nils-herrmann/clean_scirepeval huggingface dataset:
Reducing Snapshots to Points: A Visual Analytics Approach to Dynamic Network Exploration

We propose a visual analytics approach for the exploration and analysis of dynamic networks. We consider snapshots of the network as points in high-dimensional space and project these to two dimensions for visualization and interaction using two juxtaposed views: one for showing a snapshot and one for showing the evolution of the network. With this approach users are enabled to detect stable states, recurring states, outlier topologies, and gain knowledge about the transitions between states and the network evolution in general. The components of our approach are discretization, vectorization and normalization, dimensionality reduction, and visualization and interaction, which are discussed in detail. The effectiveness of the approach is shown by applying it to artificial and real-world dynamic networks.
An exoskeleton using controlled energy storage and release to aid ankle propulsion

Symmetric ankle propulsion is the cornerstone of efficient human walking. The ankle plantar flexors provide the majority of the mechanical work for the step-to-step transition and much of this work is delivered via elastic recoil from the Achilles' tendon — making it highly efficient. Even though the plantar flexors play a central role in propulsion, body-weight support and swing initiation during walking, very few assistive devices have focused on aiding ankle plantarflexion. Our goal was to develop a portable ankle exoskeleton taking inspiration from the passive elastic mechanisms at play in the human triceps surae-Achilles' tendon complex during walking. The challenge was to use parallel springs to provide ankle joint mechanical assistance during stance phase but allow free ankle rotation during swing phase. To do this we developed a novel ‘smart-clutch’ that can engage and disengage a parallel spring based only on ankle kinematic state. The system is purely passive — containing no motors, electronics or external power supply. This ‘energy-neutral’ ankle exoskeleton could be used to restore symmetry and reduce metabolic energy expenditure of walking in populations with weak ankle plantar flexors (e.g. stroke, spinal cord injury, normal aging).
MAX-MIN Ant System

Ant System, the first Ant Colony Optimization algorithm, showed to be a viable method for attacking hard combinatorial optimization problems. Yet, its performance, when compared to more fine-tuned algorithms, was rather poor for large instances of traditional benchmark problems like the Traveling Salesman Problem. To show that Ant Colony Optimization algorithms could be good alternatives to existing algorithms for hard combinatorial optimization problems, recent research in this area has mainly focused on the development of algorithmic variants which achieve better performance than Ant System. In this paper, we present MAX–MIN Ant System ( MMAS), an Ant Colony Optimization algorithm derived from Ant System.MMAS differs from Ant System in several important aspects, whose usefulness we demonstrate by means of an experimental study. Additionally, we relate one of the characteristics specific to MMAS — that of using a greedier search than Ant System — to results from the search space analysis of the combinatorial optimization problems attacked in this paper. Our computational results on the Traveling Salesman Problem and the Quadratic Assignment Problem show that MMAS is currently among the best performing algorithms for these problems.
MODELDB: A System for Machine Learning Model Management

Building a machine learning model is an iterative process. A data scientist will build many tens to hundreds of models before arriving at one that meets some acceptance criteria (e.g. AUC cutoff, accuracy threshold). However, the current style of model building is ad-hoc and there is no practical way for a data scientist to manage models that are built over time. As a result, the data scientist must attempt to "remember" previously constructed models and insights obtained from them. This task is challenging for more than a handful of models and can hamper the process of sensemaking. Without a means to manage models, there is no easy way for a data scientist to answer questions such as "Which models were built using an incorrect feature?", "Which model performed best on American customers?" or "How did the two top models compare?" In this paper, we describe our ongoing work on ModelDB, a novel end-to-end system for the management of machine learning models. ModelDB clients automatically track machine learning models in their native environments (e.g. scikit-learn, spark.ml), the ModelDB backend introduces a common layer of abstractions to represent models and pipelines, and the ModelDB frontend allows visual exploration and analyses of models via a web-based interface.
A Variance Reduced Stochastic Newton Method

Quasi-Newton methods are widely used in practise for convex loss minimization problems. These methods exhibit good empirical performanc e o a wide variety of tasks and enjoy super-linear convergence to the optimal s olution. For largescale learning problems, stochastic Quasi-Newton methods ave been recently proposed. However, these typically only achieve sub-linea r convergence rates and have not been shown to consistently perform well in practice s nce noisy Hessian approximations can exacerbate the effect of high-variance stochastic gradient estimates. In this work we propose V ITE, a novel stochastic Quasi-Newton algorithm that uses an existing first-order technique to reduce this va r ance. Without exploiting the specific form of the approximate Hessian, we show that V ITE reaches the optimum at a geometric rate with a constant step-size when de aling with smooth strongly convex functions. Empirically, we demonstrate im provements over existing stochastic Quasi-Newton and variance reduced stochast i gradient methods.
Beyond GPS: determining the camera viewing direction of a geotagged image

