nils-herrmann/clean_scirepeval huggingface dataset:
Atlas-based rib-bone detection in chest X-rays

This paper investigates using rib-bone atlases for automatic detection of rib-bones in chest X-rays (CXRs). We built a system that takes patient X-ray and model atlases as input and automatically computes the posterior rib borders with high accuracy and efficiency. In addition to conventional atlas, we propose two alternative atlases: (i) automatically computed rib bone models using Computed Tomography (CT) scans, and (ii) dual energy CXRs. We test the proposed approach with each model on 25 CXRs from the Japanese Society of Radiological Technology (JSRT) dataset and another 25 CXRs from the National Library of Medicine CXR dataset. We achieve an area under the ROC curve (AUC) of about 95% for Montgomery and 91% for JSRT datasets. Using the optimal operating point of the ROC curve, we achieve a segmentation accuracy of 88.91±1.8% for Montgomery and 85.48±3.3% for JSRT datasets. Our method produces comparable results with the state-of-the-art algorithms. The performance of our method is also excellent on challenging X-rays as it successfully addressed the rib-shape variance between patients and number of visible rib-bones due to patient respiration.
MovieGEN : A Movie Recommendation System

In this paper we introduce MovieGEN, an expert system for movie recommendation. We implement the system using machine learning and cluster analysis based on a hybrid recommendation approach. The system takes in the users’ personal information and predicts their movie preferences using well-trained support vector machine (SVM) models. Based on the SVM prediction it selects movies from the dataset, clusters the movies and generates questions to the users. Based on the users’ answers, it refines its movie set and it finally recommends movies for the users. In the process we traverse the parameter space, thus enabling the customizability of the system.
CCVis : Visual Analytics of Student Online Learning Behaviors Using Course Clickstream Data

As more and more college classrooms utilize online platforms to facilitate teaching and learning activities, analyzing student online behaviors becomes increasingly important for instructors to effectively monitor and manage student progress and performance. In this paper, we present CCVis, a visual analytics tool for analyzing the course clickstream data and exploring student online learning behaviors. Targeting a large college introductory course with over two thousand student enrollments, our goal is to investigate student behavior patterns and discover the possible relationships between student clickstream behaviors and their course performance. We employ higher-order network and structural identity classification to enable visual analytics of behavior patterns from the massive clickstream data. CCVis includes four coordinated views (the behavior pattern, behavior breakdown, clickstream comparative, and grade distribution views) for user interaction and exploration. We demonstrate the effectiveness of CCVis through case studies along with an ad-hoc expert evaluation. Finally, we discuss the limitation and extension of this work.
A Framework for Using Hypothesis-Driven Approaches to Support Data-Driven Learning Analytics in Measuring Computational Thinking in Block-Based Programming Environments

Systematic endeavors to take computer science (CS) and computational thinking (CT) to scale in middle and high school classrooms are underway with curricula that emphasize the enactment of authentic CT skills, especially in the context of programming in block-based programming environments. There is, therefore, a growing need to measure students’ learning of CT in the context of programming and also support all learners through this process of learning computational problem solving. The goal of this research is to explore hypothesis-driven approaches that can be combined with data-driven ones to better interpret student actions and processes in log data captured from block-based programming environments with the goal of measuring and assessing students’ CT skills. Informed by past literature and based on our empirical work examining a dataset from the use of the Fairy Assessment in the Alice programming environment in middle schools, we present a framework that formalizes a process where a hypothesis-driven approach informed by Evidence-Centered Design effectively complements data-driven learning analytics in interpreting students’ programming process and assessing CT in block-based programming environments. We apply the framework to the design of Alice tasks for high school CS to be used for measuring CT during programming.
Multi-User Energy Consumption Monitoring and Anomaly Detection with Partial Context Information

Anomaly detection is an important problem in building energy management in order to identify energy theft and inefficiencies. However, it is hard to differentiate actual anomalies from the genuine changes in energy consumption due to seasonal variations and changes in personal settings such as holidays. One of the important drawbacks of existing anomaly detection algorithms is that various unknown context variables, such as seasonal variations, can affect the energy consumption of users in ways that appear anomalous to existing time series based anomaly detection algorithms.
 In this paper, we present a system for monitoring the energy consumption of multiple users within a neighborhood and a novel algorithm for detecting anomalies by combining data from multiple users. For each user, the neighborhood is defined as the set of all other users that have similar characteristics (function, location or demography), and are therefore likely to react and consume energy in the similar way in response to the external conditions. The neighborhood can be predefined based on prior customer information, or can be identified through an analysis of historical energy consumption. The proposed algorithm works as a two-step process. In the first step, the algorithm periodically computes an anomaly score for each user by just considering their own energy consumption and variations in the consumption of the past. In the second step, the anomaly score for a user is adjusted by analyzing the energy consumption data in the neighborhood. The collation of data within the neighborhood allows the proposed algorithm to differentiate between these genuine effects and real anomalous behavior of users. Unlike multivariate time series anomaly detection algorithms, the proposed algorithm can identify specific users that are exhibiting anomalous behavior. The capabilities of the algorithm are demonstrated using several year-long real-world data sets, for commercial as well as residential consumers.
A fast transient LDO based on dual loop FVF with high PSRR

