nils-herrmann/clean_scirepeval huggingface dataset:
Interactive metagenomic visualization in a Web browser

A critical output of metagenomic studies is the estimation of abundances of taxonomical or functional groups. The inherent uncertainty in assignments to these groups makes it important to consider both their hierarchical contexts and their prediction confidence. The current tools for visualizing metagenomic data, however, omit or distort quantitative hierarchical relationships and lack the facility for displaying secondary variables. Here we present Krona, a new visualization tool that allows intuitive exploration of relative abundances and confidences within the complex hierarchies of metagenomic classifications. Krona combines a variant of radial, space-filling displays with parametric coloring and interactive polar-coordinate zooming. The HTML5 and JavaScript implementation enables fully interactive charts that can be explored with any modern Web browser, without the need for installed software or plug-ins. This Web-based architecture also allows each chart to be an independent document, making them easy to share via e-mail or post to a standard Web server. To illustrate Krona's utility, we describe its application to various metagenomic data sets and its compatibility with popular metagenomic analysis tools. Krona is both a powerful metagenomic visualization tool and a demonstration of the potential of HTML5 for highly accessible bioinformatic visualizations. Its rich and interactive displays facilitate more informed interpretations of metagenomic analyses, while its implementation as a browser-based application makes it extremely portable and easily adopted into existing analysis packages. Both the Krona rendering code and conversion tools are freely available under a BSD open-source license, and available from: http://krona.sourceforge.net .
The comparation of text mining with Naive Bayes classifier, nearest neighbor, and decision tree to detect Indonesian swear words on Twitter

Twitter is one of world most famous social media. There are many statement expresed in Twitter like happiness, sadness, public information, etc. Unfortunately, people may got angry to each other and write it down as a tweet on Twitter. Some tweet may contain Indonesian swear words. It's serious problem because many Indonesians may not tolerated swear words. Some Indonesian swear words may have multiple means, not always an Indonesian swear word means insulting. Twitter has provide tweet's data by account, trending topics, and advance keyword. This work try to analyze many tweet about political news, political event, and some Indonesian famous person because the tweet assumed contains many Indonesian swear word. The derived tweets will process in text mining and then analyzed by classification process using Naive Bayes Classifier, Nearest Neighbor, and Decision Tree to detect Indonesian swear word. This work expected to discover the high accurate classification model. It means, the model can differentiate the real meaning of Indonesian swear word contained in tweet.
Radiometric calibration from a single image

Photometric methods in computer vision require calibration of the camera's radiometric response, and previous works have addressed this problem using multiple registered images captured under different camera exposure settings. In many instances, such an image set is not available, so we propose a method that performs radiometric calibration from only a single image, based on measured RGB distributions at color edges. This technique automatically selects appropriate edge information for processing, and employs a Bayesian approach to compute the calibration. Extensive experimentation has shown that accurate calibration results can be obtained using only a single input image.
On the Limits of LoRaWAN Channel Access

The rising tide of the Internet of Things has brought to the surface numerous low-power, long-range and low-bitrate wireless network technologies. One of them, LoRaWAN, is being intensely popularized as a solution for sensor networks, however, its potential and limitations are unclear, because there is still neither accurate study nor massive LoRaWAN deployment. This paper surveys and analyzes LoRaWAN operation, focusing on performance evaluation of its channel access as the most crucial component for massive machine type communication. We reveal and point out weaknesses of the LoRaWAN specification and propose solutions to improve LoRaWAN performance.
Style Finder: Fine-Grained Clothing Style Detection and Retrieval

With the rapid proliferation of smartphones and tablet computers, search has moved beyond text to other modalities like images and voice. For many applications like Fashion, visual search offers a compelling interface that can capture stylistic visual elements beyond color and pattern that cannot be as easily described using text. However, extracting and matching such attributes remains an extremely challenging task due to high variability and deformability of clothing items. In this paper, we propose a fine-grained learning model and multimedia retrieval framework to address this problem. First, an attribute vocabulary is constructed using human annotations obtained on a novel fine-grained clothing dataset. This vocabulary is then used to train a fine-grained visual recognition system for clothing styles. We report benchmark recognition and retrieval results on Women's Fashion Coat Dataset and illustrate potential mobile applications for attribute-based multimedia retrieval of clothing items and image annotation.
An analytical framework for data stream mining techniques based on challenges and requirements

