nils-herrmann/clean_scirepeval huggingface dataset:
Animating paper using shape memory alloys

Our aim is to make shape memory alloys (SMAs) accessible and visible as creative crafting materials by combining them with paper. In this paper, we begin by presenting mechanisms for actuating paper with SMAs along with a set of design guidelines for achieving dramatic movement. We then describe how we tested the usability and educational potential of one of these mechanisms in a workshop where participants, age 9 to 15, made actuated electronic origami cranes. We found that participants were able to successfully build constructions integrating SMAs and paper, that they enjoyed doing so, and were able to learn skills like circuitry design and soldering over the course of the workshop.
Learning Multi-Modal Grounded Linguistic Semantics by Playing "I Spy"

Grounded language learning bridges words like ‘red’ and ‘square’ with robot perception. The vast majority of existing work in this space limits robot perception to vision. In this paper, we build perceptual models that use haptic, auditory, and proprioceptive data acquired through robot exploratory behaviors to go beyond vision. Our system learns to ground natural language words describing objects using supervision from an interactive humanrobot “I Spy” game. In this game, the human and robot take turns describing one object among several, then trying to guess which object the other has described. All supervision labels were gathered from human participants physically present to play this game with a robot. We demonstrate that our multi-modal system for grounding natural language outperforms a traditional, vision-only grounding framework by comparing the two on the “I Spy” task. We also provide a qualitative analysis of the groundings learned in the game, visualizing what words are understood better with multi-modal sensory information as well as identifying learned word meanings that correlate with physical object properties (e.g. ‘small’ negatively correlates with object weight).
Randomized General Regression Network for Identification of Defect Patterns in Semiconductor Wafer Maps

Defect detection and classification in semiconductor wafers has received an increasing attention from both industry and academia alike. Wafer defects are a serious problem that could cause massive losses to the companies' yield. The defects occur as a result of a lengthy and complex fabrication process involving hundreds of stages, and they can create unique patterns. If these patterns were to be identified and classified correctly, then the root of the fabrication problem can be recognized and eventually resolved. Machine learning (ML) techniques have been widely accepted and are well suited for such classification-/identification problems. However, none of the existing ML model's performance exceeds 96% in identification accuracy for such tasks. In this paper, we develop a state-of-the-art classifying algorithm using multiple ML techniques, relying on a general-regression-network-based consensus learning model along with a powerful randomization technique. We compare our proposed method with the widely used ML models in terms of model accuracy, stability, and time complexity. Our method has proved to be more accurate and stable as compared to any of the existing algorithms reported in the literature, achieving its accuracy of 99.8%, stability of 1.128, and TBM of 15.8 s.
Product Barcode and Expiry Date Detection for the Visually Impaired Using a Smartphone

The use of an expired product may cause harm to its designated target. If the product is for human consumption, e.g. medicine, the result can be fatal. While most people can check the expiration date easily before using the product, it is very difficult for a visually impaired or a totally blind person to do so independently. This paper therefore proposes a solution that helps the visually impaired to identify a product and subsequently `read' the expiration date on a product using a handheld Smartphone. While there are a few commercial barcode decoder and text recognition applications for the mobile phone, they require the user to point the phone to the correct location - which is extremely hard for the visually impaired. We thus focus our research on helping the blind user to locate the barcode and the expiration date on a product package. After that, existing barcode decoding and OCR algorithms can be utilized to obtain the required information. A field trial with several bind- folded/totally-blind participants is conducted and shows that the proposed solution is effective in guiding a visually impaired user towards the barcode and expiry information, although some issues remain with the reliability of the off-the-shelf decoding algorithms on low-resolution videos.
User Modeling in Human–Computer Interaction

A fundamental objective of human–computer interaction research is to make systems more usable, more useful, and to provide users with experiences fitting their specific background knowledge and objectives. The challenge in an information-rich world is not only to make information available to people at any time, at any place, and in any form, but specifically to say the “right” thing at the “right” time in the “right” way. Designers of collaborative human–computer systems face the formidable task of writing software for millions of users (at design time) while making it work as if it were designed for each individual user (only known at use time). User modeling research has attempted to address these issues. In this article, I will first review the objectives, progress, and unfulfilled hopes that have occurred over the last ten years, and illustrate them with some interesting computational environments and their underlying conceptual frameworks. A special emphasis is given to high-functionality applications and the impact of user modeling to make them more usable, useful, and learnable. Finally, an assessment of the current state of the art followed by some future challenges is given.
Unsupervised Multiway Data Analysis: A Literature Survey

