nils-herrmann/clean_scirepeval huggingface dataset:
Historiographic Mapping of Knowledge Domains Literature

To better understand the topic of this colloquium, we have created a series of databases related to knowledge domains (dynamic systems [small world/Milgram], information visualization [Tufte], co-citation [Small], bibliographic coupling [Kessler], and scientometrics [Scientometrics]). I have used a software package called HistCite which generates chronological maps of subject (topical) collections resulting from searches of the ISI Web of Science or ISI citation indexes (SCI, SSCI, and/or AHCI) on CD-ROM. When a marked list is created on WoS, an export file is created which contains all cited references for each source document captured. These bibliographic collections, saved as ASCII files, are processed by HistCite in order to generate chronological and other tables as well as historiographs which highlight the most-cited works in and outside the collection. HistCite also includes a module for detecting and editing errors or variations in cited references as well as a vocabulary analyzer which generates both ranked word lists and word pairs used in the collection. Ideally the system will be used to help the searcher quickly identify the most significant work on a topic and trace its year-by-year historical development. In addition to the collections mentioned above, historiographs based on collections of papers that cite the Watson-Crick 1953 classic paper identifying the helical structure of DNA were created. Both year-by-year as well as month-by-month displays of papers from 1953 to 1958 were necessary to highlight the publication activity of those years.
A Convolution Kernel Method for Color Recognition

Color recognition for out-door images is important for low-level computer vision, but it is a difficult task due to the effect of circumstances such as illumination, weather and so on. In this paper, we propose a novel convolution kernel method to extract color information from out-door images.When two images are compared, the proposed kernel maps images onto a high-dimentional feature space of which features are image fragments of two images and then the similarity between them is obtained through the inner-production of two image vectors. To evaluate the proposed kernel, it is applied to the vehicle color recognition problem. In the experiments on 500 vehicle images, the vehicle color recognition model with the proposed kernel shows about 92% of precision and 92% of recall. On the other hands, the model with a linear kernel shows about 45% of precision and 45% of recall. These experimental results imply that the proposed kernel is a plausible approach for the color recognition task.
A Deep Learning Approach to Machine Transliteration

In this paper we present a novel transliteration technique which is based on deep belief networks. Common approaches use finite state machines or other methods similar to conventional machine translation. Instead of using conventional NLP techniques, the approach presented here builds on deep belief networks, a technique which was shown to work well for other machine learning problems. We show that deep belief networks have certain properties which are very interesting for transliteration and possibly also for translation and that a combination with conventional techniques leads to an improvement over both components on an Arabic-English transliteration task.
Fingerprint Based ATM Security by using ARM 7 1

The purpose of this project is to increase the security that customer use the ATM machine. Once user's bank card is lost and the password is stolen, the criminal will draw all cash in the shortest time, which will bring enormous financial losses to customer, so to rectify this problem we are implementing this project. The chip of LPC2148 is used for the core of microprocessor in ARM7, furthermore, an improved enhancement algorithm of fingerprint image increase the security that customer use the ATM machine.
STRANDS: Interactive Simulation of Thin Solids using Cosserat Models

STRANDS are thin elastic solids that are visually well approximated as smooth curves, and yet possess essential physical behaviors characteristic of solid objects such as twisting. Common examples in computer graphics include: sutures, catheters, and tendons in surgical simulation; hairs, ropes, and vegetation in animation. Physical models based on spring meshes or 3D finite elements for such thin solids are either inaccurate or inefficient for interactive simulation. In this paper we show that models based on the Cosserat theory of elastic rods are very well suited for interactive simulation of these objects. The physical model reduces to a system of spatial ordinary differential equations that can be solved efficiently for typical boundary conditions. The model handles the important geometric non-linearity due to large changes in shape. We introduce Cosserat-type physical models, describe efficient numerical methods for interactive simulation of these models, and implementation results.
" Intelligent Heart Disease Prediction System Using Data Mining Techniques "

