nils-herrmann/clean_scirepeval huggingface dataset:
Geofencing 2.0: taking location-based notifications to the next level

Recently, Location-based Services (LBS) became proactive by supporting smart notifications in case the user enters or leaves a specific geographical area, well-known as Geofencing. However, different geofences cannot be temporally related to each other. Therefore, we introduce a novel method to formalize sophisticated Geofencing scenarios as state and transition-based geofence models. Such a model considers temporal relations between geofences as well as duration constraints for the time being within a geofence or in transition between geofences. These are two highly important aspects in order to cover sophisticated scenarios in which a notification should be triggered only in case the user crosses multiple geofences in a defined temporal order or leaves a geofence after a certain amount of time. As a proof of concept, we introduce a prototype of a suitable user interface for designing complex geofence models in conjunction with the corresponding proactive LBS.
Enlightening Deep Neural Networks with Knowledge of Confounding Factors

Deep learning techniques have demonstrated significant capacity in modeling some of the most challenging real world problems of high complexity. Despite the popularity of deep models, we still strive to better understand the underlying mechanism that drives their success. Motivated by observations that neurons in trained deep nets predict variation explaining factors indirectly related to the training tasks, we recognize that a deep network learns representations more general than the task at hand in order to disentangle impacts of multiple confounding factors governing the data, isolate the effects of the concerning factors, and optimize the given objective. Consequently, we propose to augment training of deep models with auxiliary information on explanatory factors of the data, in an effort to boost this disentanglement. Such deep networks, trained to comprehend data interactions and distributions more accurately, possess improved generalizability and compute better feature representations. Since pose is one of the most dominant confounding factors for object recognition, we adopt this principle to train a pose-aware deep convolutional neural network to learn both the class and pose of an object, so that it can make more informed classification decisions taking into account image variations induced by the object pose. We demonstrate that auxiliary pose information improves the classification accuracy in our experiments on Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) tasks. This general principle is readily applicable to improve the recognition and classification performance in various deep-learning applications.
Conditional Similarity Networks

What makes images similar? To measure the similarity between images, they are typically embedded in a feature-vector space, in which their distance preserve the relative dissimilarity. However, when learning such similarity embeddings the simplifying assumption is commonly made that images are only compared to one unique measure of similarity. A main reason for this is that contradicting notions of similarities cannot be captured in a single space. To address this shortcoming, we propose Conditional Similarity Networks (CSNs) that learn embeddings differentiated into semantically distinct subspaces that capture the different notions of similarities. CSNs jointly learn a disentangled embedding where features for different similarities are encoded in separate dimensions as well as masks that select and reweight relevant dimensions to induce a subspace that encodes a specific similarity notion. We show that our approach learns interpretable image representations with visually relevant semantic subspaces. Further, when evaluating on triplet questions from multiple similarity notions our model even outperforms the accuracy obtained by training individual specialized networks for each notion separately.
High-Linearity CMOS T/R Switch Design Above 20 GHz Using Asymmetrical Topology and AC-Floating Bias

This paper presents circuit techniques to achieve high linearity and good isolation for CMOS transmit/receive (T/R) switches above 20 GHz. A comparison between the conventional symmetrical and the proposed asymmetrical switch topology is presented with an emphasis on the linearity performance. The substrate loading effects on T/R switch figure of merit are analyzed quantitatively based on a compact model of triple-well (TW) NMOS device. AC-floating bias techniques used for the T/R switch and the associated performance tradeoffs are discussed. By combining these techniques, an LC-tuned 24-GHz single-pole double-throw T/R switch is implemented in a 90-nm TW CMOS process. The switch uses 1.2-V digital control signals for both T/R mode selection and source/drain biases. The design achieves a measured P1dB of 28.7 dBm, which represents the highest linearity reported to date for CMOS millimeter-wave switches. The measured insertion loss and return loss at 24 GHz are better than 3.5 and 10 dB, respectively.
PWM rectifier with LCL-filter using different current control structures

The control of a PWM rectifier with LCL-filter using a minimum number of sensors is analyzed. In addition to the DC-link voltage either the converter or line current is measured. Two different ways of current control are shown, analyzed and compared by simulations as well as experimental investigations. Main focus is spent on active damping of the LCL filter resonance and on robustness against line inductance variations.
Design procedure for two-stage CMOS transconductance operational amplifiers: a tutorial

