nils-herrmann/clean_scirepeval huggingface dataset:
A Survey of Image Processing Algorithms in Digital Mammography

Mammography is at present the best available technique for early detection of breast cancer. The most common breast abnormalities that may indicate breast cancer are masses and calcifications. In some cases, subtle signs that can also lead to a breast cancer diagnosis, such as architectural distortion and bilateral asymmetry, are present. Breast abnormalities are defined with wide range of features and may be easily missed or misinterpreted by radiologists while reading large amount of mammographic images provided in screening programs. To help radiologists provide an accurate diagnosis, a computer-aided detection (CADe) and computer-aided diagnosis (CADx) algorithms are being developed. CADe and CADx algorithms help reducing the number of false positives and they assist radiologists in deciding between follow up and biopsy. This chapter gives a survey of image processing algorithms that have been developed for detection of masses and calcifications. An overview of algorithms in each step (segmentation step, feature extraction step, feature selection step, classification step) of the mass detection algorithms is given. Wavelet detection methods and other recently proposed methods for calcification detection are presented. An overview of contrast enhancement and noise equalization methods is given as well as an overview of calcification classification algorithms.
Filing, piling, and everything in between: The dynamics of E-mail inbox management

Managing the constant flow of incoming messages is a daily challenge faced by knowledge workers who use technologies such as e-mail and other digital communication tools. This study focuses on the most ubiquitous of these technologies, e-mail, and unobtrusively explores the ongoing inbox-management activities of thousands of users worldwide over a period of 8 months. The study describes the dynamics of these inboxes throughout the day and the week as users strive to handle incoming messages, read them, classify them, respond to them in a timely manner, and archive them for future reference, all while carrying out the daily tasks of knowledge workers. It then tests several hypotheses about the influence of specific inbox-management behaviors in mitigating the causes of e-mail overload, and proposes a continuous index that quantifies one of these inbox-management behaviors. This inbox clearing index (ICI) expands on the widely cited trichotomous classification of users into frequent filers, spring cleaners, and no filers, as suggested by Whittaker and Sidner (1996). We propose that the ICI allows shifting the focus, from classifying users to characterizing a diversity of user behaviors and measuring the relationships between these behaviors and desired outcomes.
Broadband Reflectarray Antenna Using Subwavelength Elements Based on Double Square Meander-Line Rings

A linearly polarized broadband reflectarray is presented employing a novel single layer subwavelength phase shifting element. The size of the element is a fifth of a wavelength at the center frequency of 10 GHz and the element consists of double concentric square rings of meander lines. By changing the length of the meander line, a 420° phase variation range is achieved at the center frequency. This characteristic makes the proposed configuration unique, as most of the reported subwavelength reflectarray elements can only realize a phase range far less than 360°. In addition, the slope of the phase response remains almost constant from 9 to 11 GHz, demonstrating a broadband property. A $48 \times 48$-element reflectarray antenna is simulated, fabricated, and measured. Good agreement is obtained between simulated and measured results. A measured 1.5-dB gain bandwidth of 18% and 56.5% aperture efficiency is achieved.
MaxMax: A Graph-Based Soft Clustering Algorithm Applied to Word Sense Induction

This paper introduces a linear time graph-based soft clustering algorithm. The algorithm applies a simple idea: given a graph, vertex pairs are assigned to the same cluster if either vertex has maximal affinity to the other. Clusters of varying size, shape, and density are found automatically making the algorithm suited to tasks such Word Sense Induction (WSI), where the number of classes is unknown and where class distributions may be skewed. The algorithm is applied to two WSI tasks, obtaining results comparable with those of systems adopting existing, state-of-the-art methods.
A REAL-TIME GENERIC TRACKER USING CONVOLUTIONAL NEURAL NETWORKS

This paper presents a novel frame-pair based method for visual object tracking. Instead of adopting two-stream Convolutional Neural Networks (CNNs) to represent each frame, we stack frame pairs as the input, resulting in a single-stream CNN tracker with much fewer parameters. The proposed tracker can learn generic motion patterns of objects with much less annotated videos than previous methods. Besides, it is found that trackers trained using two successive frames tend to predict the centers of searching windows as the locations of tracked targets. To alleviate this problem, we propose a novel sampling strategy for off-line training. Specifically, we construct a pair by sampling two frames with a random offset. The offset controls the moving smoothness of objects. Experiments on the challenging VOT14 and OTB datasets show that the proposed tracker performs on par with recently developed generic trackers, but with much less memory. In addition, our tracker can run in a speed of over 100 (30) fps with a GPU (CPU), much faster than most deep neural network based trackers.
Attitudes, satisfaction and usage: Factors contributing to each in the acceptance of information technology