A growing number of applications that generate massive streams of data need intelligent data processing and online analysis. Real-time surveillance systems, telecommunication systems, sensor networks and other dynamic environments are such examples. The imminent need for turning such data into useful information and knowledge augments the development of systems, algorithms and frameworks that address streaming challenges. The storage, querying and mining of such data sets are highly computationally challenging tasks. Mining data streams is concerned with extracting knowledge structures represented in models and patterns in non stopping streams of information. Generally, two main challenges are designing fast mining methods for data streams and need to promptly detect changing concepts and data distribution because of highly dynamic nature of data streams. The goal of this article is to analyze and classify the application of diverse data mining techniques in different challenges of data stream mining. In this paper, we present the theoretical foundations of data stream analysis and propose an analytical framework for data stream mining techniques.
KCT: a MATLAB toolbox for motion control of KUKA robot manipulators

The Kuka Control Toolbox (KCT) is a collection of MATLAB functions for motion control of KUKA robot manipulators, developed to offer an intuitive and high-level programming interface to the user. The toolbox, which is compatible with all 6 DOF small and low payload KUKA robots that use the Eth.RSIXML, runs on a remote computer connected with the KUKA controller via TCP/IP. KCT includes more than 30 functions, spanning operations such as forward and inverse kinematics computation, point-to-point joint and Cartesian control, trajectory generation, graphical display and diagnostics. The flexibility, ease of use and reliability of the toolbox is demonstrated through two applicative examples.
Face recognition under partial occlusion using HMM and Face Edge Length Model

There are many applications which use Face Recognition for identification or verification of a person. In this study, a face recognition system based on HMM has been proposed to handle the problem of partial occlusion. Face is represented by eight isolated regions: Hairs, Forehead, Eyebrows, Eyes, Nose, Upper Lips, Mouth and Chin. The non-occluded region in face image of testing and training image is used for processing. Further, to increase the accuracy and robustness in the face recognition system HMM is coupled with Face Edge Length Model (FELM) in recognition phase. FELM contains various lengths between the any two edge points on the face. The proposed model is more flexible as it handles general occlusion. Experiments are performed only for sunglasses and scarf occlusions in AR database Experimental results reveal that the proposed algorithm outperforms state-of-art as well as those methods that uses only HMM in recognition phase.
Recoding Color Transfer as a Color Homography

Color transfer is an image editing process that adjusts the colors of a picture to match a target picture’s color theme. A natural color transfer not only matches the color styles but also prevents after-transfer artifacts due to image compression, noise, and gradient smoothness change. The recently discovered color homography theorem proves that colors across a change in photometric viewing condition are related by a homography. In this paper, we propose a color-homography-based color transfer decomposition which encodes color transfer as a combination of chromaticity shift and shading adjustment. A powerful form of shading adjustment is shown to be a global shading curve by which the same shading homography can be applied elsewhere. Our experiments show that the proposed color transfer decomposition provides a very close approximation to many popular color transfer methods. The advantage of our approach is that the learned color transfer can be applied to many other images (e.g. other frames in a video), instead of a frame-toframe basis. We demonstrate two applications for color transfer enhancement and video color grading re-application. This simple model of color transfer is also important for future color transfer algorithm design.
An Intelligent Tutoring System for Entity Relationship Modelling

The paper presents KERMIT, a Knowledge-based Entity Relationship Modelling Intelligent Tutor. KERMIT is a problem-solving environment for the university-level students, in which they can practise conceptual database design using the Entity-Relationship data model. KERMIT uses Constraint-Based Modelling (CBM) to model the domain knowledge and generate student models. We have used CBM previous in tutors that teach SQL and English punctuation rules. The research presented in this paper is significant because we show that CBM can be used to support students learning design tasks, which are very different from domains we dealt with in earlier tutors. The paper describes the system’s architecture and functionality. The system observes students’ actions and adapts to their knowledge and learning abilities. KERMIT has been evaluated in the context of genuine teaching activities. We present the results of two evaluation studies with students taking database courses, which show that KERMIT is an effective system. The students have enjoyed the system’s adaptability and found it a valuable asset to their learning.
The Anatomy of a Context-Aware Application

We describe a sensor-driven, or sentient, platform for context-aware computing that enables applications to follow mobile users as they move around a building. The platform is particularly suitable for richly equipped, networked environments. The only item a user is required to carry is a small sensor tag, which identifies them to the system and locates them accurately in three dimensions. The platform builds a dynamic model of the environment using these location sensors and resource information gathered by telemetry software, and presents it in a form suitable for application programmers. Use of the platform is illustrated through a practical example, which allows a user’s current working desktop to follow them as they move around the environment.
Interactive exploratory search for multi page search results

