nils-herrmann/clean_scirepeval huggingface dataset:
EventJava: An Extension of Java for Event Correlation

Event correlation has become the cornerstone of many reactive applications, particularly in distributed systems. However, support for programming with complex events is still rather specific and rudimentary. This paper presents EventJava, an extension of Java with generic support for event-based distributed programming. EventJava seamlessly integrates events with methods, and broadcasting with unicasting of events; it supports reactions to combinations of events, and predicates guarding those reactions. EventJava is implemented as a framework to allow for customization of event semantics, matching, and dispatching. We present its implementation, based on a compiler transforming specific primitives to Java, along with a reference implementation of the framework. We discuss ordering properties of EventJava through a formalization of its core as an extension of Featherweight Java. In a performance evaluation, we show that EventJava compares favorably to a highly tuned database-backed event correlation engine as well as to a comparably lightweight concurrency mechanism.
Exploiting GNU radio and USRP: An economical test bed for real time communication systems

Communication experiments using normal lab setup, which includes more hardware and less software raises the cost of the total system. The method proposed here provides a new approach through which all the analog and digital experiments can be performed using a single hardware-USRP (Universal Software Radio Peripheral) and software-GNU Radio Companion (GRC). Initially, networking setup is formulated using SDR technology. Later on, one of the analog communication experiments is demonstrated in real time using the GNU Radio Companion, RTL-SDR and USRP. The entire communication system is less expensive as the system uses a single reprogrammable hardware and most of the focus of the system deals with the software part.
Loughborough University Institutional Repository Understanding Generation Y and their use of social media : a review and research agenda

The purpose of this paper is to review what we know – and don’t know – about Generation Y’s use of social media and to assess the implications for individuals, firms and society.  The paper distinguishes Generation Y from other cohorts in terms of systematic differences in values, preferences and behavior that are stable over time (as opposed to maturational or other differences). It describes their social media use and highlights evidence of intra-generational variance arising from environmental factors (including economic, cultural, technological and political/legal factors) and individual factors. Individual factors include stable factors (including socio-economic status, age and lifecycle stage) and dynamic, endogenous factors (including goals, emotions, and social norms).The paper discusses how Generation Y’s use of social media influences individuals, firms and society. It develops managerial implications and a research agenda.  Prior research on the social media use of Generation Y raises more questions than it answers. It: focuses primarily on the USA and/or (at most) one other country, ignoring other regions with large and fast-growing Generation Y populations where social-media use and its determinants may differ significantly; tends to study students whose behaviors may change over their life cycle stages; relies on self-reports by different age groups to infer Generation Y’s social media use; and does not examine the drivers and outcomes of social-media use. This paper’s conceptual framework yields a detailed set of research questions.  This paper provides a conceptual framework for considering the antecedents and consequences of Generation Y’s social media usage. It identifies unanswered questions about Generation Y’s use of social media, as well as practical insights for managers.
Estimation of missing markers in human motion capture

Motion capture is a prevalent technique for capturing and analyzing human articulations. A common problem encountered in motion capture is that some marker positions are often missing due to occlusions or ambiguities. Most methods for completing missing markers may quickly become ineffective and produce unsatisfactory results when a significant portion of the markers are missing for extended periods of time. We propose a data-driven, piecewise linear modeling approach to missing marker estimation that is especially beneficial in this scenario. We model motion sequences of a training set with a hierarchy of low-dimensional local linear models characterized by the principal components. For a new sequence with missing markers, we use a pre-trained classifier to identify the most appropriate local linear model for each frame and then recover the missing markers by finding the least squares solutions based on the available marker positions and the principal components of the associated model. Our experimental results demonstrate that our method is efficient in recovering the full-body motion and is robust to heterogeneous motion data.
The gender paradox in suicide.

In most Western countries females have higher rates of suicidal ideation and behavior than males, yet mortality from suicide is typically lower for females than for males. This article explores the gender paradox of suicidal behavior, examines its validity, and critically examines some of the explanations, concluding that the gender paradox of suicidal behavior is a real phenomenon and not a mere artifact of data collection. At the same time, the gender paradox in suicide is a more culture-bound phenomenon than has been traditionally assumed; cultural expectations about gender and suicidal behavior strongly determine its existence. Evidence from the United States and Canada suggests that the gender gap may be more prominent in communities where different suicidal behaviors are expected of females and males. These divergent expectations may affect the scenarios chosen by females and males, once suicide becomes a possibility, as well as the interpretations of those who are charged with determining whether a particular behavior is suicidal (e.g., coroners). The realization that cultural influences play an important role in the gender paradox of suicidal behaviors holds important implications for research and for public policy.
Manual training of mental rotation in children

