nils-herrmann/clean_scirepeval huggingface dataset:
A robust 2D point-sequence curve offset algorithm with multiple islands for contour-parallel tool path

An offset algorithm is important to the contour-parallel tool path generation process. Usually, it is necessary to offset with islands. In this paper a new offset algorithm for a 2D point-sequence curve (PS-curve) with multiple islands is presented. The algorithm consists of three sub-processes, the islands bridging process, the raw offset curve generation and the global invalid loops removal. The input of the algorithm is a set of PS-curves, in which one of them is the outer profile and the others are islands. The bridging process bridges all the islands to the outer profile with the Delaunay triangulation method, forming a single linked PS-curve.With the fact that local problems are caused by intersections of adjacent bisectors, the concept of stuck circle is proposed. Based on stuck circle, local problems are fixed by updating the original profile with the proposed basic rule and append rule, so that a raw offset curve can be generated. The last process first reports all the self-intersections on the raw offset PS-curve, and then a procedure called tree analysis puts all the self-intersections into a tree. All the points between the nodes in even depth and its immediate children are collected using the collecting rule. The collected points form the valid loops, which is the output of the proposed algorithm. Each sub-process can be complete in near linear time, so the whole algorithm has a near linear time complexity. This can be proved by the examples tested in the paper.
Soft switching interleaved PWM buck converter with one auxiliary switch

This paper proposes an interleaved zero voltage transition (ZVT) PWM buck converter. The proposed converter consists of two identical buck converter modules and an auxiliary circuit. The auxiliary circuit provides zero voltage switching condition for the main switches. Also, zero current switching condition for the auxiliary switch and diodes is achieved. The interleaved buck converter with the proposed auxiliary switch is analyzed. The simulation results of a 300W ZVT interleaved buck converter operating at 100 KHz are presented to justify the validity of theoretical analysis.
Transforming Models with ATL 1

This paper presents ATL (ATLAS Transformation Language): a hybrid model transformation language that allows both declarative and imperative constructs to be used in transformation definitions. The paper describes the language syntax by using examples. Language semantics is described in pseudo-code and various optimizations of transformation executions are discussed. ATL is supported by a set of development tools such as an editor, a compiler, a virtual machine, and a debugger. A case study shows the applicability of the language constructs. Alternative ways for implementing the case study are outlined. ATL language features are classified according to a model that captures the commonalities and variabilities of the model transformations domain.
Yelp Dataset Challenge: Review Rating Prediction

Review websites, such as TripAdvisor and Yelp, allow users to post online reviews for various businesses, products and services, and have been recently shown to have a significant influence on consumer shopping behaviour. An online review typically consists of free-form text and a star rating out of 5. The problem of predicting a user’s star rating for a product, given the user’s text review for that product, is called Review Rating Prediction and has lately become a popular, albeit hard, problem in machine learning. In this paper, we treat Review Rating Prediction as a multi-class classification problem, and build sixteen different prediction models by combining four feature extraction methods, (i) unigrams, (ii) bigrams, (iii) trigrams and (iv) Latent Semantic Indexing, with four machine learning algorithms, (i) logistic regression, (ii) Näıve Bayes classification, (iii) perceptrons, and (iv) linear Support Vector Classification. We analyse the performance of each of these sixteen models to come up with the best model for predicting the ratings from reviews. We use the dataset provided by Yelp for training and testing the models.
Adaptive Sliding-Window Coded Modulation in Cellular Networks

The sliding-window superposition coding scheme aims to mitigate intercell interference at the physical layer by achieving the simultaneous decoding performance with point-to-point channel codes, low- complexity decoding, and minimal coordination overhead. The associated sliding-window coded modulation (SWCM) scheme can be readily implemented using standard off-the-shelf codes, such as the standard LTE turbo code, and tracks the information-theoretical performance guarantee of sliding-window superposition coding. This paper investigates how the basic SWCM scheme performs for the Ped-B fading interference channel model and proposes several improvements in transceiver design, such as soft decoding, input bit-mapping and layer optimization, and power control. Our enhanced SWCM scheme achieves the rates higher than those of the basic SWCM scheme by 10% to 20%, which already shows a significant gain over existing schemes that ignore modulation or coding information of interfering signals. This result confirms the potential of SWCM as a basic building block for physical-layer interference management in 5G and subsequent generations of cellular networks.
A New Approach for Resource Scheduling with Deep Reinforcement Learning

