nils-herrmann/clean_scirepeval huggingface dataset:
Multi-level partition of unity implicits

1. Briefly summarize the paper's contributions. Does it address a new problem? Does it present a new approach? Does it show new types of results?  [AS] This paper presents a new approach to construct surface models from extremely large point sets using piecewise quadratic functions that capture the local shape of the surface, and then blending them together using multi-level partitions of unity as weighting functions. This is the first method to use partitions of the point set into regions to fit implicit functions to, as opposed to function fitting at every point, in previous methods.  [DS] The paper presents a surface representation using multi-level partition of unity (MPU) implicit surfaces. Presents an approach with the 3 key concepts: 1. Piecewise quadratic functions used as local shape estimates for the surface. 2. Weighing functions (partition of unity) to blend the local shape functions. 3. Octree subdivision that adapts based on shape complexity. This approach is adaptive based on the desired accuracy. The use of implicit functions provide data repairing capabilities for scattered data and an easy way to perform implicit modeling operations.  [FP] This paper proposes a method to implicitly define a surface as the zero level of a distance function. The space is partitioned in octree cells and a shape function is fitted to each cell. In contrast to previous approaches (such as Hoppe 92), the distance to the surface (i.e., the implicit function evaluation) is not computed respect to a single element of the space partition (in this case, an octree cell) but is a weighted sum. Due to the partition of unity approach and the use of smooth weighting function, the distance function changes smoothly along the 3D space.  [JD]  [LF] The paper introduces the partition of unity approach for approximation of the signed distance function of a set of scattered points with normals. The method breaks up the point set using octree subdivision (can handle complex topology). It then uses piecewise quadratic function as local approximations of the surface. These local approximations are blended together using smooth, local weights (can represent sharp features).  [MK] This paper proposes an efficient, octree-based, algorithm for reconstructing surfaces from oriented point samples. The approach falls somewhere between global implicit methods and MLS approaches, as the approach partitions space, computes a local implicit surface around each partition, and then glues these together using a partition …
Mid-face Rejuvenation Mr Dalvi Humzah and

With age, the mid-face changes due to a combination of remodelling of the maxilla and pyriform aperture, volumetric and positional changes of the superficial and deep mid-facial fat pads, and changes in skin tone and texture. It is widely accepted that these changes significantly influence many of the visual changes in the lower face. These may be effectively treated with a variety of injectable techniques to rejuvenate the mid-face. The current literature concerning the pertinent anatomy for mid-facial rejuvenation is therefore discussed within this article.  In recent years, the way we analyse and plan the non-surgical approach to the mid-face has changed dramatically. The focus has shifted away from treating visible lines/folds as they present, and is now centred on treating the age-related changes at the root cause. The restoration of a dynamic and harmonious volume distribution is a key factor in non-surgical rejuvenation of the mid-face. There is a growing body of literature demonstrating the multifactorial nature of facial ageing. These indicate that facial analysis and subsequent rejuvenation should initially address such anatomical changes. An understanding of the anatomical changes associated with ageing is required to formulate effective strategies to rejuvenate the ageing mid-face.
Development of an Electromagnetic Actuated Microdisplacement Module

This paper presents the mechanical design and control system design of an electromagnetic actuator-based microdisplacement module. The microdisplacement module composed of a symmetrical leaf-spring parallelogram mechanism and an electromagnetic actuator. The characteristics of the mechanism in terms of stiffness and natural frequencies are derived and verified. Both leakage flux and core inductance are taken into consideration during modeling the mathematic model of the electromagnetic actuator, and based on which, the system dynamic model is established. Due to the nonlinearity characteristics of the system, a dynamic sliding-mode controller is designed without linearizing the system dynamics. A prototype of the microdisplacement module is fabricated, and the parameters of the system are identified and calibrated. Finally, the designed dynamic sliding-mode controller is applied; step response and tracking performance are studied. Experimental results demonstrate that a submicrometer accuracy can be achieved by the module, which validate the effectiveness of the proposed mechanism and controller design as well. The research results show that the electromagnetic actuator-based module can be extended to wide applications in the field of micro/nanopositioning and manipulation.
High Level Sensor Data Fusion Approaches For Object Recognition In Road Environment

Application of high level fusion approaches demonstrate a sequence of significant advantages in multi sensor data fusion and automotive safety fusion systems are no exception to this. High level fusion can be applied to automotive sensor networks with complementary or/and redundant field of views. The advantage of this approach is that it ensures system modularity and allows benchmarking, as it does not permit feedbacks and loops inside the processing. In this paper two specific high level data fusion approaches are described including a brief architectural and algorithmic presentation. These approaches differ mainly in their data association part: (a) track level fusion approach solves it with the point to point association with emphasis on object continuity and multidimensional assignment, and (b) grid based fusion approach that proposes a generic way to model the environment and to perform sensor data fusion. The test case for these approaches is a multi sensor equipped PReVENT/ProFusion2 truck demonstrator vehicle.
The development of initial trust in an online company by new customers