When deciding whether two stimuli rotated in space are identical or mirror reversed, subjects employ mental rotation to solve the task. In children mental rotation can be trained by extensive repetition of the task, but the improvement seems to rely on the retrieval of previously learned stimuli. We assumed that due to the close relation between mental and manual rotation in children a manual training should improve the mental rotation process itself. The manual training we developed indeed ameliorated mental rotation and the training effect was not limited to learned stimuli. While boys outperformed girls in the mental rotation test before the manual rotation training, we found no gender differences in the results of the manual rotation task. 2006
Self-image resilience and dissonance: the role of affirmational resources.

It was predicted that high self-esteem Ss (HSEs) would rationalize an esteem-threatening decision less than low self-esteem Ss (LSEs), because HSEs presumably had more favorable self-concepts with which to affirm, and thus repair, their overall sense of self-integrity. This prediction was supported in 2 experiments within the "free-choice" dissonance paradigm--one that manipulated self-esteem through personality feedback and the other that varied it through selection of HSEs and LSEs, but only when Ss were made to focus on their self-concepts. A 3rd experiment countered an alternative explanation of the results in terms of mood effects that may have accompanied the experimental manipulations. The results were discussed in terms of the following: (a) their support for a resources theory of individual differences in resilience to self-image threats--an extension of self-affirmation theory, (b) their implications for self-esteem functioning, and (c) their implications for the continuing debate over self-enhancement versus self-consistency motivation.
Full-automatic recognition of various parking slot markings using a hierarchical tree structure

Ho Gi Jung Hanyang University Department of Automotive Engineering 222 Wangsimni-ro, Seongdong-gu Seoul 133-791, Republic of Korea E-mail: hogijung@hanyang.ac.kr Abstract. A full-automatic method for recognizing parking slot markings is proposed. The proposed method recognizes various types of parking slot markings by modeling them as a hierarchical tree structure. This method mainly consists of two processes: bottom-up and top-down. First, the bottom-up process climbs up the hierarchical tree structure to excessively generate parking slot candidates so as not to lose the correct slots. This process includes corner detection, junction and slot generation, and type selection procedures. After that, the top-down process confirms the final parking slots by eliminating falsely generated slots, junctions, and corners based on the properties of the parking slot marking type by climbing down the hierarchical tree structure. The proposed method was evaluated in 608 real-world parking situations encompassing a variety of different parking slot markings. The experimental result reveals that the proposed method outperforms the previous semiautomatic method while requiring a small amount of computational costs even though it is fully automatic.
GSET somi: a game-specific eye tracking dataset for somi

In this paper, we present an eye tracking dataset of computer game players who played the side-scrolling cloud game Somi. The game was streamed in the form of video from the cloud to the player. This dataset can be used for designing and testing game-specific visual attention models. The source code of the game is also available to facilitate further modifications and adjustments. For collecting this data, male and female candidates were asked to play the game in front of a remote eye-tracking device. For each player, we recorded gaze points, video frames of the gameplay, and mouse and keyboard commands. For each video frame, a list of its game objects with their locations and sizes was also recorded. This data, synchronized with eye-tracking data, allows one to calculate the amount of attention that each object or group of objects draw from each player. As a benchmark, we also show various attention patterns could be identified among players.
No . E 04-337 A Model of Reference-Dependent Preferences

We develop a model that fleshes out, extends, and modifies existing models of reference dependent preferences and loss aversion while accomodating most of the evidence motivating these models. Our approach makes reference-dependent theory more broadly applicable by avoiding some of the ways that prevailing models—if applied literally and without ancillary assumptions—make variously weak and incorrect predictions. Our model combines the reference-dependent gain-loss utility with standard economic “consumption utility” and clarifies the relationship between the two. Most importantly, we posit that a person’s reference point is her recent expectations about outcomes (rather than the status quo), and assume that behavior accords to a personal equilibrium: The person maximizes utility given her rational expectations about outcomes, where these expectations depend on her own anticipated behavior. We apply our theory to consumer behavior, and emphasize that a consumer’s willingness to pay for a good is endogenously determined by the market distribution of prices and how she expects to respond to these prices. Because a buyer’s willingness to buy depends on whether she anticipates buying the good, for a range of market prices there are multiple personal equilibria. This multiplicity disappears when the consumer is sufficiently uncertain about the price she will face. Because paying more than she anticipated induces a sense of loss in the buyer, the lower the prices at which she expects to buy the lower will be her willingness to pay. In some situations, a known stochastic decrease in prices can even lower the quantity demanded.
Human-machine-interaction in the industry 4.0 era

