nils-herrmann/clean_scirepeval huggingface dataset:
A New Level Shifter with Low Power in Multi-Voltage System

A new level shifter used in multiple voltage digital circuits is presented. It combines the merit of conventional level shifter and single supply level shifter, which can shifter any voltage level signal to a desired higher level with low leakage current. The circuits was designed in 180nm CMOS technology and simulated in SPICE. The simulation results showed that the proposed level shifter circuit has 36% leakage power dissipation reduction compared to the conventional level shifter
A Web of Things-Based Emerging Sensor Network Architecture for Smart Control Systems

The Web of Things (WoT) plays an important role in the representation of the objects connected to the Internet of Things in a more transparent and effective way. Thus, it enables seamless and ubiquitous web communication between users and the smart things. Considering the importance of WoT, we propose a WoT-based emerging sensor network (WoT-ESN), which collects data from sensors, routes sensor data to the web, and integrate smart things into the web employing a representational state transfer (REST) architecture. A smart home scenario is introduced to evaluate the proposed WoT-ESN architecture. The smart home scenario is tested through computer simulation of the energy consumption of various household appliances, device discovery, and response time performance. The simulation results show that the proposed scheme significantly optimizes the energy consumption of the household appliances and the response time of the appliances.
Overview of the CL-SciSumm 2016 Shared Task

The CL-SciSumm 2016 Shared Task is the first medium-scale shared task on scientific document summarization in the computational linguistics (CL) domain. The task built off of the experience and training data set created in its namesake pilot task, which was conducted in 2014 by the same organizing committee. The track included three tasks involving: (1A) identifying relationships between citing documents and the referred document, (1B) classifying the discourse facets, and (2) generating the abstractive summary. The dataset comprised 30 annotated sets of citing and reference papers from the open access research papers in the CL domain. This overview paper describes the participation and the official results of the second CL-SciSumm Shared Task, organized as a part of the Joint Workshop onBibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL 2016), held in New Jersey,USA in June, 2016. The annotated dataset used for this shared task and the scripts used for evaluation can be accessed and used by the community at: https://github.com/WING-NUS/scisumm-corpus.
Edge Computing Architecture for Mobile Crowdsensing

Mobile crowdsensing (MCS) is a human-driven Internet of Things service empowering citizens to observe the phenomena of individual, community, or even societal value by sharing sensor data about their environment while on the move. Typical MCS service implementations utilize cloud-based centralized architectures, which consume a lot of computational resources and generate significant network traffic, both in mobile networks and toward cloud-based MCS services. Mobile edge computing (MEC) is a natural choice to distribute MCS solutions by moving computation to network edge, since an MEC-based architecture enables significant performance improvements due to the partitioning of problem space based on location, where real-time data processing and aggregation is performed close to data sources. This in turn reduces the associated traffic in mobile core and will facilitate MCS deployments of massive scale. This paper proposes an edge computing architecture adequate for massive scale MCS services by placing key MCS features within the reference MEC architecture. In addition to improved performance, the proposed architecture decreases privacy threats and permits citizens to control the flow of contributed sensor data. It is adequate for both data analytics and real-time MCS scenarios, in line with the 5G vision to integrate a huge number of devices and enable innovative applications requiring low network latency. Our analysis of service overhead introduced by distributed architecture and service reconfiguration at network edge performed on real user traces shows that this overhead is controllable and small compared with the aforementioned benefits. When enhanced by interoperability concepts, the proposed architecture creates an environment for the establishment of an MCS marketplace for bartering and trading of both raw sensor data and aggregated/processed information.
A Statistical Framework for Product Description Generation

We present in this paper a statistical framework that generates accurate and fluent product description from product attributes. Specifically, after extracting templates and learning writing knowledge from attribute-description parallel data, we use the learned knowledge to decide what to say and how to say for product description generation. To evaluate accuracy and fluency for the generated descriptions, in addition to BLEU and Recall, we propose to measure what to say (in terms of attribute coverage) and to measure how to say (by attribute-specified generation) separately. Experimental results show that our framework is effective.
PEOPLE AND ORGANIZATIONAL CULTURE : A PROFILE COMPARISON APPROACH TO ASSESSING PERSON-ORGANIZATION FIT

