nils-herrmann/clean_scirepeval huggingface dataset:
Fail-operational in safety-related automotive multi-core systems

Dynamically expendable real-time systems are an essential improvement over current future automotive E/E architectures. New functions and applications like automated driving or the subsequent activation of features require a different approach. This also applies to existing architectures which lack enhanced safety concepts beyond common fail-safe systems. Especially, electronic components without mechanical fallback require a fail-operational implementation to guarantee a correct safety-behavior. Although common hardware architectures already provide a couple of safety features, hardware-supported features to realize such systems are under research. In our work-in-progress paper we provide an overview on existing approaches and present future concepts for the implementation of fail-operational systems on a multi-core processor.
Levels of ICT Integration among Teacher Educators in a Teacher Education Academic College

This article examines the perspective of teacher educators and academic officials in an academic teacher education program regarding the integration of ICT in the teacher education program. The study portrays the current state of the ICT integration process and the implementation of the program for “Adapting Teacher Training Colleges to 21st Century Education” in a specific academic college in one of Israel’s outlying areas. This mixed methods study combined quantitative and qualitative methods. Data was collected by means of a closed questionnaire, an open-ended questionnaire for the teacher educators (N = 68), and semi-structured interviews conducted with the academic officials (N = 12). Findings revealed a hierarchical range of ICT integration in teaching, which reflects different profiles of teacher educators who integrate innovative pedagogies. The three integration levels (the basic level, the focused level, and the creative level) reflect the scope of ICT integration in the context of teacher training creating a continuum of integration and implementation, which can serve as an infrastructure for the effective adoption and integration of this innovative pedagogy by teacher educators and academic officials in academic teacher training colleges.
Estimating Smart City sensors data generation

Nowadays, Smart Cities are positioned as one of the most challenging and important research topics, highlighting major changes in people's lifestyle. Technologies such as smart energy, smart transportation or smart health are being designed to improve citizen's quality of life. Smart Cities leverage the deployment of a network of devices - sensors and mobile devices-, all connected through different means and/or technologies, according to their network availability and capacities, setting a novel framework feeding end-users with an innovative set of smart services. Aligned to this objective, a typical Smart City architecture is organized into layers, including a sensing layer (generates data), a network layer (moves the data), a middleware layer (manages all collected data and makes it ready for usage) and an application layer (provides the smart services benefiting from this data). In this paper a real Smart City is analyzed, corresponding to the city of Barcelona, with special emphasis on the layers responsible for collecting the data generated by the deployed sensors. The amount of daily sensors data transmitted through the network has been estimated and a rough projection has been made assuming an exhaustive deployment that fully covers all city. Finally, we discuss some solutions to both reduce the data transmission and improve the data management.
Probabilistic Models for Expert Finding

A common task in many applications is to find persons who are knowledgeable about a given topic (i.e., expert finding). In this paper, we propose and develop a general probabilistic framework for studying expert finding problem and derive two families of generative models (candidate generation models and topic generation models) from the framework. These models subsume most existing language models proposed for expert finding. We further propose several techniques to improve the estimation of the proposed models, including incorporating topic expansion, using a mixture model to model candidate mentions in the supporting documents, and defining an email count-based prior in the topic generation model. Our experiments show that the proposed estimation strategies are all effective to improve retrieval accuracy.
Radar cross section measurement with 77 GHz automotive FMCW radar

In this paper, radar cross section (RCS) measurement for human subjects and vehicles in 77 GHz automotive frequency modulated continuous wave (FMCW) radar system is presented. In this system, it is impossible to utilize conventional RCS definition due to high frequency band and the modulation technique used. Therefore, we introduce a new parameter called pseudo-RCS that can replace the conventional RCS. Then, we conduct actual experiment in the road to measure the RCS of the human subjects and the vehicles. In the measurement, data of four human subjects and four different kinds of vehicles are recorded with the 77 GHz FMCW radar. From the actual measured data, we find RCS distributions of the human subjects and the vehicles. For the human subjects, the RCS values are distributed following the Nakagami distribution. On the other hand, the log-normal distribution is well fit for the RCS values in the case of the vehicle.
Are educational computer microgames engaging and effective for knowledge acquisition at high-schools ? A quasi-experimental study

