nils-herrmann/clean_scirepeval huggingface dataset:
Miniaturized NFC Antenna Design for a Tablet PC With a Narrow Border and Metal Back-Cover

A novel structure of the near-field communication (NFC) antenna design for a tablet PC is proposed. This tablet PC has a narrow border and full metallic back-cover. A miniaturized loop antenna design is achieved by attaching ferrite sheets on both sides of the loop antenna. The ferrite sheets may reduce eddy currents induced on the adjacent metallic back-cover by the loop antenna to improve the communication range of the NFC. Only the edge of the tablet PC allows the antenna to radiate due to the full metallic back-cover. Thus, the NFC antenna needs to be narrow to be installed on the edge of the tablet PC. Therefore, we propose a miniaturized NFC antenna with the dimensions of 41.5 (L) × 7.5 (W) × 0.45 (T) mm3 only. Simulated magnetic field distributions are consistent with measured voltage distributions. This design has a good communication range more than 6 cm in front of the touchscreen panel and reaches 2 cm over the other side above the metal back-cover.
TnT -- A Statistical Part-of-Speech Tagger

Trigrams'n'Tags (TnT) is an efficient statistical part-of-speech tagger. Contrary to claims found elsewhere in the literature, we argue that a tagger based on Markov models performs at least as well as other current approaches, including the Maximum Entropy framework. A recent comparison has even shown that TnT performs significantly better for the tested corpora. We describe the basic model of TnT, the techniques used for smoothing and for handling unknown words. Furthermore, we present evaluations on two corpora.
Implementation of Naive Bayesian Classifier and Ada-Boost Algorithm Using Maize Expert System

Machine learning  is concerned with the design and development of algorithms that allow computers to evolve intelligent behaviors based on empirical data. Weak learner is a learning algorithm with accuracy less than 50%. Adaptive Boosting (Ada-Boost) is a machine learning algorithm may be used to increase accuracy for any weak learning algorithm. This can be achieved by running it on a given weak learner several times, slightly alters data and combines the hypotheses. In this paper, Boost algorithm is used to increase the accuracy of the weak learner Naïve-Bayesian classifier. The Ada-Boost algorithm iteratively works on the Naïve-Bayesian classifier with normalized weights and it classifies the given input into different classes with some attributes. Maize Expert System is developed to identify the diseases of Maize crop using Ada-Boost algorithm logic as inference mechanism. A separate user interface for the Maize expert system consisting of three different interfaces namely, End-user/farmer, Expert and Admin are presented here. End-user/farmer module may be used for identifying the diseases for the symptoms entered by the farmer. Expert module may be used for adding rules and questions to data set by a domain expert. Admin module may be used for maintenance of the system.
Haze: privacy-preserving real-time traffic statistics

We consider mobile applications that let users learn traffic conditions based on reports from other users. However, the providers of these mobile services have access to such sensitive information as timestamped locations and movements of its users. In this paper, we introduce the model and general approach of Haze, a system for traffic-update applications that supports the creation of traffic statistics from user reports while protecting the privacy of the users. We also present preliminary experiments that indicate potential for a practical deployment of Haze.
A Radius and Ulna TW3 Bone Age Assessment System

An end-to-end system to automate the well-known Tanner-Whitehouse (TW3) clinical procedure to estimate the skeletal age in childhood is proposed. The system comprises the detailed analysis of the two most important bones in TW3: the radius and ulna wrist bones. First, a modified version of an adaptive clustering segmentation algorithm is presented to properly semi-automatically segment the contour of the bones. Second, up to 89 features are defined and extracted from bone contours and gray scale information inside the contour, followed by some well- founded feature selection mathematical criteria, based on the ideas of maximizing the classes' separability. Third, bone age is estimated with the help of a generalized softmax perceptron (GSP) neural network (NN) that, after supervised learning and optimal complexity estimation via the application of the recently developed posterior probability model selection (PPMS) algorithm, is able to accurately predict the different development stages in both radius and ulna from which and with the help of the TW3 methodology, we are able to conveniently score and estimate the bone age of a patient in years, in what can be understood as a multiple- class (multiple stages) pattern recognition approach with posterior probability estimation. Finally, numerical results are presented to evaluate the system performance in predicting the bone stages and the final patient bone age over a private hand image database, with the help of the pediatricians and the radiologists expert diagnoses.
Heterogeneous Information Network Embedding for Recommendation

