images. Performance evaluations show that the G3PD method consistently outperforms existing methods in terms of segmentation accuracy.
Epidemiological modeling of news and rumors on Twitter

Characterizing information diffusion on social platforms like Twitter enables us to understand the properties of underlying media and model communication patterns. As Twitter gains in popularity, it has also become a venue to broadcast rumors and misinformation. We use epidemiological models to characterize information cascades in twitter resulting from both news and rumors. Specifically, we use the SEIZ enhanced epidemic model that explicitly recognizes skeptics to characterize eight events across the world and spanning a range of event types. We demonstrate that our approach is accurate at capturing diffusion in these events. Our approach can be fruitfully combined with other strategies that use content modeling and graph theoretic features to detect (and possibly disrupt) rumors.
The Effect of Edmodo Learning Network on Students ’ Perception , Self-Regulated Learning Behaviors and Learning Performance

As the use of social networking sites and online collaborative tools increases, educators can utilize these technologies to improve student engagement in the virtual classrooms. Edmodo, an internet-based learning management system (LMS), offers a secure virtual classroom that can be moderated by an instructor. In this study Edmodo was used to create supportive learning by facilitating channels for students to communicate with the teacher and participate in class activities. The virtual class space provided students opportunity to share ideas, follow up with class assignments, and take quizzes. The main aim of this study was to investigate the effects of integrating this networking system regarding students’ perception towards the learning platform. Self-regulated learning (SRL) behaviors of students along with their learning performances were also examined. Students were to sign up for Edmodo classroom and work with provided class activities. Learning materials were uploaded for students and preand post-test scores were collected and analyzed. Self-reported survey questionnaires about SRL and students’ perceptions were administered at the end of the course. The statistical analysis revealed that learning activities provided on the LMS significantly improved selfregulated learning behaviors and learning performance of students, with a positive perception towards the online learning
Cement hydration from hours to centuries controlled by di ff usion through barrier shells of CSH

Although a few good models for cement hydration exist, they have some limitations. Some do not take into account the complete range of variation of pore relative humidity and temperature, and apply over durations limited from up a few months to up to about a year. The ones that are applicable for long durations are either computationally too intensive for use in finite element programs or predict the hydration to terminate after few months. However, recent tests of autogenous shrinkage and swelling in water imply that the hydration may continue, at decaying rate, for decades, provided that a not too low relative pore humidity (above 0.7) persists for a long time, as expected for the cores of thick concrete structural members. Therefore, and because design lifetimes of over hundred years are required for large concrete structures, a new hydration model for a hundred year lifespan and beyond is developed. The new model considers that, after the first day of hydration, the remnants of anhydrous cement grains, gradually consumed by hydration, are enveloped by contiguous, gradually thickening, spherical barrier shells of calciumsilicate hydrate (C-S-H). The hydration progress is controlled by transport of water from capillary pores through the barrier shells toward the interface with anhydrous cement. The transport is driven by a difference of humidity, defined by equivalence with the difference in chemical potential of water. Although, during the period of 4–24 h, the C-S-H forms discontinuous nano-globules around the cement grain, an equivalent barrier shell control was formulated for this period, too, for ease and effectiveness of calculation. The entire model is calibrated and validated by published test data on the evolution of hydration degree for various cement types, particle size distributions, water-cement ratios and temperatures. Computationally, this model is sufficiently effective for calculating the evolution of hydration degree (or aging) at every integration point of every finite element in a large structure.
Extracting Parallel Sentences from Comparable Corpora using Document Level Alignment

The quality of a statistical machine translation (SMT) system is heavily dependent upon the amount of parallel sentences used in training. In recent years, there have been several approaches developed for obtaining parallel sentences from non-parallel, or comparable data, such as news articles published within the same time period (Munteanu and Marcu, 2005), or web pages with a similar structure (Resnik and Smith, 2003). One resource not yet thoroughly explored is Wikipedia, an online encyclopedia containing linked articles in many languages. We advance the state of the art in parallel sentence extraction by modeling the document level alignment, motivated by the observation that parallel sentence pairs are often found in close proximity. We also include features which make use of the additional annotation given by Wikipedia, and features using an automatically induced lexicon model. Results for both accuracy in sentence extraction and downstream improvement in an SMT system are presented.
Cyclical Learning Rates for Training Neural Networks

It is known that the learning rate is the most important hyper-parameter to tune for training deep neural networks. This paper describes a new method for setting the learning rate, named cyclical learning rates, which practically eliminates the need to experimentally find the best values and schedule for the global learning rates. Instead of monotonically decreasing the learning rate, this method lets the learning rate cyclically vary between reasonable boundary values. Training with cyclical learning rates instead of fixed values achieves improved classification accuracy without a need to tune and often in fewer iterations. This paper also describes a simple way to estimate "reasonable bounds" – linearly increasing the learning rate of the network for a few epochs. In addition, cyclical learning rates are demonstrated on the CIFAR-10 and CIFAR-100 datasets with ResNets, Stochastic Depth networks, and DenseNets, and the ImageNet dataset with the AlexNet and GoogLeNet architectures. These are practical tools for everyone who trains neural networks.
Gender Classification From the Same Iris Code Used for Recognition