This article brings together three current themes in organizational behavior: (1) a renewed interest in assessing person-situation interactional constructs, (2) the quantitative assessment of organizational culture, and (3) the application of "Q-sort," or template-matching, approaches to assessing person-situation interactions. Using longitudinal data from accountants and M.B.A. students and cross-sectional data from employees of government agencies and public accounting firms, we developed and validated an instrument for assessing personorganization fit, the Organizational Culture Profile (OCP). Results suggest that the dimensionality of individual preferences for organizational cultures and the existence of these cultures are interpretable. Further, person-organization fit predicts job satisfaction and organizational commitment a year after fit was measured and actual turnover after two years. This evidence attests to the importance of understanding the fit between individuals' preferences and organizational cultures.
Teacher perspectives on integrating ICT into subject teaching : Commitment , constraints , caution and change

This paper examines how secondary teachers of the core subjects of English, Mathematics and Science have begun to integrate information and communication technology (ICT) into mainstream classroom practice in English schools. It draws on an analysis of 18 focus group interviews with core subject departments. The analysis culminated in a thematic model of professional thinking about how the integrated use of ICT can support subject teaching and learning. Evident commitment to incorporating ICT was tempered by a cautious, critical approach and the influence of external constraints operating. Teacher accounts emphasised both the use of ICT to enhance and extend existing classroom practice, and change in terms of emerging forms of activity which complemented or modified practice. A gradual process of pedagogical evolution was apparent; teachers were developing and trialling new strategies specifically for mediating ICT-supported learning. In particular, these overcame the potentially obstructive role of some forms of ICT by focusing pupils’ attention onto underlying learning objectives.
GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs

We propose a new network architecture, Gated Attention Networks (GaAN), for learning on graphs. Unlike the traditional multi-head attention mechanism, which equally consumes all attention heads, GaAN uses a convolutional sub-network to control each attention head’s importance. We demonstrate the effectiveness of GaAN on the inductive node classification problem on large graphs. Moreover, with GaAN as a building block, we construct the Graph Gated Recurrent Unit (GGRU) to address the traffic speed forecasting problem. Extensive experiments on three realworld datasets show that our GaAN framework achieves state-of-the-art results on both tasks.
Count-Based Exploration with Neural Density Models

The PixelCNN model used in this paper is a lightweight variant of the Gated PixelCNN introduced in (van den Oord et al., 2016a). It consists of a 7 × 7 masked convolution, followed by two residual blocks with 1×1 masked convolutions with 16 feature planes, and another 1×1 masked convolution producing 64 features planes, which are mapped by a final masked convolution to the output logits. Inputs are 42 × 42 greyscale images, with pixel values quantized to 8 bins.
Semantic Instance Segmentation via Deep Metric Learning

We propose a new method for semantic instance segmentation, by first computing how likely two pixels are to belong to the same object, and then by grouping similar pixels together. Our similarity metric is based on a deep, fully convolutional embedding model. Our grouping method is based on selecting all points that are sufficiently similar to a set of “seed points’, chosen from a deep, fully convolutional scoring model. We show competitive results on the Pascal VOC instance segmentation benchmark.
Provenance Data in Social Media

Social media shatters the barrier to communicate anytime anywhere for people of all walks of life. The publicly available, virtually free information in social media poses a new challenge to consumers who have to discern whether a piece of information published in social media is reliable. For example, it can be difficult to understand the motivations behind a statement passed from one user to another, without knowing the person who originated the message. Additionally, false information can be propagated through social media, resulting in embarrassment or irreversible damages. Provenance data associated with a social media statement can help dispel rumors, clarify opinions, and confirm facts. However, provenance data about social media statements is not readily available to users today. Currently, providing this data to users requires changing the social media infrastructure or offering subscription services. Taking advantage of social media features, research in this nascent field spearheads the search for a way to provide provenance data to social media users, thus leveraging social media itself by mining it for the provenance data. Searching for provenance data reveals an interesting problem space requiring the development and application of new metrics in order to provide meaningful provenance data to social media users. This lecture reviews the current research on information provenance, explores exciting research opportunities to address pressing needs, and shows how data mining can enable a social media user to make informed judgements about statements published in social media.
Critical-path planning and scheduling

Among the major problems facing technical management today are those involving the coordination of many diverse activities toward a common goal. In a large engineering project, for example, almost all the engineering and craft skills are involved as well as the functions represented by research, development, design, procurement, construction, vendors, fabricators and the customer. Management must devise plans which will tell with as much accuracy as possible how the efforts of the people representing these functions should be directed toward the project's completion. In order to devise such plans and implement them, management must be able to collect pertinent information to accomplish the following tasks:
 (1) To form a basis for prediction and planning
 (2) To evaluate alternative plans for accomplishing the objective
 (3) To check progress against current plans and objectives, and
 (4) To form a basis for obtaining the facts so that decisions can be made and the job can be done.
A Survey on High Efficiency Wireless Local Area Networks: Next Generation WiFi

