nils-herrmann/clean_scirepeval huggingface dataset:
Get another label? improving data quality and data mining using multiple, noisy labelers

This paper addresses the repeated acquisition of labels for data items when the labeling is imperfect. We examine the improvement (or lack thereof) in data quality via repeated labeling, and focus especially on the improvement of training labels for supervised induction. With the outsourcing of small tasks becoming easier, for example via Rent-A-Coder or Amazon's Mechanical Turk, it often is possible to obtain less-than-expert labeling at low cost. With low-cost labeling, preparing the unlabeled part of the data can become considerably more expensive than labeling. We present repeated-labeling strategies of increasing complexity, and show several main results. (i) Repeated-labeling can improve label quality and model quality, but not always. (ii) When labels are noisy, repeated labeling can be preferable to single labeling even in the traditional setting where labels are not particularly cheap. (iii) As soon as the cost of processing the unlabeled data is not free, even the simple strategy of labeling everything multiple times can give considerable advantage. (iv) Repeatedly labeling a carefully chosen set of points is generally preferable, and we present a robust technique that combines different notions of uncertainty to select data points for which quality should be improved. The bottom line: the results show clearly that when labeling is not perfect, selective acquisition of multiple labels is a strategy that data miners should have in their repertoire; for certain label-quality/cost regimes, the benefit is substantial.
Using support vector machine in FoRex predicting

The trend of currency rates can be predicted with supporting from supervised machine learning in the transaction systems such as support vector machine. Not only representing models in use of machine learning techniques in learning, the support vector machine (SVM) model also is implemented with actual FoRex transactions. This might help automatically to make the transaction decisions of Bid/Ask in Foreign Exchange Market by using Expert Advisor (Robotics). The experimental results show the advantages of use SVM compared to the transactions without use SVM ones.
A Review on Business Intelligence and Big Data

Improvement of data generating, processing, storing and networking technologies has made storing, capturing and sharing of data easier and cheaper than before and has enabled organizations to handle the huge volume of data at high velocity and variety, named as big data. Big data offers many opportunities when the associated difficulties are addressed properly. Business Intelligence (BI) basically focuses on transforming raw data into usable, valuable and actionable information for decision-making. It can be classified as a kind of data-driven decision support system. Although big data related papers have increased for last fifteen years, there are not sufficient papers that directly overviews big data impact on BI. As data is growing exponentially, storage, process, and analytics tools and technologies become more important for BI solutions. With the advent of big data, BI’s concept, architecture, and capabilities are meant to be changed. Unlike decades before, BI now is to be extract value from huge data ocean by using big data tools as well as classical ones. So, an interclusion has emerged between big data and BI. This paper overviews the current state of the art of BI and big data, and discuss how big data era affects BI solutions in general context.
Smart Grid — The New and Improved Power Grid: A Survey

The Smart Grid, regarded as the next generation power grid, uses two-way flows of electricity and information to create a widely distributed automated energy delivery network. In this article, we survey the literature till 2011 on the enabling technologies for the Smart Grid. We explore three major systems, namely the smart infrastructure system, the smart management system, and the smart protection system. We also propose possible future directions in each system. colorred{Specifically, for the smart infrastructure system, we explore the smart energy subsystem, the smart information subsystem, and the smart communication subsystem.} For the smart management system, we explore various management objectives, such as improving energy efficiency, profiling demand, maximizing utility, reducing cost, and controlling emission. We also explore various management methods to achieve these objectives. For the smart protection system, we explore various failure protection mechanisms which improve the reliability of the Smart Grid, and explore the security and privacy issues in the Smart Grid.
Auxiliary Image Regularization for Deep CNNs with Noisy Labels

Precisely-labeled data sets with sufficient amount of samples are very important for training deep convolutional neural networks (CNNs). However, many of the available real-world data sets contain erroneously labeled samples and those errors substantially hinder the learning of very accurate CNN models. In this work, we consider the problem of training a deep CNN model for image classification with mislabeled training samples – an issue that is common in real image data sets with tags supplied by amateur users. To solve this problem, we propose an auxiliary image regularization technique, optimized by the stochastic Alternating Direction Method of Multipliers (ADMM) algorithm, that automatically exploits the mutual context information among training images and encourages the model to select reliable images to robustify the learning process. Comprehensive experiments on benchmark data sets clearly demonstrate our proposed regularized CNN model is resistant to label noise in training data.
Review on Fragmentation in Distributed Database Environment