Curricular schooling can benefit from the usage of educational computer games, but it is difficult to integrate them in the formal schooling system. Here, we investigate one possible approach to this integration, which capitalizes on using a micro-game that can be played with a teacher’s guidance as a supplement after a traditional expository lecture followed by a debriefing. The game’s purpose is to reinforce and integrate part of the knowledge learnt during the lecture. We investigated feasibility of this approach in a quasi-experimental study in 70 min long seminars on the topic of animal learning at 5 classes at 4 different high-schools in the Czech Republic. Each class was divided to two groups randomly. After an expository lecture, the game group played a game called Orbis Pictus Bestialis while the control group received an extra lecture that used media-rich materials. The time allotment was the same in both groups. We investigated the immediate and one month delayed effects of the game on students’ knowledge reinforced and integrated by the game as well as on knowledge learnt during the expository lecture but not strengthened by the game. We also investigated students’ overall appeal towards the seminar and its perceived educational value. Data from 100 students were analysed. The results showed that a) the game-playing is comparable to the traditional form of teaching concerning immediate knowledge gains and has a significant medium positive effect size regarding retention, b) the gameplaying is not detrimental to information transmitted in the expository lecture but not strengthened by the game, c) perceived educational value and the overall appeal were high in the game group, nevertheless the perceived educational value was slightly lower in the game group comparing to the traditional group. Our results suggest that the proposed approach of harnessing educational computer games at high-schools is promising.
ANMPI-BASED PARALLEL AND DISTRIBUTED MACHINE LEARNING PLATFORM ON LARGE-SCALE HPC CLUSTERS

This paper presents the design of an MPI-based parallel and distributed machine learning platform on large-scale HPC clusters. Researchers and practitioners can implement easily a class of parallelizable machine learning algorithms on the platform, or port quickly an existing non-parallel implementation of a parallelizable algorithm to the platform with only minor modifications. Complicated functions in parallel programming such as scheduling, caching and load balancing are handled automatically by the platform. The platform performance was evaluated in a series of stress tests by using a k-means clustering task on 7,500 hours of speech data (about 2.7 billion 52dimensional feature vectors). Good scalability is demonstrated on an HPC cluster with thousands of CPU cores.
The Use of Gamification in Higher Education : An Empirical Study

The use of gamification in higher education has increased considerably over the past decades. An empirical study was conducted in Hungary with two groups of students to investigate their behaviour while interacting with Kahoot! The results were analyzed based on the technology acceptance model. They indicate that the positive attitude, good experience and ease of availability contributed to improve student performance which strengthened the intention to use the application. Besides these, the perceived utility was positively influenced by the ease of use as consequence. Gamification; education; Hungary; technology acceptance model; university student
Remote Sensing and Control of an Irrigation System Using a Distributed Wireless Sensor Network

Efficient water management is a major concern in many cropping systems in semiarid and arid areas. Distributed in-field sensor-based irrigation systemsoffer a potential solution to support site-specific irrigation management that allows producers to maximize their productivity while saving water. This paper describes details of the design and instrumentation of variable rate irrigation, a wireless sensor network, and software for real-time in-field sensing and control of a site-specific precision linear-move irrigation system. Field conditions were site-specifically monitored by six in-field sensor stations distributed across the field based on a soil property map, and periodically sampled and wirelessly transmitted to a base station. An irrigation machine was converted to be electronically controlled by a programming logic controller that updates georeferenced location of sprinklers from a differential Global Positioning System (GPS) and wirelessly communicates with a computer at the base station. Communication signals from the sensor network and irrigation controller to the base station were successfully interfaced using low-cost Bluetooth wireless radio communication. Graphic user interface-based software developed in this paper offered stable remote access to field conditions and real-time control and monitoring of the variable-rate irrigation controller.
Balun and power divider based on multilayer ring resonators (MRR)

This paper presents novel super compact microwave power dividers and balun (balanced-to-unbalanced) circuits. The proposed devices are based on multilayer ring resonators (MRR) structure. These new microwave devices are highly compact and flexible in design that can operate within various desirable bandwidths from narrow-band to ultrawideband (UWB), hence performing as bandpass filters simultaneously along with their own functions. It is also possible to have arbitrary power divisions. By this technique, a balun can be simply converted to a power diver and vice versa. Sample circuits are designed and the scattering characteristics are provided using electromagnetic simulation software. The dimensions of the devices are 2.3 mm χ 2.3 mm χ 1.5 mm.
RCD: Rapid Close to Deadline Scheduling for datacenter networks