Two-way arrays or matrices are often not enough to represent all the information in the data and standard two-way analysis techniques commonly applied on matrices may fail to find the underlying structures in multi-modal datasets. Multiway data analysis has recently become popular as an exploratory analysis tool in discovering the structures in higher-order datasets, where data have more than two modes. We provide a review of significant contributions in the literature on multiway models, algorithms as well as their applications in diverse disciplines including chemometrics, neuroscience, social network analysis, text mining and computer vision.
PsychSim: Modeling Theory of Mind with Decision-Theoretic Agents

Agent-based modeling of human social behavior is an increasingly important research area. A key factor in human social interaction is our beliefs about others, a theory of mind. Whether we believe a message depends not only on its content but also on our model of the communicator. How we act depends not only on the immediate effect but also on how we believe others will react. In this paper, we discuss PsychSim, an implemented multiagent-based simulation tool for modeling interactions and influence. While typical approaches to such modeling have used first-order logic, PsychSim agents have their own decision-theoretic model of the world, including beliefs about its environment and recursive models of other agents. Using these quantitative models of uncertainty and preferences, we have translated existing psychological theories into a decision-theoretic semantics that allow the agents to reason about degrees of believability in a novel way. We discuss PsychSim’s underlying architecture and describe its application to a school violence scenario for illustration.
Surface light fields for 3D photography

A surface light field is a function that assigns a color to each ray originating on a surface. Surface light fields are well suited to constructing virtual images of shiny objects under complex lighting conditions. This paper presents a framework for construction, compression, interactive rendering, and rudimentary editing of surface light fields of real objects. Generalization of vector quantization and principal component analysis are used to construct a compressed representation of an object's surface light field from photographs and range scans. A new rendering algorithm achieves interactive rendering of images from the compressed representation, incorporating view-dependent geometric level-of-detail control. The surface light field representation can also be directly edited to yield plausible surface light fields for small changes in surface geometry and reflectance properties.
A de-identifier for medical discharge summaries

Clinical records contain significant medical information that can be useful to researchers in various disciplines. However, these records also contain personal health information (PHI) whose presence limits the use of the records outside of hospitals. The goal of de-identification is to remove all PHI from clinical records. This is a challenging task because many records contain foreign and misspelled PHI; they also contain PHI that are ambiguous with non-PHI. These complications are compounded by the linguistic characteristics of clinical records. For example, medical discharge summaries, which are studied in this paper, are characterized by fragmented, incomplete utterances and domain-specific language; they cannot be fully processed by tools designed for lay language.



In this paper, we show that we can de-identify medical discharge summaries using a de-identifier, Stat De-id, based on support vector machines and local context (F-measure=97% on PHI). Our representation of local context aids de-identification even when PHI include out-of-vocabulary words and even when PHI are ambiguous with non-PHI within the same corpus. Comparison of Stat De-id with a rule-based approach shows that local context contributes more to de-identification than dictionaries combined with hand-tailored heuristics (F-measure=85%). Comparison with two well-known named entity recognition (NER) systems, SNoW (F-measure=94%) and IdentiFinder (F-measure=36%), on five representative corpora show that when the language of documents is fragmented, a system with a relatively thorough representation of local context can be a more effective de-identifier than systems that combine (relatively simpler) local context with global context. Comparison with a Conditional Random Field De-identifier (CRFD), which utilizes global context in addition to the local context of Stat De-id, confirms this finding (F-measure=88%) and establishes that strengthening the representation of local context may be more beneficial for de-identification than complementing local with global context.
Scenario-Based Analysis of Software Architecture

Software architecture is one of the most important tools for designing and understanding a system, whether that system is in preliminary design, active deployment, or maintenance. Scenarios are important tools for exercising an architecture in order to gain information about a system’s fitness with respect to a set of desired quality attributes. This paper presents an experiential case study illustrating the methodological use of scenarios to gain architecture-level understanding and predictive insight into large, real-world systems in various domains. A structured method for scenario-based architectural analysis is presented, using scenarios to analyze architectures with respect to achieving quality attributes. Finally, lessons and morals are presented, drawn from the growing body of experience in applying scenario-based architectural analysis techniques.
Analysis of SEER Dataset for Breast Cancer Diagnosis using C4.5 Classification Algorithm