This paper deals with well-defined design criteria for two-stage CMOS transconductance operational amplifiers. A novel and simple design procedure is presented, which allows electrical parameters to be univocally related to the value of each circuit element and biasing value. Unlike previous methods, the proposed one is suited for a pencil-and-paper design and yields accurate performance optimization without introducing unnecessary circuit constraints. Bandwidth optimization strategies are also discussed. SPICE simulations based on the proposed procedures are given which closely agree the expected results.
Conversations in context: a Twitter case for social media systems design

Conversations are the lifeblood of collaborative communities. Social media like microblogging tool Twitter have great potential for supporting these conversations. However, just studying the role of these media from a tool perspective is not sufficient. To fully unlock their power, they need to examined from a socio-technical perspective. We introduce a socio-technical context framework which can be used to analyze the role of systems of tools supporting goal-oriented conversations. Central to this framework is the communicative workflow loop, which is grounded in the Language/Action Perspective. We show how socio-technical conversation contexts can be used to match the communicative requirements of collaborative communities with enabling tool functionalities. This social media systems design process is illustrated with a case on Twitter.
Mobile Robot Miniaturisation: A Tool for Investigation in Control Algorithms

The interaction of an autonomous mobile robot with the real world critically depends on the robots morphology and on its environment. Building a model of these aspects is extremely complex, making simulation insu cient for accurate validation of control algorithms. If simulation environments are often very e cient, the tools for experimenting with real robots are often inadequate. The traditional programming languages and tools seldom provide enought support for realtime experiments, thus hindering the understanding of the control algorithms and making the experimentation complex and time-consuming. A miniature robot is presented: it has a cylindrical shape measuring 55 mm in diameter and 30 mm in height. Due to its small size, experiments can be performed quickly and cost-e ectively in a small working area. Small peripherals can be designed and connected to the basic module and can take advantage of a versatile communication scheme. A serial-link is provided to run control algorithms on a workstation during debugging, thereby giving the user the opportunity of employing all available graphical tools. Once debugged, the algorithm can be downloaded to the robot and run on its own processor. Experimentation with groups of robots is hardly possible with commercially available hardware. The size and the price of the described robot open the way to cost-e ective investigations into collective behaviour. This aspect of research drives the design of the robot described in this paper. Experiments with some twenty units are planned for the near future.
Speech emotion recognition using deep neural network and extreme learning machine

Speech emotion recognition is a challenging problem partly because it is unclear what features are effective for the task. In this paper we propose to utilize deep neural networks (DNNs) to extract high level features from raw data and show that they are effective for speech emotion recognition. We first produce an emotion state probability distribution for each speech segment using DNNs. We then construct utterance-level features from segment-level probability distributions. These utterancelevel features are then fed into an extreme learning machine (ELM), a special simple and efficient single-hidden-layer neural network, to identify utterance-level emotions. The experimental results demonstrate that the proposed approach effectively learns emotional information from low-level features and leads to 20% relative accuracy improvement compared to the stateof-the-art approaches.
Shader metaprogramming

Modern graphics accelerators have embedded programmable components in the form of vertex and fragment shading units. Current APIs permit specification of the programs for these components using an assembly-language level interface. Compilers for high-level shading languages are available but these read in an external string specification, which can be inconvenient.It is possible, using standard C++, to define a high-level shading language directly in the API. Such a language can be nearly indistinguishable from a special-purpose shading language, yet permits more direct interaction with the specification of textures and parameters, simplifies implementation, and enables on-the-fly generation, manipulation, and specialization of shader programs. A shading language built into the API also permits the lifting of C++ host language type, modularity, and scoping constructs into the shading language without any additional implementation effort.
Learning to estimate query difficulty: including applications to missing content detection and distributed information retrieval

In this article we present novel learning methods for estimating the quality of results returned by a search engine in response to a query. Estimation is based on the agreement between the top results of the full query and the top results of its sub-queries. We demonstrate the usefulness of quality estimation for several applications, among them improvement of retrieval, detecting queries for which no relevant content exists in the document collection, and distributed information retrieval. Experiments on TREC data demonstrate the robustness and the effectiveness of our learning algorithms.
Design of high efficiency line start permanent magnet motor for submersible pumps

This paper deals with the development of 3-phase line-start permanent magnet motor used to the submersible pumps. LSPM motor has similar characteristics such as the synchronous machine in the continuous mode. Also, LSPM motor is not required the specific drive hardware. LSPM motor can be applied instead of the induction motor and it can achieve a high-efficiency characteristic. In this paper, we discussed the design and analysis for 11kW LSPM motor. And, a formula was developed for the maximum torque calculation method in LSPM.
Examining the Relationship Between Parental Involvement and Student Motivation