Increasingly, geographic information is being associated with personal photos. Recent research results have shown that the additional global positioning system (GPS) information helps visual recognition for geotagged photos by providing location context. However, the current GPS data only identifies the camera location, leaving the viewing direction uncertain. To produce more precise location information, i.e. the viewing direction for geotagged photos, we utilize both Google Street View and Google Earth satellite images. Our proposed system is two-pronged: 1) visual matching between a user photo and any available street views in the vicinity determine the viewing direction, and 2) when only an overhead satellite view is available, near-orthogonal view matching between the user photo and satellite imagery computes the viewing direction. Experimental results have shown the promise of the proposed framework.
Towards a Cortical Prosthesis: Implementing A Spike-Based HMAX Model of Visual Object Recognition in Silico

Object recognition and categorization are computationally difficult tasks that are performed effortlessly by humans. Attempts have been made to emulate the computations in different parts of the primate cortex to gain a better understanding of the cortex and to design brain–machine interfaces that speak the same language as the brain. The HMAX model proposed by Riesenhuber and Poggio and extended by Serre <etal/> attempts to truly model the visual cortex. In this paper, we provide a spike-based implementation of the HMAX model, demonstrating its ability to perform biologically-plausible MAX computations as well as classify basic shapes. The spike-based model consists of 2514 neurons and 17<formula formulatype="inline"><tex Notation="TeX">$\thinspace$</tex> </formula>305 synapses (S1 Layer: 576 neurons and 7488 synapses, C1 Layer: 720 neurons and 2880 synapses, S2 Layer: 576 neurons and 1152 synapses, C2 Layer: 640 neurons and 5760 synapses, and Classifier: 2 neurons and 25 synapses). Without the limits of the retina model, it will take the system 2 min to recognize rectangles and triangles in 24<formula formulatype="inline"><tex Notation="TeX">$\,\times\,$</tex> </formula>24 pixel images. This can be reduced to 4.8 s by rearranging the lookup table so that neurons which have similar responses to the same input(s) can be placed on the same row and affected in parallel.
Vehicle detection for autonomous parking using a Soft-Cascade AdaBoost classifier

This paper presents a monocular algorithm for front and rear vehicle detection, developed as part of the FP7 V-Charge project's perception system. The system is made of an AdaBoost classifier with Haar Features Decision Stump. It processes several virtual perspective images, obtained by un-warping 4 monocular fish-eye cameras mounted all-around an autonomous electric car. The target scenario is the automated valet parking, but the presented technique fits well in any general urban and highway environment. A great attention has been given to optimize the computational performance. The accuracy in the detection and a low computation costs are provided by combining a multiscale detection scheme with a Soft-Cascade classifier design. The algorithm runs in real time on the project's hardware platform. The system has been tested on a validation set, compared with several AdaBoost schemes, and the corresponding results and statistics are also reported.
Loneliness and health: potential mechanisms.

Two studies using cross-sectional designs explored four possible mechanisms by which loneliness may have deleterious effects on health: health behaviors, cardiovascular activation, cortisol levels, and sleep.



In Study 1, we assessed autonomic activity, salivary cortisol levels, sleep quality, and health behaviors in 89 undergraduate students selected based on pretests to be among the top or bottom quintile in feelings of loneliness. In Study 2, we assessed blood pressure, heart rate, salivary cortisol levels, sleep quality, and health behaviors in 25 older adults whose loneliness was assessed at the time of testing at their residence.



Total peripheral resistance was higher in lonely than nonlonely participants, whereas cardiac contractility, heart rate, and cardiac output were higher in nonlonely than lonely participants. Lonely individuals also reported poorer sleep than nonlonely individuals. Study 2 indicated greater age-related increases in blood pressure and poorer sleep quality in lonely than nonlonely older adults. Mean salivary cortisol levels and health behaviors did not differ between groups in either study.