Lack of trust in online companies is a primary reason why many web users do not shop online. This study proposes a model to explain how new customers of a web-based company develop initial trust in the company after their first visit. The model is empirically tested using a questionnaire-based field study. The results indicate that perceived company reputation and willingness to customize products and services can significantly affect initial trust. Perceived web site usefulness, ease of use, and security control are also significant antecedents of initial trust. Finally, we found no support for the hypothesized effect of individual customer trust propensity on initial trust.
SCARE: Side-Channel Analysis Based Reverse Engineering for Post-Silicon Validation

Engineering (RE) has been historically considered as a powerful approach to understand electronic hardware in order to gain competitive intelligence or accomplish piracy. In recent years, it has also been looked at as a way to authenticate hardware intellectual properties in the court of law. In this paper, we propose a beneficial role of RE in post-silicon validation of integrated circuits (IC) with respect to IC functionality, reliability and integrity. Unlike traditional destructive RE approaches, we propose a fast non-destructive side-channel analysis approach that can hierarchically extract structural information from an IC through its transient current signature. Such a top-down side-channel analysis approach is capable of reliably identifying pipeline stages and functional blocks. It is also suitable to distinguish sequential elements from combinational gates. For extraction of random logic structures (e.g. control blocks and finite state machines) we combine side-channel analysis with logic testing based Boolean function extraction. The proposed approach is amenable to automation, scalable, and can be applied as part of post-silicon validation process to verify that each IC implements exclusively the functionality described in the specification and is free from malicious modification or Trojan attacks. Simulation results on a pipelined DLX processor demonstrate the effectiveness of the proposed approach.
Slant: A Blackboard System to Generate Plot, Figuration, and Narrative Discourse Aspects of Stories

We introduce Slant, a system that integrates more than a decade of research into computational creativity, and specifically story generation, by connecting subsystems that deal with plot, figuration, and the narrative discourse using a blackboard. The process of integrating these systems highlights differences in the representation of story and has led to a better understanding of how story can be usefully abstracted. The plot generator MEXICA and a component of Curveship are used with little modification in Slant, while the figuration subsystem Fig-S and the template generator GRIOT-Gen, inspired by GRIOT, are also components. The development of the new subsystem Verso, which deals with genre, shows how different genres can be computationally modeled and applied to in-development stories to generate results that are surprising in terms of their connections and valuable in terms of their relationship to cultural questions. Example stories are discussed, as is the potential of the system to allow for broader collaboration, the empirical testing of how subsystems interrelate, and possible contributions in literary and artistic contexts.
Aspect extraction in sentiment analysis: comparative analysis and survey

Sentiment analysis (SA) has become one of the most active and progressively popular areas in information retrieval and text mining due to the expansion of the World Wide Web (WWW). SA deals with the computational treatment or the classification of user’s sentiments, opinions and emotions hidden within the text. Aspect extraction is the most vital and extensively explored phase of SA to carry out the classification of sentiments in precise manners. During the last decade, enormous number of research has focused on identifying and extracting aspects. Therefore, in this survey, a comprehensive overview has been attempted for different aspect extraction techniques and approaches. These techniques have been categorized in accordance with the adopted approach. Despite being a traditional survey, a comprehensive comparative analysis is conducted among different approaches of aspect extraction, which not only elaborates the performance of any technique but also guides the reader to compare the accuracy with other state-of-the-art and most recent approaches.
1kW Home Inverter Using Cascaded Current Fed Push Pull Converter and SPWM Inverter

DC to AC converters or inverters are widely used in un-interruptible power supply systems, AC motor drives, induction heating and renewable energy source systems. The conventional form of an inverter is the full bridge inverter where an H bridge is controlled according to SPWM principle. The resulting sine wave is stepped up using a transformer to produce voltage level close to the utility voltage(230V 50Hz). This method has a disadvantage of low efficiency due to high losses in low frequency transformer. This paper eliminates the usage of low frequency transformer by implementing a current fed push pull converter with a voltage doubler circuit which directly gives a high step up dc voltage to the inverter input. This converter also provides isolation between battery and the AC line. The boosted DC voltage is inverted using SPWM technique.
Post-processing NPR effects for video games