This research tests and develops the Technology Acceptance Model (TAM), introduced by Davis (1986), which attempts to explain end users’ attitudes to computing technologies. It introduces several new variables, including compatibility, user characteristics, system rating and the enduser computing satisfaction (EUCS) construct, a surrogate measure for IT success and acceptance. A questionnaire with over seventy items was completed by a large number of users and LISREL, a technique for modelling a system of structural equations, was used to analyse the responses. The output shows the model as a whole ® ts the data very well and indicates signi® cant relationships between variables in the model. These results con® rm that TAM is a valuable tool for predicting attitudes, satisfaction, and usage from beliefs and external variables. They also show that relative advantage of the system contributed most to attitudes and satisfaction. Compatibility (of the system to the task performed) contributed most to usage and was the most important antecedent of the belief variables, including relative advantage.
LIDAR-based autonomous wheelchair

Electric wheelchairs restore mobility and independence to those with injuries limiting their movement. However, individuals suffering from arthritis or motor neuron degenerative diseases e.g. amyotrophic lateral sclerosis (ALS) lack the fine motor control to fluently control electric wheelchairs. These individuals are left dependent on others, drastically reducing their independence and quality of life. This study seeks to mitigate these problems by presenting a wheelchair capable of autonomous navigation, with minimal directive from the user. The wheelchair uses the robot operating system (ROS), microcontrollers, rotary encoders, and a LIDAR unit to navigate. The LI-DAR unit provides vital measurements of the environment which are used to construct a map of the surroundings. ROS receives input from the LIDAR sensor along with the rotary encoders to determine a navigable path to a user-defined destination. ROS sends movement commands to a microcontroller to move the wheelchair along the designated path. The navigation system is designed to adapt to changes in the environment and reroute the wheelchair if new obstacles emerge. During the autonomous system testing, involving ten trials, the wheelchair successfully navigated to the expected destination in 100% of the trials. Based on these preliminary results, the autonomous wheelchair has the potential to restore independent indoor mobility. Further development could provide autonomous navigation capabilities in outdoor environments.
Neurocognitive effects of clozapine, olanzapine, risperidone, and haloperidol in patients with chronic schizophrenia or schizoaffective disorder.

Newer antipsychotic drugs have shown promise in ameliorating neurocognitive deficits in patients with schizophrenia, but few studies have compared newer antipsychotic drugs with both clozapine and conventional agents, particularly in patients who have had suboptimal response to prior treatments.



The authors examined the effects of clozapine, olanzapine, risperidone, and haloperidol on 16 measures of neurocognitive functioning in a double-blind, 14-week trial involving 101 patients. A global score was computed along with scores in four neurocognitive domains: memory, attention, motor function, and general executive and perceptual organization.



Global neurocognitive function improved with olanzapine and risperidone treatment, and these improvements were superior to those seen with haloperidol. Patients treated with olanzapine exhibited improvement in the general and attention domains but not more than that observed with other treatments. Patients treated with risperidone exhibited improvement in memory that was superior to that of both clozapine and haloperidol. Clozapine yielded improvement in motor function but not more than in other groups. Average effect sizes for change were in the small to medium range. More than half of the patients treated with olanzapine and risperidone experienced "clinically significant" improvement (changes in score of at least one-half standard deviation relative to baseline). These findings did not appear to be mediated by changes in symptoms, side effects, or blood levels of medications.



Patients with a history of suboptimal response to conventional treatments may show cognitive benefits from newer antipsychotic drugs, and there may be differences between atypical antipsychotic drugs in their patterns of cognitive effects.
A Semi-automatized Modular Annotation Tool for Ancient Manuscript Annotation

In this paper, we present DIVAnnotation, an ancient document annotation tool which is freely available as open source. This software is easily modular thanks to the splitting of the different annotation steps through the use of a tabbed graphical user iterface. State-of-the-art document image analysis methods are included through web services, thus allowing users to generate automatically annotations and correct them manually when needed. The annotations are stored into a highly structured TEI file which makes data access and manipulation simple. A Java library for managing TEI files generated by DIVAnnotation is also provided as open source.
DeepSurveyCam - A Deep Ocean Optical Mapping System