Datacenter-based Cloud Computing services provide a flexible, scalable and yet economical infrastructure to host online services such as multimedia streaming, email and bulk storage. Many such services perform geo-replication to provide necessary quality of service and reliability to users resulting in frequent large inter-datacenter transfers. In order to meet tenant service level agreements (SLAs), these transfers have to be completed prior to a deadline. In addition, WAN resources are quite scarce and costly, meaning they should be fully utilized. Several recently proposed schemes, such as B4 , TEMPUS [2], and SWAN  have focused on improving the utilization of inter-datacenter transfers through centralized scheduling, however, they fail to provide a mechanism to guarantee that admitted requests meet their deadlines. Also, in a recent study, authors propose Amoeba , a system that allows tenants to define deadlines and guarantees that the specified deadlines are met, however, to admit new traffic, the proposed system has to modify the allocation of already admitted transfers. In this paper, we propose Rapid Close to Deadline Scheduling (RCD), a close to deadline traffic allocation technique that is fast and efficient. Through simulations, we show that RCD is up to 15 times faster than Amoeba, provides high link utilization along with deadline guarantees, and is able to make quick decisions on whether a new request can be fully satisfied before its deadline.
A Dynamic Approach for MPE and Weighted MAX-SAT

The problem of Most Probable Explanation (MPE) arises in the scenario of probabilistic inference: finding an assignment to all variables that has the maximum likelihood given some evidence. We consider the more general CNF-based MPE problem, where each literal in a CNF-formula is associated with a weight. We describe reductions between MPE and weighted MAX-SAT, and show that both can be solved by a variant of weighted model counting. The MPE-SAT algorithm is quite competitive with the state-of-the-art MAX-SAT, WCSP, and MPE solvers on a variety of problems.
Automatic Text Classification: A Technical Review

Automatic Text Classification is a semi-supervised machine learning task that automatically assigns a given document to a set of pre-defined categories based on its textual content and extracted features. Automatic Text Classification has important applications in content management, contextual search, opinion mining, product review analysis, spam filtering and text sentiment mining. This paper explains the generic strategy for automatic text classification and surveys existing solutions to major issues such as dealing with unstructured text, handling large number of attributes and selecting a machine learning technique appropriate to the text-classification application.
Interoperable Data Migration between NoSQL Columnar Databases

NoSQL databases have emerged as the solution to handle large quantities of user-generated contents still guaranteeing fault tolerance, availability and scalability. Each NoSQL database offers differentiated properties and characteristics as well as different data models and architectures. As a result, the development of applications exploiting such kind of technology is strictly dependent on the specific NoSQL solution being adopted, and the migration from a NoSQL to the other requires the development of ad-hoc code managing the transfer of data. In order to mitigate such issue, this paper proposes an interoperable migration system for columnar NoSQL databases. The proposed approach is based on an orginal Metamodel, capable of preserving both strong and weak consistency between data updates, secondary indexes and various data types. Moreover, the approach allows developers to easily add support for new databases.
FAtiMA Modular: Towards an Agent Architecture with a Generic Appraisal Framework

This paper presents a generic and flexible architecture for emotional agents, with what we consider to be the minimum set of functionalities that allows us to implement and compare different appraisal theories in a given scenario. FAtiMA Modular, the architecture proposed is composed of a core algorithm and by a set of components that add particular functionality (either in terms of appraisal or behaviour) to the architecture, which makes the architecture more flexible and easier to extend.
Accident prediction model for railway-highway interfaces.

Considerable past research has explored relationships between vehicle accidents and geometric design and operation of road sections, but relatively little research has examined factors that contribute to accidents at railway-highway crossings. Between 1998 and 2002 in Korea, about 95% of railway accidents occurred at highway-rail grade crossings, resulting in 402 accidents, of which about 20% resulted in fatalities. These statistics suggest that efforts to reduce crashes at these locations may significantly reduce crash costs. The objective of this paper is to examine factors associated with railroad crossing crashes. Various statistical models are used to examine the relationships between crossing accidents and features of crossings. The paper also compares accident models developed in the United States and the safety effects of crossing elements obtained using Korea data. Crashes were observed to increase with total traffic volume and average daily train volumes. The proximity of crossings to commercial areas and the distance of the train detector from crossings are associated with larger numbers of accidents, as is the time duration between the activation of warning signals and gates. The unique contributions of the paper are the application of the gamma probability model to deal with underdispersion and the insights obtained regarding railroad crossing related vehicle crashes.
Seismic surveying with drone-mounted geophones

Seismic imaging is the primary technique for subsurface exploration. Traditional seismic imaging techniques rely heavily on manual labor to plant sensors, lay miles of cabling, and then recover the sensors. Often sites of resource or rescue interest may be difficult or hazardous to access. Thus, there is a substantial need for unmanned sensors that can be deployed by air and potentially in large numbers. This paper presents working prototypes of autonomous drones equipped with geophones (vibration sensors) that can fly to a site, land, listen for echoes and vibrations, store the information on-board, and subsequently return to home base. The design uses four geophone sensors (with spikes) in place of the landing gear. This provides a stable landing attitude, redundancy in sensing, and ensures the geophones are oriented perpendicular to the ground. The paper describes hardware experiments demonstrating the efficacy of this technique and a comparison with traditional manual techniques. The performance of the seismic drone was comparable to a well planted geophone, proving the drone mount system is a feasible alternative to traditional seismic sensors.
Characteristics of Mobile Web Content

