nils-herrmann/clean_scirepeval huggingface dataset:
Editorial: “Business Process Intelligence: Connecting Data and Processes”

This introduction to the special issue on Business Process Intelligence (BPI) discusses the relation between data and processes. The recent attention for Big Data illustrates that organizations are aware of the potential of the torrents of data generated by today's information systems. However, at the same time, organizations are struggling to extract value from this overload of data. Clearly, there is a need for data scientists able to transform event data into actionable information. To do this, it is crucial to take a process perspective. The ultimate goal of BPI is not to improve information systems or the recording of data; instead the focus should be in improving the process. For example, we may want to aim at reducing costs, minimizing response times, and ensuring compliance. This requires a “confrontation” between process models and event data. Recent advances in process mining allow us to automatically learn process models showing the bottlenecks from “raw” event data. Moreover, given a normative model, we can use conformance checking to quantify and understand deviations. Automatically learned models may also be used for prediction and recommendation. BPI is rapidly developing as a field linking data science to business process management. This article aims to provide an overview thereby paving the way for the other contributions in this special issue.
Decision support for diffuse pollution management

The effort to manage diffuse pollution at the catchment scale is an ongoing challenge that needs to take into account trade-offs between environmental and economic objectives. Best Management Practices (BMPs) are gaining ground as a means to address the problem, but their application (and impact) is highly dependant on the characteristics of the crops and of the land in which they are to be applied. In this paper, we demonstrate a new methodology and associated decision support tool that suggests the optimal location for placing BMPs to minimise diffuse surface water pollution at the catchment scale, by determining the trade-off among economic and multiple environmental objectives. The decision support tool consists of a non-point source (NPS) pollution estimator, the SWAT (Soil and Water Assessment Tool) model, a genetic algorithm (GA), which serves as the optimisation engine for the selection and placement of BMPs across the agricultural land of the catchment, and of an empirical economic function for the estimation of the mean annual cost of BMP implementation. In the proposed decision support tool, SWAT was run a number of times equal to the number of tested BMPs, to predict nitrates nitrogen (N-NO3) and total phosphorus (TP) losses from all the agricultural Hydrologic Response Units (HRUs) and possible BMPs implemented on them. The results were then saved in a database which was subsequently used for the optimisation process. Fifty different BMPs, including sole or combined changes in livestock, crop, soil and nutrient application management in alfalfa, corn and pastureland fields, were evaluated in the reported application of the tool in a catchment in Greece, by solving a three-objective optimisation process (cost, TP and N-NO3). The relevant two-dimensional trade-off curves of cost-TP, cost-N-NO3 and N-NO3eTP are presented and discussed. The strictest environmental target, expressed as a 45% reduction of TP at the catchment outlet, which also resulted in a 25% reduction of the annual N-NO3 yield was met at an affordable annual cost of 25 V/person by establishing an optimal combination of BMPs. The methodology could be used to assist in a more cost-effective implementation of environmental
GSM Based Electrical Control System for Smart Home Application

This paper presents the development of GSM-based control home appliances for smart home system. The main aim of the prototype development is to reduce electricity wastage. GSM module was used for receiving short message service (SMS) from user’s mobile phone that automatically enable the controller to take any further action such as to switch ON and OFF the home appliances such as light, airconditioner etc. The system was integrated with microcontroller and GSM network interface using assembly language. MPLAB software was utilized to accomplish the integration. The system is activated when user sends the SMS to controller at home. Upon receiving the SMS command, the microcontroller unit then automatically controls the electrical home appliances by switching ON or OFF the device according to the user order. In other word, it read message from the mobile phone and response to control the devices according to the received message. The prototype has been successfully developed and it could provide an effective mechanism in utilizing the energy source efficiently.
Bluetooth positioning using RSSI and triangulation methods

Location based services are the hottest applications on mobile devices nowadays and the growth is continuing. Indoor wireless positioning is the key technology to enable location based services to work well indoors, where GPS normally could not work. Bluetooth has been widely used in mobile devices like phone, PAD etc. therefore Bluetooth based indoor positioning has great market potential. Radio Signal Strength (RSS) is a key parameter for wireless positioning. New Bluetooth standard (since version 2.1) enables RSS to be discovered without time consuming pre-connection. In this research, general wireless positioning technologies are firstly analysed. Then RSS based Bluetooth positioning using the new feature is studied. The mathematical model is established to analyse the relation between RSS and the distance between two Bluetooth devices. Three distance-based algorithms are used for Bluetooth positioning: Least Square Estimation, Three-border and Centroid Method. Comparison results are analysed and the ways to improve the positioning accuracy are discussed.
Effects of attribute and valence of e-WOM on message adoption: Moderating roles of subjective knowledge and regulatory focus