In this work, the design of a dual loop flipped voltage follower (FVF) based low-dropout regulator (LDO) to achieve fast transient response is proposed for all-digital phase locked loop (ADPLL) in the RFID application. With the help of an additional reference generation loop in FVF LDO and the cascaded structure, high power supply rejection ratio (PSRR) is achievable. This design is fabricated in GF 40nm CMOS technology. The FVF LDO core only occupies small area of 0.036 mm2. This area also includes 80pF on chip capacitor. Without large off-chip capacitor, this LDO is suitable for system-on-chip (SoC) requirement. Post layout simulation shows that fast response of 45ns and high PSRR of −42dB through up to 10GHz frequency range.
Collective classification for packed executable identification

Malware is any software designed to harm computers. Commercial anti-virus are based on signature scanning, which is a technique effective only when the malicious executables have been previously analysed and identified. Malware writers employ several techniques in order to hide their actual behaviour. Executable packing consists in encrypting or hiding the real payload of the executable. Generic unpacking techniques do not depend on the packer used, as they execute the binary within an isolated environment (namely `sandbox') to gather the real code of the packed executable. However, this approach is slow and, therefore, a filter step is required to determine when an executable has been packed. To this end, supervised machine learning approaches trained with static features from the executables have been proposed. Notwithstanding, supervised learning methods need the identification and labelling of a high number of packed and not packed executables. In this paper, we propose a new method for packed executable detection that adopts a collective learning approach to reduce the labelling requirements of completely supervised approaches. We performed an empirical validation demonstrating that the system maintains a high accuracy rate while the labelling efforts are lower than when using supervised learning.
Design of permanent-magnet generators for wind turbines

There has been much interest and studies in high efficiency wind generators with permanent magnet excitation due to the increasing availability of permanent magnet materials, especially Nd-Fe-. The present paper is aimed to outline the design, analysis of such a PM generator. The generator that is being used will be an 8-pole permanent magnet generator rated at 5 kW and using NdFeB for the field excitation. The details of the permanent magnet generator are also presented.
11 Sociocultural Theory and Second Language Learning

The intent of this chapter is to familiarize readers with the principles and constructs of an approach to learning and mental development known as Sociocultural Theory.1 Sociocultural Theory (SCT) has its origins in the writings of the Russian psychologist L. S. Vygotsky and his colleagues. SCT argues that human mental functioning is fundamentally a mediated process that is organized by cultural artifacts, activities, and concepts (Ratner, 2002).2 Within this framework, humans are understood to utilize existing cultural artifacts and to create new ones that allow them to regulate their biological and behavioral activity. Language use, organization, and structure are the primary means of mediation. Practically speaking, developmental processes take place through participation in cultural, linguistic, and historically formed settings such as family life and peer group interaction, and in institutional contexts like schooling, organized sports activities, and work places, to name only a few. SCT argues that while human neurobiology is a necessary condition for higher order thinking, the most important forms of human cognitive activity develop
ONLI: An ontology-based system for querying DBpedia using natural language paradigm

The Semantic Web has emerged as an extension of the current Web, in which Web content has well-defined meaning through the addition of logic-based metadata. However, current mechanisms for information retrieval from semantic knowledge bases restrict their use to only experienced users. To address this gap, the natural language processing (NLP) is deemed to be very intuitive from a use point of view, due to it hides the formality of a knowledge base as well as the executable query language. This paper presents a novel ontology-based information retrieval system for DBpedia called ONLI (Ontology-based Natural Language Interface). ONLI proposes the use of an ontology model in order to represent both the syntactic question’s structure and the question’s context. This model allows inferring the answer type expected by the user through an established question’s classification. These features allow reducing the search space thus increasing the probability of providing the correct answer. From this perspective, ONLI was evaluated in terms of their ability to find the correct answer into DBpedia’s content, achieving promising results and proving to be very useful to non-experienced users.
A Two-Stage Deanonymization Attack against Anonymized Social Networks

