nils-herrmann/clean_scirepeval huggingface dataset:
Quantifying fungal infection of plant leaves by digital image analysis using Scion Image software.

A digital image analysis method previously used to evaluate leaf color changes due to nutritional changes was modified to measure the severity of several foliar fungal diseases. Images captured with a flatbed scanner or digital camera were analyzed with a freely available software package, Scion Image, to measure changes in leaf color caused by fungal sporulation or tissue damage. High correlations were observed between the percent diseased leaf area estimated by Scion Image analysis and the percent diseased leaf area from leaf drawings. These drawings of various foliar diseases came from a disease key previously developed to aid in visual estimation of disease severity. For leaves of Nicotiana benthamiana inoculated with different spore concentrations of the anthracnose fungus Colletotrichum destructivum, a high correlation was found between the percent diseased tissue measured by Scion Image analysis and the number of leaf spots. The method was adapted to quantify percent diseased leaf area ranging from 0 to 90% for anthracnose of lily-of-the-valley, apple scab, powdery mildew of phlox and rust of golden rod. In some cases, the brightness and contrast of the images were adjusted and other modifications were made, but these were standardized for each disease. Detached leaves were used with the flatbed scanner, but a method using attached leaves with a digital camera was also developed to make serial measurements of individual leaves to quantify symptom progression. This was successfully applied to monitor anthracnose on N. benthamiana leaves. Digital image analysis using Scion Image software is a useful tool for quantifying a wide variety of fungal interactions with plant leaves.
Towards Geo-Distributed Machine Learning

Latency to end-users and regulatory requirements push large companies to build data centers all around the world. The resulting data is “born” geographically distributed. On the other hand, many Machine Learning applications require a global view of such data in order to achieve the best results. These types of applications form a new class of learning problems, which we call Geo-Distributed Machine Learning (GDML). Such applications need to cope with: 1) scarce and expensive cross-data center bandwidth, and 2) growing privacy concerns that are pushing for stricter data sovereignty regulations. Current solutions to learning from geo-distributed data sources revolve around the idea of first centralizing the data in one data center, and then training locally. As Machine Learning algorithms are communication-intensive, the cost of centralizing the data is thought to be offset by the lower cost of intra-data center communication during training. In this work, we show that the current centralized practice can be far from optimal, and propose a system architecture for doing geo-distributed training. Furthermore, we argue that the geo-distributed approach is structurally more amenable to dealing with regulatory constraints, as raw data never leaves the source data center. Our empirical evaluation on three real datasets confirms the general validity of our approach, and shows that GDML is not only possible but also advisable in many scenarios.
Sentiment analysis: A literature review

Sentiment analysis has aroused the interest of many researchers in recent years, since subjective texts are useful for many applications. In particular, sentiment analysis on online reviews has become a hot research field. Studies on sentiment analysis mainly focus on framework and lexicon construction, feature extraction, and polarity determination. This paper presents a survey on the latest development in sentiment analysis, and makes an in-depth introduction of its research and application in business and Blogsphere. The methods used in current research are especially emphasized and the existing problems of those studies are discussed. Finally, some possible future directions of research are pointed out.
Assessing the Attack Threat due to IRC Channels

This practical experience report presents the results of an investigation into the threat of attacks associated with the chat medium IRC. A combination of simulated users (i.e., bots), some configured with scripts that simulated conversations, and regular users were used. The average number of attacks per day a user on IRC can expect, the effect of channel activity, gender based on the name, and network type on the number of attacks were determined. The social structure of IRC channels and the types of users that use it were analyzed. The results indicate that attacks through IRC channels come from human users selecting targets rather than automated scripts targeting every user in a channel
Making Sense Out of a Jungle of JavaScript Frameworks - Towards a Practitioner-Friendly Comparative Analysis

The field of Web development is entering the HTML5 and CSS3 era and JavaScript is becoming increasingly influential. A large number of JavaScript frameworks have been recently promoted. Practitioners applying the latest technologies need to choose a suitable JavaScript framework (JSF) in order to abstract the frustrating and complicated coding steps and to provide a cross-browser compatibility. Apart from benchmark suites and recommendation from experts, there is little research helping practitioners to select the most suitable JSF to a given situation. The few proposals employ software metrics on the JSF, but practitioners are driven by different concerns when choosing a JSF. As an answer to the critical needs, this paper is a call for action. It proposes a research design towards a comparative analysis framework of JSF, which merges researcher needs and practitioner needs.
Component-based robotic engineering (Part I) [Tutorial]