0747-5632/$ see front matter 2012  A http://dx.doi.org/10.1016/j.chb.2012.05.018 ⇑ Corresponding author. ; fax E-mail addresses: leekt@knu.ac.kr (K.-T. Lee), unlim 1 Tel.: +82 53 950 7322; fax: +82 53 950 6247. The current study proposes a model to test whether online review valence and attributes have an effect on credibility, and whether regulatory focus and subjective knowledge have moderating effects. Three hundred nineteen university students participated in online experiments with a 2 (positive vs. negative review valence) by 2 (objective vs. subjective review attributes) between subject design. The experiment demonstrated that objective and negative online reviews have a significant positive and negative impact, respectively, on message credibility, which affects review adoption. The results also showed that the moderating effect produced by objective information and a consumer’s subjective knowledge is supported. This study contributes to explaining the inconsistent results between review valence/attribute and credibility found in previous studies.
A Simple Weight Decay Can Improve Generalization

It has been observed in numerical simulations that a weight decay can improve generalization in a feed-forward neural network. This paper explains why. It is proven that a weight decay has two effects in a linear network. First, it suppresses any irrelevant components of the weight vector by choosing the smallest vector that solves the learning problem. Second, if the size is chosen right, a weight decay can suppress some of the effects of static noise on the targets, which improves generalization quite a lot. It is then shown how to extend these results to networks with hidden layers and non-linear units. Finally the theory is confirmed by some numerical simulations using the data from NetTalk.
Optimizing machine translation by learning to search

We present a novel approach to training discriminative tree-structured machine translation systems by learning to search. We describe three primary innovations in this work: a new parsing coordinator architecture and algorithms to generate the required training examples for the learning algorithm; a new semiring that provides an unbiased way to compare translations; and a new training objective that measures whether a translation inference improves the quality of a translation. We also apply the reinforcement learning concept of exploration to SMT. Finally, we empirically evaluate our innovations.
A binary linear programming formulation of the graph edit distance

A binary linear programming formulation of the graph edit distance for unweighted, undirected graphs with vertex attributes is derived and applied to a graph recognition problem. A general formulation for editing graphs is used to derive a graph edit distance that is proven to be a metric, provided the cost function for individual edit operations is a metric. Then, a binary linear program is developed for computing this graph edit distance, and polynomial time methods for determining upper and lower bounds on the solution of the binary program are derived by applying solution methods for standard linear programming and the assignment problem. A recognition problem of comparing a sample input graph to a database of known prototype graphs in the context of a chemical information system is presented as an application of the new method. The costs associated with various edit operations are chosen by using a minimum normalized variance criterion applied to pairwise distances between nearest neighbors in the database of prototypes. The new metric is shown to perform quite well in comparison to existing metrics when applied to a database of chemical graphs
Advances in rapid detection methods for foodborne pathogens.

Food safety is increasingly becoming an important public health issue, as foodborne diseases present a widespread and growing public health problem in both developed and developing countries. The rapid and precise monitoring and detection of foodborne pathogens are some of the most effective ways to control and prevent human foodborne infections. Traditional microbiological detection and identification methods for foodborne pathogens are well known to be time consuming and laborious as they are increasingly being perceived as insufficient to meet the demands of rapid food testing. Recently, various kinds of rapid detection, identification, and monitoring methods have been developed for foodborne pathogens, including nucleic-acid-based methods, immunological methods, and biosensor-based methods, etc. This article reviews the principles, characteristics, and applications of recent rapid detection methods for foodborne pathogens.
THE EXPOSURE OF YOUTH TO UNWANTED SEXUAL MATERIAL ON THE INTERNET A National Survey of Risk , Impact , and Prevention

This national survey of youth, ages 10 to 17, and their caretakers has several implications for the current debate about young people and Internet pornography. Twentyfive percent of youth had unwanted exposure to sexual pictures on the Internet in the past year, challenging the prevalent assumption that the problem is primarily about young people motivated to actively seek out pornography. Most youth had no negative reactions to their unwanted exposure, but one quarter said they were very or extremely upset, suggesting a priority need for more research on and interventions directed toward such negative effects. The use of filtering and blocking software was associated with a modest reduction in unwanted exposure, suggesting that it may help but is far from foolproof. Various forms of parental supervision were not associated with any reduction in exposure. The authors urge that social scientific research be undertaken to inform this highly contentious public policy controversy.
Inertial Motion Capture Costume Design Study