In Traditional environments, there are many advantages of distributed data warehouses. Distributed processing is the efficient way to increase efficiency of data. But the efficiency of query processing is a critical issue in data warehousing system, as decision support applications require minimum response times to answer complex, ad-hoc queries having aggregations, multi-ways joins overvast repositories of data. To achieve this, the fragmentation of data warehouse is the best to reduce the query execution time. The execution time reduces when queries runs over smaller datasets. The system performance is increased by allowing data to be spread across datamarts. So, it is very important to manage an appropriate methodology for data fragmentation and fragment allocation. Here focus is on the distributed data warehouses, which combines the known predicate construction techniques with a clustering method to fragment data warehouse relations by using the data miningbased horizontal fragmentation methodology for a relational DDW environment. DW decentralization gives the better performance; in the fragments are allocated to the corresponding site according to their frequency.
Wheelchair obstacle avoidance based on fuzzy controller and ultrasonic sensors

Electric wheelchair is one of the most used for the movement of disabled and aged people. This work introduces an obstacle avoidance system aimed for providing more autonomous navigation of a electric wheelchair (EW) in unknown indoor environments. These technologies seek to increase the independence of people with disabilities and improve their quality of life by making the most of each individuals abilities. Furthermore, the integration of an ultrasonic sensor to avoid obstacle and a fuzzy controller to generates velocity for aim to join the target position. A prototype of EW has been equipped with control unit based on two micro-controller. The first for manage the motors velocity. The second for explore the ultrasonic sensors. Two micro-controller exchanges the information with a PC board type PC 104, which is used to process data from sensors and encoders. The information is processed by a control algorithm based on fuzzy logic. The control algorithm is optimized using the gradient method to minimize the path traveled to reach the desired position. The practical implementation demonstrates the algorithm validity for obstacle avoidance and goal achievement with a minimum path and greater security.
Very long-term survival and durability of mitral valve repair for mitral valve prolapse.

Mitral regurgitation (MR) due to mitral valve prolapse (MVP) is often treatable by surgical repair. However, the very long-term (>10-year) durability of repair in both anterior leaflet prolapse (AL-MVP) and posterior leaflet prolapse (PL-MVP) is unknown.



In 917 patients (aged 65+/-13 years, 68% male), surgical correction of severe isolated MR due to MVP (679 repairs and 238 replacements [MVRs]) was performed between 1980 and 1995. Survival after repair was better than survival after MVR for both PL-MVP (at 15 years, 41+/-5% versus 31+/-6%, respectively; P=0.0003) and AL-MVP (at 14 years, 42+/-8% versus 31+/-5%, respectively; P=0.003). In multivariate analysis adjusting for predictors of survival, repair was independently associated with lower mortality in PL-MVP (adjusted risk ratio [RR] 0.61, 95% CI 0.44 to 0.85; P=0.0034) and in AL-MVP (adjusted RR 0.67, 95% CI 0.47 to 0.96; P=0.028). The reoperation rate was not different after repair or MVR overall (at 19 years, 20+/-5% for repair versus 23+/-5% for MVR; P=0.4) or separately in PL-MVP (P=0.3) or AL-MVP (P=0.3). However, the reoperation rate was higher after repair of AL-MVP than after repair of PL-MVP (at 15 years, 28+/-7% versus 11+/-3%, respectively; P=0.0006). From the 1980s to the 1990s, the RR of reoperation after repair of AL-MVP versus PL-MVP did not change (RR 2.5 versus 2.7, respectively; P=0.58), but the absolute rate of reoperation decreased similarly in PL-MVP and AL-MVP (at 10 years, from 10+/-3% to 5+/-2% and from 24+/-6% to 10+/-2%, respectively; P=0.04).



In severe MR due to MVP, mitral valve repair compared with MVR provides improved very long-term survival after surgery for both AL-MVP and PL-MVP. Reoperation is similarly required after repair or replacement but is more frequent after repair of AL-MVP. Recent improvement in long-term durability of repair suggests that it should be the preferred mode of surgical correction of MVP whether it affects anterior or posterior leaflets and is an additional incentive for early surgery of severe MR due to MVP.
Pattern Matching on Encrypted Streams: Applications to DPI and searches on genomic data