This paper describes different interactive, non photorealistic techniques which can be easily applied to video games. Based on a study of art and games, an emphasis is put on considering NPR tools as basic elements to provide different styles and moods, to convey different emotional and experiential representations of a game. We restrict ourselves to screen based effects, which permits any existing game to use our framework with practically no integration cost. This allows us not only to comply with user preferences in rendering style, but also the creation of multiple gaming experiences out of the same game. We show the resulting effects in an in-house videogame and in standard Unity demos, and show how the users can change the style of the videogame by means of a menu.
A combination of artificial neural network and random walk models for financial time series forecasting

Properly comprehending and modeling the dynamics of financial data has indispensable practical importance. The prime goal of a financial time series model is to provide reliable future forecasts which are crucial for investment planning, fiscal risk hedging, governmental policy making, etc. These time series often exhibit notoriously haphazard movements which make the task of modeling and forecasting extremely difficult. As per the research evidence, the random walk (RW) is so far the best linear model for forecasting financial data. Artificial neural network (ANN) is another promising alternative with the unique capability of nonlinear self-adaptive modeling. Numerous comparisons of the performances of RW and ANN models have also been carried out in the literature with mixed conclusions. In this paper, we propose a combination methodology which attempts to benefit from the strengths of both RW and ANN models. In our proposed approach, the linear part of a financial dataset is processed through the RW model, and the remaining nonlinear residuals are processed using an ensemble of feedforward ANN (FANN) and Elman ANN (EANN) models. The forecasting ability of the proposed scheme is examined on four real-world financial time series in terms of three popular error statistics. The obtained results clearly demonstrate that our combination method achieves reasonably better forecasting accuracies than each of RW, FANN and EANN models in isolation for all four financial time series.
Artificial intelligence techniques for bioinformatics.

This review provides an overview of the ways in which techniques from artificial intelligence (AI) can be usefully employed in bioinformatics, both for modelling biological data and for making new discoveries. The paper covers three techniques: symbolic machine learning approaches (nearest neighbour and identification tree techniques), artificial neural networks and genetic algorithms. Each technique is introduced and supported with examples taken from the bioinformatics literature. These examples include folding prediction, viral protease cleavage prediction, classification, multiple sequence alignment and microarray gene expression analysis.
AutoSVD++: An Efficient Hybrid Collaborative Filtering Model via Contractive Auto-encoders

Collaborative filtering (CF) has been successfully used to provide users with personalized products and services. However, dealing with the increasing sparseness of user-item matrix still remains a challenge. To tackle such issue, hybrid CF such as combining with content based filtering and leveraging side information of users and items has been extensively studied to enhance performance. However, most of these approaches depend on hand-crafted feature engineering, which is usually noise-prone and biased by different feature extraction and selection schemes. In this paper, we propose a new hybrid model by generalizing contractive auto-encoder paradigm into matrix factorization framework with good scalability and computational efficiency, which jointly models content information as representations of effectiveness and compactness, and leverage implicit user feedback to make accurate recommendations. Extensive experiments conducted over three large-scale real datasets indicate the proposed approach outperforms the compared methods for item recommendation.
From Word To Sense Embeddings: A Survey on Vector Representations of Meaning

Over the past years, distributed semantic representations have proved to be effective and flexible keepers of prior knowledge to be integrated into downstream applications. This survey focuses on the representation of meaning. We start from the theoretical background behind word vector space models and highlight one of their major limitations: the meaning conflation deficiency, which arises from representing a word with all its possible meanings as a single vector. Then, we explain how this deficiency can be addressed through a transition from the word level to the more fine-grained level of word senses (in its broader acceptation) as a method for modelling unambiguous lexical meaning. We present a comprehensive overview of the wide range of techniques in the two main branches of sense representation, i.e., unsupervised and knowledge-based. Finally, this survey covers the main evaluation procedures and applications for this type of representation, and provides an analysis of four of its important aspects: interpretability, sense granularity, adaptability to different domains and compositionality.
A 3.4 – 6.2 GHz Continuously tunable electrostatic MEMS resonator with quality factor of 460–530

In this paper we present the first MEMS electrostatically-tunable loaded-cavity resonator that simultaneously achieves a very high continuous tuning range of 6.2 GHz:3.4 GHz (1.8:1) and quality factor of 460–530 in a volume of 18×30×4 mm3 including the actuation scheme and biasing lines. The operating principle relies on tuning the capacitance of the loaded-cavity by controlling the gap between an electrostatically-actuated membrane and the cavity post underneath it. Particular attention is paid on the fabrication of the tuning mechanism in order to avoid a) quality factor degradation due to the biasing lines and b) hysteresis and creep issues. A single-crystal silicon membrane coated with a thin gold layer is the key to the success of the design.
Evaluation and management of children with holoprosencephaly.

