nils-herrmann/clean_scirepeval huggingface dataset:
Improving Consistency and Correctness of Sequence Inpainting using Semantically Guided Generative Adversarial Network

Contemporary benchmark methods for image inpainting are based on deep generative models and specifically leverage adversarial loss for yielding realistic reconstructions. However, these models cannot be directly applied on image/video sequences because of an intrinsic drawbackthe reconstructions might be independently realistic, but, when visualized as a sequence, often lacks fidelity to the original uncorrupted sequence. The fundamental reason is that these methods try to find the best matching latent space representation near to natural image manifold without any explicit distance based loss. In this paper, we present a semantically conditioned Generative Adversarial Network (GAN) for sequence inpainting. The conditional information constrains the GAN to map a latent representation to a point in image manifold respecting the underlying pose and semantics of the scene. To the best of our knowledge, this is the first work which simultaneously addresses consistency and correctness of generative model based inpainting. We show that our generative model learns to disentangle pose and appearance information; this independence is exploited by our model to generate highly consistent reconstructions. The conditional information also aids the generator network in GAN to produce sharper images compared to the original GAN formulation. This helps in achieving more appealing inpainting performance. Though generic, our algorithm was targeted for inpainting on faces. When applied on CelebA and Youtube Faces datasets, the proposed method results in significant improvement over the current benchmark, both in terms of quantitative evaluation (Peak Signal to Noise Ratio) and human visual scoring over diversified combinations of resolutions and deformations.  Exemplary success of our model in simultaneously preserving facial semantics(appearance and expressions) and improving inpaiting quality. Benchmark generative models such as DIP [49] are agnostic to holistic facial semantics and thus generate independently realistic, yet structurally inconsistent solutions.
Measuring and defining the experience of immersion in games

Despite the word's common usage by gamers and reviewers alike, it is still not clear what immersion means. This paper explores immersion further by investigating whether immersion can be defined quantitatively, describing three experiments in total. The first experiment investigated participants' abilities to switch from an immersive to a non-immersive task. The second experiment investigated whether there were changes in participants' eye movements during an immersive task. The third experiment investigated the effect of an externally imposed pace of interaction on immersion and affective measures (state-anxiety, positive affect, negative affect). Overall the findings suggest that immersion can be measured subjectively (through questionnaires) as well as objectively (task completion time, eye movements). Furthermore, immersion is not only viewed as a positive experience: negative emotions and uneasiness (i.e. anxiety) also run high.
A three-fingered cable-driven gripper for underwater applications

In this paper, the design and experimental evaluation of a cable driven robotic gripper for underwater applications is presented. The gripper has three fingers and is characterised by a large workspace if compared with other similar devices reported in literature. Its kinematic configuration allows to execute both parallel and precision grasps on objects with very different dimensions. The gripper has 8 degrees of freedom actuated by only three motors by means of a suitable coupling of the joints obtained through the cable transmission. Moreover, in order to facilitate the execution of complex tasks, special force/torque sensors are mounted on the fingertips. The paper reports the main specifications deriving from the particular tasks in which the gripper is involved, and illustrates the proposed design solutions. Results obtained from real underwater experiments are provided as well, in order to demonstrate the capabilities of the gripper.
A deep bidirectional LSTM approach for video-realistic talking head

This paper proposes a deep bidirectional long short-term memory approach in modeling the long contextual, nonlinear mapping between audio and visual streams for video-realistic talking head. In training stage, an audio-visual stereo database is firstly recorded as a subject talking to a camera. The audio streams are converted into acoustic feature, i.e. Mel-Frequency Cepstrum Coefficients (MFCCs), and their textual labels are also extracted. The visual streams, in particular, the lower face region, are compactly represented by active appearance model (AAM) parameters by which the shape and texture variations can be jointly modeled. Given pairs of the audio and visual parameter sequence, a DBLSTM model is trained to learn the sequence mapping from audio to visual space. For any unseen speech audio, whether it is original recorded or synthesized by text-to-speech (TTS), the trained DBLSTM model can predict a convincing AAM parameter trajectory for the lower face animation. To further improve the realism of the proposed talking head, the trajectory tiling method is adopted to use the DBLSTM predicted AAM trajectory as a guide to select a smooth real sample image sequence from the recorded database. We then stitch the selected lower face image sequence back to a background face video of the same subject, resulting in a video-realistic talking head. Experimental results show that the proposed DBLSTM approach outperforms the existing HMM-based approach in both objective and subjective evaluations.
A Discriminative Latent Variable Model for Online Clustering