Pattern matching is essential in applications such as deep-packet inspection (DPI), searching on genomic data, or analyzing medical data. A simple task to do on plaintext data, pattern matching is much harder to do when the privacy of the data must be preserved. Existent solutions involve searchable encryption mechanisms with at least one of these three drawbacks: requiring an exhaustive (and static) list of keywords to be prepared before the data is encrypted (like in symmetric searchable encryption); requiring tokenization, i.e., breaking up the data to search into substrings and encrypting them separately (e.g., like BlindBox); relying on symmetrickey cryptography, thus implying a token-regeneration step for each encrypted-data source (e.g., user). Such approaches are ill-suited for pattern-matching with evolving patterns (e.g., updating virus signatures), variable searchword lengths, or when a single entity must filter ciphertexts from multiple parties. In this work, we introduce Searchable Encryption with Shiftable Trapdoors (SEST): a new primitive that allows for pattern matching with universal tokens (usable by all entities), in which keywords of arbitrary lengths can be matched to arbitrary ciphertexts. Our solution uses public-key encryption and bilinear pairings. It consists of projecting keywords on polynomials of degree equal to the length of the keyword and using a sliding-window-like technique to match the trapdoor to successive fragments of the encrypted data. In addition, very minor modifications to our solution enable it to take into account regular expressions, such as fullyor partly-unknown characters in a keyword (wildcards and interval/subset searches). Our trapdoor size is at most linear in the keyword length (and independent of the plaintext size), and we prove that the leakage to the searcher is only the trivial one: since the searcher learns whether the pattern occurs and where, it can distinguish based on different search results of a single trapdoor on two different plaintexts. To better show the usability of our scheme, we implemented it to run DPI on all the SNORT rules. We show that even for very large plaintexts, our encryption algorithm scales well. The patternmatching algorithm is slightly slower, but extremely parallelizable, and it can thus be run even on very large data. Although our proofs use a (marginally) interactive assumption, we argue that this is a relatively small price to pay for the flexibility and privacy that we are able to attain.
L+2, an improved line of sight guidance law for UAVs

This paper describes a new guidance law that extends the pursuit guidance law previously developed by Park et. al. Several improvements are presented that allow operation in the real world. A stability analysis accounts for the dynamic response of the bank angle commands which leads to the definition of regions of instability. Another extension accounts for situations where the pursuit aim point is not defined by the previous work. A third extension changes the pursuit distance-to-go from a constant to a constant time-to-go so that the linearized transient response is independent of ground speed. Yet another extension defines a “homing” mode in which the UAV flies to a goal point without a defined path, commonly used as a “return-to-base,” either as a safety measure or as an end-of-mission order. Since there is no constraint that the goal point be stationary, we demonstrate that the new law can be used to follow a moving target whose location is known, such as a mobile ground control station. Simulations with a 6 degree-of-freedom aircraft model demonstrate these features.
S-Surge: Novel Portable Surgical Robot with Multiaxis Force-Sensing Capability for Minimally Invasive Surgery

To achieve a compact and lightweight surgical robot with force-sensing capability, in this paper, we propose a surgical robot called “S-surge,” which is developed for robot-assisted minimally invasive surgery, focusing mainly on its mechanical design and force-sensing system. The robot consists of a 4-degree-of-freedom (DOF) surgical instrument and a 3-DOF remote center-of-motion manipulator. The manipulator is designed by adopting a double-parallelogram mechanism and spherical parallel mechanism to provide advantages such as compactness, simplicity, improved accuracy, and high stiffness. Kinematic analysis was performed in order to optimize workspace. The surgical instrument enables multiaxis force sensing including a three-axis pulling force and single-axis grasping force. In this study, it will be verified that it is feasible to carry the entire robot around thanks to its light weight (4.7 kg); therefore, allowing the robot to be applicable for telesurgery in remote areas. Finally, it will be explained how we experimented with the performance of the robot and conducted tissue manipulating task using the motion and force sensing capability of the robot in a simulated surgical setting.
ORB-SLAM: A Versatile and Accurate Monocular SLAM System

This paper presents ORB-SLAM, a feature-based monocular simultaneous localization and mapping (SLAM) system that operates in real time, in small and large indoor and outdoor environments. The system is robust to severe motion clutter, allows wide baseline loop closing and relocalization, and includes full automatic initialization. Building on excellent algorithms of recent years, we designed from scratch a novel system that uses the same features for all SLAM tasks: tracking, mapping, relocalization, and loop closing. A survival of the fittest strategy that selects the points and keyframes of the reconstruction leads to excellent robustness and generates a compact and trackable map that only grows if the scene content changes, allowing lifelong operation. We present an exhaustive evaluation in 27 sequences from the most popular datasets. ORB-SLAM achieves unprecedented performance with respect to other state-of-the-art monocular SLAM approaches. For the benefit of the community, we make the source code public.
The Scrum Software Development Process for Small Teams