Today medical services have come a long way to treat patients with various diseases. Among the most lethal one is the heart disease problem which cannot be seen with a naked eye and comes instantly when its limitations are reached. Today diagnosing patients correctly and administering effective treatments have become quite a challenge. Poor clinical decisions may end to patient death and which cannot be afforded by the hospital as it loses its reputation. The cost to treat a patient with a heart problem is quite high and not affordable by every patient. To achieve a correct and cost effective treatment computer-based information and/or decision support Systems can be developed to do the task. Most hospitals today use some sort of hospital information systems to manage their healthcare or patient data. These systems typically generate huge amounts of data which take the form of numbers, text, charts and images. Unfortunately, these data are rarely used to support clinical decision making. There is a wealth of hidden information in these data that is largely untapped. This raises an important question: " How can we turn data into useful information that can enable healthcare practitioners to make intelligent clinical decisions? " So there is need of developing a master's project which will help practitioners predict the heart disease before it occurs.The diagnosis of diseases is a vital and intricate job in medicine. The recognition of heart disease from diverse features or signs is a multi-layered problem that is not free from false assumptions and is frequently accompanied by impulsive effects. Thus the attempt to exploit knowledge and experience of several specialists and clinical screening data of patients composed in databases to assist the diagnosis procedure is regarded as a valuable option.
Glutaminolysis and Transferrin Regulate Ferroptosis.

Ferroptosis has emerged as a new form of regulated necrosis that is implicated in various human diseases. However, the mechanisms of ferroptosis are not well defined. This study reports the discovery of multiple molecular components of ferroptosis and its intimate interplay with cellular metabolism and redox machinery. Nutrient starvation often leads to sporadic apoptosis. Strikingly, we found that upon deprivation of amino acids, a more rapid and potent necrosis process can be induced in a serum-dependent manner, which was subsequently determined to be ferroptosis. Two serum factors, the iron-carrier protein transferrin and amino acid glutamine, were identified as the inducers of ferroptosis. We further found that the cell surface transferrin receptor and the glutamine-fueled intracellular metabolic pathway, glutaminolysis, played crucial roles in the death process. Inhibition of glutaminolysis, the essential component of ferroptosis, can reduce heart injury triggered by ischemia/reperfusion, suggesting a potential therapeutic approach for treating related diseases.
SnappyData: A Unified Cluster for Streaming, Transactions and Interactice Analytics

Many modern applications are a mixture of streaming, transactional and analytical workloads. However, traditional data platforms are each designed for supporting a specific type of workload. The lack of a single platform to support all these workloads has forced users to combine disparate products in custom ways. The common practice of stitching heterogeneous environments has caused enormous production woes by increasing complexity and the total cost of ownership. To support this class of applications, we present SnappyData as the first unified engine capable of delivering analytics, transactions, and stream processing in a single integrated cluster. We build this hybrid engine by carefully marrying a big data computational engine (Apache Spark) with a scale-out transactional store (Apache GemFire). We study and address the challenges involved in building such a hybrid distributed system with two conflicting components designed on drastically different philosophies: one being a lineage-based computational model designed for high-throughput analytics, the other a consensusand replication-based model designed for low-latency operations.
Learning, invariance, and generalization in high-order neural networks.

High-order neural networks have been shown to have impressive computational, storage, and learning capabilities. This performance is because the order or structure of a high-order neural network can be tailored to the order or structure of a problem. Thus, a neural network designed for a particular class of problems becomes specialized but also very efficient in solving those problems. Furthermore, a priori knowledge, such as geometric invariances, can be encoded in high-order networks. Because this knowledge does not have to be learned, these networks are very efficient in solving problems that utilize this knowledge.
Mining advices from weblogs

Weblog, one of the fastest growing user generated contents, often contains key learnings gleaned from people's past experiences which are really worthy to be well presented to other people. One of the key learnings contained in weblogs is often vented in the form of advice. In this paper, we aim to provide a methodology to extract sentences that reveal advices on weblogs. We observed our data to discover the characteristics of advices contained in weblogs. Based on this observation, we define our task as a classification problem using various linguistic features. We show that our proposed method significantly outperforms the baseline. The presence or absence of imperative mood expression appears to be the most important feature in this task. It is also worth noting that the work presented in this paper is the first attempt on mining advices from English data.
Electric Load Forecasting

This article illustrates the application of a nonlinear system identification technique to the problem of STLF. Five NARX models are estimated using fixed-size LS-SVM, and two of the models are later modified into AR-NARX structures following the exploration of the residuals. The forecasting performance, assessed for different load series, is satisfactory. The MSE levels on the test data are below 3% in most cases. The models estimated with fixed-size LS-SVM give better results than a linear model estimated with the same variables and also better than a standard LS-SVM in dual space estimated using only the last 1000 data points. Furthermore, the good performance of the fixed-size LS-SVM is obtained based on a subset of M = 1000 initial support vectors, representing a small fraction of the available sample. Further research on a more dedicated definition of the initial input variables (for example, incorporation of external variables to reflect industrial activity, use of explicit seasonal information) might lead to further improvements and the extension toward other types of load series.
Fast Finite Shearlet Transform : a tutorial