This paper presents a latent variable structured prediction model for discriminative supervised clustering of items called the Latent Left-linking Model (LM). We present an online clustering algorithm for LM based on a feature-based item similarity function. We provide a learning framework for estimating the similarity function and present a fast stochastic gradient-based learning technique. In our experiments on coreference resolution and document clustering, LM outperforms several existing online as well as batch supervised clustering techniques.
Automated construction of robotic manipulation programs

Society is becoming more automated with robots beginning to perform most tasks in factories and starting to help out in home and office environments. One of the most important functions of robots is the ability to manipulate objects in their environment. Because the space of possible robot designs, sensor modalities, and target tasks is huge, researchers end up having to manually create many models, databases, and programs for their specific task, an effort that is repeated whenever the task changes. Given a specification for a robot and a task, the presented framework automatically constructs the necessary databases and programs required for the robot to reliably execute manipulation tasks. It includes contributions in three major components that are critical for manipulation tasks. The first is a geometric-based planning system that analyzes all necessary modalities of manipulation planning and offers efficient algorithms to formulate and solve them. This allows identification of the necessary information needed from the task and robot specifications. Using this set of analyses, we build a planning knowledge-base that allows informative geometric reasoning about the structure of the scene and the robot’s goals. We show how to efficiently generate and query the information for planners. The second is a set of efficient algorithms considering the visibility of objects in cameras when choosing manipulation goals. We show results with several robot platforms using grippers cameras to boost accuracy of the detected objects and to reliably complete the tasks. Furthermore, we use the presented planning and visibility infrastructure to develop a completely automated extrinsic camera calibration method and a method for detecting insufficient calibration data. The third is a vision-centric database that can analyze a rigid object’s surface for stable and discriminable features to be used in pose extraction programs. Furthermore, we show work towards a new voting-based object pose extraction algorithm that does not rely on 2D/3D feature correspondences and thus reduces the early-commitment problem plaguing the generality of traditional vision-based pose extraction algorithms. In order to reinforce our theoric contributions with a solid implementation basis, we discuss the open-source planning environment OpenRAVE, which began and evolved as a result of the work done in this thesis. We present an analysis of its architecture and provide insight for successful robotics software environments.
A Novel Conformal RFID-Enabled Module Utilizing Inkjet-Printed Antennas and Carbon Nanotubes for Gas-Detection Applications

This letter introduces for the first time the integration of a conformal radio frequency identification (RFID) antenna with a single-walled carbon nanotube (SWCNT) composite in a chipless RFID node for toxic gas detection. The electrical performance characterization of the inkjet-printed SWCNT film is also reported for the first time up to 1 GHz. The whole module is realized by inkjet printing on a low-cost paper-based substrate, and the RFID tag is designed for the European UHF RFID band. The electrical conductivity of the SWCNT film changes in the presence of very small quantities of toxic gases like ammonia and nitrogen oxide, resulting in the variation of the backscattered power level, which can be easily detected by the RFID reader to realize reliable wireless toxic gas sensing.
Real-time facial animation with image-based dynamic avatars

We present a novel image-based representation for dynamic 3D avatars, which allows effective handling of various hairstyles and headwear, and can generate expressive facial animations with fine-scale details in real-time. We develop algorithms for creating an image-based avatar from a set of sparsely captured images of a user, using an off-the-shelf web camera at home. An optimization method is proposed to construct a topologically consistent morphable model that approximates the dynamic hair geometry in the captured images. We also design a real-time algorithm for synthesizing novel views of an image-based avatar, so that the avatar follows the facial motions of an arbitrary actor. Compelling results from our pipeline are demonstrated on a variety of cases.
DCSim: A data centre simulation tool for evaluating dynamic virtualized resource management

Computing today is shifting from hosting services in servers owned by individual organizations to data centres providing resources to a number of organizations on a shared infrastructure. Managing such a data centre presents a unique set of goals and challenges. Through the use of virtualization, multiple users can run isolated virtual machines (VMs) on a single physical host, allowing for a higher server utilization. By consolidating VMs onto fewer physical hosts, infrastructure costs can be reduced in terms of the number of servers required, power consumption, and maintenance. To meet constantly changing workload levels, running VMs may need to be migrated (moved) to another physical host. Algorithms to perform dynamic VM reallocation, as well as dynamic resource provisioning on a single host, are open research problems. Experimenting with such algorithms on the data centre scale is impractical. Thus, there is a need for simulation tools to allow rapid development and evaluation of data centre management techniques. We present DCSim, an extensible simulation framework for simulating a data centre hosting an Infrastructure as a Service cloud. We evaluate the scalability of DCSim, and demonstrate its usefulness in evaluating VM management techniques.
Fuzzy control of inverted pendulum and concept of stability using Java application

