nils-herrmann/clean_scirepeval huggingface dataset:
Cognitive walkthrough for the web

This paper proposes a transformation of the Cognitive Walkthrough (CW), a theory-based usability inspection method that has proven useful in designing applications that support use by exploration. The new Cognitive Walkthrough for the Web (CWW) is superior for evaluating how well websites support users' navigation and information search tasks. The CWW uses Latent Semantic Analysis to objectively estimate the degree of semantic similarity (information scent) between representative user goal statements (100-200 words) and heading/link texts on each web page. Using an actual website, the paper shows how the CWW identifies three types of problems in web page designs. Three experiments test CWW predictions of users' success rates in accomplishing goals, verifying the value of CWW for identifying these usability problems
Social, ethical and legal barriers to E-health

Information technology such as electronic medical records (EMRs), electronic prescribing and decision support systems are recognized as essential tools in Europe, the U.S., Canada, Australia, and New Zealand. But significant barriers impede wide-scale adoption of these tools, especially EMR systems.



The objectives of this study were to investigate the present status of information technology in health care, the perceived benefits and barriers by primary care physicians.



Literature analysis and survey data from primary care physicians on adoption of information technology are reviewed.



The U.S. trails European countries as well as Canada, Australia and New Zealand in the use of information technology in primary care. The results of the study indicate that physicians in general perceive benefits to information technology, but also cite major barriers to its implementation in their practices. These barriers include lack of access to capital by health care providers, complex systems and lack of data standards that permit exchange of clinical data, privacy concerns and legal barriers.



Overcoming these barriers will require subsidies and performance incentives by payers and government; certification and standardization of vendor applications that permit clinical data exchange; removal of legal barriers; and greater security of medical data to convince practitioners and patients of the value of EMRs.
A vision-based method for weeds identification through the Bayesian decision theory

One of the objectives of precision agriculture is to minimize the volume of herbicides that are applied to the fields through the use of site-specific weed management systems. This paper outlines an automatic computer vision-based approach for the detection and differential spraying of weeds in corn crops. The method is designed for post-emergence herbicide applications where weeds and corn plants display similar spectral signatures and the weeds appear irregularly distributed within the crop’s field. The proposed strategy involves two processes: image segmentation and decision making. Image segmentation combines basic suitable image processing techniques in order to extract cells from the image as the low level units. Each cell is described by two area-based measuring relationships between crop and weeds. The decision making determines the cells to be sprayed based on the computation of a posterior probability under a Bayesian framework. The a priori probability in this framework is computed taking into account the dynamic of the physical system (tractor) where the method is embedded. The main contributions of this paper are: (1) the combination of the image segmentation and decision making processes and (2) the decision making itself which exploits a previous knowledge which is mapped as the a priori probability. The performance of the method is illustrated by comparative analysis against some existing strategies.
A multiparadigm intelligent tutoring system for robotic arm training

To assist learners during problem-solving activities, an intelligent tutoring system (ITS) has to be equipped with domain knowledge that can support appropriate tutoring services. Providing domain knowledge is usually done by adopting one of the following paradigms: building a cognitive model, specifying constraints, integrating an expert system, and using data mining algorithms to learn domain knowledge. However, for some ill-defined domains, each single paradigm may present some advantages and limitations in terms of the required resources for deploying it, and tutoring support that can be offered. To address this issue, we propose using a multiparadigm approach. In this paper, we explain how we have applied this idea in CanadarmTutor, an ITS for learning to operate the Canadarm2 robotic arm. To support tutoring services in this ill-defined domain, we have developed a multiparadigm model combining: 1) a cognitive model to cover well-defined parts of the task and spatial reasoning, 2) a data mining approach for automatically building a task model from user solutions for ill-defined parts of the task, and 3) a 3D path-planner to cover other parts of the task for which no user data are available. The multiparadigm version of CanadarmTutor allows providing a richer set of tutoring services than what could be offered with previous single paradigm versions of CanadarmTutor.
A centralized detection and prevention technique against ARP poisoning

The Man-in-the-Middle (MITM) attack on ARP is presently a common attack and nuisance to the typical LAN environment. This type of MITM is brought to effect by ARP cache poisoning which is achieved using forged ARP packets. ARP poisoning is a mechanism in which a node poisons ARP cache table of another node forcing it to send packets to a destination other than the intended one. This paper presents a feasible solution to the ARP cache poisoning, removing inconsistencies from all ARP tables of all hosts in the network. This paper uses a centralized system and ARP Central Server (ACS) to manage ARP table entries in all hosts. All hosts in the network uses the ACS to validate their ARP table entries. The ACS validates and corrects the poisoned ARP entries of the attacked hosts and hence prevents ARP poisoning in the network.
The study on feature selection in customer churn prediction modeling