This article is the first of a two-part series intended as an introduction to component-based software engineering (CBSE) in robotics. In this tutorial, we regard a component as a piece of software that implements robotic functionality (e.g., path planning). The focus of this article is on design principles and implementation guidelines that enable the development of reusable and maintainable software-building blocks, which can be assembled to build robotic applications.
Growing a Graph Matching from a Handful of Seeds

In many graph–mining problems, two networks from different domains have to be matched. In the absence of reliable node attributes, graph matching has to rely on only the link structures of the two networks, which amounts to a generalization of the classic graph isomorphism problem. Graph matching has applications in social–network reconciliation and de-anonymization, protein–network alignment in biology, and computer vision. The most scalable graph–matching approaches use ideas from percolation theory, where a matched node pair “infects” neighbouring pairs as additional potential matches. This class of matching algorithm requires an initial seed set of known matches to start the percolation. The size and correctness of the matching is very sensitive to the size of the seed set. In this paper, we give a new graph–matching algorithm that can operate with a much smaller seed set than previous approaches, with only a small increase in matching errors. We characterize a phase transition in matching performance as a function of the seed set size, using a random bigraph model and ideas from bootstrap percolation theory. We also show the excellent performance in matching several real large-scale social networks, using only a handful of seeds.
Sentiment Classification using Distant Supervision

We introduce a novel approach for automatically classifying the sentiment of Twitter messages. These messages are classified as either positive or negative with respect to a query term. This is useful for consumers who want to research the sentiment of products before purchase, or companies that want to monitor the public sentiment of their brands. There is no previous research on classifying sentiment of messages on microblogging services like Twitter. We present the results of machine learning algorithms for classifying the sentiment of Twitter messages using distant supervision. Our training data consists of Twitter messages with emoticons, which are used as noisy labels. This type of training data is abundantly available and can be obtained through automated means. We show that machine learning algorithms (Naive Bayes, Maximum Entropy, and SVM) have accuracy above 80% when trained with emoticon data. This paper also describes the preprocessing steps needed in order to achieve high accuracy. The main contribution of this paper is the idea of using tweets with emoticons for distant supervised learning.
The shortest-path problem for graphs with random arc-lengths

We consider the problem of finding the shortest distance between all pairs of vertices in a complete digraph on n vertices, whose arc-lengths are non-negative random variables. We describe an algorithm which solves this problem in O(n(m + n log n)) expected time, where m is the expected number of arcs with finite length. If m is small enough, this represents a small improvement over the bound in Bloniarz . We consider also the case when the arc-lengths are random variables which are independently distributed with distribution function F, where F(0) = 0 and F is differentiable at 0; for this case, we describe an algorithm which runs in O(n 2log n) expected time. In our treatment of the shortest-path problem we consider the following problem in combinatorial probability theory. A town contains n people, one of whom knows a rumour. At the first stage he tells someone chosen randomly from the town; at each stage, each person who knows the rumour tells someone else, chosen randomly from the town and independently of all other choices. Let Sn be the number of stages before the whole town knows the rumour. We show that Sn/log2n--" 1 + loge 2 in probability as n ~ 0% and estimate the probabilities of large deviations in Sn.
Gecko inspired micro-fibrillar adhesives for wall climbing robots on micro/nanoscale rough surfaces

This paper presents the fabrication, characterization and testing of bio-inspired synthetic dry adhesive fiber arrays. Fibers were fabricated via micromolding followed by spatula tip formation via dipping. Arrays of fibers with diameters between 28 mum and 57 mum and a height of 114 mum were fabricated with high uniformity on 6.25 cm2 areas with up to 95% yield. Adaptation to uneven surfaces was observed with fiber elongations over 6 times the original height of the fiber. The unstructured sample exhibited 1.6 times as much adhesion as the fiber array sample for the flat punch indenter. However, fibrillar samples demonstrated up to 5.3 times as much adhesion as the unstructured sample for the hemispherical indenter. The fibrillar adhesive sample was implemented on a wall climbing robot which was able to carry itself and climb a distance on a painted wall and wood door.
Optimization of a Parallel Shoulder Mechanism to Achieve a High-Force, Low-Mass, Robotic-Arm Exoskeleton

This paper describes a robotic-arm exoskeleton that uses a parallel mechanism inspired by the human forearm to allow naturalistic shoulder movements. The mechanism can produce large forces through a substantial portion of the range of motion (RoM) of the human arm while remaining lightweight. This paper describes the optimization of the exoskeleton's torque capabilities by the modification of the key geometric design parameters.
WaveNet: A Generative Model for Raw Audio