Results point to two potentially orthogonal predisease mechanisms that warrant special attention: cardiovascular activation and sleep dysfunction. Health behavior and cortisol regulation, however, may require more sensitive measures and large sample sizes to discern their roles in loneliness and health.
Technology Selection for Big Data and Analytical Applications

The term Big Data has become pervasive in recent years, as smart phones, televisions, washing machines, refrigerators, smart meters, diverse sensors, eyeglasses, and even clothes connect to the Internet. However, their generated data is essentially worthless without appropriate data analytics that utilizes information retrieval, statistics, as well as various other techniques. As Big Data is commonly too big for a single person or institution to investigate, appropriate tools are being used that go way beyond a traditional data warehouse and that have been developed in recent years. Unfortunately, there is no single solution but a large variety of different tools, each of which with distinct functionalities, properties and characteristics. Especially small and medium-sized companies have a hard time to keep track, as this requires time, skills, money, and specific knowledge that, in combination, result in high entrance barriers for Big Data utilization. This paper aims to reduce these barriers by explaining and structuring different classes of technologies and the basic criteria for proper technology selection. It proposes a framework that guides especially small and mid-sized companies through a suitable selection process that can serve as a basis for further advances. TYPE OF PAPER AND
Modelling and Analysis of Single Diode Photovoltaic Module using MATLAB / Simulink

The growing concern over environmental degradation resulting from combustion of fossil fuels and depleting fossil fuel reserves has raised awareness about alternative energy options. Renewable energy system is perfect solution of this problem. This paper presents a mathematical model of single diode solar photovoltaic (SPV) module. SPV cell generates electricity when exposed to sunlight but this generation depends on whether condition like temperature and irradiance, for better accuracy all the parameters are considered including shunt, series resistance and simulated in MATLAB/Simulink. The output is analyzed by varying the temperature and irradiance and effect of change in shunt and series resistance is also observed.
Mining Individual Life Pattern Based on Location History

The increasing pervasiveness of location-acquisition technologies (GPS, GSM networks, etc.) enables people to conveniently log their location history into spatial-temporal data, thus giving rise to the necessity as well as opportunity to discovery valuable knowledge from this type of data. In this paper, we propose the novel notion of individual life pattern, which captures individual's general life style and regularity. Concretely, we propose the life pattern normal form (the LP-normal form) to formally describe which kind of life regularity can be discovered from location history; then we propose the LP-Mine framework to effectively retrieve life patterns from raw individual GPS data. Our definition of life pattern focuses on significant places of individual life and considers diverse properties to combine the significant places. LP-Mine is comprised of two phases: the modelling phase and the mining phase. The modelling phase pre-processes GPS data into an available format as the input of the mining phase. The mining phase applies separate strategies to discover different types of pattern. Finally, we conduct extensive experiments using GPS data collected by volunteers in the real world to verify the effectiveness of the framework.
Gigascope: A Stream Database for Network Applications

We have developed Gigascope, a stream database for network applications including traffic analysis, intrusion detection, router configuration analysis, network research, network monitoring, and performance monitoring and debugging. Gigascope is undergoing installation at many sites within the AT&T network, including at OC48 routers, for detailed monitoring. In this paper we describe our motivation for and constraints in developing Gigascope, the Gigascope architecture and query language, and performance issues. We conclude with a discussion of stream database research problems we have found in our application.
Moral conviction: another contributor to attitude strength or something more?

Attitudes held with strong moral conviction (moral mandates) were predicted to have different interpersonal consequences than strong but nonmoral attitudes. After controlling for indices of attitude strength, the authors explored the unique effect of moral conviction on the degree that people preferred greater social (Studies 1 and 2) and physical (Study 3) distance from attitudinally dissimilar others and the effects of moral conviction on group interaction and decision making in attitudinally homogeneous versus heterogeneous groups (Study 4). Results supported the moral mandate hypothesis: Stronger moral conviction led to (a) greater preferred social and physical distance from attitudinally dissimilar others, (b) intolerance of attitudinally dissimilar others in both intimate (e.g., friend) and distant relationships (e.g., owner of a store one frequents), (c) lower levels of good will and cooperativeness in attitudinally heterogeneous groups, and (d) a greater inability to generate procedural solutions to resolve disagreements.
Sensor Anomaly Detection in Wireless Sensor Networks for Healthcare

Wireless Sensor Networks (WSN) are vulnerable to various sensor faults and faulty measurements. This vulnerability hinders efficient and timely response in various WSN applications, such as healthcare. For example, faulty measurements can create false alarms which may require unnecessary intervention from healthcare personnel. Therefore, an approach to differentiate between real medical conditions and false alarms will improve remote patient monitoring systems and quality of healthcare service afforded by WSN. In this paper, a novel approach is proposed to detect sensor anomaly by analyzing collected physiological data from medical sensors. The objective of this method is to effectively distinguish false alarms from true alarms. It predicts a sensor value from historic values and compares it with the actual sensed value for a particular instance. The difference is compared against a threshold value, which is dynamically adjusted, to ascertain whether the sensor value is anomalous. The proposed approach has been applied to real healthcare datasets and compared with existing approaches. Experimental results demonstrate the effectiveness of the proposed system, providing high Detection Rate (DR) and low False Positive Rate (FPR).
Some computer science issues in ubiquitous computing