When the customer churn prediction model is built, a large number of features bring heavy burdens to the model and even decrease the accuracy. This paper is aimed to review the feature selection, to compare the algorithms from different fields and to design a framework of feature selection for customer churn prediction. Based on the framework, the author experiment on the structured module with some telecom operator's marketing data to verify the efficiency of the feature selection framework.
A fast APRIORI implementation

The efficiency of frequent itemset mining algorithms is determined mainly by three factors: the way candidates are generated, the data structure that is used and the implementation details. Most papers focus on the first factor, some describe the underlying data structures, but implementation details are almost always neglected. In this paper we show that the effect of implementation can be more important than the selection of the algorithm. Ideas that seem to be quite promising, may turn out to be ineffective if we descend to the implementation level. We theoretically and experimentally analyze APRIORI which is the most established algorithm for frequent itemset mining. Several implementations of the algorithm have been put forward in the last decade. Although they are implementations of the very same algorithm, they display large differences in running time and memory need. In this paper we describe an implementation of APRIORI that outperforms all implementations known to us. We analyze, theoretically and experimentally, the principal data structure of our solution. This data structure is the main factor in the efficiency of our implementation. Moreover, we present a simple modification of APRIORI that appears to be faster than the original algorithm.
TCAD simulation of Ge-GaAs hetrojunction dopingless tunnel field effect transistor

This paper presents a TCAD study on the performance of Si and Ge-GaAs hetrojunction dopingless tunnel field effect transistor (TFETs). Si tunnel device have low on current. So hetrojunction architecture is combined with dopingless structure for improving on current with smaller off current. Dopingless architecture uses low thermal budget for fabrication so hetrojunction dopingless TFET are suitable for low power low cost application. By comparison it is found that Ge-GaAs and Ge-Si devices have increased on current in compare to Si dopingless TFET but off current in Ge-GaAs devices is lower than Ge-Si device. In further steps capacitance of Ge-GaAs are also compared to Si Dopingless TFET. All simulation are done using Sentaurus TCAD simulator.
Cardiac pulse detection in BCG signals implemented on a regular classroom chair integrated to an emotional and learning model for personalization of learning resources

Emotions are related with learning processes, and physiological signals can be used to detect them. In this work, it is presented a model for the dynamic personalization of a learning environment. In the model, a specific combination of emotions and cognition processes are connected and integrated with the concept of ‘flow’, since it may provide a way to improve learning abilities. Physiological signals can be used to relate temporal emotions and subject's learning processes; the cardiac pulse is a reliable signal that carries useful information about the subject's emotional condition, which is detected using a classroom chair adapted with non invasive Electro-Mechanical Film (EMFi) sensors and an acquisition system that generates a ballistocardiogram (BCG), which is analyzed by a developed algorithm to obtain cardiac pulse statistics. A study including different data obtained from the chair's sensors was carried out and interpreted in terms of emotions, which together with a cognitive model is used for the personalization of content in a learning scenario. Such model establishes a relation between the learner's knowledge, the content of a course or a learning activity and the emotional condition of each student.
Business Intelligence in the Hospitality Industry

The hospitality industry is one that has been driven by customer loyalty. Many customers pick their hotel of choice and will stay with that same hotel because of the experiences, service, and even the price. Customers have recently been rewarded through hotel, credit card, and airline points that help drive and maintain this loyalty. Utilizing data to their advantage, the hotel industry has been actively exploring and implementing business intelligence. While many see IT systems as a foundation utility that can be easily imitated, business intelligence can act as a driver to maintain sustained competitive advantage over competitors in the hospitality industry. It can serve means of preserving existing customer loyalty while facing competitive pressures. This paper discusses the importance of BI to the hospitality industry, indicates how BI can serve as a barrier to competitive pressures and discusses future capabilities harnessed through BI that are not yet main stream but are expected to transform
Gujarati Script Recognition : A Review

This paper is step to locate the researchers, their track in the way for recognizing the characters from various regional scripts in India. This survey paper would provide a path for developing recognition tools for Indian scripts where there is still a scope of recognition accuracy. It provides the reasons for researchers to work for recognition of Indian scripts. In this paper, the various scripts along with their special properties, there feature extraction and recognition techniques are described. Simultaneously a detailed comparison is made on the fronts of techniques used and recognition of Gujarati script.
Chapter 5 PARALLEL ALGORITHM DESIGN FOR BRANCH AND BOUND

