nils-herrmann/clean_scirepeval huggingface dataset:
Adaptive Linked Data-Driven Web Components: Building Flexible and Reusable Semantic Web Interfaces - Building Flexible and Reusable Semantic Web Interfaces

Due to the increasing amount of Linked Data openly published on the Web, user-facing Linked Data Applications (LDAs) are gaining momentum. One of the major entrance barriers for Web developers to contribute to this wave of LDAs is the required knowledge of Semantic Web (SW) technologies such as the RDF data model and SPARQL query language. This paper presents an adaptive componentbased approach together with its open source implementation for creating flexible and reusable SW interfaces driven by Linked Data. Linked Data-driven (LD-R) Web components abstract the complexity of the underlying SW technologies in order to allow reuse of existing Web components in LDAs, enabling Web developers who are not experts in SW to develop interfaces that view, edit and browse Linked Data. In addition to the modularity provided by the LD-R components, the proposed RDF-based configuration method allows application assemblers to reshape their user interface for different use cases, by either reusing existing shared configurations or by creating their proprietary configurations.
Distributed Manufacturing Scheduling Using Intelligent Agents

reflect the temporal relationships between manufacturing activities and the capacity limitations of a set of shared resources. The allocation also affects a schedule's opti-mality with respect to criteria such as cost, lateness, or throughput. The globalization of manufacturing makes such optimization increasingly important. To survive in this competitive market, manufacturing enterprises must increase their productivity and profitability through greater shop floor agility. Agent-based manufacturing scheduling systems are a promising way to provide this optimization. Scheduling problems are not exclusive to manufacturing systems. Similar situations happen routinely in publishing houses, universities, hospitals, airports, transportation companies, and so on. Scheduling problems are typically NP-hard; that is, finding an optimal solution is impossible without using an essentially enumerative algorithm , and the computation time increases exponentially with the problem size. Manufacturing scheduling is one of the most difficult scheduling problems. A well-known manufacturing scheduling problem is classical job shop scheduling, which involves a set of jobs and a set of machines. Each machine can handle at most one job at a time. Each job consists of a chain of operations , each of which must be processed during an uninterrupted time period of given length on a given machine. The purpose is to find a best schedule—that is, an allocation of the operations to time intervals on the machines that has the minimum duration required to complete all jobs. The total possible solutions for this problem with n jobs and m machines is (n!) m. The problem becomes even more complex when it includes other variables. Additional resources In a real manufacturing enterprise, production managers or human schedulers must consider all kinds of manufacturing resources, not just jobs and machines. For example, a classical job shop scheduling problem with n jobs, m machines, and k operators could have ((n!) m) k possible solutions. Simultaneous planning and scheduling Traditional approaches separating process planning and scheduling can obtain suboptimal solutions at two separate phases. Global optimization of a manufacturing system is only possible when process planning and scheduling are integrated. However, this makes the scheduling problem much more difficult to solve. Unforeseen dynamic situations In a job shop manufacturing environment, things rarely go as expected. Scheduled jobs get canceled and new jobs are inserted. Certain resources become unavailable and additional resources are introduced. A scheduled task takes more or less time than anticipated, and tasks arrive early or late. Other uncertainties include power system failures, …
Distributed denial of service attack detection using Naive Bayes Classifier through Info Gain Feature Selection

Distributed Denial of Service (DDoS) attack brings revenue loss, productivity loss, reputation damage, theft, etc. to huge banking and business firms. This leads to the necessity of a good DDoS prevention and detection techniques. The paper aims to provide a better solution to these problems using features analysis. The statistical characteristics or parameters of the incoming packets viz. MTI (mean time intervals), POIP (probability of occurrence of IP), TTL (time to live), ACK value, SYN value, time stamp field, differentiated service field and sequence number are analyzed. The incoming packets are classified into normal and attack packets by deploying Naïve Bayes classifier algorithm using the attack and normal profile from previously available datasets. Information gain algorithm is used to decrease the computation time, memory usage and increase efficiency of detection by reducing the number of parameters. The performance increases with more consistency after the application of information gain. The efficiencies of detection before and after the application of information gain are 98% and 99.5% respectively. The computation time is also reduced by 46.6%. In the paper, we use CAIDA 2008 and CAIDA anonymous trace 2015 datasets for feature selection and classification.
Towards more accurate retrieval of duplicate bug reports