3 Computation of the shearlet transform 13 3.1 Finite discrete shearlets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 3.2 A discrete shearlet frame .  3.3 Inversion of the shearlet transform . . . . . . . . . . . . . . . . . . . . . . . . . 20 3.4 Smooth shearlets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3.5 Implementation details .  3.5.2 Computation of spectra . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.6 Short documentation .  31 3.9 Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Affordance Prediction via Learned Object Attributes

We present a novel method for learning and predicting the affordances of an object based on its physical and visual attributes. Affordance prediction is a key task in autonomous robot learning, as it allows a robot to reason about the actions it can perform in order to accomplish its goals. Previous approaches to affordance prediction have either learned direct mappings from visual features to affordances, or have introduced object categories as an intermediate representation. In this paper, we argue that physical and visual attributes provide a more appropriate mid-level representation for affordance prediction, because they support informationsharing between affordances and objects, resulting in superior generalization performance. In particular, affordances are more likely to be correlated with the attributes of an object than they are with its visual appearance or a linguistically-derived object category. We provide preliminary validation of our method experimentally, and present empirical comparisons to both the direct and category-based approaches of affordance prediction. Our encouraging results suggest the promise of the attributebased approach to affordance prediction.
Contraction Sensing With Smart Braid McKibben Muscles

The inherent compliance of soft fluidic actuators makes them attractive for use in wearable devices and soft robotics. Their flexible nature permits them to be used without traditional rotational or prismatic joints. Without these joints, however, measuring the motion of the actuators is challenging. Actuator-level sensors could improve the performance of continuum robots and robots with compliant or multi-degree-of-freedom joints. We make the reinforcing braid of a pneumatic artificial muscle (PAM or McKibben muscle) “smart” by weaving it from conductive insulated wires. These wires form a solenoid-like circuit with an inductance that more than doubles over the PAM contraction. The reinforcing and sensing fibers can be used to measure the contraction of a PAM actuator with a simple linear function of the measured inductance, whereas other proposed self-sensing techniques rely on the addition of special elastomers or transducers, the technique presented in this paper can be implemented without modifications of this kind. We present and experimentally validate two models for Smart Braid sensors based on the long solenoid approximation and the Neumann formula, respectively. We test a McKibben muscle made from a Smart Braid in quasi-static conditions with various end loads and in dynamic conditions. We also test the performance of the Smart Braid sensor alongside steel.
Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction

We propose split-brain autoencoders, a straightforward modification of the traditional autoencoder architecture, for unsupervised representation learning. The method adds a split to the network, resulting in two disjoint sub-networks. Each sub-network is trained to perform a difficult task &#x2013; predicting one subset of the data channels from another. Together, the sub-networks extract features from the entire input signal. By forcing the network to solve cross-channel prediction tasks, we induce a representation within the network which transfers well to other, unseen tasks. This method achieves state-of-the-art performance on several large-scale transfer learning benchmarks.
High voltage pulse generator based on DC-to-DC boost converter with capacitor-diode voltage multipliers for bacterial decontamination

High voltage pulse generators can be used effectively in water treatment applications, as applying a pulsed electric field on the infected sample guarantees killing of harmful germs and bacteria. In this paper, a new high voltage pulse generator with closed loop control on its output voltage is proposed. The proposed generator is based on DC-to-DC boost converter in conjunction with capacitor-diode voltage multiplier (CDVM), and can be fed from low-voltage low-frequency AC supply, i.e. utility mains. The proposed topology provides transformer-less operation which reduces size and enhances the overall efficiency. A Detailed design of the proposed pulse generator has been presented as well. The proposed approach is validated by simulation as well as experimental results.
Grip force control for an elastic finger using vision-based incipient slip feedback

In this paper, a grip-force control of an elastic object is proposed based on a visual slip margin feedback. When an elastic object is pressed and slid slightly on a rigid plate, a partial slip, called "incipient slip" occurs on the contact surface. The slip margin between an elastic object and a rigid plate is estimated based on the analytic solution of Hertzian contact model. A 1 DOF gripper consists of a camera and a force sensor is developed. The slip margin can be estimated from the tangential force measured by a force sensor, the deformation of the elastic object and the radius on the contact area both measured by a camera. In the proposed method, the friction coefficient is not explicitly needed. The grip force is controlled by a direct feedback of the estimated slip margin, whose stability is analytically guaranteed. As a result, the slip margin is maintained to a desired value without occurring the gross slip against a disturbance load force to the object.
An effective personal mobile robot agent through symbiotic human-robot interaction