Due to the flexibility in modelling data heterogeneity, heterogeneous information network (HIN) has been adopted to characterize complex and heterogeneous auxiliary data in recommender systems, called HIN based recommendation. It is challenging to develop effective methods for HIN based recommendation in both extraction and exploitation of the information from HINs. Most of HIN based recommendation methods rely on path based similarity, which cannot fully mine latent structure features of users and items. In this paper, we propose a novel heterogeneous network embedding based approach for HIN based recommendation, called HERec. To embed HINs, we design a meta-path based random walk strategy to generate meaningful node sequences for network embedding. The learned node embeddings are first transformed by a set of fusion functions, and subsequently integrated into an extended matrix factorization (MF) model. The extended MF model together with fusion functions are jointly optimized for the rating prediction task. Extensive experiments on three real-world datasets demonstrate the effectiveness of the HERec model. Moreover, we show the capability of the HERec model for the cold-start problem, and reveal that the transformed embedding information from HINs can improve the recommendation performance.
Density Based Smart Traffic System with Real Time Data Analysis Using IoT

This paper aims to develop a convenient traffic system that allows a smooth movement of cars which will help build a smarter city. The traffic system currently implemented in many areas is not based on the density of traffic and every road is allotted a preset time. This results in traffic congestion due to large red-light delays and timings allotted for roads in a city that should vary during peak on-off hours, but in reality don't. These traditional systems are not adaptable and fail to support traffic during an unexpected situation or during an accident, and this makes them inefficient. In order to calculate the density of traffic various sensors can be used, each having their merits and demerits. In our proposed system Ultrasound Sensors are used along with Image Processing(using live feed from a camera) that works on a Raspberry Pi platform and calculates the vehicle density and dynamically allots time for different levels of traffic. This in turn allows better signal control and effective management of traffic thereby reducing the probability of a collision. By using Internet Of Things(IoT) real time data from the system can be collected, stored and managed on a cloud. This data can be used to interpret the signal duration in-case any of the sensing equipment fail, and also for future analysis.
Anomaly detection methods for bankruptcy prediction

Bankruptcy prediction is remarkable for its importance in modern economics. Many related work have proposed novel supervised learning methods for predicting financial distress. Conventional classification algorithms are likely to mistaken minority class for majority class because their cost function simply optimizes error rate. This paper applies anomaly detection algorithms to the bankruptcy prediction problem in an attempt to suggest a new stable model taking the data distribution into consideration. The efficacy of anomaly detection techniques is tested on bankruptcy prediction datasets viz. Polish banks. Our empirical evaluation shows that Isolation Forest outperforms multivariate Gaussian distribution, one-class SVM, and other classification estimators in terms of the ROC curve.
Translating imperative code to MapReduce

We present an approach for automatic translation of sequential, imperative code into a parallel MapReduce framework. Automating such a translation is challenging: imperative updates must be translated into a functional MapReduce form in a manner that both preserves semantics and enables parallelism. Our approach works by first translating the input code into a functional representation, with loops succinctly represented by fold operations. Then, guided by rewrite rules, our system searches a space of equivalent programs for an effective MapReduce implementation. The rules include a novel technique for handling irregular loop-carried dependencies using group-by operations to enable greater parallelism. We have implemented our technique in a tool called Mold. It translates sequential Java code into code targeting the Apache Spark runtime. We evaluated Mold on several real-world kernels and found that in most cases Mold generated the desired MapReduce program, even for codes with complex indirect updates.
Simulation-based efficiency analysis of an in-plant milk-run operator under disturbances

This study examines the efficiency, interactions and impact of a milk-run operator on a typical assembly line. The idea behind the milk-run lies in organizing the material supply to production areas using a specialized logistic worker travelling in cycles between the warehouse and production. A discrete-event simulation model was developed to evaluate the interactions of the milk-run operator and a typical 10 workstation assembly line. We present an analysis of the various kinds of disturbances occurring in the production environment (time variability of technological operations and supply cycle, delays of supply cycles) and management decisions (takt time presence or absence, buffer capacity, supply cycle duration) on the production stability and performance (assembly line throughput rate, milk-run operator utilization, workstation starvation and work in process). Recommendations for designers of in-plant logistics are provided.
Expanded HP memristor model and simulation in STDP learning

Based on the classical HP memristor found by HP Lab, this paper presents an expanded model that making fully consideration of the influence of R on, that is, R on is the similar order of magnitude of R off. Simulations proved that in some particular conditions, the hysteresis effect of the expanded model is the same as HP memristor. A comparison was made between these two models under some given conditions. Then, we built several simulations to test the classical characteristics of the expanded HP memristor. Simulation results demonstrate that the expanded model is superior to the original in some aspects like easy switching and power saving. At last, we applied the expanded HP memristor in STDP learning simulation, which shows it is a good candidate for neural network when a threshold voltage function is proposed.
CORE: Context-Aware Open Relation Extraction with Factorization Machines