Digital traces left by users of online social networking services, even after anonymization, are susceptible to privacy breaches. This is exacerbated by the increasing overlap in user-bases among various services. To alert fellow researchers in both the academia and the industry to the feasibility of such an attack, we propose an algorithm, Seed-and-Grow, to identify users from an anonymized social graph, based solely on graph structure. The algorithm first identifies a seed subgraph, either planted by an attacker or divulged by a collusion of a small group of users, and then grows the seed larger based on the attacker's existing knowledge of the users' social relations. Our work identifies and relaxes implicit assumptions taken by previous works, eliminates arbitrary parameters, and improves identification effectiveness and accuracy. Simulations on real-world collected data sets verify our claim.
An IQ-modulator based heterodyne 77-GHz FMCW colocated MIMO radar system

In this work the realization of a 77-GHz frequency-division multiple-access-based frequency-modulated continuous-wave multiple-input multiple-output radar with four transceiver channels in conjunction with a non-uniform linear antenna array is presented. The radar system, consisting of an RF-frontend utilizes transceiver chips with integrated inphase/quadrature-modulators to generate the frequency shifted transmit signals and a field programmable gate array-based baseband board, is used for test measurements to verify the system performance and to demonstrate the beamforming capability as well as the accuracy of the digital-beamforming method.
Towards High Performance Video Object Detection

There has been significant progresses for image object detection in recent years. Nevertheless, video object detection has received little attention, although it is more challenging and more important in practical scenarios. Built upon the recent works [37, 36], this work proposes a unified approach based on the principle of multi-frame end-to-end learning of features and cross-frame motion. Our approach extends prior works with three new techniques and steadily pushes forward the performance envelope (speed-accuracy tradeoff), towards high performance video object detection.
The Balancing Cube: A Dynamic Sculpture As Test Bed for Distributed Estimation and Control

The balancing cube is a dynamic sculpture that can balance autonomously on any of its edges or corners (see Figures 14). When standing on a corner, the cube represents a three-dimensional (3-D) inverted pendulum with multiple actuation, sensing, and control units that are interconnected over a communication network. The main structural components are the cube body (a rigid aluminum structure with a cubic shape) and six identical rotating arms located on each of the cube's inner faces. The rotating arms are self-contained units carrying sensors, actuation, a computer, and a battery. Due to their modular design, these units are referred to as modules. As they rotate, they shift the overall center of mass of the system, exert forces on the cube structure, and can, as a result, influence the cube's motion. The modules constitute the agents in the distributed and networked control system; their joint objective is the stabilization of the cube. A video of the cube can be found on the project Web site .
Keeping up to date: An academic researcher's information journey

Keeping up to date with research developments is a central activity of academic researchers, but researchers face difficulties in managing the rapid growth of available scientific information. This study examined how researchers stay up to date, using the information journey model as a framework for analysis and investigating which dimensions influence information behaviors. We designed a 2-round study involving semistructured interviews and prototype testing with 61 researchers with 3 levels of seniority (PhD student to professor). Data were analyzed following a semistructured qualitative approach. Five key dimensions that influence information behaviors were identified: level of seniority, information sources, state of the project, level of familiarity, and how well defined the relevant community is. These dimensions are interrelated and their values determine the flow of the information journey. Across all levels of professional expertise, researchers used similar hard (formal) sources to access content, while soft (interpersonal) sources were used to filter information. An important “pain point” that future information tools should address is helping researchers filter information at the point of need.
Arabic Automatic Speech Recognition Enhancement

In this paper, we propose three approaches for Arabic automatic speech recognition. For pronunciation modeling, we propose a pronunciation variant generation with decision tree. For acoustic modeling, we propose the Hybrid approach to adapt the native acoustic model using another native acoustic model. Regarding the language model, we improve the language model using processed text. The experimental results show that the proposed pronunciation model approach has reduction in WER around 1%. The acoustic modeling reduce the WER by 1.2% and the adapted language modeling show reduction in WER by 1.9%.
Forward Light Cuts: A Scalable Approach to Real-Time Global Illumination

We present Forward Light Cuts, a novel approach to real-time global illumination using forward rendering techniques. We focus on unshadowed diffuse interactions for the first indirect light bounce in the context of large models such as the complex scenes usually encountered in CAD application scenarios. Our approach efficiently generates and uses a multiscale radiance cache by exploiting the geometry-specific stages of the graphics pipeline, namely the tessellator unit and the geometry shader. To do so, we assimilate virtual point lights to the scene’s triangles and design a stochastic decimation process chained with a partitioning strategy that accounts for both close-by strong light reflections, and distant regions from which numerous virtual point lights collectively contribute strongly to the end pixel. Our probabilistic solution is supported by a mathematical analysis and a number of experiments covering a wide range of application scenarios. As a result, our algorithm requires no precomputation of any kind, is compatible with dynamic view points, lighting condition, geometry and materials, and scales to tens of millions of polygons on current graphics hardware.
Attention, self-regulation and consciousness.