The development of Industry 4.0 will be accompanied by changing tasks and demands for the human in the factory. As the most flexible entity in cyber-physical production systems, workers will be faced with a large variety of jobs ranging from specification and monitoring to verification of production strategies. Through technological support it is guaranteed that workers can realize their full potential and adopt the role of strategic decision-makers and flexible problem-solvers. The use of established interaction technologies and metaphors from the consumer goods market seems to be promising. This paper demonstrates solutions for the technological assistance of workers, which implement the representation of a cyber-physical world and the therein occurring interactions in the form of intelligent user interfaces. Besides technological means, the paper points out the requirement for adequate qualification strategies, which will create the required, inter-disciplinary understanding for Industry 4.0.
An entropy based classification scheme for land applications of polarimetric SAR

In this paper we outline a new scheme for parameterizing polarimetric scattering problems, which has application in the quantitative analysis of polarimetric SAR data. The method relies on an eigenvalue analysis of the coherency matrix and employs a three-level Bernoulli statistical model to generate estimates of the average target scattering matrix parameters from the data. The scattering entropy is a key parameter is determining the randomness in this model and is seen as a fundamental parameter in assessing the importance of polarimetry in remote sensing problems. We show application of the method to some important classical random media scattering problems and apply it to POLSAR data from the NASA/JPL AIRSAR data base.
A MATLAB-simulink-based solar photovoltaic array (SPVA) module with MPPT

The performance of a solar photovoltaic array (SPVA) is dependent upon the temperature and irradiance level and it is necessary to study the characteristics of photovoltaic (PV) array. To utilize PV power or extract maximum power from PV array, the maximum power point tracking (MPPT) technique is essential to study and implement. In this paper, an equivalent electrical circuit of PV system has been modelled and its characteristics are studied. Response of the PV array with different irradiance level is also obtained. Incremental conductance algorithm of MPPT is modelled and developed to extract maximum power from PV array. The design of dc-dc boost converter has been carried out. It is observed that maximum power from PV array is achieved at input and output side of the dc-dc converter. The simulation work is done in MATLAB/SIMULINK environment.
Interdigital sensors and transducers

This review paper focuses on interdigital electrodes-a geometric structure encountered in a wide variety of sensor and transducer designs. Physical and chemical principles behind the operation of these devices vary so much across different fields of science and technology that the common features present in all devices are often overlooked. This paper attempts to bring under one umbrella capacitive, inductive, dielectric, piezoacoustic, chemical, biological, and microelectromechanical interdigital sensors and transducers. The paper also provides historical perspective, discusses fabrication techniques, modeling of sensor parameters, application examples, and directions of future research.
Medical Applications of Wireless Body Area Networks

Wireless Body Area Networks (WBANs) provide efficient communication solutions to the ubiquitous healthcare systems. Health monitoring, telemedicine, military, interactive entertainment, and portable audio/video systems are some of the applications where WBANs can be used. The miniaturized sensors together with advance micro-electro-mechanical systems (MEMS) technology create a WBAN that continuously monitors the health condition of a patient. This paper presents a comprehensive discussion on the applications of WBANs in smart healthcare systems. We highlight a number of projects that enable WBANs to provide unobtrusive long-term healthcare monitoring with real-time updates to the health center. In addition, we list many potential medical applications of a WBAN including epileptic seizure warning, glucose monitoring, and cancer detection.
Mining Frequent Subgraph Patterns from Uncertain Graph Data

In many real applications, graph data is subject to uncertainties due to incompleteness and imprecision of data. Mining such uncertain graph data is semantically different from and computationally more challenging than mining conventional exact graph data. This paper investigates the problem of mining uncertain graph data and especially focuses on mining frequent subgraph patterns on an uncertain graph database. A novel model of uncertain graphs is presented, and the frequent subgraph pattern mining problem is formalized by introducing a new measure, called expected support. This problem is proved to be NP-hard. An approximate mining algorithm is proposed to find a set of approximately frequent subgraph patterns by allowing an error tolerance on expected supports of discovered subgraph patterns. The algorithm uses efficient methods to determine whether a subgraph pattern can be output or not and a new pruning method to reduce the complexity of examining subgraph patterns. Analytical and experimental results show that the algorithm is very efficient, accurate, and scalable for large uncertain graph databases. To the best of our knowledge, this paper is the first one to investigate the problem of mining frequent subgraph patterns from uncertain graph data.
A Capacitorless LDO Regulator With Fast Feedback Technique and Low-Quiescent Current Error Amplifier