Several researchers, present authors included, envision personal mobile robot agents that can assist humans in their daily tasks. Despite many advances in robotics, such mobile robot agents still face many limitations in their perception, cognition, and action capabilities. In this work, we propose a symbiotic interaction between robot agents and humans to overcome the robot limitations while allowing robots to also help humans. We introduce a visitor’s companion robot agent, as a natural task for such symbiotic interaction. The visitor lacks knowledge of the environment but can easily open a door or read a door label, while the mobile robot with no arms cannot open a door and may be confused about its exact location, but can plan paths well through the building and can provide useful relevant information to the visitor. We present this visitor companion task in detail with an enumeration and formalization of the actions of the robot agent in its interaction with the human. We briefly describe the wifi-based robot localization algorithm and show results of the different levels of human help to the robot during its navigation. We then test the value of robot help to the visitor during the task to understand the relationship tradeoffs. Our work has been fully implemented in a mobile robot agent, CoBot, which has successfully navigated for several hours and continues to navigate in our indoor environment.
600V LPT-CSTBT™ on advanced thin wafer technology

Electrical characteristics of the fabricated 600V class CSTBT™ with a Light Punch Through (LPT) structure on an advanced thin wafer technology are presented for the first time. The electrical characteristics of LPT-CSTBT are superior to the conventional Punch Through type (PT) one, especially in low current density regions because of the inherent lower built-in potential. Furthermore, we also have evaluated the effects of the mechanical stress on the device characteristics after soldering, utilizing a novel evaluation method with a very small size sub-chip layout. The results validate the proposed tool is useful to examine the influence of the mechanical stress on the electrical characteristics.
A New Perspective on Uncalibrated Photometric Stereo

We investigate the problem of reconstructing normals, albedo and lights of Lambertian surfaces in uncalibrated photometric stereo under the perspective projection model. Our analysis is based on establishing the integrability constraint. In the orthographic projection case, it is well-known that when such constraint is imposed, a solution can be identified only up to 3 parameters, the so-called generalized bas-relief (GBR) ambiguity. We show that in the perspective projection case the solution is unique. We also propose a closed-form solution which is simple, efficient and robust. We test our algorithm on synthetic data and publicly available real data. Our quantitative tests show that our method outperforms all prior work of uncalibrated photometric stereo under orthographic projection.
A Survey of Software Estimation Techniques and Project Planning Practices

Paper provides in depth review of software and project estimation techniques existing in industry and literature, its strengths and weaknesses. Usage, popularity and applicability of such techniques are elaborated. In order to improve estimation accuracy, such knowledge is essential. Many estimation techniques, models, methodologies exists and applicable in different categories of projects. None of them gives 100% accuracy but proper use of them makes estimation process smoother and easier. Organizations should automate estimation procedures, customize available tools and calibrate estimation approaches as per their requirements. Proposed future work is to study factors involved in Software Engineering Approaches (Software Estimation in focus) for Offshore and Outsourced Software Development taking Pakistani IT Industry as a Case Study
A Deeper Understanding of Sequence in Narrative Visualization

Conveying a narrative with visualizations often requires choosing an order in which to present visualizations. While evidence exists that narrative sequencing in traditional stories can affect comprehension and memory, little is known about how sequencing choices affect narrative visualization. We consider the forms and reactions to sequencing in narrative visualization presentations to provide a deeper understanding with a focus on linear, 'slideshow-style' presentations. We conduct a qualitative analysis of 42 professional narrative visualizations to gain empirical knowledge on the forms that structure and sequence take. Based on the results of this study we propose a graph-driven approach for automatically identifying effective sequences in a set of visualizations to be presented linearly. Our approach identifies possible transitions in a visualization set and prioritizes local (visualization-to-visualization) transitions based on an objective function that minimizes the cost of transitions from the audience perspective. We conduct two studies to validate this function. We also expand the approach with additional knowledge of user preferences for different types of local transitions and the effects of global sequencing strategies on memory, preference, and comprehension. Our results include a relative ranking of types of visualization transitions by the audience perspective and support for memory and subjective rating benefits of visualization sequences that use parallelism as a structural device. We discuss how these insights can guide the design of narrative visualization and systems that support optimization of visualization sequence.