Underwater photogrammetry and in particular systematic visual surveys of the deep sea are by far less developed than similar techniques on land or in space. The main challenges are the rough conditions with extremely high pressure, the accessibility of target areas (container and ship deployment of robust sensors, then diving for hours to the ocean floor), and the limitations of localization technologies (no GPS). The absence of natural light complicates energy budget considerations for deep diving flash-equipped drones. Refraction effects influence geometric image formation considerations with respect to field of view and focus, while attenuation and scattering degrade the radiometric image quality and limit the effective visibility. As an improvement on the stated issues, we present an AUV-based optical system intended for autonomous visual mapping of large areas of the seafloor (square kilometers) in up to 6000 m water depth. We compare it to existing systems and discuss tradeoffs such as resolution vs. mapped area and show results from a recent deployment with 90,000 mapped square meters of deep ocean floor.
Deep learning for health informatics: Recent trends and future directions

Health informatics has emerged as a growing domain of interest among researchers world-wide owing to its major implications on society. Applications of machine learning in healthcare range from disease prediction to patient-level personalized services. The prevalence of big data in healthcare has paved the way for applications based on deep learning techniques in the past few years. This paper reviews recent trends and applications of deep learning applied to the healthcare domain. We highlight recent research work, identify challenges and suggest possible future directions that could be pursued further in this domain.
Combining Data Mining and Machine Learning for Effective Fraud Detection *

This paper describes the automatic design of methods for detecting fraudulent behavior. Much of the design is accomplished using a series of machine learning methods. In particular, we combine data mining and constructive induction with more standard machine learning techniques to design methods for detecting fraudulent usage of cellular telephones based on profiling customer behavior. Specifically, we use a rulelearning program to uncover indicators of fraudulent behavior from a large database of cellular calls. These indicators are used to create profilers, which then serve as features to a system that combines evidence from multiple profilers to generate high-confidence alarms. Experiments indicate that this automatic approach performs nearly as well as the best hand-tuned methods for detecting fraud.
Blitz: A Principled Meta-Algorithm for Scaling Sparse Optimization

By reducing optimization to a sequence of small subproblems, working set methods achieve fast convergence times for many challenging problems. Despite excellent performance, theoretical understanding of working sets is limited, and implementations often resort to heuristics to determine subproblem size, makeup, and stopping criteria. We propose BLITZ, a fast working set algorithm accompanied by useful guarantees. Making no assumptions on data, our theory relates subproblem size to progress toward convergence. This result motivates methods for optimizing algorithmic parameters and discarding irrelevant variables as iterations progress. Applied to `1-regularized learning, BLITZ convincingly outperforms existing solvers in sequential, limited-memory, and distributed settings. BLITZ is not specific to `1-regularized learning, making the algorithm relevant to many applications involving sparsity or constraints.
The effect of five prosthetic feet on the gait and loading of the sound limb in dysvascular below-knee amputees.

The purpose of this study was to determine the effects of prosthetic foot design on the vertical ground reaction forces experienced by the sound and amputated limbs in a group of persons with dysvascular below-knee amputations. Stride characteristics, joint motion, and ground reaction forces were recorded simultaneously during a self-selected free walking velocity in seven subjects wearing five different prosthetic feet (SACH, Flex-Foot, Carbon Copy II, Seattle, Quantum). Subjects used each foot for one month prior to testing. Results indicated that the sound limb was exposed to higher vertical ground reaction forces than normal despite a reduced walking velocity. Use of the Flex-Foot resulted in the lowest sound limb vertical forces, which appears to be related to its large arc of dorsiflexion motion. In addition, there was increased loading response knee flexion of the sound limb indicating an attempt by these subjects to modulate floor impact. These results suggest that the intact lower extremity is susceptible to excessive floor impact, and that prosthetic foot design can have an effect on the magnitude of the vertical forces experienced by the limb.
Health of Things Algorithms for Malignancy Level Classification of Lung Nodules

Lung cancer is one of the leading causes of death world wide. Several computer-aided diagnosis systems have been developed to help reduce lung cancer mortality rates. This paper presents a novel structural co-occurrence matrix (SCM)-based approach to classify nodules into malignant or benign nodules and also into their malignancy levels. The SCM technique was applied to extract features from images of nodules and classifying them into malignant or benign nodules and also into their malignancy levels. The computed tomography exams from the lung image database consortium and image database resource initiative datasets provide information concerning nodule positions and their malignancy levels. The SCM was applied on both grayscale and Hounsfield unit images with four filters, to wit, mean, Laplace, Gaussian, and Sobel filters creating eight different configurations. The classification stage used three well-known classifiers: multilayer perceptron, support vector machine, and <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-nearest neighbors algorithm and applied them to two tasks: (<inline-formula> <tex-math notation="LaTeX">$i$ </tex-math></inline-formula>) to classify the nodule images into malignant or benign nodules and (<inline-formula> <tex-math notation="LaTeX">$ii$ </tex-math></inline-formula>) to classify the lung nodules into malignancy levels (1 to 5). The results of this approach were compared to four other feature extraction methods: gray-level co-occurrence matrix, local binary patterns, central moments, and statistical moments. Moreover, the results here were also compared to the results reported in the literature. Our approach outperformed the other methods in both tasks; it achieved 96.7% for both accuracy and F-Score metrics in the first task, and 74.5% accuracy and 53.2% F-Score in the second. These experimental results reveal that the SCM successfully extracted features of the nodules from the images and, therefore may be considered as a promising tool to support medical specialist to make a more precise diagnosis concerning the malignancy of lung nodules.
Implicit Discourse Relation Classification via Multi-Task Neural Networks