The paper describes a scalable, wearable multi-sensor system for motion capture based on inertial measurement units (IMUs). Such a unit is composed of accelerometer, gyroscope and magnetometer. The final quality of an obtained motion arises from all the individual parts of the described system. The proposed system is a sequence of the following stages: sensor data acquisition, sensor orientation estimation, system calibration, pose estimation and data visualisation. The construction of the system's architecture with the dataflow programming paradigm makes it easy to add, remove and replace the data processing steps. The modular architecture of the system allows an effortless introduction of a new sensor orientation estimation algorithms. The original contribution of the paper is the design study of the individual components used in the motion capture system. The two key steps of the system design are explored in this paper: the evaluation of sensors and algorithms for the orientation estimation. The three chosen algorithms have been implemented and investigated as part of the experiment. Due to the fact that the selection of the sensor has a significant impact on the final result, the sensor evaluation process is also explained and tested. The experimental results confirmed that the choice of sensor and orientation estimation algorithm affect the quality of the final results.
High-efficiency line-start interior permanent-magnet synchronous motors

This paper presents a successful design of a high-efficiency small but novel interior permanent-magnet motor using NdBFe magnets. It is designed to operate both at line and variable frequencies. Line start with high inertia load was a special consideration. Time-stepping finite-element analysis has been used to successfully predict the dynamic and transient performances of the prototype motors. It has been found that the proposed design has yielded successful simulation and experimental results. The maximum load inertia corresponding to the rotor-bar depth has been determined from the simulation results.
Modeling and Analyzing the Reliability and Cost of Service Composition in the IoT: A Probabilistic Approach

Recently, many efforts have been devoted to explore the integration of Internet of Things (IoT) and Service-Oriented Computing (SOC). These works allow the real-world devices to provide their functionality as web services. However, two important issues, unreliable service providing and resource constraints, make the modeling and analysis of service composition in IoT a big challenge. In this paper, we propose a probabilistic approach to formally describe and analyze the reliability and cost-related properties of the service composition in IoT. First, a service composition in IoT is modeled as a finite state machine (FSM) which focuses on the functional part. Then, we extend this FSM model to a Markov Decision Process (MDP), which can specify the reliability of service operations. Furthermore, we extend MDP with cost structure, which can represent the different service quality attributes for each operation, such as energy consumption, communication cost, etc. The desirable quality properties of the service composition are specified by a probabilistic extension of temporal logic PCTL. We adopt a well-established probabilistic model checker PRISM to verify and analyze those properties of our service composition models.
Tradeoffs Between Profit and Customer Satisfaction for Service Provisioning in the Cloud

The recent cloud computing paradigm represents a trend of moving business applications to platforms run by parties located in different administrative domains. A cloud platform is often highly scalable and cost-effective through its pay-as-you-go pricing model. However, being shared by a large number of users, the running of applications in the platform faces higher performance uncertainty compared to a dedicated platform. Existing Service Level Agreements (SLAs) cannot sufficiently address the performance variation issue. In this paper, we use utility theory leveraged from economics and develop a new utility model for measuring customer satisfaction in the cloud. Based on the utility model, we design a mechanism to support utility-based SLAs in order to balance the performance of applications and the cost of running them. We consider an infrastructure-as-a-service type cloud platform (e.g., Amazon EC2), where a business service provider leases virtual machine (VM) instances with spot prices from the cloud and gains revenue by serving its customers. Particularly, we investigate the interaction of service profit and customer satisfaction. In addition, we present two scheduling algorithms that can effectively bid for different types of VM instances to make tradeoffs between profit and customer satisfaction. We conduct extensive simulations based on the performance data of different types of Amazon EC2 instances and their price history. Our experimental results demonstrate that the algorithms perform well across the metrics of profit, customer satisfaction and instance utilization.
A Robotic Skin for Collision Avoidance and Affective Touch Recognition

We describe a flexible robotic skin module that can measure proximity, contact and force, as well as algorithms for detecting obstacles, human hands, and affective touch gestures. The computational requirements of the proposed architecture are in line with the capabilities of a small microcontroller, allowing for a majority of the data to be processed colocated with the skin. The resulting system only communicates low-bandwidth information of interest, thereby solving challenges in routing high-bandwidth information and reducing the burden on a robot's central processing unit. We describe design and manufacturing of a 10.8 <inline-formula><tex-math notation="LaTeX">$\times$ </tex-math></inline-formula> 10.8 cm<inline-formula><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> skin patch containing 64 sensors, classification results for six different affective touch gestures, and a method that allows the skin to differentiate between an approaching human hand and various obstacles.
Vacant parking space detector for outdoor parking lot by using surveillance camera and FCM classifier