We propose CORE, a novel matrix factorization model that leverages contextual information for open relation extraction. Our model is based on factorization machines and integrates facts from various sources, such as knowledge bases or open information extractors, as well as the context in which these facts have been observed. We argue that integrating contextual information—such as metadata about extraction sources, lexical context, or type information—significantly improves prediction performance. Open information extractors, for example, may produce extractions that are unspecific or ambiguous when taken out of context. Our experimental study on a large real-world dataset indicates that CORE has significantly better prediction performance than state-ofthe-art approaches when contextual information is available.
Exploring the Influence of Online Consumers ’ Perception on Purchase Intention as Exemplified with an Online Bookstore

The purpose of this study is to use structural equation modeling (SEM) to explore the influence of online bookstore consumers’ perception on their purchase intention. Through literature review, four constructs were used to establish a causal relationship between perception of online shopping and consumers’ purchase intention. Questionnaires based on these four constructs were designed and distributed to the customers of an online bookstore; AMOS software as analytical tool was used to build and confirm a SEM model. Results of this study show that product perception, shopping experience, and service quality have positive and significant influence on consumers’ purchase intention, but perceived risk has negative influence on consumers’ purchase intention, and shopping experience is most important.
Project Lachesis: Parsing and Modeling Location Histories

A datatype with increasing importance in GIS is what we call the location history–a record of an entity’s location in geographical space over an interval of time. This paper proposes a number of rigorously defined data structures and algorithms for analyzing and generating location histories. Stays are instances where a subject has spent some time at a single location, and destinations are clusters of stays. Using stays and destinations, we then propose two methods for modeling location histories probabilistically. Experiments show the value of these data structures, as well as the possible applications of probabilistic models of location histories.
Network Packet Analysis for Detecting Malicious Insider

With the wide spread use of the Internet, the cyber crimes have also increased. The industries are facing more and more cyber threats. The threats to an organization come not only from the outside but also from within the organization. The malicious insider in an organization uses the network facility to do the industrial espionage. The information in the form of packets that is being transferred over the network is essential to be analyzed to identify the suspicious activities. The paper proposes a framework to identify the various activities being performed by the user based on the network packet analysis. A methodology is proposed to identify the malicious insider in an organization.
An architecture for more realistic conversational systems

In this paper, we describe an architecture for conversational systems that enables human-like performance along several important dimensions. First, interpretation is incremental, multi-level, and involves both general and task- and domain-specific knowledge. Second, generation is also incremental, proceeds in parallel with interpretation, and accounts for phenomena such as turn-taking, grounding and interruptions. Finally, the overall behavior of the system in the task at hand is determined by the (incremental) results of interpretation, the persistent goals and obligations of the system, and exogenous events of which it becomes aware. As a practical matter, the architecture supports a separation of responsibilities that enhances portability to new tasks and domains.
Real Time Localization and 3D Reconstruction

In this paper we describe a method that estimates the motion of a calibrated camera (settled on an experimental vehicle) and the tridimensional geometry of the environment. The only data used is a video input. In fact, interest points are tracked and matched between frames at video rate. Robust estimates of the camera motion are computed in real-time, key-frames are selected and permit the features 3D reconstruction. The algorithm is particularly appropriate to the reconstruction of long images sequences thanks to the introduction of a fast and local bundle adjustment method that ensures both good accuracy and consistency of the estimated camera poses along the sequence. It also largely reduces computational complexity compared to a global bundle adjustment. Experiments on real data were carried out to evaluate speed and robustness of the method for a sequence of about one kilometer long. Results are also compared to the ground truth measured with a differential GPS.
Simple, Fast, and Practical Non-Blocking and Blocking Concurrent Queue Algorithms

Drawing ideas from previous authors, we present a new non-blocking concurrent queue algorithm and a new twolock queue algorithm in which one enqueue and one dequeue can proceed concurrently. Both algorithms are simple, fast, and practical; we were surprised not to find them in the literature. Experiments on a 12-node SGI Challenge multiprocessor indicate that the new non-blocking queue consistently outperforms the best known alternatives; it is the clear algorithm of choice for machines that provide a universal atomic primitive (e.g. compare and swap or load linked/store conditional). The two-lock concurrent queue outperforms a single lock when several processes are competing simultaneously for access; it appears to be the algorithm of choice for busy queues on machines with non-universal atomic primitives (e.g. test and set). Since much of the motivation for non-blocking algorithms is rooted in their immunity to large, unpredictable delays in process execution,we report experimental results both for systems with dedicated processors and for systems with several processes multiprogrammed on each processor.
Blind Kriging: Implementation and performance analysis