In this paper, a fuzzy controller for an inverted pendulum system is presented in two stages. These stages are: investigation of fuzzy control system modeling methods and solution of the “Inverted Pendulum Problem” by using Java programming with Applets for internet based control education. In the first stage, fuzzy modeling and fuzzy control system investigation, Java programming language, classes and multithreading were introduced. In the second stage specifically, simulation of the inverted pendulum problem was developed with Java Applets and the simulation results were given. Also some stability concepts are introduced.
TweetAlert: Semantic Analytics in Social Networks for Citizen Opinion Mining in the City of the Future

In this paper a highly configurable, real-time analysis system to automatically record, analyze and visualize high level aggregated information of user interventions in Twitter is described. The system is designed to provide public entities with a powerful tool to rapidly and easily understand what the citizen behavior trends are, what their opinion about city services, events, etc. is, and also may used as a primary alert system that may improve the efficiency of emergency systems. The citizen is here observed as a proactive city sensor capable of generating huge amounts of very rich, high-level and valuable data through social media platforms, which, after properly processed, summarized and annotated, allows city administrators to better understand citizen necessities. The architecture and component blocks are described and some key details of the design, implementation and scenarios of application are discussed.
Locally Weighted Projection Regression : An O(n) Algorithm for Incremental Real Time Learning in High Dimensional Space

Locally weighted projection regression is a new algorithm that achieves nonlinear function approximation in high dimensional spaces with redundant and irrelevant input dimensions. At its core, it uses locally linear models, spanned by a small number of univariate regressions in selected directions in input space. This paper evaluates different methods of projection regression and derives a nonlinear function approximator based on them. This nonparametric local learning system i) learns rapidly with second order learning methods based on incremental training, ii) uses statistically sound stochastic cross validation to learn iii) adjusts its weighting kernels based on local information only, iv) has a computational complexity that is linear in the number of inputs, and v) can deal with a large number of possibly redundant inputs, as shown in evaluations with up to 50 dimensional data sets. To our knowledge, this is the first truly incremental spatially localized learning method to combine all these properties.
Big Data and Specific Analysis Methods for Insurance Fraud Detection Big Data and Specific Analysis Methods for Insurance Fraud Detection

Analytics is the future of big data because only transforming data into information gives them value and can turn data in business in competitive advantage. Large data volumes, their variety and the increasing speed their growth, stretch the boundaries of traditional data warehouses and ETL tools. This paper investigates the benefits of Big Data technology and main methods of analysis that can be applied to the particular case of fraud detection in public health insurance system in Romania.
A Novel Approach for Vehicle Inertial Parameter Identification Using a Dual Kalman Filter

This paper proposes a novel algorithm to identify three inertial parameters: sprung mass, yaw moment of inertia, and longitudinal position of the center of gravity. A four-wheel nonlinear vehicle model with roll dynamics and a correlation between the inertial parameters is used for a dual unscented Kalman filter to simultaneously identify the inertial parameters and the vehicle state. A local observability analysis on the nonlinear vehicle model is used to activate and deactivate different modes of the proposed algorithm. Extensive CarSim simulations and experimental tests show the performance and robustness of the proposed approach on a flat road with a constant tire-road friction coefficient.
An Analysis of students' performance using classification algorithms

In recent years, the analysis and evaluation of students‟ performance and retaining the standard of education is a very important problem in all the educational institutions. The most important goal of the paper is to analyze and evaluate the school students‟ performance by applying data mining classification algorithms in weka tool. The data mining tool has been generally accepted as a decision making tool to facilitate better resource utilization in terms of students‟ performance. The various classification algorithms could be specifically mentioned as J48, Random Forest, Multilayer Perceptron, IB1 and Decision Table are used. The results of such classification model deals with accuracy level, confusion matrices and also the execution time. Therefore conclusion could be reached that the Random Forest performance is better than that of different algorithms.
Recurrent Neural Networks for Customer Purchase Prediction on Twitter

The abundance of data posted to Twitter enables companies to extract useful information, such as Twitter users who are dissatisfied with a product. We endeavor to determine which Twitter users are potential customers for companies and would be receptive to product recommendations through the language they use in tweets after mentioning a product of interest. With Twitter’s API, we collected tweets from users who tweeted about mobile devices or cameras. An expert annotator determined whether each tweet was relevant to customer purchase behavior and whether a user, based on their tweets, eventually bought the product. For the relevance task, among four models, a feed-forward neural network yielded the best cross-validation accuracy of over 80% per product. For customer purchase prediction of a product, we observed improved performance with the use of sequential input of tweets to recurrent models, with an LSTM model being best; we also observed the use of relevance predictions in our model to be more effective with less powerful RNNs and on more difficult tasks.
An ensemble classifier system for early diagnosis of acute lymphoblastic leukemia in blood microscopic images