The emerging paradigm of the Internet of Everything, along with the increasing demand of Internet services everywhere, results in a remarkable and continuous growth of the global Internet traffic. As a cost-effective Internet access solution, WiFi networks currently generate a major portion of the global Internet traffic. Furthermore, the number of WiFi public hotspots worldwide is expected to increase by more than sevenfold by 2018. To face this huge increase in the number of densely deployed WiFi networks, and the massive amount of data to be supported by these networks in indoor and outdoor environments, it is necessary to improve the current WiFi standard and define specifications for high efficiency wireless local area networks (HEWs). This paper presents potential techniques that can be applied for HEWs, in order to achieve the required performance in dense HEW deployment scenarios, as expected in the near future. The HEW solutions under consideration includes physical layer techniques, medium access control layer strategies, spatial frequency reuse schemes, and power saving mechanisms. To accurately assess a newly proposed HEW scheme, we discuss suitable evaluation methodologies, by defining simulation scenarios that represent future HEW usage models, performance metrics that reflect HEW user experience, traffic models for dominant HEW applications, and channel models for indoor and outdoor HEW deployments. Finally, we highlight open issues for future HEW research and development.
Compact Topside Millimeter-Wave Waveguide-to-Microstrip Transitions

This letter presents two different waveguide-to-microstrip transition designs for the 76-81 GHz frequency band. Both transitions are fabricated on a grounded single layer substrate using a standard printed circuit board (PCB) fabrication process. A coplanar patch antenna and a feed technique at the non-radiating edge are used for the impedance transformation. In the first design, a conventional WR-10 waveguide is connected. In the second design, a WR-10 waveguide flange with an additional inductive waveguide iris is employed to improve the bandwidth. Both designs were developed for the integration of multi-channel array systems allowing an element spacing of λ0/2 or less. Measurement results of the first transition without the iris show a bandwidth of 8.5 GHz (11%) for 10 dB return loss and a minimum insertion loss (IL) of 0.35 dB. The transition using the iris increases the bandwidth to 12 GHz (15%) for 10 dB return loss and shows a minimum insertion loss of 0.6 dB at 77 GHz.
Investigation of cloudless solar radiation with PV module employing Matlab-Simulink

This paper focuses an novel approach on the prediction of Voltage-Current(V-I) characteristics of a Photovoltaic panel under varying weather conditions and also the modelling of hourly cloudless solar radiation to provide the insolation on a PV module of any orientation, located at any site. The empirical model developed in this study uses standard specifications together with actual solar radiation and cell temperature. This proposed work develops a Matlab-Simulink block to generate solar radiation at any location and for any time of the year. A new model for Current-Voltage (I-V) characteristics and Maximum Power operation of a Photovoltaic (PV) module is also presented which aims to model the effect on I-V and P-V curves of varying climatic conditions. Moreover, this model has been implemented using the Matlab-Simulink and is used to investigate the effect of meteorological conditions on the performance of a PV module generator. Thus the combined model of cloudless solar radiation and the photovoltaic module provide a tool that may be loaded in the library for analysis purpose. It is found that the predicted solar radiation strongly agrees with the experimental data.
Large Scaled Relation Extraction With Reinforcement Learning

Sentence relation extraction aims to extract relational facts from sentences, which is an important task in natural language processing field. Previous models rely on the manually labeled supervised dataset. However, the human annotation is costly and limits to the number of relation and data size, which is difficult to scale to large domains. In order to conduct largely scaled relation extraction, we utilize an existing knowledge base to heuristically align with texts, which not rely on human annotation and easy to scale. However, using distant supervised data for relation extraction is facing a new challenge: sentences in the distant supervised dataset are not directly labeled and not all sentences that mentioned an entity pair can represent the relation between them. To solve this problem, we propose a novel model with reinforcement learning. The relation of the entity pair is used as distant supervision and guide the training of relation extractor with the help of reinforcement learning method. We conduct two types of experiments on a publicly released dataset. Experiment results demonstrate the effectiveness of the proposed method compared with baseline models, which achieves 13.36% improvement.
What is this evasive beast we call user satisfaction?