Without discourse connectives, classifying implicit discourse relations is a challenging task and a bottleneck for building a practical discourse parser. Previous research usually makes use of one kind of discourse framework such as PDTB or RST to improve the classification performance on discourse relations. Actually, under different discourse annotation frameworks, there exist multiple corpora which have internal connections. To exploit the combination of different discourse corpora, we design related discourse classification tasks specific to a corpus, and propose a novel Convolutional Neural Network embedded multi-task learning system to synthesize these tasks by learning both unique and shared representations for each task. The experimental results on the PDTB implicit discourse relation classification task demonstrate that our model achieves significant gains over baseline systems.
Clustering Student Programming Assignments to Multiply Instructor Leverage

A challenge in introductory and intermediate programming courses is understanding how students approached solving a particular programming problem, in order to provide feedback on how they might improve. In both Massive Open Online Courses (MOOCs) and large residential courses, such feedback is difficult to provide for each student individually. To multiply the instructor's leverage, we would like to group student submissions according to the general problem-solving strategy they used, as the first stage of a ``feedback pipeline''. We describe ongoing explorations of a variety of clustering algorithms and similarity metrics using a corpus of over 800 student submissions to a simple programming assignment from a programming MOOC. We find that for a majority of submissions, it is possible to automatically create clusters such that an instructor ``eyeballing'' some representative submissions from each cluster can readily describe qualitatively what the common elements are in student submissions in that cluster. This information can be the basis for feedback to the students or for comparing one group of students' approach with another's.
Elastic Scheduling of Scientific Workflows under Deadline Constraints in Cloud Computing Environments

Scientific workflow applications are collections of several structured activities and fine-grained computational tasks. Scientific workflow scheduling in cloud computing is a challenging research topic due to its distinctive features. In cloud environments, it has become critical to perform efficient task scheduling resulting in reduced scheduling overhead, minimized cost and maximized resource utilization while still meeting the user-specified overall deadline. This paper proposes a strategy, Dynamic Scheduling of Bag of Tasks based workflows (DSB), for scheduling scientific workflows with the aim to minimize financial cost of leasing Virtual Machines (VMs) under a user-defined deadline constraint. The proposed model groups the workflow into Bag of Tasks (BoTs) based on data dependency and priority constraints and thereafter optimizes the allocation and scheduling of BoTs on elastic, heterogeneous and dynamically provisioned cloud resources called VMs in order to attain the proposed method’s objectives. The proposed approach considers pay-as-you-go Infrastructure as a Service (IaaS) clouds having inherent features such as elasticity, abundance, heterogeneity and VM provisioning delays. A trace-based simulation using benchmark scientific workflows representing real world applications, demonstrates a significant reduction in workflow computation cost while the workflow deadline is met. The results validate that the proposed model produces better success rates to meet deadlines and cost efficiencies in comparison to adapted state-of-the-art algorithms for similar problems.
Hybridly Connected Structure for Hybrid Beamforming in mmWave Massive MIMO Systems

In this paper, we propose a hybridly connected structure for hybrid beamforming in millimeter-wave (mmWave) massive MIMO systems, where the antenna arrays at the transmitter and receiver consist of multiple sub-arrays, each of which connects to multiple radio frequency (RF) chains, and each RF chain connects to all the antennas corresponding to the sub-array. In this structure, through successive interference cancelation, we decompose the precoding matrix optimization problem into multiple precoding sub-matrix optimization problems. Then, near-optimal hybrid digital and analog precoders are designed through factorizing the precoding sub-matrix for each sub-array. Furthermore, we compare the performance of the proposed hybridly connected structure with the existing fully and partially connected structures in terms of spectral efficiency, the required number of phase shifters, and energy efficiency. Finally, simulation results are presented to demonstrate that the spectral efficiency of the hybridly connected structure is better than that of the partially connected structure and that its spectral efficiency can approach that of the fully connected structure with the increase in the number of RF chains. Moreover, the proposed algorithm for the hybridly connected structure is capable of achieving higher energy efficiency than existing algorithms for the fully and partially connected structures.