Large and/or computationally expensive optimization problems sometimes require parallel or high-performance computing systems to achieve reasonable running times. This chapter gives an introduction to parallel computing for those familiar with serial optimization. We present techniques to assist the porting of serial optimization codes to parallel systems and discuss more fundamentally parallel approaches to optimization. We survey the state-of-the-art in distributedand shared-memory architectures and give an overview of the programming models appropriate for efficient algorithms on these platforms. As concrete examples, we discuss the design of parallel branch-and-bound algorithms for mixed-integer programming on a distributed-memory system, quadratic assignment problem on a grid architecture, and maximum parsimony in evolutionary trees on a shared-memory system.
A novel approach to American sign language recognition using MAdaline neural network

Sign language interpretation is gaining a lot of research attention because of its social contributions which is proved to be extremely beneficial for the people suffering from hearing or speaking disabilities. This paper proposes a novel image processing sign language detection framework that employs MAdaline network for classification purpose. This paper mainly highlights two novel aspects, firstly it introduces an advanced feature set comprising of seven distinct features that has not been used widely for sign language interpretation purpose, more over utilization of such features negates the cumbersome step of cropping of irrelevant background image, thus reducing system complexity. Secondly it suggests a possible solution of the concerned problem can be obtained using an extension of the traditional Adaline network, formally termed as MAdaline Network. Although the concept of MAdaline network has originated much earlier, the provision of application of this framework in this domain definitely help in designing an improved sign language interpreting interface. The newly formulated framework has been implemented to recognize standardized American sign language containing 26 English alphabets from ‘A’ to ‘Z’. The performance of the proposed algorithm has also been compared with the standardized algorithms, and in each case the former one outperformed its contender algorithms by a large margin establishing the efficiency of the same.
Hashed and hierarchical timing wheels: efficient data structures for implementing a timer facility

The performance of timer algorithms is crucial to many network protocol implementations that use timers for failure recovery and rate control. Conventional algorithms to implement an Operating System timer module takeO(n) time to start or maintain a timer, where n is the number of outstanding timers: this is expensive for largen. This paper shows that by using a circular buffer or timing wheel, it takes O(1) time to start, stop, and maintain timers within the range of the wheel. Two extensions for larger values of the interval are described. In the first, the timer interval is hashed into a slot on the timing wheel. In the second, a hierarchy of timing wheels with different granularities is used to span a greater range of intervals. The performance of these two schemes and various implementation tradeoffs are discussed. We have used one of our schemes to replace the current BSD UNIX callout and timer facilities. Our new implementation can support thousands of outstanding timers without much overhead. Our timer schemes have also been implemented in other operating systems and network protocol packages.
Elastic Bands: Connecting Path Planning and Control

Elastic bands are proposed as the basis for a new framework to close the gap between global path planning and real-time sensor-based robot control. An elastic band is a deformable collision-free path. The initial shape of the elastic is the free path generated by a planner. Subjected to artificial forces, the elastic band deforms in real time to a short and smooth path that maintains clearance from the obstacles. The elastic continues to deform as changes in the environment are detected by sensors, enabling the robot to accommodate uncertainties and react to unexpected and moving obstacles. While providing a tight connection between the robot and its environment, the elastic band preserves the global nature of the planned path. This paper outlines the framework and discusses an efficient implementation based on bubbles.
A multiscale random field model for Bayesian image segmentation

Many approaches to Bayesian image segmentation have used maximum a posteriori (MAP) estimation in conjunction with Markov random fields (MRF). Although this approach performs well, it has a number of disadvantages. In particular, exact MAP estimates cannot be computed, approximate MAP estimates are computationally expensive to compute, and unsupervised parameter estimation of the MRF is difficult. The authors propose a new approach to Bayesian image segmentation that directly addresses these problems. The new method replaces the MRF model with a novel multiscale random field (MSRF) and replaces the MAP estimator with a sequential MAP (SMAP) estimator derived from a novel estimation criteria. Together, the proposed estimator and model result in a segmentation algorithm that is not iterative and can be computed in time proportional to MN where M is the number of classes and N is the number of pixels. The also develop a computationally efficient method for unsupervised estimation of model parameters. Simulations on synthetic images indicate that the new algorithm performs better and requires much less computation than MAP estimation using simulated annealing. The algorithm is also found to improve classification accuracy when applied to the segmentation of multispectral remotely sensed images with ground truth data.
Value from hedonic experience and engagement.