With the rapid development of deep learning, deep reinforcement learning (DRL) began to appear in the field of resource scheduling in recent years. Based on the previous research on DRL in the literature, we introduce online resource scheduling algorithm DeepRM2 and the offline resource scheduling algorithm DeepRM_Off. Compared with the state-of-the-art DRL algorithm DeepRM and heuristic algorithms, our proposed algorithms have faster convergence speed and better scheduling efficiency with regarding to average slowdown time, job completion time and rewards.
A Short-Term Rainfall Prediction Model Using Multi-task Convolutional Neural Networks

Precipitation prediction, such as short-term rainfall prediction, is a very important problem in the field of meteorological service. In practice, most of recent studies focus on leveraging radar data or satellite images to make predictions. However, there is another scenario where a set of weather features are collected by various sensors at multiple observation sites. The observations of a site are sometimes incomplete but provide important clues for weather prediction at nearby sites, which are not fully exploited in existing work yet. To solve this problem, we propose a multi-task convolutional neural network model to automatically extract features from the time series measured at observation sites and leverage the correlation between the multiple sites for weather prediction via multi-tasking. To the best of our knowledge, this is the first attempt to use multi-task learning and deep learning techniques to predict short-term rainfall amount based on multi-site features. Specifically, we formulate the learning task as an end-to-end multi-site neural network model which allows to leverage the learned knowledge from one site to other correlated sites, and model the correlations between different sites. Extensive experiments show that the learned site correlations are insightful and the proposed model significantly outperforms a broad set of baseline models including the European Centre for Medium-range Weather Forecasts system (ECMWF).
Inclusion of e-commerce workflow with NoSQL DBMS: MongoDB document store

In today's open market, e-commerce has developed its strategy to process various business applications. There applications are developed with the platforms like Magento, Zen Cart, Prestashop, Spree etc., to run a successful online store. These are also used for customer e-commerce apps and require a scalable database for storing the data. The NoSQL database provides an efficient storage access and processing environment with horizontally scalable and replicable strategy over RDBMS. Because of its schema less structure, NoSQL databases provides access to the users to dynamically change the data model applications. Due to this characteristics, NoSQL databases are being used in real time analysis. In order to build a successful online and scalable store, in this study, we choose the database as Document-Oriented NoSQL database for the inclusion of e-commerce workflow. As a new finding, in this study, we will ascertain design and working strategy of e-commerce based on MongoDB, which is a widely used Document-Oriented NoSQL database to process large scale business applications.
Ultra Fast H.264/AVC to HEVC Transcoder

The emerging High Efficiency Video Coding (HEVC) standard achieves significant performance improvement over H.264/AVC standard at a cost of much higher complexity. In this paper, we propose a ultra fast H.264/AVC to HEVC transcoder for multi-core processors implementing Wave front Parallel Processing (WPP) and SIMD acceleration, along with expedited motion estimation (ME) and mode decision (MD) by utilizing information extracted from the input H.264/AVC stream. Experiments using standard HEVC test bit streams show that the proposed transcoder achieves 70x speed up over the HEVC HM 8.1 reference software (including H.264 encoding) at very small rate distortion (RD) performance loss.
Agent based Architecture for Modeling and Analysis of Self Adaptive Systems using Formal Methods

Self-adaptive systems (SAS) can modify their behavior during execution; this modification is done because of change in internal or external environment. The need for selfadaptive software systems has increased tremendously in last decade due to ever changing user requirements, improvement in technology and need for building software that reacts to user preferences. To build this type of software we need well establish models that have the flexibility to adjust to the new requirements and make sure that the adaptation is efficient and reliable. Feedback loop has proven to be very effective in modeling and developing SAS, these loops help the system to sense, analyze, plan, test and execute the adaptive behavior at runtime. Formal methods are well defined, rigorous and reliable mathematical techniques that can be effectively used to reason and specify behavior of SAS at design and run-time. Agents can play an important role in modeling SAS because they can work independently, with other agents and with environment as well. Using agents to perform individual steps in feedback loop and formalizing these agents using Petri nets will not only increase the reliability, but also, the adaptation can be performed efficiently for taking decisions at run time with increased confidence. In this paper, we propose a multi-agent framework to model self-adaptive systems using agent based modeling. This framework will help the researchers in implementation of SAS, which is more dependable, reliable, autonomic and flexible because of use of multi-agent based formal approach. Formal methods; self-adaptive systems; agent based modeling; feedback loop; Petri nets
The examination timetabling problem at Universiti Malaysia Pahang: Comparison of a constructive heuristic with an existing software solution