In a bug tracking system, different testers or users may submit multiple reports on the same bugs, referred to as duplicates, which may cost extra maintenance efforts in triaging and fixing bugs. In order to identify such duplicates accurately, in this paper we propose a retrieval function (REP) to measure the similarity between two bug reports. It fully utilizes the information available in a bug report including not only the similarity of textual content in summary and description fields, but also similarity of non-textual fields such as product, component, version, etc. For more accurate measurement of textual similarity, we extend BM25F - an effective similarity formula in information retrieval community, specially for duplicate report retrieval. Lastly we use a two-round stochastic gradient descent to automatically optimize REP for specific bug repositories in a supervised learning manner. We have validated our technique on three large software bug repositories from Mozilla, Eclipse and OpenOffice. The experiments show 10 -- 27% relative improvement in recall rate@k and 17 -- 23% relative improvement in mean average precision over our previous model. We also applied our technique to a very large dataset consisting of 209,058 reports from Eclipse, resulting in a recall rate@k of 37 -- 71% and mean average precision of 47%.
A Bayesian Analysis of Some Nonparametric Problems

Bayesian approach remained rather unsuccessful in treating nonparametric problems. This is primarily due to the difficulty in finding workable prior distribution on the parameter space , which in nonparametric problems is taken to be a set of probability distributions on a given sample space. Two Desirable Properties of a Prior 1. The support of the prior should be large. 2. Posterior distribution given a sample of observation should be manageable analytically. These properties are antagonistic : One may be obtained at the expense of other.
Multilingual Subjectivity Analysis Using Machine Translation

Although research in other languages is increasing, much of the work in subjectivity analysis has been applied to English data, mainly due to the large body of electronic resources and tools that are available for this language. In this paper, we propose and evaluate methods that can be employed to transfer a repository of subjectivity resources across languages. Specifically, we attempt to leverage on the resources available for English and, by employing machine translation, generate resources for subjectivity analysis in other languages. Through comparative evaluations on two different languages (Romanian and Spanish), we show that automatic translation is a viable alternative for the construction of resources and tools for subjectivity analysis in a new target language.
A Compact Flicker-Free Transformer-Less LED Driver With an Enhanced Power Factor for Omnidirectional Multichannel Smart Bulb Applications

In this paper, a flicker-free transformer-less multichannel light-emitting diode (LED) driver that adopts a new power factor enhancement technique is presented. The proposed LED driver divides the transmission path of the current to the LED string in two ways depending on the instantaneous input voltage in order to improve the PF relative to that of a conventional compact flicker-free LED driver that uses a fixed LED current regulation scheme with only one electrolytic capacitor. The proposed driver can be implemented with a minimum number of external components leading to a compact module that allows a light bulb with omnidirectional light emission. In order to enhance the expandability of the smart bulb, a conventional buck converter with 3-V output is integrated with the proposed LED driver to supply the peripherals of the module. The proposed LED driver IC is implemented in a 0.35-μm ultrahigh voltage CMOS process and occupies an area of 2.9 mm × 2.0 mm. A 10-W smart bulb prototype that adopts the proposed LED driver is flicker free and shows a power factor of 0.715 and power conversion efficiency of 77.5% for an ac supply of 120 V.
Giving voice to office customers: Best practices in how office handles verbatim text feedback

Microsoft Office users submit hundreds of thousands of pieces of verbatim feedback per month. How can an engineer or manager in Office find the signal in this data to make business decisions? This paper presents an overview of the Office Customer Voice (OCV) system. OCV combines classification, on-demand clustering and other machine learning techniques with a rich web UI to solve this problem. In this paper, we describe the different types of feedback received. Next, we outline the architecture used to build OCV. We then detail the text processing, classification and clustering done to reason on the data. Finally, we present challenges, future plans, and best practices that may be relevant to other teams analyzing customer feedback. We argue that this multi-pronged approach to handling customer feedback presents a pattern that other organizations can use to mature their handling of customer feedback.
An Initial Study on Personalized Filtering Thresholds in Defending Sequential Spear Phishing Attacks

Different from spam and regular phishing attacks, spear phishing attacks target a small group of people, and the attackers usually make elaborate plans before attacking. There is existing work on classifying spear phishing emails where a threshold value is used to balance misclassified normal emails and misclassified malicious emails. However, most existing systems use a uniform threshold for all users, while in reality users may differ in how susceptible they are to phishing attacks and their access to critical information. Existing work on setting personalized thresholds assumes that the attacker compromises multiple users simultaneously to maximize his expected utility. However, an attacker may be only interested in specific credential information, which could be accessed by a group of users. In this situation, a sequential attack is more reasonable for the attacker to reduce the cost of launching attacks and the likelihood of detection. We propose a Stackelberg game model to calculate the optimal solution for the sequential attack situation and formulate a bilevel optimization problem for the defender. By exploiting the structure of the bilevel problem, we propose a single level formulation called PEDS that is equivalent to the bilevel problem. Experimental results show that PEDS can solved within 60 seconds even when the number of users is 70, and the thresholds computed by PEDS lead to significant higher defender utilities as compared with existing approaches.
Evaluating the language resources of chatbots for their potential in english as a second language