Recognizing that value involves experiencing pleasure or pain is critical to understanding the psychology of value. But hedonic experience is not enough. I propose that it is also necessary to recognize that strength of engagement can contribute to experienced value through its contribution to the experience of motivational force--an experience of the intensity of the force of attraction to or repulsion from the value target. The subjective pleasure/pain properties of a value target influence strength of engagement, but factors separate from the hedonic properties of the value target also influence engagement strength and thus contribute to the experience of attraction or repulsion. These additional sources of engagement strength include opposition to interfering forces, overcoming personal resistance, using the right or proper means of goal pursuit, and regulatory fit between the orientation and manner of goal pursuit. Implications of the contribution of engagement strength to value are discussed for judgment and decision making, persuasion, and emotional experiences.
Knowledge Graph Embedding with Numeric Attributes of Entities

Knowledge Graph (KG) embedding projects entities and relations into low dimensional vector space, which has been successfully applied in KG completion task. The previous embedding approaches only model entities and their relations, ignoring a large number of entities’ numeric attributes in KGs. In this paper, we propose a new KG embedding model which jointly model entity relations and numeric attributes. Our approach combines an attribute embedding model with a translation-based structure embedding model, which learns the embeddings of entities, relations, and attributes simultaneously. Experiments of link prediction on YAGO and Freebase show that the performance is effectively improved by adding entities’ numeric attributes in the embedding model.
Palm vein recognition with Local Binary Patterns and Local Derivative Patterns

Palm vein feature extraction from near infrared images is a challenging problem in hand pattern recognition. In this paper, a promising new approach based on local texture patterns is proposed. First, operators and histograms of multi-scale Local Binary Patterns (LBPs) are investigated in order to identify new efficient descriptors for palm vein patterns. Novel higher-order local pattern descriptors based on Local Derivative Pattern (LDP) histograms are then investigated for palm vein description. Both feature extraction methods are compared and evaluated in the framework of verification and identification tasks. Extensive experiments on CASIA Multi-Spectral Palmprint Image Database V1.0 (CASIA database) identify the LBP and LDP descriptors which are better adapted to palm vein texture. Tests on the CASIA datasets also show that the best adapted LDP descriptors consistently outperform their LBP counterparts in both palm vein verification and identification.
A Deep Learning Approach to Human Activity Recognition Based on Single Accelerometer

In this paper, we propose an acceleration-based human activity recognition method using popular deep architecture, Convolution Neural Network (CNN). In particular, we construct a CNN model and modify the convolution kernel to adapt the characteristics of tri-axial acceleration signals. Also, for comparison, we use some widely used methods to accomplish the recognition task on the same dataset. The large dataset we constructed consists of 31688 samples from eight typical activities. The experiment results show that the CNN works well, which can reach an average accuracy of 93.8% without any feature extraction methods.
Multiple hypothesis tracking for multiple target tracking

Multiple hypothesis tracking (MHT) is generally accepted as the preferred method for solving the data association problem in modern multiple target tracking (MTT) systems. This paper summarizes the motivations for MHT, the basic principles behind MHT and the alternative implementations in common use. It discusses the manner in which the multiple data association hypotheses formed by MHT can be combined with multiple filter models, such as used by the interacting multiple model (IMM) method. An overview of the studies that show the advantages of MHT over the conventional single hypothesis approach is given. Important current applications and areas of future research and development for MHT are discussed.
Future Internet of Things: open issues and challenges

Internet of Things (IoT) and its relevant technologies have been attracting the attention of researchers from academia, industry, and government in recent years. However, since the requirements of the IoT are quite different from what the Internet today can offer, several innovative techniques have been gradually developed and incorporated into IoT, which is referred to as the Future Internet of Things (FIoT). Among them, how to extract ‘‘data’’ and transfer them into ‘‘knowledge’’ from sensing layer to application layer has become a vital issue. This paper begins with an overview of IoT and FIoT, followed by discussions on how to apply data mining and computational intelligence to FIoT. An intelligent data management framework inspired by swarm optimization will then given. Finally, open issues and future trends of this field
A computer‐based multi‐media tutor for beginning piano students

The Piano Tutor provides computer-based instruction to beginning piano students. Intended as a supplement to traditional instruction, the Piano Tutor helps students by correcting mistakes before they become ingrained through practice and by teaching new material as soon as the student is ready. The Piano Tutor combines an expert system with state-of-the-art music recognition software and multimedia output devices to provide a stimulating learning environment that tailors instruction to the student’s needs.