The most prevailing approach now for parking lot vacancy detecting system is to use sensor-based techniques. The main impediments to the camera-based system in applying to parking lots on rooftop and outside building are the glaring sun light and dark shadows in the daytime, and low-light intensity and back-lighting in the nighttime. To date, no camera-based detecting systems for outdoor parking lots have been in practical use. A few engineering firms provide the camera-based system, which is only for underground and indoor parking lots. This paper reports on the new camera based system called ParkLotD for detecting vacancy/occupancy in parking lots. ParkLotD uses a classifier based on fuzzy c-means (FCM) clustering and hyper-parameter tuning by particle swarm optimization (PSO). The test result of the detection error rate for the indoor multi-story parking lot has improved by an order of magnitude compared to the current system based on the edge detection approach. ParkLotD demonstrates high detection performance and enables the camera-based system to achieve the practical use in outdoor parking lots.
Mechanical Programming of Soft Actuators by Varying Fiber Angle

In this work we investigate the influence of fiber angle on the deformation of fiber-reinforced soft fluidic actuators. We demonstrate that, by simply varying the fiber angle, we can tune the actuators to achieve a wide range of motions, including axial extension, radial expansion, and twisting. We investigate the relationship between fiber angle and actuator deformation by performing finite element simulations for actuators with a range of different fiber angles, and we verify the simulation results by experimentally characterizing the actuators. By combining actuator segments in series, we can achieve combinations of motions tailored to specific tasks. We demonstrate this by using the results of simulations of separate actuators to design a segmented wormlike soft robot capable of propelling itself through a tube and performing an orientation-specific peg insertion task at the end of the tube. Understanding the relationship between fiber angle and motion of these soft fluidic actuators enables rapid exploration of the design space, opening the door to the iteration of exciting soft robot concepts such as flexible and compliant endoscopes, pipe inspection devices, and assembly line robots.
SQLEM: Fast Clustering in SQL using the EM Algorithm

Clustering is one of the most important tasks performed in Data Mining applications. This paper presents an efficient SQL implementation of the EM algorithm to perform clustering in very large databases. Our version can effectively handle high dimensional data, a high number of clusters and more importantly, a very large number of data records. We present three strategies to implement EM in SQL: horizontal, vertical and a hybrid one. We expect this work to be useful for data mining programmers and users who want to cluster large data sets inside a relational DBMS.
Adaptive duplicate detection using learnable string similarity measures

The problem of identifying approximately duplicate records in databases is an essential step for data cleaning and data integration processes. Most existing approaches have relied on generic or manually tuned distance metrics for estimating the similarity of potential duplicates. In this paper, we present a framework for improving duplicate detection using trainable measures of textual similarity. We propose to employ learnable text distance functions for each database field, and show that such measures are capable of adapting to the specific notion of similarity that is appropriate for the field's domain. We present two learnable text similarity measures suitable for this task: an extended variant of learnable string edit distance, and a novel vector-space based measure that employs a Support Vector Machine (SVM) for training. Experimental results on a range of datasets show that our framework can improve duplicate detection accuracy over traditional techniques.
Creating SERS hot spots on MoS(2) nanosheets with in situ grown gold nanoparticles.

Herein, a reliable surface-enhanced Raman scattering (SERS)-active substrate has been prepared by synthesizing gold nanoparticles (AuNPs)-decorated MoS2 nanocomposite. The AuNPs grew in situ on the surface of MoS2 nanosheet to form efficient SERS hot spots by a spontaneous redox reaction with tetrachloroauric acid (HAuCl4) without any reducing agent. The morphologies of MoS2 and AuNPs-decorated MoS2 nanosheet were characterized by TEM, HRTEM, and AFM. The formation of hot spots greatly depended on the ratio of MoS2 and HAuCl4. When the concentration of HAuCl4 was 2.4 mM, the as-prepared AuNPs@MoS2-3 nanocomposite exhibited a high-quality SERS activity toward probe molecule due to the generated hot spots. The spot-to-spot SERS signals showed that the relative standard deviation (RSD) in the intensity of the main Raman vibration modes (1362, 1511, and 1652 cm(-1)) of Rhodamine 6G were about 20%, which displayed good uniformity and reproducibility. The AuNPs@MoS2-based substrate was reliable, sensitive, and reproducible, which showed great potential to be an excellent SERS substrate for biological and chemical detection.