This paper introduces WaveNet, a deep neural network for generating raw audio waveforms. The model is fully probabilistic and autoregressive, with the predictive distribution for each audio sample conditioned on all previous ones; nonetheless we show that it can be efficiently trained on data with tens of thousands of samples per second of audio. When applied to text-to-speech, it yields state-ofthe-art performance, with human listeners rating it as significantly more natural sounding than the best parametric and concatenative systems for both English and Mandarin. A single WaveNet can capture the characteristics of many different speakers with equal fidelity, and can switch between them by conditioning on the speaker identity. When trained to model music, we find that it generates novel and often highly realistic musical fragments. We also show that it can be employed as a discriminative model, returning promising results for phoneme recognition.
A Circularly Polarized Cylindrical Dielectric Resonator Antenna Using a Helical Exciter

A novel circularly polarized cylindrical dielectric resonator antenna excited by an external tape helix is presented. The helix is fed by a coaxial line through a small hole on a finite size ground plane. The configuration offers a compact and easy to fabricate feeding network providing a 3 dB axial-ratio bandwidth of 6.4%. A prototype of the proposed configuration is fabricated and measured. Measured and simulated return loss, axial-ratio, radiation pattern, and realized gain are presented and discussed together with design guidelines.
Energy-efficient GPU design with reconfigurable in-package graphics memory

We propose an energy-efficient reconfigurable in-package graphics memory design that integrates wide-interface graphics DRAMs with GPU on a silicon interposer. We reduce the memory power consumption by scaling down the supply voltage and frequency while maintaining the same or higher peak bandwidth. Furthermore, we design a reconfigurable memory interface and propose two reconfiguration mechanisms to optimize system energy efficiency and throughput. The proposed memory architecture can reduce memory power consumption up to 54%, without reconfiguration. The reconfigurable interface can improve system energy efficiency by 23% and throughput by 30% under a power budget of 240W*.
Phase-change materials for rewriteable data storage.

Phase-change materials are some of the most promising materials for data-storage applications. They are already used in rewriteable optical data storage and offer great potential as an emerging non-volatile electronic memory. This review looks at the unique property combination that characterizes phase-change materials. The crystalline state often shows an octahedral-like atomic arrangement, frequently accompanied by pronounced lattice distortions and huge vacancy concentrations. This can be attributed to the chemical bonding in phase-change alloys, which is promoted by p-orbitals. From this insight, phase-change alloys with desired properties can be designed. This is demonstrated for the optical properties of phase-change alloys, in particular the contrast between the amorphous and crystalline states. The origin of the fast crystallization kinetics is also discussed.
Online grocery retailing: what do consumers think?

To use the theory of planned behavior (TPB) as a theoretical framework to explore in depth the range of beliefs held by consumers about internet shopping in general and internet grocery shopping in particular.  Seven focus group interviews, four in the United Kingdom and three in Denmark, were conducted among consumers with different degrees of experience with internet grocery shopping. This diversification of respondents was chosen to capture a broad range of the consumer beliefs that predict intentions to buy groceries online or not. The TPB framework was used to construct the interview guide that was followed in all focus groups.  An unexpected result of the explorative study was that the seven groups consisting of more or less experienced internet shoppers differed only little in their pool of beliefs (outcome and control beliefs). Beliefs about internet grocery shopping, positive as well as negative, were remarkably congruent across groups. In the minds of consumers, internet grocery shopping is an advantage compared with conventional grocery shopping in terms of convenience, product range and price. Disadvantages, which could act as mental barriers, are, for instance, the risk of receiving inferior quality groceries and the loss of the recreational aspect of grocery shopping.  An important potential limitation of this research is the choice of focus groups as research methodology, which can prevent the elicitation of certain types of beliefs. If important beliefs concern issues of a more sensitive, personal character they are not likely to be mentioned in a focus group. Another limitation is the explorative nature of the research, which makes it impossible to attach weights to the importance of the elicited beliefs in predicting internet shopping behavior.  The findings could be used to direct attention to consumer beliefs about internet grocery shopping which have the potential of acting as barriers to this line of e-commerce.  To shed some light on the role of consumers in an underperforming and understudied branch of internet retailing. Barriers in the consumers’ minds to shop for groceries online are identified using an established theoretical framework.
Nipah Virus-associated Encephalitis Outbreak, Siliguri, India