Previous researchers have explored various approaches for predicting the gender of a person based on the features of the iris texture. This paper is the first to predict gender directly from the same binary iris code that could be used for recognition. We found that the information for gender prediction is distributed across the iris, rather than localized in particular concentric bands. We also found that using selected features representing a subset of the iris region achieves better accuracy than using features representing the whole iris region. We used the measures of mutual information to guide the selection of bits from the iris code to use as features in gender prediction. Using this approach, with a person-disjoint training and testing evaluation, we were able to achieve 89% correct gender prediction using the fusion of the best features of iris code from the left and right eyes.
Multilingual Neural Machine Translation for Low Resource Languages

Neural Machine Translation (NMT) has been shown to be more effective in translation tasks compared to the Phrase-Based Statistical Machine Translation (PBMT). However, NMT systems are limited in translating low-resource languages (LRL), due to the fact that neural methods require a large amount of parallel data to learn effective mappings between languages. In this work we show how so-called multilingual NMT can help to tackle the challenges associated with LRL translation. Multilingual NMT forces words and subwords representation in a shared semantic space across multiple languages. This allows the model to utilize a positive parameter transfer between different languages, without changing the standard attentionbased encoder-decoder architecture and training modality. We run preliminary experiments with three languages (English, Italian, Romanian) covering six translation directions and show that for all available directions the multilingual approach, i.e. just one system covering all directions is comparable or even outperforms the single bilingual systems. Finally, our approach achieve competitive results also for language pairs not seen at training time using a pivoting (x-step) translation. Italiano. La traduzione automatica con reti neurali (neural machine translation, NMT) ha dimostrato di essere più efficace in molti compiti di traduzione rispetto a quella basata su frasi (phrase-based machine translation, PBMT). Tuttavia, i sistemi NMT sono limitati nel tradurre lingue con basse risorse (LRL). Questo è dovuto al fatto che i metodi di deep learning richiedono grandi quantit di dati per imparare una mappa efficace tra le due lingue. In questo lavoro mostriamo come un modello NMT multilingua può aiutare ad affrontare i problemi legati alla traduzione di LRL. La NMT multilingua costringe la rappresentrazione delle parole e dei segmenti di parole in uno spazio semantico condiviso tra multiple lingue. Questo consente al modello di usare un trasferimento di parametri positivo tra le lingue coinvolte, senza cambiare l’architettura NMT encoder-decoder basata sull’attention e il modo di addestramento. Abbiamo eseguito esperimenti preliminari con tre lingue (inglese, italiano e rumeno), coprendo sei direzioni di traduzione e mostriamo che per tutte le direzioni disponibili l’approccio multilingua, cioè un solo sistema che copre tutte le direzioni è confrontabile o persino migliore dei singolo sistemi bilingue. Inoltre, il nostro approccio ottiene risultati competitivi anche per coppie di lingue non viste durante il trainig, facendo uso di traduzioni con pivot.
Generating Images with Perceptual Similarity Metrics based on Deep Networks

Discriminator architecture is shown in Figure 1 . In our setup the job of the discriminator is to analyze the local statistics of images. Therefore, after five convolutional layers with occasional stride we perform global average pooling. The result is processed by two fully connected layers, followed by a 2-way softmax. We perform 50% dropout after the global average pooling layer and the first fully connected layer.
Concept Graph Learning from Educational Data

This paper addresses an open challenge in educational data mining, i.e., the problem of using observed prerequisite relations among courses to learn a directed universal concept graph, and using the induced graph to predict unobserved prerequisite relations among a broader range of courses. This is particularly useful to induce prerequisite relations among courses from different providers (universities, MOOCs, etc.). We propose a new framework for inference within and across two graphs---at the course level and at the induced concept level---which we call Concept Graph Learning (CGL). In the training phase, our system projects the course-level links onto the concept space to induce directed concept links; in the testing phase, the concept links are used to predict (unobserved) prerequisite links for test-set courses within the same institution or across institutions. The dual mappings enable our system to perform an interlingua-style transfer learning, e.g. treating the concept graph as the interlingua, and inducing prerequisite links in a transferable manner across different universities. Experiments on our newly collected data sets of courses from MIT, Caltech, Princeton and CMU show promising results, including the viability of CGL for transfer learning.
Non-inverting buck-boost power-factor-correction converter with wide input-voltage-range applications

This paper presents a non-inverting buck-boost based power-factor-correction (PFC) converter operating in the boundary-conduction-mode (BCM) for the wide input-voltage-range applications. Unlike other conventional PFC converters, the proposed non-inverting buck-boost based PFC converter has both step-up and step-down conversion functionalities to provide positive DC output-voltage. In order to reduce the turn-on switching-loss in high frequency applications, the BCM current control is employed to achieve zero current turn-on for the power switches. Besides, the relationships of the power factor versus the voltage conversion ratio between the BCM boost PFC converter and the proposed BCM non-inverting buck-boost PFC converter are also provided. Finally, the 70-watt prototype circuit of the proposed BCM buck-boost based PFC converter is built for the verification of the high frequency and wide input-voltage-range.
Naturalizing a Programming Language via Interactive Learning