This paper presents a real-world, capacitated examination timetabling problem from Universiti Malaysia Pahang (UMP), Malaysia. The problem has constraints which have not been modelled before, these being the distance between examination rooms and splitting exams across several rooms. These constraints provide additional challenges in defining a suitable model and in developing a constructive heuristic. One of the contributions of this paper is to formally define this real-world problem. A further contribution is the constructive heuristic that is able to produce good quality solutions for the problem, which are superior to the solutions that are produced using the university’s current software. Moreover, our method adheres to all hard constraints which the current systems fails to do.
Deep MIML Network

In many real world applications, the concerned objects are with multiple labels, and can be represented as a bag of instances. Multi-instance Multi-label (MIML) learning provides a framework for handling such task and has exhibited excellent performance in various domains. In a MIML setting, the feature representation of instances usually has big impact on the final performance; inspired by the recent deep learning studies, in this paper, we propose the DeepMIML network which exploits deep neural network formation to generate instance representation for MIML. The sub-concept learning component of the DeepMIML structure reserves the instance-label relation discovery ability of MIML algorithms; that is, it can automatically locating the key input patterns that trigger the labels. The effectiveness of DeepMIML network is validated by experiments on various domains of data.
Exploring knowledge sharing in ERP implementation: an organizational culture framework

This is a multi-site case study of firms that have implemented enterprise resource planning (ERP) systems. It examines eight dimensions of culture and their impact on how ERP implementation teams are able to effectively share knowledge across diverse functions and perspectives during ERP implementation. Through synthesizing the data, we develop a cultural configuration that shows the dimensions of culture that best facilitate knowledge sharing in ERP implementation. The results also indicate ways that firms may overcome cultural barriers to knowledge sharing. A model is developed that demonstrates the link between the dimensions of culture and knowledge sharing during ERP implementation. Possible research questions on which future research can be based are also identified. D 2004 Elsevier B.V. All rights reserved.
A Context-Aware Approach for Detecting Worth-Checking Claims in Political Debates

In the context of investigative journalism, we address the problem of automatically identifying which claims in a given document are most worthy and should be prioritized for fact-checking. Despite its importance, this is a relatively understudied problem. Thus, we create a new corpus of political debates, containing statements that have been fact-checked by nine reputable sources, and we train machine learning models to predict which claims should be prioritized for fact-checking, i.e., we model the problem as a ranking task. Unlike previous work, which has looked primarily at sentences in isolation, in this paper we focus on a rich input representation modeling the context: relationship between the target statement and the larger context of the debate, interaction between the opponents, and reaction by the moderator and by the public. Our experiments show state-of-the-art results, outperforming a strong rivaling system by a margin, while also confirming the importance of the contextual information.
Airport Security Screener Competency : A Cross-Sectional and Longitudinal Analysis

The performance of 5,717 aviation security screeners in detecting prohibited items in x-ray images of passenger bags was analyzed over a period of 4 years. The measure used in this study was detection performance on the X-Ray Competency Assessment Test (X-Ray CAT) and performance on this measure was obtained on an annual basis. Between tests, screeners performed varying amounts of training in the task of prohibited item detection using an adaptive computer-based training system called X-Ray Tutor (XRT). For both XRT and X-Ray CAT, prohibited items are categorized into guns, knives, improvised explosive devices (IEDs), and other prohibited items. Cross-sectional and longitudinal analyses of the training and test data were performed. Both types of analysis revealed a strong improvement of X-Ray CAT performance as a result of XRT training for all 4 item categories. The results of the study indicate that screener competency in detecting prohibited items in x-ray images of passenger bags can be greatly improved through routine XRT training.
Homogenization of material properties in additively manufactured structures

Additive manufacturing transforms material into three-dimensional parts incrementally, layer by layer or path by path. Subject to the build direction and machine resolution, an additively manufactured part deviates from its design model in terms of both geometry and mechanical performance. In particular, the material inside the fabricated part often exhibits spatially varying material distribution (heterogeneity) and direction dependent behavior (anisotropy), indicating that the design model is no longer a suitable surrogate to consistently estimate the mechanical performance of the printed component. We propose a new two-stage approach tomodeling and estimating effective elastic properties of parts fabricated by fused deposition modeling (FDM) process. First, we construct an implicit representation of an effective mesoscale geometry–material model of the printed structure that captures the details of the particular process and published material information. This representation of mesoscale geometry and material of the printed structure is then homogenized at macro scale through a solution of an integral equation formulated using Green’s function. We show that the integral equation can be converted into a system of linear equations that is symmetric and positive definite and can be solved efficiently using conjugate gradient method and Fourier transform. The computed homogenized properties are validated by both finite element method and experiment results. The proposed two-stage approach can be used to estimate other effective material properties in a variety of additive manufacturing processes, whenever a similar effective mesoscale geometry–material model can be constructed.
Exploiting location based social networks in business predictions