This brief presents an ultralow quiescent class-AB error amplifier (ERR AMP) of low dropout (LDO) and a slew-rate (SR) enhancement circuit to minimize compensation capacitance and speed up transient response designed in the 0.11-μm 1-poly 6-metal CMOS process. In order to increase the current capability with a low standby quiescent current under large-signal operation, the proposed scheme has a class-AB-operation operational transconductance amplifier (OTA) that acts as an ERR AMP. As a result, the new OTA achieved a higher dc gain and faster settling time than conventional OTAs, demonstrating a dc gain improvement of 15.8 dB and a settling time six times faster than that of a conventional OTA. The proposed additional SR enhancement circuit improved the response based on voltage-spike detection when the voltage dramatically changed at the output node.
An Ultra-Low-Power Long Range Battery/Passive RFID Tag for UHF and Microwave Bands With a Current Consumption of 700 nA at 1.5 V

We present for the first time, a fully integrated battery powered RFID integrated circuit (IC) for operation at ultrahigh frequency (UHF) and microwave bands. The battery powered RFID IC can also work as a passive RFID tag without a battery or when the battery has died (i.e., voltage has dropped below 1.3 V); this novel dual passive and battery operation allays one of the major drawbacks of currently available active tags, namely that the tag cannot be used once the battery has died. When powered by a battery, the current consumption is 700 nA at 1.5 V (400 nA if internal signals are not brought out on test pads). This ultra-low-power consumption permits the use of a very small capacity battery of 100 mA-hr for lifetimes exceeding ten years; as a result a battery tag that is very close to a passive tag both in form factor and cost is made possible. The chip is built on a 1-mum digital CMOS process with dual poly layers, EEPROM and Schottky diodes. The RF threshold power at 2.45 GHz is -19 dBm which is the lowest ever reported threshold power for RFID tags and has a range exceeding 3.5 m under FCC unlicensed operation at the 2.4-GHz microwave band. The low threshold is achieved with architectural choices and low-power circuit design techniques. At 915 MHz, based on the experimentally measured tag impedance (92-j837) and the threshold spec of the tag (200 mV), the theoretical minimum range is 24 m. The tag initially is in a "low-power" mode to conserve power and when issued the appropriate command, it operates in "full-power" mode. The chip has on-chip voltage regulators, clock and data recovery circuits, EEPROM and a digital state machine that implements the ISO 18000-4 B protocol in the "full-power" mode. We provide detailed explanation of the clock recovery circuits and the implementation of the binary sort algorithm, which includes a pseudorandom number generator. Other than the antenna board and a battery, no external components are used.
Intention Analysis for Sales, Marketing and Customer Service

In recent years, social media has become a customer touch-point for the business functions of marketing, sales and customer service. We aim to show that intention analysis might be useful to these business functions and that it can be performed effectively on short texts (at the granularity level of a single sentence). We demonstrate a scheme of categorization of intentions that is amenable to automation using simple machine learning techniques that are language-independent. We discuss the grounding that this scheme of categorization has in speech act theory. In the demonstration we go over a number of usage scenarios in an attempt to show that the use of automatic intention detection tools would benefit the business functions of sales, marketing and service. We also show that social media can be used not just to convey pleasure or displeasure (that is, to express sentiment) but also to discuss personal needs and to report problems (to express intentions). We evaluate methods for automatically discovering intentions in text, and establish that it is possible to perform intention analysis on social media with an accuracy of 66.97%± 0.10%.
Evaluation of Docker containers based on hardware utilization

Docker is an open platform for developers and system administrators to build, ship, and run distributed applications using Docker Engine, a portable, lightweight runtime and packaging tool, and Docker Hub, a cloud service for sharing applications and automating workflows. The main advantage is that, Docker can get code tested and deployed into production as fast as possible. Different applications can be run over Docker containers with language independency. In this paper the performance of these Docker containers are evaluated based on their system performance. That is based on system resource utilization. Different benchmarking tools are used for this. Performance based on file system is evaluated using Bonnie++. Other system resources such as CPU utilization, memory utilization etc. are evaluated based on the benchmarking code (using psutil) developed using python. Detail results obtained from all these tests are also included in this paper. The results include CPU utilization, memory utilization, CPU count, CPU times, Disk partition, network I/O counter etc.