Medical professionals need a reliable prediction methodology to diagnose cancer and distinguish between the different stages in cancer. Classification is a data mining function that assigns items in a collection to target groups or classes. C4.5 classification algorithm has been applied to SEER breast cancer dataset to classify patients into either “Carcinoma in situ” (beginning or pre-cancer stage) or “Malignant potential” group. Pre-processing techniques have been applied to prepare the raw dataset and identify the relevant attributes for classification. Random test samples have been selected from the pre-processed data to obtain classification rules. The rule set obtained was tested with the remaining data. The results are presented and discussed.  Breast Cancer Diagnosis, Classification, Clinical Data, SEER Dataset, C4.5 Algorithm
AgOnt: Ontology for Agriculture Internet of Things

Recent advances in networking and sensor technologies allow various physical world objects connected to form the Internet of Things (IOT). As more sensor networks are being deployed in agriculture today, there is a vision of integrating different agriculture IT system into the agriculture IOT. The key challenge of such integration is how to deal with semantic heterogeneity of multiple information resources. The paper proposes an ontology-based approach to describe and extract the semantics of agriculture objects and provides a mechanism for sharing and reusing agriculture knowledge to solve the semantic interoperation problem. AgOnt, ontology for the agriculture IOT, is built from agriculture terminologies and the lifecycles including seeds, grains, transportation, storage and consumption. According to this unified meta-model, heterogeneous agriculture data sources can be integrated and accessed seamlessly.
Voltage Doubler/Tripler Current-Mode Charge Pump Topology with simple "Gear Box"

DC to DC converters which are realized as a switched capacitor type are very common in display and lighting low power solutions. This applications requires a lot of different supply voltage rails with different demand on the output power. E.g. a thin-film-transistor (TFT) LCD panel requires 24V - 35V at 10mA - 50mA to switch pixel transistors. A current-mode regulated charge pump generates this rail from a 12V -18V input supply. The selected charge pump topology uses a simple "gear box" controlled by a single feedback amplifier to select between doubler and tripler modes of operation. This feedback amplifier simultaneously regulates output voltage and selects the operating mode. The new topology allows less circuit complexity compare to common "gear box" solutions. Therefore this converter system with flexible conversion ratios to support different VIN/VOUT ratios and load currents is the ideal solution to be most efficient. Also the advantages of current-mode control where one transistor in the output stage operates as a regulated current source on the low voltage rail which can theoretically support any power stage topology will be demonstrated. The proposed charge pump is realized in a BICMOS process with HV extension. Finally the measurement results especially regarding mode transitions will be shown.
Isotropic Remeshing of Surfaces: A Local Parameterization Approach

We present a method for isotropic remeshing of arbitrary genus surfaces. The method is based on a mesh adaptation process, namely, a sequence of local modifications performed on a copy of the original mesh, while referring to the original mesh geometry. The algorithm has three stages. In the first stage the required number or vertices are generated by iterative simplification or refinement. The second stage performs an initial vertex partition using an area-based relaxation method. The third stage achieves precise isotropic vertex sampling prescribed by a given density function on the mesh. We use a modification of Lloyd’s relaxation method to construct a weighted centroidal Voronoi tessellation of the mesh. We apply these iterations locally on small patches of the mesh that are parameterized into the 2D plane. This allows us to handle arbitrary complex meshes with any genus and any number of boundaries. The efficiency and the accuracy of the remeshing process is achieved using a patch-wise parameterization technique.  Surface mesh generation, isotropic triangle meshing, centroidal Voronoi tessellation, local parameterization. ∗ Technion, Haifa, Israel † INRIA Sophia-Antipolis ‡ Technion, Haifa, Israel Remaillage isotrope de surfaces utilisant une paramétrisation locale Résumé : Cet article décrit une méthode de remaillage isotrope de surfaces triangulées. L’approche repose sur une technique d’adaptation locale du maillage. L’idée consiste à opérer une séquence d’opérations élémentaires sur une copie du maillage original, tout en faisant référence au maillage original pour la géométrie. L’algorithme comporte trois étapes. La première étape ramène la complexité du maillage au nombre de sommets désiré par raffinement ou décimation itérative. La seconde étape opère une première répartition des sommets via une technique de relaxation optimisant un équilibrage local des aires sur les triangles. La troisième étape opère un placement isotrope des sommets via une relaxation de Lloyd pour construire une tessellation de Voronoi centrée. Les itérations de relaxation de Lloyd sont appliquées localement dans un espace paramétrique 2D calculé à la volée sur un sous-ensemble de la triangulation originale de telle que sorte que les triangulations de complexité et de genre arbitraire puissent être efficacement remaillées. Mots-clés : Maillage de surfaces, maillage triangulaire isotrope, diagrammes de Voronoi centrés, paramétrisation locale.  Remeshing of Surfaces
Software defined radios for small satellites