Leukemia is a malignant neoplasm of the blood or bone marrow that affects both children and adults and remains a leading cause of death around the world. Acute lymphoblastic leukemia (ALL) is the most common type of leukemia and is more common among children and young adults. ALL diagnosis through microscopic examination of the peripheral blood and bone marrow tissue samples is performed by hematologists and has been an indispensable technique long since. However, such visual examinations of blood samples are often slow and are also limited by subjective interpretations and less accurate diagnosis. The objective of this work is to improve the ALL diagnostic accuracy by analyzing morphological and textural features from the blood image using image processing. This paper aims at proposing a quantitative microscopic approach toward the discrimination of lymphoblasts (malignant) from lymphocytes (normal) in stained blood smear and bone marrow samples and to assist in the development of a computer-aided screening of ALL. Automated recognition of lymphoblasts is accomplished using image segmentation, feature extraction, and classification over light microscopic images of stained blood films. Accurate and authentic diagnosis of ALL is obtained with the use of improved segmentation methodology, prominent features, and an ensemble classifier, facilitating rapid screening of patients. Experimental results are obtained and compared over the available image data set. It is observed that an ensemble of classifiers leads to 99 % accuracy in comparison with other standard classifiers, i.e., naive Bayesian (NB), K-nearest neighbor (KNN), multilayer perceptron (MLP), radial basis functional network (RBFN), and support vector machines (SVM).
Location based Applications for Mobile Augmented Reality

In this work we investigate building indoor location based applications for a mobile augmented reality system. We believe that augmented reality is a natural interface to visualize spacial information such as position or direction of locations and objects for location based applications that process and present information based on the user’s position in the real world. To enable such applications we construct an indoor tracking system that covers a substantial part of a building. It is based on visual tracking of fiducial markers enhanced with an inertial sensor for fast rotational updates. To scale such a system to a whole building we introduce a space partitioning scheme to reuse fiducial markers throughout the environment. Finally we demonstrate two location based applications built upon this facility, an indoor navigation aid and a library search applica-
A Bi-model based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling

Intent detection and slot filling are two main tasks for building a spoken language understanding(SLU) system. Multiple deep learning based models have demonstrated good results on these tasks . The most effective algorithms are based on the structures of sequence to sequence models (or ”encoder-decoder” models), and generate the intents and semantic tags either using separate models((Yao et al., 2014; Mesnil et al., 2015;  2015; Kurata et al., 2016; Hahn et al., 2011)) or a joint model ((Liu and Lane, 2016a; HakkaniTür et al., 2016; Guo et al., 2014)). Most of the previous studies, however, either treat the intent detection and slot filling as two separate parallel tasks, or use a sequence to sequence model to generate both semantic tags and intent. Most of these approaches use one (joint) NN based model (including encoderdecoder structure) to model two tasks, hence may not fully take advantage of the crossimpact between them. In this paper, new Bi-model based RNN semantic frame parsing network structures are designed to perform the intent detection and slot filling tasks jointly, by considering their cross-impact to each other using two correlated bidirectional LSTMs (BLSTM). Our Bi-model structure with a decoder achieves state-of-the-art result on the benchmark ATIS data (Hemphill et al., 1990; Tur et al., 2010), with about 0.5% intent accuracy improvement and 0.9 % slot filling improvement.
American sign language recognition in game development for deaf children

ASL) game, which uses gesture recognition technology to help young deaf children practice ASL skills. We describe a brief history of the game, an overview of recent user studies, and the results of recent work on the problem of continuous, user-independent sign language recognition in classroom settings. Our database of signing samples was collected from user studies of deaf children playing aWizard of Oz version of the game at the Atlanta Area School for the Deaf (AASD). Our data set is characterized by disfluencies inherent in continuous signing, varied user characteristics including clothing and skin tones, and illumination changes in the classroom. The dataset consisted of 541 phrase samples and 1,959 individual sign samples of five children signing game phrases from a 22 word vocabulary. Our recognition approach uses color histogram adaptation for robust hand segmentation and tracking. The children wear small colored gloves with wireless accelerometers mounted on the back of their wrists. The hand shape information is combined with accelerometer data and used to train hidden Markov models for recognition. We evaluated our approach by using leave-one-out validation; this technique iterates through each child, training on data from four children and testing on the remaining child's data. We achieved average word accuracies per child ranging from 91.75% to 73.73% for the user-independent models.