When analysing data from computationally expensive simulation codes or process measurements, surrogate modelling methods are firmly established as facilitators for design space exploration, sensitivity analysis, visualisation and optimisation. Kriging is a popular surrogate modelling technique for data based on deterministic computer experiments. There exist several types of Kriging, mostly differing in the type of regression function used. Recently a promising new variable selection technique was proposed to identify a regression function in the Kriging framework. In this paper this type of Kriging, i.e., blind Kriging, has been efficiently implemented in Matlab® and has been extended. The implementation is validated and tested on several examples to illustrate the strength and weaknesses of this new, promising modelling technique. It is shown that the performance of blind Kriging is as good as, or better than ordinary Kriging. Though, blind Kriging comes at double the computational cost with respect to ordinary Kriging.
Tracking-based interaction for object creation in mobile augmented reality

In this work, we evaluate the feasibility of tracking-based interaction using a mobile phone's or tablet's camera in order to create and edit 3D objects in augmented reality applications. We present a feasibility study investigating if and how gestures made with your finger can be used to create such objects. A revised interface design is evaluated in a user study with 24 subjects that reveals a high usability and entertainment value, but also identifies issues such as ergonomic discomfort and imprecise input for complex tasks. Hence, our results suggest huge potential for this type of interaction in the entertainment, edutainment, and leisure domain, but limited usefulness for serious applications.
Speaking in tongues: SQL access to NoSQL systems

Non-relational data stores, which are usually called NoSQL systems, have become an important class of data management systems. They often outperform the relational systems. Yet there is no common way to interface with NoSQL systems. The Structured Query Language (SQL) has already proven to be useful to provide a uniform query language for all the relational systems. We identify a subset of SQL for access to NoSQL systems. Our extensible middleware translates SQL queries to the query languages of the connected NoSQL systems. The migration between these systems is thereby greatly simplified as well as the comparison of the supported NoSQL systems.
STUDENTS ACCEPTANCE OF MOBILE LEARNING FOR HIGHER EDUCATION IN SAUDI ARABIA

Mobile learning is the next step in the development of distance learning. Widespread access to mobile devices and the opportunity to learn regardless of time and place make the mobile learning an important tool for lifelong learning. The research objectives are to examine the possibility of acceptance in mobile learning (m-Learning) and study main factors that affect using mLearning that focus on higher education students in Saudi Arabia. The researcher used a quantitative approach survey of 80 students. The modified acceptance framework that based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model is adopted to determine the factors that influence the students’ intention to use m-Learning. The results from statistical analysis show that the acceptance level of students on m-Learning is in the high level.
A three-stage approach for aircraft line maintenance personnel rostering using MIP, discrete event simulation and DEA

Personnel scheduling problems need to cope with personnel preferences, coverage constraints, legal restrictions, and many other constraints. We present a three-stage methodology that can be used to select personnel rosters. In the first stage we generate multiple personnel rosters with a mathematical programming model. In the second stage, the performance of the rosters regarding a number of service criteria is evaluated through discrete event simulation. In the third stage, a ranking is made using Data Envelopment Analysis. The methodology is tested on a personnel scheduling problem for aircraft line maintenance.
Twitter Topic Modeling for Breaking News Detection

Social media platforms like Twitter have become increasingly popular for the dissemination and discussion of current events. Twitter makes it possible for people to share stories that they find interesting with their followers, and write updates on what is happening around them. In this paper we attempt to use topic models of tweets in real time to identify breaking news. Two different methods, Latent Dirichlet Allocation (LDA) and Hierarchical Dirichlet Process (HDP) are tested with each tweet in the training corpus as a document by itself, as well as with all the tweets of a unique user regarded as one document. This second approach emulates Author-Topic modeling (AT-modeling). The evaluation of methods relies on manual scoring of the accuracy of the modeling by volunteered participants. The experiments indicate topic modeling on tweets in real-time is not suitable for detecting breaking news by itself, but may be useful in analyzing and describing news tweets.
Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks

We study the problem of synthesizing a number of likely future frames from a single input image. In contrast to traditional methods, which have tackled this problem in a deterministic or non-parametric way, we propose to model future frames in a probabilistic manner. Our probabilistic model makes it possible for us to sample and synthesize many possible future frames from a single input image. To synthesize realistic movement of objects, we propose a novel network structure, namely a Cross Convolutional Network; this network encodes image and motion information as feature maps and convolutional kernels, respectively. In experiments, our model performs well on synthetic data, such as 2D shapes and animated game sprites, as well as on real-world video frames. We also show that our model can be applied to visual analogy-making, and present an analysis of the learned network representations.