Recent advances in genetics and neuroimaging have greatly contributed to our understanding of the spectrum of midline brain and craniofacial malformations known as holoprosencephaly. Neuroradiologic studies have provided detailed characteristics of four major types of holoprosencephaly: alobar, semilobar, lobar, and middle interhemispheric variant. Clinical studies in children with these types of holoprosencephaly have revealed a wide range of survival and neurologic outcomes. Motor and developmental dysfunctions correlate with the severity of the brain malformation in holoprosencephaly. These findings have implications in the management of medical problems associated with holoprosencephaly and overall prognostication.
A magnetostatic 2-axis MEMS scanner with I-section rib-reinforcement and slender permanent magnet patterns

This study demonstrates the 2-axis epitaxial silicon scanner driven by the coil-less magnetostatic force using electroplated permanent magnet (CoNiMnP) film. The present approach has four merits: (1) the process employs the cheap silicon wafer with epitaxial layer; and the electrochemical etching stop technique is used to precisely control the thickness of scanner; (2) the I-section rib-reinforced structure is implemented to provide high stiffness of the mirror plate; (3) the magnetostatic driving force on scanner is increased by electroplated permanent magnet film with slender patterns; (4) the size of packaged scanner is reduced since the assembled permanent magnets are not required.
Active intelligent antenna system for car2car

In this paper an innovative antenna system for car2car applications is described. The antenna system is based on an active antenna module containing the antennas as well as the complete car2car chip hardware and software. This module is very small, so that it can be integrated e.g. into a shark fin antenna on the cars rooftop. Moreover additional modules can be mounted somewhere else in the car and connected with the main module via Ethernet. This allows high flexibility and re-configurability of the system. Two test cars have been equipped with the new car2car demonstrator systems and measurements have been performed in the 3D measurement chamber as well as in dynamic outdoor scenarios. The new system shows excellent performance in measurements and field trials.
Clustering Algorithms Applied in Educational Data Mining

Fifty years ago there were just a handful of universities across the globe that could provide for specialized educational courses. Today Universities are generating not only graduates but also massive amounts of data from their systems. So the question that arises is how can a higher educational institution harness the power of this didactic data for its strategic use? This review paper will serve to answer this question. To build an Information system that can learn from the data is a difficult task but it has been achieved successfully by using various data mining approaches like clustering, classification, prediction algorithms etc. However the use of these algorithms with educational dataset is quite low. This review paper focuses to consolidate the different types of clustering algorithms as applied in Educational Data Mining context.
DDoS: Survey of Traceback Methods

The problem of identifying Distributed Denial of Service (DDoS) is one of the hardest threats in the internet security. It is important to protect the resource and trace from the Denial of Service (DoS) attack, but it is difficult to distinguish normal traffic and DoS attack traffic because the DoS generally hide their identities/origins. Especially the attackers often use incorrect or spoofed source IP address, so tracing the source of the denial of service is hardest in internet. Lot of techniques and methodologies are used to trace the DDoS attacks. This paper presents some of the mostly used predicting traceback techniques to solve the problem. The main goal of this paper is appraise the different traceback techniques of the DDoS. This paper evaluates the different traceback methods of the
Clinical Information Extraction via Convolutional Neural Network

We report an implementation of a clinical information extraction tool that leverages deep neural network to annotate event spans and their attributes from raw clinical notes and pathology reports. Our approach uses context words and their part-of-speech tags and shape information as features. Then we hire temporal (1D) con-volutional neural network to learn hidden feature representations. Finally, we use Multilayer Perceptron (MLP) to predict event spans. The empirical evaluation demonstrates that our approach significantly outperforms baselines.
Recommendations Using Modified K-Means Clustering and Voting Theory

Recommender Systems are popularly used for generating user-specific recommendations for services, products and information. In particular, for the process of generating recommendations, a combination of K-Means and Collaborative Filtering algorithm is used extensively. K-Means is a standard data clustering algorithm which suffers from the problem of low recommendation quality due to random cluster center initialization. After generating the clusters, Collaborative filtering is a commonly used technique for generating recommendations. But this technique suffers from the problems of data sparsity and scalability. In this paper, we have used a modified K-Means clustering algorithm for partitioning users into independent clusters. Voting theory has been used to find out the most suited cluster for which the new user can be a part of, and then recommend items to the user. The proposed method attempts to reduce the cost for generating clusters, and to make the clustering method scalable.  Recommender Systems; Collaborative Filtering; K-Means; Voting theory.