Why Scrum? As members of the Software Technology Group, our group is responsible for introducing new technologies and processes into our organization at AG Communication Systems in Phoenix, Arizona. We research new approaches and sponsor their introduction and growth. We also conduct development project checkups for ongoing projects and postmortems for completed ones. In our periodic postmortems and checkups, we noticed some recurring problems for our New Business Opportunity (NBO) projects.  1 lists some comments arising from projects at our organization that faced significant challenges. In the new telecommunications market where our company operates, change is overwhelming. Software developers have always complained about changing requirements, but in traditional approaches they assumed they would understand the requirements before moving on to the next phase. In the current environment, however, project requirements might be unclear or unknown even as the project gets underway. Indeed, the market might not be defined—it might even be that no one clearly understands the product under development. Most development teams respond with, “Make the chaos go away! Give us better requirements!” Unfortunately or not, chaos is the reality in this new business environfocus
Resource Allocation for Energy Harvesting-Powered D2D Communication Underlaying UAV-Assisted Networks

In this paper, we investigate the resource allocation problem for unmanned aerial vehicle (UAV)-assisted networks, where a UAV acting as an energy source provides radio frequency energy for multiple energy harvesting-powered device-to-device (D2D) pairs with much information to be transmitted. The goal is to maximize the average throughput within a time horizon while satisfying the energy causality constraint under a generalized harvest-transmit-store model, which results in a non-convex problem. By introducing the Lagrangian relaxation method, we analytically show that the behavior of all D2D pairs at each time slot is exclusive: harvesting energy or transmitting information signals. The formulated non-convex optimization problem is thus transformed into a mixed integer nonlinear programming (MINIP). We then design an efficient resource allocation algorithm to solve this MINIP, where D.C. (difference of two convex functions) programming and golden section method are combined to achieve a suboptimal solution. Furthermore, we provide an idea to reduce the computational complexity for facilitating the application in practice. Simulations are conducted to validate the effectiveness of the proposed algorithm and evaluate the system throughput performance.
Weakly- and Semi-supervised Panoptic Segmentation

We present a weakly supervised model that jointly performs both semanticand instance-segmentation – a particularly relevant problem given the substantial cost of obtaining pixel-perfect annotation for these tasks. In contrast to many popular instance segmentation approaches based on object detectors, our method does not predict any overlapping instances. Moreover, we are able to segment both “thing” and “stuff” classes, and thus explain all the pixels in the image. “Thing” classes are weakly-supervised with bounding boxes, and “stuff” with image-level tags. We obtain state-of-the-art results on Pascal VOC, for both full and weak supervision (which achieves about 95% of fullysupervised performance). Furthermore, we present the first weakly-supervised results on Cityscapes for both semanticand instance-segmentation. Finally, we use our weakly supervised framework to analyse the relationship between annotation quality and predictive performance, which is of interest to dataset creators.
Pseudo-task Augmentation: From Deep Multitask Learning to Intratask Sharing - and Back

Deep multitask learning boosts performance by sharing learned structure across related tasks. This paper adapts ideas from deep multitask learning to the setting where only a single task is available. The method is formalized as pseudo-task augmentation, in which models are trained with multiple decoders for each task. Pseudo-tasks simulate the effect of training towards closelyrelated tasks drawn from the same universe. In a suite of experiments, pseudo-task augmentation improves performance on single-task learning problems. When combined with multitask learning, further improvements are achieved, including state-of-the-art performance on the CelebA dataset, showing that pseudo-task augmentation and multitask learning have complementary value. All in all, pseudo-task augmentation is a broadly applicable and efficient way to boost performance in deep learning systems.
The conceptual model to solve the problem of interoperability in health information systems

In the domain of e-health, there are diverse and heterogeneous health care systems with different brands on various platforms. One of the most important challenges in this field is the interoperability which plays a key role on information exchange and sharing. Achieving the interoperability is a difficult task because of complexity and diversity of systems, standards, and kinds of information. The lack of interoperability would lead to increase costs and errors of medical operation in hospitals. The purpose of this article is to present a conceptual model for solving interoperability in health information systems. A Health Service Bus (HSB) as an integrated infrastructure is suggested to facilitate Service Oriented Architecture. A scenario-based evaluation on the proposed conceptual model shows that adopting web service technology is an effective way for this task.
Mapping the Paraphrase Database to WordNet

WordNet has facilitated important research in natural language processing but its usefulness is somewhat limited by its relatively small lexical coverage. The Paraphrase Database (PPDB) covers 650 times more words, but lacks the semantic structure of WordNet that would make it more directly useful for downstream tasks. We present a method for mapping words from PPDB to WordNet synsets with 89% accuracy. The mapping also lays important groundwork for incorporating WordNet’s relations into PPDB so as to increase its utility for semantic reasoning in applications.