During January and February 2001, an outbreak of febrile illness associated with altered sensorium was observed in Siliguri, West Bengal, India. Laboratory investigations at the time of the outbreak did not identify an infectious agent. Because Siliguri is in close proximity to Bangladesh, where outbreaks of Nipah virus (NiV) infection were recently described, clinical material obtained during the Siliguri outbreak was retrospectively analyzed for evidence of NiV infection. NiV-specific immunoglobulin M (IgM) and IgG antibodies were detected in 9 of 18 patients. Reverse transcription-polymerase chain reaction (RT-PCR) assays detected RNA from NiV in urine samples from 5 patients. Sequence analysis confirmed that the PCR products were derived from NiV RNA and suggested that the NiV from Siliguri was more closely related to NiV isolates from Bangladesh than to NiV isolates from Malaysia. NiV infection has not been previously detected in India.
Designing location-based mobile games with a purpose: collecting geospatial data with CityExplorer

The games with a purpose paradigm proposed by Luis von Ahn [9] is a new approach for game design where useful but boring tasks, like labeling a random image found in the web, are packed within a game to make them entertaining. But there are not only large numbers of internet users that can be used as voluntary data producers but legions of mobile device owners, too. In this paper we describe the design of a location-based mobile game with a purpose: CityExplorer. The purpose of this game is to produce geospatial data that is useful for non-gaming applications like a location-based service. From the analysis of four use case studies of CityExplorer we report that such a purposeful game is entertaining and can produce rich geospatial data collections.
Click Fraud Detection with Bot Signatures

Click Fraud Bots pose a significant threat to the online economy. To-date efforts to filter bots have been geared towards identifiable useragent strings, as epitomized by the IAB's Robots and Spiders list. However bots designed to perpetrate malicious activity or fraud, are designed to avoid detection with these kinds of lists, and many use very sophisticated schemes for cloaking their activities. In order to combat this emerging threat, we propose the creation of Bot Signatures for training and evaluation of candidate Click Fraud Detection Systems. Bot signatures comprise keyed records connected to case examples. We demonstrate the technique by developing 8 simulated examples of Bots described in the literature including Click Bot A.
On-Street and Off-Street Parking Availability Prediction Using Multivariate Spatiotemporal Models

Parking guidance and information (PGI) systems are becoming important parts of intelligent transportation systems due to the fact that cars and infrastructure are becoming more and more connected. One major challenge in developing efficient PGI systems is the uncertain nature of parking availability in parking facilities (both on-street and off-street). A reliable PGI system should have the capability of predicting the availability of parking at the arrival time with reliable accuracy. In this paper, we study the nature of the parking availability data in a big city and propose a multivariate autoregressive model that takes into account both temporal and spatial correlations of parking availability. The model is used to predict parking availability with high accuracy. The prediction errors are used to recommend the parking location with the highest probability of having at least one parking spot available at the estimated arrival time. The results are demonstrated using real-time parking data in the areas of San Francisco and Los Angeles.
Probabilistic Matrix Factorization with Non-random Missing Data

We propose a probabilistic matrix factorization model for collaborative filtering that learns from data that is missing not at random (MNAR). Matrix factorization models exhibit state-of-the-art predictive performance in collaborative filtering. However, these models usually assume that the data is missing at random (MAR), and this is rarely the case. For example, the data is not MAR if users rate items they like more than ones they dislike. When the MAR assumption is incorrect, inferences are biased and predictive performance can suffer. Therefore, we model both the generative process for the data and the missing data mechanism. By learning these two models jointly we obtain improved performance over state-of-the-art methods when predicting the ratings and when modeling the data observation process. We present the first viable MF model for MNAR data. Our results are promising and we expect that further research on NMAR models will yield large gains in collaborative filtering.
MUTE: Majority under-sampling technique

An application which operates on an imbalanced dataset loses its classification performance on a minority class, which is rare and important. There are a number of over-sampling techniques, which insert minority instances into a dataset, to adjust the class distribution. Unfortunately, these instances highly affect the computation of generating a classifier. In this paper, a new simple and effective under-sampling called MUTE is proposed. Its strategy is to get rid of noise majority instances which over-lap with minority instances. The removal majority instances are considered based on their safe levels relying on the Safe-Level-SMOTE concept. MUTE not only reduces the classifier construction time because of a downsizing dataset but also improves the prediction rate on a minority class. The experimental results show that MUTE improves F-measure by comparing to SMOTE techniques.
Decentralized dynamic spectrum access for cognitive radios: cooperative design of a non-cooperative game

We consider dynamic spectrum access among cognitive radios from an adaptive, game theoretic learning perspective. Spectrum-agile cognitive radios compete for channels temporarily vacated by licensed primary users in order to satisfy their own demands while minimizing interference. For both slowly varying primary user activity and slowly varying statistics of "fast" primary user activity, we apply an adaptive regret based learning procedure which tracks the set of correlated equilibria of the game, treated as a distributed stochastic approximation. This procedure is shown to perform very well compared with other similar adaptive algorithms. We also estimate channel contention for a simple CSMA channel sharing scheme.