The notion of ‘user satisfaction’ plays a prominent role in HCI, yet it remains evasive. This exploratory study reports three experiments from an ongoing research program. In this program we aim to uncover (1) what user satisfaction is, (2) whether it is primarily determined by user expectations or by the interactive experience, (3) how user satisfaction may be related to perceived usability, and (4) the extent to which satisfaction rating scales capture the same interface qualities as uncovered in self-reports of interactive experiences. In all three experiments reported here user satisfaction was found to be a complex construct comprising several concepts, the distribution of which varied with the nature of the experience. Expectations were found to play an important role in the way users approached a browsing task. Satisfaction and perceived usability was assessed using two methods: scores derived from unstructured interviews and from the Web site Analysis MeasureMent Inventory (WAMMI) rating scales. Scores on these two instruments were somewhat similar, but conclusions drawn across all three experiments differed in terms of satisfaction ratings, suggesting that rating scales and interview statements may tap different interface qualities. Recent research suggests that ‘beauty’, or ‘appeal’ is linked to perceived usability so that what is ‘beautiful’ is also perceived to be usable [Interacting with Computers 13 (2000) 127]. This was true in one experiment here using a web site high in perceived usability and appeal. However, using a site with high appeal but very low in perceived usability yielded very high satisfaction, but low perceived usability scores, suggesting that what is ‘beautiful’ need not also be perceived to be usable. The results suggest that web designers may need to pay attention to both visual appeal and usability. q 2002 Elsevier Science B.V. All rights reserved.
Controller selection in a Wireless Mesh SDN under network partitioning and merging scenarios

In this paper we consider a Wireless Mesh Networ k (WMN) integrating SDN principles. The Wireless Mesh Routers (WMR) are OpenFlow capable switches that ca n be controlled by SDN controllers, according to the wmSDN (wireless mesh SDN) architecture that we have intro duced in a previous work. We consider the issue of controller selection in a scenario with intermittent connectivity. We assume that over time a single WMN can become split in two or more p artitions and that separate partitions can merge into a large r one. We assume that a set of SDN controllers can potentiall y take control of the WMRs. At a given time only one contr oller should be the master of a WMR and it should be the most appropriate one according to some metric. We argue that the state of the art solutions for “master election” among distributed controllers are not suitable in a mesh networking environment, as they could easily be affected by inconsistencies. We envisage a “master selection” a pproach which is under the control of each WMR, and guarant ees that at a given time only one controller will be master of a WMR. We designed a specific master selection procedure w hich is very simple in terms of the control logic to be exe cuted in the WMR. We have implemented the proposed solution and deployed it over a network emulator (CORE) and over the combination of two physical wireless testbeds (NITOS and wiLab.t).
Subsampling-Based Approximate Monte Carlo for Discrete Distributions

Drawing a sample from a discrete distribution is one of the building components for Monte Carlo methods. Like other sampling algorithms, discrete sampling also suffers from high computational burden in large-scale inference problems. We study the problem of sampling a discrete random variable with a high degree of dependency that is typical in large-scale Bayesian inference and graphical models, and propose an efficient approximate solution with a subsampling approach. We make a novel connection between the discrete sampling and Multi-Armed Bandits problems with a finite reward population and provide three algorithms with theoretical guarantees. Empirical evaluations show the robustness and efficiency of the approximate algorithms in both synthetic and real-world large-scale problems.
Providing accident detection in vehicular networks through OBD-II devices and Android-based smartphones

The increasing activity in the Intelligent Transportation Systems (ITS) area faces a strong limitation: the slow pace at which the automotive industry is making cars "smarter". On the contrary, the smartphone industry is advancing quickly. Existing smartphones are endowed with multiple wireless interfaces and high computational power, being able to perform a wide variety of tasks. By combining smartphones with existing vehicles through an appropriate interface we are able to move closer to the smart vehicle paradigm, offering the user new functionalities and services when driving. In this paper we propose an Android-based application that monitors the vehicle through an On Board Diagnostics (OBD-II) interface, being able to detect accidents. Our proposed application estimates the G force experienced by the passengers in case of a frontal collision, which is used together with airbag triggers to detect accidents. The application reacts to positive detection by sending details about the accident through either e-mail or SMS to pre-defined destinations, immediately followed by an automatic phone call to the emergency services. Experimental results using a real vehicle show that the application is able to react to accident events in less than 3 seconds, a very low time, validating the feasibility of smartphone based solutions for improving safety on the road.