Modern information retrieval interfaces typically involve multiple pages of search results, and users who are recall minded or engaging in exploratory search using ad hoc queries are likely to access more than one page. Document rankings for such queries can be improved by allowing additional context to the query to be provided by the user herself using explicit ratings or implicit actions such as clickthroughs. Existing methods using this information usually involved detrimental UI changes that can lower user satisfaction. Instead, we propose a new feedback scheme that makes use of existing UIs and does not alter user's browsing behaviour; to maximise retrieval performance over multiple result pages, we propose a novel retrieval optimisation framework and show that the optimal ranking policy should choose a diverse, exploratory ranking to display on the first page. Then, a personalised re-ranking of the next pages can be generated based on the user's feedback from the first page. We show that document correlations used in result diversification have a significant impact on relevance feedback and its effectiveness over a search session. TREC evaluations demonstrate that our optimal rank strategy (including approximative Monte Carlo Sampling) can naturally optimise the trade-off between exploration and exploitation and maximise the overall user's satisfaction over time against a number of similar baselines.
Securing SMS Based One Time Password Technique from Man in the Middle Attack

Security of financial transactions in E-Commerce is difficult to implement and there is a risk that user’s confidential data over the internet may be accessed by hackers. Unfortunately, interacting with an online service such as a banking web application often requires certain degree of technical sophistication that not all Internet users possess. For the last couple of year such naive users have been increasingly targeted by phishing attacks that are launched by miscreants who are aiming to make an easy profit by means of illegal financial transactions. In this paper, we have proposed an idea for securing e-commerce transaction from phishing attack. An approach already exists where phishing attack is prevented using one time password which is sent on user’s registered mobile via SMS for authentication. But this method can be counter attacked by “Man in the Middle”. In our paper, a new idea is proposed which is more secure compared to the existing online payment system using OTP. In this mechanism OTP is combined with the secure key and is then passed through RSA algorithm to generate the Transaction password. A Copy of this password is maintained at the server side and is being generated at the user side using a mobile application; so that it is not transferred over the insecure network leading to a fraudulent transaction. Phishing, Replay attack, MITM attack, RSA, Random Generator.
A power line communication network infrastructure for the smart home

Low voltage electrical wiring in homes has largely been dismissed as too noisy and unpredictable to support high speed communication signals. However, recent advances in communication and modulation methodologies as well as in adaptive digital signal processing and error detection and correction have spawned novel media access control (MAC) and physical layer (PHY) protocols, capable of supporting power line communication networks at speeds comparable to wired local area networks (LANs). In this paper we motivate the use of power line LAN’s as a basic infrastructure for building integrated ”smart homes,” wherein information appliances (IA)—ranging from simple control or monitoring devices to multimedia entertainment systems—are seamlessly interconnected by the very wires which provide them electricity. By simulation and actual measurements using ”reference design” prototype commercial powerline products, we show that the HomePlugMAC and PHY layers can guarantee QoS for real-time communications, supporting delay sensitive data streams for ”Smart Home” applications.
Dynamic Programming for Linear-Time Incremental Parsing

Incremental parsing techniques such as shift-reduce have gained popularity thanks to their efficiency, but there remains a major problem: the search is greedyand only explores a tiny fraction of the whole space (even with beam search) as opposed to dynamic programming. We show that, surprisingly, dynamic programming is in fact possible for many shift-reduce parsers, by merging “equivalent” stacks based on feature values. Empirically, our algorithm yields up to a five-fold speedup over a state-of-the-art shift-reduce dependency parser with no loss in accuracy. Better search also leads to better learning, and our final parser outperforms all previously reported dependency parsers for English and Chinese, yet is much faster.
Structural XML Classification in Concept Drifting Data Streams

Classification of large, static collections of XML data has been intensively studied in the last several years. Recently however, the data processing paradigm is shifting from static to streaming data, where documents have to be processed online using limited memory and class definitions can change with time in an event called concept drift. As most existing XML classifiers are capable of processing only static data, there is a need to develop new approaches dedicated for streaming environments. In this paper, we propose a new classification algorithm for XML data streams called XSC. The algorithm uses incrementally mined frequent subtrees and a tree-subtree similarity measure to classify new documents in an associative manner. The proposed approach is experimentally evaluated against eight state-of-the-art stream classifiers on real and synthetic data. The results show that XSC performs significantly better than competitive algorithms in terms of accuracy and memory usage.
The development of sexual orientation in women.