Parent involvement has a sound research base attesting to the many potential benefits it can offer in education. However, student motivation as an academic outcome of parental involvement has only recently been investigated. The purpose of this article is to show how parent involvement is related to students’ motivation. Studies of students from the elementary school to high school show a beneficial relationship between parental involvement and the following motivational constructs: school engagement, intrinsic/extrinsic motivation, perceived competence, perceived control, self-regulation, mastery goal orientation, and motivation to read. From the synthesis of the parent involvement and motivation literature, we offer potential explanations for their relationship. Directions for areas of continued research are also presented.
Benefit of pattern diversity via two-element array of circular patch antennas in indoor clustered MIMO channels

In this paper, we analyze a multiple-input multiple-output (MIMO) array consisting of two circular microstrip antennas, designed to exploit pattern diversity. We analytically derive the spatial correlation coefficients of this array as a function of the mode excited, for realistic clustered MIMO channel models. We compare the performance of the circular patch array (CPA) against an array of two spaced dipoles. In particular, we compute a theoretical tradeoff to predict when the pattern diversity provided by the CPA is more effective than space diversity from the uniform linear array (ULA), based on the eigenvalues of the spatial correlation matrix. Through simulations, we show that CPAs yield better performance or satisfy more restrictive size constraints than ULAs in clustered MIMO channels, depending on the element spacing of the ULA. These results make the CPA an attractive solution for miniaturized MIMO arrays for portable devices or access points.
6–18GHz 13W reactive matched GaN power amplifier MMIC

The design and performance of a compact 6–18GHz power amplifier MMIC utilizing 0.2 pm gallium nitride (GaN) high electron mobility transistor (HEMT) technology is presented. An output power of 41.4 dBm to 43.3 dBm (average 42.3dBm) with over 16 dB power gain and a power added efficiency (PAE) of 21% to 35% over the band of 6–18 GHz under a drain voltage of 28 V in CW mode have been achieved. The chip size is compact with the size of 2.6∗3.6 mm2 and it delivers an average output power density 1.83 W/mm2 over the chip area. The thermal resistance is 1.55 T7W measured in CW mode.
Unleashing the Power of Mobile Cloud Computing using ThinkAir

Smartphones have exploded in popularity in recent years, becoming ever more sophisticated and capable. As a result, developers worldwide are building increasingly complex applications that require ever increasing amounts of computational power and energy. In this paper we propose ThinkAir, a framework that makes it simple for developers to migrate their smartphone applications to the cloud. ThinkAir exploits the concept of smartphone virtualization in the cloud and provides method level computation offloading. Advancing on previous works, it focuses on the elasticity and scalability of the server side and enhances the power of mobile cloud computing by parallelizing method execution using multiple Virtual Machine (VM) images. We evaluate the system using a range of benchmarks starting from simple micro-benchmarks to more complex applications. First, we show that the execution time and energy consumption decrease two orders of magnitude for the N -queens puzzle and one order of magnitude for a face detection and a virus scan application, using cloud offloading. We then show that if a task is parallelizable, the user can request more than one VM to execute it, and these VMs will be provided dynamically. In fact, by exploiting parallelization, we achieve a greater reduction on the execution time and energy consumption for the previous applications. Finally, we use a memory-hungry image combiner tool to demonstrate that applications can dynamically request VMs with more computational power in order to meet their computational requirements.
Probabilistic Graph-based Dependency Parsing with Convolutional Neural Network

This paper presents neural probabilistic parsing models which explore up to thirdorder graph-based parsing with maximum likelihood training criteria. Two neural network extensions are exploited for performance improvement. Firstly, a convolutional layer that absorbs the influences of all words in a sentence is used so that sentence-level information can be effectively captured. Secondly, a linear layer is added to integrate different order neural models and trained with perceptron method. The proposed parsers are evaluated on English and Chinese Penn Treebanks and obtain competitive accuracies.
Effects of bilingualism and aging on executive function and working memory.