Our goal is to create a convenient natural language interface for performing wellspecified but complex actions such as analyzing data, manipulating text, and querying databases. However, existing natural language interfaces for such tasks are quite primitive compared to the power one wields with a programming language. To bridge this gap, we start with a core programming language and allow users to “naturalize” the core language incrementally by defining alternative, more natural syntax and increasingly complex concepts in terms of compositions of simpler ones. In a voxel world, we show that a community of users can simultaneously teach a common system a diverse language and use it to build hundreds of complex voxel structures. Over the course of three days, these users went from using only the core language to using the naturalized language in 85.9% of the last 10K utterances.
Efficient Wireless Electric Power Transmission Using Magnetic Resonance Coupling

This paper presents how one can effectively transfer electric power wirelessly using magnetic resonance coupling, this paper also shows the results obtained from effective wireless electric power transmission, these results are obtained by transmitting magnetic waves at specific resonance frequency between two coils. This paper also provides some ways to improve wireless power transmission efficiency. In the end this paper gives a lot of practical applications where results of this project can efficiently use.
Blockchain solutions for big data challenges: A literature review

The popularity of Blockchain technology and the huge extent of its application, results with much ongoing research in different practical and scientific areas. Although still new and in experimenting phase, the Blockchain is being seen as a revolutionary solution, addressing modern technology concerns like decentralization, trust, identity, data ownership and data-driven decisions. At the same time, the world is facing an expansion in quantity and diversity of digital data that are generated by both users and machines. While actively searching for the best way to store, organize and process Big Data, the Blockchain technology comes in providing significant input. Its proposed solutions about decentralized management of private data, digital property resolution, IoT communication and public institutions' reforms are having significant impact on how Big Data may evolve. This paper presents the novel solutions associated with some of the Big Data areas that can be empowered by the Blockchain technology.
Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels

We consider the problem of improving the efficiency of random ized Fourier feature maps to accelerate training and testing speed of kernel methods on large dat asets. These approximate feature maps arise as Monte Carlo approximations to integral representations of shift-invariant kernel functions (e.g., Gaussian kernel). In this paper, we propose to use Quasi-Monte Carlo(QMC) approximations instead, where the relevant integrands are evaluated on a low-discrepancy sequence of points as opposed to random point sets as in the Monte Carlo approach. We derive a new disc repancy measure called box discrepancy based on theoretical characterizations of the integration error with respect to a given sequence. We then propose to learn QMC sequences adapted to our setting based o n explicit box discrepancy minimization. Our theoretical analyses are complemented with empirical r esults that demonstrate the effectiveness of classical and adaptive QMC techniques for this problem.
Music and social bonding: “self-other” merging and neurohormonal mechanisms

It has been suggested that a key function of music during its development and spread amongst human populations was its capacity to create and strengthen social bonds amongst interacting group members. However, the mechanisms by which this occurs have not been fully discussed. In this paper we review evidence supporting two thus far independently investigated mechanisms for this social bonding effect: self-other merging as a consequence of inter-personal synchrony, and the release of endorphins during exertive rhythmic activities including musical interaction. In general, self-other merging has been experimentally investigated using dyads, which provide limited insight into large-scale musical activities. Given that music can provide an external rhythmic framework that facilitates synchrony, explanations of social bonding during group musical activities should include reference to endorphins, which are released during synchronized exertive movements. Endorphins (and the endogenous opioid system (EOS) in general) are involved in social bonding across primate species, and are associated with a number of human social behaviors (e.g., laughter, synchronized sports), as well as musical activities (e.g., singing and dancing). Furthermore, passively listening to music engages the EOS, so here we suggest that both self-other merging and the EOS are important in the social bonding effects of music. In order to investigate possible interactions between these two mechanisms, future experiments should recreate ecologically valid examples of musical activities.
IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models

This paper provides a unified account of two schools of thinking in information retrieval modelling: the generative retrieval focusing on predicting relevant documents given a query, and the discriminative retrieval focusing on predicting relevancy given a query-document pair. We propose a game theoretical minimax game to iteratively optimise both models. On one hand, the discriminative model, aiming to mine signals from labelled and unlabelled data, provides guidance to train the generative model towards fitting the underlying relevance distribution over documents given the query. On the other hand, the generative model, acting as an attacker to the current discriminative model, generates difficult examples for the discriminative model in an adversarial way by minimising its discrimination objective. With the competition between these two models, we show that the unified framework takes advantage of both schools of thinking: (i) the generative model learns to fit the relevance distribution over documents via the signals from the discriminative model, and (ii) the discriminative model is able to exploit the unlabelled data selected by the generative model to achieve a better estimation for document ranking. Our experimental results have demonstrated significant performance gains as much as 23.96% on Precision@5 and 15.50% on MAP over strong baselines in a variety of applications including web search, item recommendation, and question answering.