m U biquitous computing enhances computer use by making many computers available throughout the physical environment, while making them effectively invisible to the user. This article explains what is new and different about the computer science involved in ubiquitous computing. First, it provides a brief overview of ubiquitous computing, then elaborates through a series of examples drawn from various subdisciplines of computer science: hardware components (e.g., chips), network protocols, interaction substrates (e.g., software for screens and pens), applications, privacy, and computational methods. Ubiquitous computing offers a framework for new and exciting research across the spectrum of computer science. Since we started this work at Xerox Palo Alto Research Center (PARC) in 1988 a few places have begun work on this possible next-generation computing environment in which each person is continually interacting with hundreds of nearby wirelessly interconnected computers. The goal is to achieve the most effective kind of technology, that which is essentially invisible to the user. To bring computers to this point while retaining their power will require radically new kinds of computers of all sizes and shapes to be available to each person. I call this future world "Ubiquitous Comput ing" (Ubicomp) . The research method for ubiquitous computing is standard experimental computer science: the construction of working prototypes of the necessai-y infrastructure in sufficient quantity to debug the viability of the systems in everyday use; ourselves and a few colleagues serving as guinea pigs.
A Joint Model for Question Answering and Question Generation

We propose a generative machine comprehension model that learns jointly to ask and answer questions based on documents. The proposed model uses a sequence-to-sequence framework that encodes the document and generates a question (answer) given an answer (question). Significant improvement in model performance is observed empirically on the SQuAD corpus, confirming our hypothesis that the model benefits from jointly learning to perform both tasks. We believe the joint model’s novelty offers a new perspective on machine comprehension beyond architectural engineering, and serves as a first step towards autonomous information seeking.
Application of Data Mining Techniques for Medical Image Classification

Breast cancer represents the second leading cause of cancer deaths in women today and it is the most common type of cancer in women. This paper presents some experiments for tumour detection in digital mammography. We investigate the use of different data mining techniques, neural networks and association rule mining, for anomaly detection and classification. The results show that the two approaches performed well, obtaining a classification accuracy reaching over 70% percent for both techniques. Moreover, the experiments we conducted demonstrate the use and effectiveness of association rule mining in image categorization.
Efficient resource management for Cloud computing environments

The notion of Cloud computing has not only reshaped the field of distributed systems but also fundamentally changed how businesses utilize computing today. While Cloud computing provides many advanced features, it still has some shortcomings such as the relatively high operating cost for both public and private Clouds. The area of Green computing is also becoming increasingly important in a world with limited energy resources and an ever-rising demand for more computational power. In this paper a new framework is presented that provides efficient green enhancements within a scalable Cloud computing architecture. Using power-aware scheduling techniques, variable resource management, live migration, and a minimal virtual machine design, overall system efficiency will be vastly improved in a data center based Cloud with minimal performance overhead.
Color transfer in correlated color space

In this paper we present a process called color transfer which can borrow one image's color characteristics from another. Recently Reinhard and his colleagues reported a pioneering work of color transfer. Their technology can produce very believable results, but has to transform pixel values from RGB to lαβ. Inspired by their work, we advise an approach which can directly deal with the color transfer in any 3D space.From the view of statistics, we consider pixel's value as a three-dimension stochastic variable and an image as a set of samples, so the correlations between three components can be measured by covariance. Our method imports covariance between three components of pixel values while calculate the mean along each of the three axes. Then we decompose the covariance matrix using SVD algorithm and get a rotation matrix. Finally we can scale, rotate and shift pixel data of target image to fit data points' cluster of source image in the current color space and get resultant image which takes on source image's look and feel. Besides the global processing, a swatch-based method is introduced in order to manipulate images' color more elaborately. Experimental results confirm the validity and usefulness of our method.
Fast image restoration without boundary artifacts

Fast Fourier transform (FFT)-based restorations are fast, but at the expense of assuming that the blurring and deblurring are based on circular convolution. Unfortunately, when the opposite sides of the image do not match up well in intensity, this assumption can create significant artifacts across the image. If the pixels outside the measured image window are modeled as unknown values in the restored image, boundary artifacts are avoided. However, this approach destroys the structure that makes the use of the FFT directly applicable, since the unknown image is no longer the same size as the measured image. Thus, the restoration methods available for this problem no longer have the computational efficiency of the FFT. We propose a new restoration method for the unknown boundary approach that can be implemented in a fast and flexible manner. We decompose the restoration into a sum of two independent restorations. One restoration yields an image that comes directly from a modified FFT-based approach. The other restoration involves a set of unknowns whose number equals that of the unknown boundary values. By summing the two, the artifacts are canceled. Because the second restoration has a significantly reduced set of unknowns, it can be calculated very efficiently even though no circular convolution structure exists.