Two studies are reported in which younger and older monolingual and bilingual adults performed executive function tasks. In Study 1, 130 participants performed a Stroop task and bilinguals in both age groups showed less interference than monolinguals with a greater benefit for older adults. In Study 2, 108 participants performed a complex working memory task based on verbal or nonverbal stimuli. Bilinguals showed less interference than monolinguals, with a larger bilingual advantage in the older adult group and in the nonverbal task. Together, these results show that bilingual advantages in executive function depend on characteristics of the participants and features of the tasks, with larger effects found for older than younger adults and for complex tasks using nonverbal material.
Semantic Information Retrieval based on Wikipedia Taxonomy

Information retrieval is used to find a subset of relevant documents against a set of documents. Determining semantic similarity between two terms is a crucial problem in Web Mining for such applications as information retrieval systems and recommender systems. Semantic similarity refers to the sameness of two terms based on sameness of their meaning or their semantic contents. Recently many techniques have introduced measuring semantic similarity using Wikipedia, a free online encyclopedia. In this paper, a new technique of measuring semantic similarity is proposed. The proposed method uses Wikipedia as an ontology and spreading activation strategy to compute semantic similarity. The utility of the proposed system is evaluated by using the taxonomy of Wikipedia categories.
Time-Frequency Analysis for the Localization of Multiple Moving Targets Using Dual-Frequency Radars

A dual-frequency radar, which estimates the range of a target based on the phase difference between two closely spaced frequencies, has been shown to be a cost-effective approach to accomplish both range-to-motion estimation and tracking. This approach, however, suffers from two drawbacks: it cannot deal with multiple moving targets, and it has poor performance in noisy environments. In this letter, we propose the use of time-frequency signal representations to overcome these drawbacks. The phase, and subsequently the range information, is obtained based on the moving target instantaneous Doppler frequency law, which is provided through time-frequency signal representations. The case of multiple moving targets is handled by separating the different Doppler signatures prior to phase estimation.
Manipulating Virtual Objects with Your Hands: A Case Study on Applying Desktop Augmented Reality at the Primary School

Augmented Reality (AR) offers many opportunities as an interactive tool to improve learning and teaching processes. This paper presents a pilot study conducted on third graders where a series of AR contents about Natural and Social Sciences have been used as a teaching tool. The main objective of the application is to help the students to learn complex concepts that present a difficult understanding. A user centered approach was followed to create the AR contents, where several teachers collaborated on its definition. The AR application combines 3D models and animations, mini games and quizzes. An evaluation of the educational contents was made from the point of view efficiency (academic achievement), usability and motivation. Results confirm that the use of desktop AR has had a positive impact on the learning and teaching process and reinforce the vision of AR as an affordable and feasible technological tool to support the students' learning activities.
Think Locally, Act Locally: The Detection of Small, Medium-Sized, and Large Communities in Large Networks

It is common in the study of networks to investigate intermediate-sized (or "meso-scale") features to try to gain an understanding of network structure and function. For example, numerous algorithms have been developed to try to identify "communities," which are typically construed as sets of nodes with denser connections internally than with the remainder of a network. In this paper, we adopt a complementary perspective that communities are associated with bottlenecks of locally biased dynamical processes that begin at seed sets of nodes, and we employ several different community-identification procedures (using diffusion-based and geodesic-based dynamics) to investigate community quality as a function of community size. Using several empirical and synthetic networks, we identify several distinct scenarios for "size-resolved community structure" that can arise in real (and realistic) networks: (1) the best small groups of nodes can be better than the best large groups (for a given formulation of the idea of a good community); (2) the best small groups can have a quality that is comparable to the best medium-sized and large groups; and (3) the best small groups of nodes can be worse than the best large groups. As we discuss in detail, which of these three cases holds for a given network can make an enormous difference when investigating and making claims about network community structure, and it is important to take this into account to obtain reliable downstream conclusions. Depending on which scenario holds, one may or may not be able to successfully identify "good" communities in a given network (and good communities might not even exist for a given community quality measure), the manner in which different small communities fit together to form meso-scale network structures can be very different, and processes such as viral propagation and information diffusion can exhibit very different dynamics. In addition, our results suggest that, for many large realistic networks, the output of locally biased methods that focus on communities that are centered around a given seed node (or set of seed nodes) might have better conceptual grounding and greater practical utility than the output of global community-detection methods. They also illustrate structural properties that are important to consider in the development of better benchmark networks to test methods for community detection.
Extremely Scaled Silicon Nano-CMOS Devices

Silicon-based CMOS technology can be scaled well into the nanometer regime. High-performance, planar, ultrathin-body devices fabricated on silicon-on-insulator substrates have been demonstrated down to 15-nm gate lengths. We have also introduced the FinFET, a double-gate device structure that is relatively simple to fabricate and can be scaled to gate lengths below 10 nm. In this paper, some of the key elements of these technologies are described, including sublithographic patterning, the effects of crystal orientation and roughness on carrier mobility, gate work function engineering, circuit performance, and sensitivity to process-induced variations.