The growing use of Location Based Social Networks especially in recent years provides large amount of data transactions. These data transactions attract many data mining researchers to infer various information from them. In this paper, a geographic business prediction technique is proposed, which infers business usage by exploiting data published about venues in Location Based Social Networks. The proposed technique is beneficial for investors and business decision makers. The proposed geo-business prediction technique considers spatial and categorical factors in the prediction process. Both factors affect the prediction accuracy rather than using traditional spatial prediction techniques, which are usually used where only the location feature is involved in the prediction process. Additionally, an outlier filter is proposed and applied to the data to avoid extreme values involvement in the prediction process in order to achieve better prediction accuracy. To test the proposed technique, an experimental case study is implemented. It uses data extracted from Foursquare about business venues in Texas State in the United States of America. The proposed geo business prediction technique has shown to provide better prediction accuracy than k nearest neighbor spatial prediction. The Application of the outlier filter, results in even higher prediction accuracy for the proposed technique.
Development of Vehicle Health Monitoring System ( VHMS / WebCARE ) for Large-Sized Construction Machine

This paper describes the vehicle health monitoring system (VHMS/WebCARE) we have developed for large-sized construction machines. Large-sized construction machines are continuously operated for many hours. Once they break down, it takes substantial cost to repair them. Besides, since repairing a large-sized construction machine takes much time, it significantly af fects the machine activity rate. Therefore, construction machines, especially large ones, are required to be free of down time and capable of planned operation without interruption. In order to secure such machines, it is necessary to early detect any symptoms of machine trouble by physical examination, etc. and have the maintenance personnel take suitable measures without delay. For monitoring and diagnosing large-sized construction machines, we have developed a system which supports diagnosis for the maintenance of large-sized construction machines, including prediction of machine trouble and estimation of machine life expectancy. This system consists mainly of a controller exclusive for collection of data for self-diagnosis of the operating condition of the machine and a data base which stores collected data so that the data can be ef fectively used for the diagnosis mentioned above.
It Takes Two to Tango: Deleted Stack Overflow Question Prediction with Text and Meta Features

Stack Overflow is a popular community-based Q&A website that caters to technical needs of software developers. As of February 2015 - Stack Overflow has more than 3.9M registered users, 8.8M questions, and 41M comments. Stack Overflow provides explicit and detailed guidelines on how to post questions but, some questions are very poor in quality. Such questions are deleted by the experienced community members and moderators. Deleted questions increase maintenance cost and have an adverse impact on the user experience. Therefore, predicting deleted questions is an important task. In this study, we propose a two stage hybrid approach - DelPredictor - which combines text processing and classification techniques to predict deleted questions. In the first stage, DelPredictor converts text in the title, body, and tag fields of questions into numerical textual features via text processing and classification techniques. In the second stage, it extracts meta features that can be categorized into: profile, community, content, and syntactic features. Next, it learns and combines two independent classifiers built on the textual and meta features. We evaluate DelPredictor on 5 years (2008-2013) of deleted questions from Stack Overflow. Our experimental results show that DelPredictor improves the F1-scores over baseline prediction, a prior approach  and a text-based approach by 29.50%, 9.34%, and 28.11%, respectively.
Microwave Power Transmission Technologies for Solar Power Satellites

A solar power satellite (SPS) is a renewable energy system that converts the sun's energy into electricity in space and transmits it to Earth using microwaves. The SPS concept, first proposed in 1968 in the United States, has recently started attracting increased public attention as a promising energy system that can be used to resolve global environmental and energy problems. One of the most challenging technologies for the SPS is microwave power transmission from the geostationary orbit to the ground. The technologies for microwave power transmission have been studied for more than 40 years since the initial demonstrations in the 1960s; however, for SPS application, considerable research, especially on high-efficiency power conversion between direct current (dc) and radio frequency (RF) and on high-accuracy microwave beam control over a long range, is still needed. This paper introduces the concept of SPS and presents the technologies and issues associated with microwave power transmission from space to ground. Current research status and the future development prospects for microwave power transmission toward commercial SPS use are also described.