This paper investigates the linguistic worth of current ‘chatbot’ programs – software programs which attempt to hold a conversation, or interact, in English – as a precursor to their potential as an ESL (English as a second language) learning resource. After some initial background to the development of chatbots, and a discussion of the Loebner Prize Contest for the most ‘human’ chatbot (the ‘Turing Test’), the paper describes an in-depth study evaluating the linguistic accuracy of a number of chatbots available online. Since the ultimate purpose of the current study concerns chatbots’ potential with ESL learners, the analysis of language embraces not only an examination of features of language from a native-speaker’s perspective (the focus of the Turing Test), but also aspects of language from a second-language-user’s perspective. Analyses indicate that while the winner of the 2005 Loebner Prize is the most able chatbot linguistically, it may not necessarily be the chatbot most suited to ESL learners. The paper concludes that while substantial progress has been made in terms of chatbots’ language-handling, a robust ESL ‘conversation practice machine’ (Atwell, 1999) is still some way off being a reality.
MVR: An Architecture for Computation Offloading in Mobile Edge Computing

As communication and sensing capabilities of mobile devices increase, mobile applications are becoming increasingly complex. The ability of computation offloading, which is one of the main features of mobile edge computing gains relevance as a technique to improve the battery lifetime of mobile devices and increase the performance of applications. In this paper, we describe the offloading system model and present an innovative architecture, called "MVR", contributing to computation offloading in mobile edge computing.
Sugihara causality analysis of scalp EEG for detection of early Alzheimer's disease

Recently, Sugihara proposed an innovative causality concept, which, in contrast to statistical predictability in Granger sense, characterizes underlying deterministic causation of the system. This work exploits Sugihara causality analysis to develop novel EEG biomarkers for discriminating normal aging from mild cognitive impairment (MCI) and early Alzheimer's disease (AD). The hypothesis of this work is that scalp EEG based causality measurements have different distributions for different cognitive groups and hence the causality measurements can be used to distinguish between NC, MCI, and AD participants. The current results are based on 30-channel resting EEG records from 48 age-matched participants (mean age 75.7 years) - 15 normal controls (NCs), 16 MCI, and 17 early-stage AD. First, a reconstruction model is developed for each EEG channel, which predicts the signal in the current channel using data of the other 29 channels. The reconstruction model of the target channel is trained using NC, MCI, or AD records to generate an NC-, MCI-, or AD-specific model, respectively. To avoid over fitting, the training is based on the leave-one-out principle. Sugihara causality between the channels is described by a quality score based on comparison between the reconstructed signal and the original signal. The quality scores are studied for their potential as biomarkers to distinguish between the different cognitive groups. First, the dimension of the quality scores is reduced to two principal components. Then, a three-way classification based on the principal components is conducted. Accuracies of 95.8%, 95.8%, and 97.9% are achieved for resting eyes open, counting eyes closed, and resting eyes closed protocols, respectively. This work presents a novel application of Sugihara causality analysis to capture characteristic changes in EEG activity due to cognitive deficits. The developed method has excellent potential as individualized biomarkers in the detection of pathophysiological changes in early-stage AD.
Probabilistic Semantics and Pragmatics: Uncertainty in Language and Thought

Language is used to communicate ideas. Ideas are mental tools for coping with a complex and uncertain world. Thus human conceptual structures should be key to language meaning, and probability—the mathematics of uncertainty— should be indispensable for describing both language and thought. Indeed, probabilistic models are enormously useful in modeling human cognition (Tenenbaum et al., 2011) and aspects of natural language (Bod et al., 2003; Chater et al., 2006). With a few early exceptions (e.g. Adams, 1975; Cohen, 1999b), probabilistic tools have only recently been used in natural language semantics and pragmatics. In this chapter we synthesize several of these modeling advances, exploring a formal model of interpretation grounded, via lexical semantics and pragmatic inference, in conceptual structure. Flexible human cognition is derived in large part from our ability to imagine possibilities (or possible worlds). A rich set of concepts, intuitive theories, and other mental representations support imagining and reasoning about possible worlds—together we will call these the conceptual lexicon. We posit that this collection of concepts also forms the set of primitive elements available for lexical semantics: word meanings can be built from the pieces of conceptual structure. Larger semantic structures are then built from word meanings by composition, ultimately resulting in a sentence meaning which is a phrase in the “language of thought” provided by the conceptual lexicon. This expression is truth-functional in that it takes on a Boolean value for each imagined world, and it can thus be used as the basis for belief updating. However, the connection between cognition, semantics, and belief is not direct: because language must flexibly adapt to the context of communication, the connection between lexical representation and interpreted meaning is mediated by pragmatic inference.
Real-Time Billboard Substitution in a Video Stream