Clusters, constellations, formations, or `swarms' of small satellites are fast becoming a way to perform scientific and technological missions more affordably. As objectives of these missions become more ambitious, there are still problems in increasing the number of communication windows, supporting multiple signals, and increasing data rates over reliable intersatellite and ground links to Earth. Also, there is a shortage of available frequencies in the 2 m and 70 cm bands due to rapid increase in the number of CubeSats orbiting the Earth - leading to further regulatory issues. Existing communication systems and radio signal processing Intellectual Property (IP) cores cannot fully address these challenges. One of the possible strategies to solve these issues is by equipping satellites with a Software Defined Radio (SDR). SDR is a key area to realise various software implementations which enable an adaptive and reconfigurable communication system without changing any hardware device or feature. This paper proposes a new SDR architecture which utilises a combination of Field Programmable Gate Array (FPGA) and field programmable Radio Frequency (RF) transceiver to solve back-end and front- end challenges and thereby enabling reception of multiple signals or satellites using single user equipment.
Modeling Regulatory Networks with Weight Matrices

Systematic gene expression analyses provide comprehensive information about the transcriptional response to different environmental and developmental conditions. With enough gene expression data points, computational biologists may eventually generate predictive computer models of transcription regulation. Such models will require computational methodologies consistent with the behavior of known biological systems that remain tractable. We represent regulatory relationships between genes as linear coefficients or weights, with the "net" regulation influence on a gene's expression being the mathematical summation of the independent regulatory inputs. Test regulatory networks generated with this approach display stable and cyclically stable gene expression levels, consistent with known biological systems. We include variables to model the effect of environmental conditions on transcription regulation and observed various alterations in gene expression patterns in response to environmental input. Finally, we use a derivation of this model system to predict the regulatory network from simulated input/output data sets and find that it accurately predicts all components of the model, even with noisy expression data.
Procedural generation of villages on arbitrary terrains

Although procedural modeling of cities has attracted a lot of attention for the past decade, populating arbitrary landscapes with non-urban settlements remains an open problem. In this work, we focus on the modeling of small, European villages that took benefit of terrain features to settle in safe, sunny or simply convenient places. We introduce a three step procedural generation method. First, an iterative process based on interest maps is used to progressively generate settlement seeds and the roads that connect them. The fact that a new road attracts settlers while a new house often leads to some extension of the road network is taken into account. Then, an anisotropic conquest method is introduced to segment the land into parcels around settlement seeds. Finally, we introduce open shape grammar to generate 3D geometry that adapts to the local slope. We demonstrate the effectiveness of our method by generating different kinds of village on arbitrary terrains, from a mountain hamlet to a fisherman village, and validate through comparison with real data.
An equalization technique for orthogonal frequency-division multiplexing systems in time-variant multipath channels

A loss of subchannel orthogonality due to timevariant multipath channels in orthogonal frequency-division multiplexing (OFDM) systems leads to interchannel interference (ICI) which increases the error floor in proportion to the Doppler frequency. In this paper, a simple frequency-domain equalization technique which can compensate for the effect of ICI in a multipath fading channel is proposed. In this technique, the equalization of the received OFDM signal is achieved by using the assumption that the channel impulse response (CIR) varies in a linear fashion during a block period and by compensating for the ICI terms that significantly affect the bit-error rate (BER) performance.
Introducing computational thinking in education courses

As computational thinking becomes a fundamental skill for the 21st century, K-12 teachers should be exposed to computing principles. This paper describes the implementation and evaluation of a computational thinking module in a required course for elementary and secondary education majors. We summarize the results from open-ended and multiple-choice questionnaires given both before and after the module to assess the students' attitudes toward and understanding of computational thinking. The results suggest that given relevant information about computational thinking, education students' attitudes toward computer science becomes more favorable and they will be more likely to integrate computing principles in their future teaching.