Throughout this century, most theorizing and empirical research about women's sexual orientation have been guided by a belief in the essential masculinity of lesbians and the femininity of heterosexual women. This view can be traced to the influential inversion theories of 19th-century sexologists such as Richard von Krafft-Ebing and Havelock Ellis. Viewing heterosexuality as the biological norm, these scholars believed that homosexuality results from a biological abnormality that leads to gender-atypical ("inverted") sexual attractions and personality. Although there were differences among the various inversion theories, they shared three core elements. First and foremost, inversion theorists characterized heterosexual women as feminine and lesbians as masculine. In Psychopathia Sexualis, Krafft-Ebing (1908/1950, pp. 398-400) described the most extreme form of female homosexuality as a woman who "possesses of the feminine qualities only the genital organs; thought, sentiment, action, even external appearances are those of the man." A second core belief was that sexual orientation is primarily biological in origin. As Meyer-Bahlburg (1984, p. 375) noted, the inversion model has been "the concept guiding biological explanations of homosexuality" throughout this century. A third belief implicit in inversion theories was that social, cultural, and experiential factors have negligible influence on women's sexual orientation. The impact of this perspective has been widespread, especially in psychology, and continues to this day. The thesis of this review is that the cumulative body of empirical research on women's sexual orientation refutes each of the main inversion assumptions. In the following sections we review this research literature. Next we present an alternative perspective on women's sexual orientation, the intimate careers framework, which is compatible with available research findings, and we identify promising directions for future research. This review focuses exclusively on women.
Real-Time 3D Reconstruction and 6-DoF Tracking with an Event Camera

We propose a method which can perform real-time 3D reconstruction from a single hand-held event camera with no additional sensing, and works in unstructured scenes of which it has no prior knowledge. It is based on three decoupled probabilistic filters, each estimating 6-DoF camera motion, scene logarithmic (log) intensity gradient and scene inverse depth relative to a keyframe, and we build a real-time graph of these to track and model over an extended local workspace. We also upgrade the gradient estimate for each keyframe into an intensity image, allowing us to recover a real-time video-like intensity sequence with spatial and temporal super-resolution from the low bit-rate input event stream. To the best of our knowledge, this is the first algorithm provably able to track a general 6D motion along with reconstruction of arbitrary structure including its intensity and the reconstruction of grayscale video that exclusively relies on event camera data.
Using SAS® Enterprise BI and SAS® Enterprise Miner to Reduce Student Attrition

The true supremacy of the SAS® Enterprise Business Intelligence Server is the ability to utilize the power of SAS® Analytics to deliver real-time information to end users, who usually do not understand statistics, but have the ability to make a difference if they have easy access to the analyzed data. This paper describes the process of using SAS® Enterprise Miner to develop a model to score university students based on their risk of attrition and deliver easy-to-understand results to university personnel using SAS® EBI.
Simultaneous localization and mapping: part I

This paper describes the simultaneous localization and mapping (SLAM) problem and the essential methods for solving the SLAM problem and summarizes key implementations and demonstrations of the method. While there are still many practical issues to overcome, especially in more complex outdoor environments, the general SLAM method is now a well understood and established part of robotics. Another part of the tutorial summarized more recent works in addressing some of the remaining issues in SLAM, including computation, feature representation, and data association
A Machine Learning Approach to Pronoun Resolution in Spoken Dialogue

We apply a decision tree based approach to pronoun resolution in spoken dialogue. Our system deals with pronouns with NPand non-NP-antecedents. We present a set of features designed for pronoun resolution in spoken dialogue and determine the most promising features. We evaluate the system on twenty Switchboard dialogues and show that it compares well to Byron’s (2002) manually tuned system.
Determination of minimum inhibitory concentrations.

Minimum inhibitory concentrations (MICs) are defined as the lowest concentration of an antimicrobial that will inhibit the visible growth of a microorganism after overnight incubation, and minimum bactericidal concentrations (MBCs) as the lowest concentration of antimicrobial that will prevent the growth of an organism after subculture on to antibiotic-free media. MICs are used by diagnostic laboratories mainly to confirm resistance, but most often as a research tool to determine the in vitro activity of new antimicrobials, and data from such studies have been used to determine MIC breakpoints. MBC determinations are undertaken less frequently and their major use has been reserved for isolates from the blood of patients with endocarditis. Standardized methods for determining MICs and MBCs are described in this paper. Like all standardized procedures, the method must be adhered to and may not be adapted by the user. The method gives information on the storage of standard antibiotic powder, preparation of stock antibiotic solutions, media, preparation of inocula, incubation conditions, and reading and interpretation of results. Tables giving expected MIC ranges for control NCTC and ATCC strains are also supplied.