Consciousness has many aspects. These include awareness of the world, feelings of control over one's behaviour and mental state (volition), and the notion of continuing self. Focal (executive) attention is used to control details of our awareness and is thus closely related to volition. Experiments suggest an integrated network of neural areas involved in executive attention. This network is associated with our voluntary ability to select among competing items, to correct error and to regulate our emotions. Recent neuroimaging studies suggest that these various functions involve separate areas of the anterior cingulate. We have adopted a strategy of using marker tasks, shown to activate the brain area by imaging studies, as a means of tracing the development of attentional networks. Executive attention appears to develop first to regulate distress during the first year of life. During later childhood the ability to regulate conflict among competing stimuli builds upon the earlier cingulate anatomy to provide a means of cognitive control. During childhood the activation of cingulate structures relates both to the child's success on laboratory tasks involving conflict and to parental reports of self-regulation and emotional control. These studies indicate a start in understanding the anatomy, circuitry and development of executive attention networks that serve to regulate both cognition and emotion.
GOGMA: Globally-Optimal Gaussian Mixture Alignment

Gaussian mixture alignment is a family of approaches that are frequently used for robustly solving the point-set registration problem. However, since they use local optimisation, they are susceptible to local minima and can only guarantee local optimality. Consequently, their accuracy is strongly dependent on the quality of the initialisation. This paper presents the first globally-optimal solution to the 3D rigid Gaussian mixture alignment problem under the L2 distance between mixtures. The algorithm, named GOGMA, employs a branch-and-bound approach to search the space of 3D rigid motions SE(3), guaranteeing global optimality regardless of the initialisation. The geometry of SE(3) was used to find novel upper and lower bounds for the objective function and local optimisation was integrated into the scheme to accelerate convergence without voiding the optimality guarantee. The evaluation empirically supported the optimality proof and showed that the method performed much more robustly on two challenging datasets than an existing globally-optimal registration solution.
Parsimonious language models for information retrieval

We systematically investigate a new approach to estimating the parameters of language models for information retrieval, called parsimonious language models. Parsimonious language models explicitly address the relation between levels of language models that are typically used for smoothing. As such, they need fewer (non-zero) parameters to describe the data. We apply parsimonious models at three stages of the retrieval process: 1) at indexing time; 2) at search time; 3) at feedback time. Experimental results show that we are able to build models that are significantly smaller than standard models, but that still perform at least as well as the standard approaches.
Alzheimer’s Disease Early Diagnosis Using Manifold-Based Semi-Supervised Learning

Alzheimer's disease (AD) is currently ranked as the sixth leading cause of death in the United States and recent estimates indicate that the disorder may rank third, just behind heart disease and cancer, as a cause of death for older people. Clearly, predicting this disease in the early stages and preventing it from progressing is of great importance. The diagnosis of Alzheimer's disease (AD) requires a variety of medical tests, which leads to huge amounts of multivariate heterogeneous data. It can be difficult and exhausting to manually compare, visualize, and analyze this data due to the heterogeneous nature of medical tests; therefore, an efficient approach for accurate prediction of the condition of the brain through the classification of magnetic resonance imaging (MRI) images is greatly beneficial and yet very challenging. In this paper, a novel approach is proposed for the diagnosis of very early stages of AD through an efficient classification of brain MRI images, which uses label propagation in a manifold-based semi-supervised learning framework. We first apply voxel morphometry analysis to extract some of the most critical AD-related features of brain images from the original MRI volumes and also gray matter (GM) segmentation volumes. The features must capture the most discriminative properties that vary between a healthy and Alzheimer-affected brain. Next, we perform a principal component analysis (PCA)-based dimension reduction on the extracted features for faster yet sufficiently accurate analysis. To make the best use of the captured features, we present a hybrid manifold learning framework which embeds the feature vectors in a subspace. Next, using a small set of labeled training data, we apply a label propagation method in the created manifold space to predict the labels of the remaining images and classify them in the two groups of mild Alzheimer's and normal condition (MCI/NC). The accuracy of the classification using the proposed method is 93.86% for the Open Access Series of Imaging Studies (OASIS) database of MRI brain images, providing, compared to the best existing methods, a 3% lower error rate.