We present a system that accepts as input a continuous stream of TV broadcast images from sports events. The system automatically detects a predetermined billboard in the scene, and replaces it with a user defined pattern, with no cooperation from the billboards or camera operators. The replacement is performed seamlessly so that a viewer should not be able to detect the substitution. This allows the targeting of advertising to the appropriate audience, which is especially useful for international events. This requires several modules using state of the art computer graphics, image processing and computer vision technology. The system relies on modular design, and on a pipeline architecture, in which the search and track modules propagate their results in symbolic form throughout the pipeline buffer, and the replacement is performed at the exit of the pipe only, therefore relying on accumulated information. Also, images are processed only once. This allows the system to make replacement decisions based on complete sequences, thus avoiding mid-sequence on screen billboard changes. We present the algorithms and the overall system architecture, and discuss further applications of the technology.
A Knowledge Hunting Framework for Common Sense Reasoning

We introduce an automatic system that achieves state-of-the-art results on the Winograd Schema Challenge (WSC), a common sense reasoning task that requires diverse, complex forms of inference and knowledge. Our method uses a knowledge hunting module to gather text from the web, which serves as evidence for candidate problem resolutions. Given an input problem, our system generates relevant queries to send to a search engine, then extracts and classifies knowledge from the returned results and weighs them to make a resolution. Our approach improves F1 performance on the full WSC by 0.21 over the previous best and represents the first system to exceed 0.5 F1. We further demonstrate that the approach is competitive on the Choice of Plausible Alternatives (COPA) task, which suggests that it is generally applicable.
Compact and high repetitive pulsed power modulator based on semiconductor switches

This paper describes the design of a compact and highly repetitive pulsed power modulator based on semiconductor switches. Output specifications of the proposed modulator are as follows: variable output pulse voltage, 1-10 kV; width, 1-10 μs; pulse repetition rate (PRR), 1-50,000 pps; and average output power, 10 kW. The proposed solid-state pulsed power modulator has two main parts: an IGBT stack that hasa gate drive circuit for the pulse output with a variable pulse width and repetition rate, and a capacitor charger that has a controllable output voltage. To connect all the storage capacitors in a series for high voltage pulses, a simple and reliable IGBT stack structure was proposed based on the Marx generator. In addition, the gate driver circuit, which supplies power and signals to all the IGBTs simultaneously, was introduced. This providesa superior protection function against the arc and the short. To charge the storage capacitors, a novel 10 kW (10 kV, 1 A) high voltage capacitor charger with the combined advantage of a series-loaded resonant converter and a ZVS (zero-voltage-switching) full-bridge pulse width modulation (PWM) converter was proposed and designed especially for the solid-state pulsed power modulator. Theexperiment results verified that the proposed scheme and structure can be used effectively for a high voltage pulsed power modulator that requires variable voltage, repetition rate, and pulse width, depending on the process of the applications.
Mining the Sentiment Expectation of Nouns Using Bootstrapping Method

We propose an unsupervised bootstrapping method to generate a new type of affect knowledge base: the sentiment expectation of nouns (e.g., “high salary” is desirable while “high price” is usually undesirable, because people have opposite sentiment expectation towards “salary” and “price”). A bootstrapping framework is designed to retrieve patterns that might be used to express complaints from the Web. The sentiment expectation of a noun could be automatically predicted with the output patterns. We evaluate the retrieved patterns and show that our method yields good results. Also, they are applied to improve both sentence and document level sentiment analysis results.
Review of control algorithms for robotic ankle systems in lower-limb orthoses, prostheses, and exoskeletons.

This review focuses on control strategies for robotic ankle systems in active and semiactive lower-limb orthoses, prostheses, and exoskeletons. Special attention is paid to algorithms for gait phase identification, adaptation to different walking conditions, and motion intention recognition. The relevant aspects of hardware configuration and hardware-level controllers are discussed as well. Control algorithms proposed for other actuated lower-limb joints (knee and/or hip), with potential applicability to the development of ankle devices, are also included.