The World Wide Web is no longer tethered to our desktops and laptops. The Web has gone mobile, providing instant access to information anywhere and anytime. The mobile Web can be considered a shadow of the World Wide Web, implemented using specialized markup languages and design techniques adapted for comparatively limited mobile phones and PDAs. Despite the growing importance and usage of the mobile Web, surprising little is known about it. This paper presents the results of a study of mobile Web content conducted in May and June of 2006. The study examines the content of over one-million mobile Web pages from around the world using a search-assisted crawling methodology to locate and study pages for three of the most popular mobile Web formats - WML 1.0, WML 2.0/XHTML mobile profile (XHTML-MP) and compact HTML (C-HTML). The objective is to study the relative characteristics of these mobile Web content formats, as well as compare them with a similar sampling of non-mobile (HTML) content. We found that WML is the dominant mobile Web content type, although regional differences do exist. We found that all three mobile content types studied were on the same order of magnitude for average page characteristics such as number of links (under 10) and number of images (around 1), but pages in the newest format, XHTML-MP, are 50% larger on average than those in WML. Not surprisingly, all of these characteristics are much smaller than for HTML content pages gathered with the same methodology. In terms of specific features, only 7 % of pages used WML cards, but 50% of XHTML-MP servers dynamically adapted the content served based on the user agent. Finally, we found less than 4% of mobile pages contained ad objects, which is much less than for HTML pages.
A Recursive TF-ISF Based Sentence Retrieval Method with Local Context

modeling framework successfully use some kind of context of sentences. Unlike that there is no successful improvement of the TF-ISF method that takes into account the context of sentences. In this paper we propose a recursive TF-ISF based method that takes into account the local context of a sentence. The context is considered the previous and next sentence of current sentence. We compared the new method to the TF-ISF baseline and to an earlier unsuccessful method that also incorporates a similar context into TF-ISF. We got statistically significant improvements of the results in comparison to both of the methods. Additional benefit of our method is the clear explicit model of the context that will allow us to automatically generate a document representation with context suitable for sentence retrieval which is important for our future work.
Discriminative coupled dictionary hashing for fast cross-media retrieval

Cross-media hashing, which conducts cross-media retrieval by embedding data from different modalities into a common low-dimensional Hamming space, has attracted intensive attention in recent years. The existing cross-media hashing approaches only aim at learning hash functions to preserve the intra-modality and inter-modality correlations, but do not directly capture the underlying semantic information of the multi-modal data. We propose a discriminative coupled dictionary hashing (DCDH) method in this paper. In DCDH, the coupled dictionary for each modality is learned with side information (e.g., categories). As a result, the coupled dictionaries not only preserve the intra-similarity and inter-correlation among multi-modal data, but also contain dictionary atoms that are semantically discriminative (i.e., the data from the same category is reconstructed by the similar dictionary atoms). To perform fast cross-media retrieval, we learn hash functions which map data from the dictionary space to a low-dimensional Hamming space. Besides, we conjecture that a balanced representation is crucial in cross-media retrieval. We introduce multi-view features on the relatively ``weak'' modalities into DCDH and extend it to multi-view DCDH (MV-DCDH) in order to enhance their representation capability. The experiments on two real-world data sets show that our DCDH and MV-DCDH outperform the state-of-the-art methods significantly on cross-media retrieval.
A 3 ppm 1.5 × 0.8 mm 2 1.0 µA 32.768 kHz MEMS-Based Oscillator

This paper describes the first 32 kHz low-power MEMS-based oscillator in production. The primary goal is to provide a small form-factor oscillator (1.5 × 0.8 mm 2 ) for use as a crystal replacement in space-constrained mobile devices. The oscillator generates an output frequency of 32.768 kHz and its binary divisors down to 1 Hz. The frequency stability over the industrial temperature range (-40 °C to 85 °C) is ±100 ppm as an oscillator (XO) or ±3 ppm with optional calibration as a temperature compensated oscillator (TCXO). Supply currents are 0.9 μA for the XO and 1.0 μA for the TCXO at supply voltages from 1.4 V to 4.5 V. The MEMS resonator is a capacitively-transduced tuning fork at 524 kHz. The circuitry is fabricated in 180 nm CMOS and includes low power sustaining circuit, fractional-N PLL, temperature sensor, digital control, and low swing driver.