nils-herrmann/clean_scirepeval huggingface dataset:
Small Spherical Antennas Using Arrays of Electromagnetically Coupled Planar Elements

This letter presents the design, fabrication, and experimental characterization of small spherical antennas fabricated using arrays of non-interconnected planar conductor elements. The antennas are based upon spherical resonator structures with radiation Q-factors approaching $1.5\times$ the fundamental lower limit. The antennas are formed by coupling these resonators to an impedance-matched coplanar strip transmission line. Direct electrical connection between the feed and the antenna are made only to conductor elements coplanar with the transmission line, simplifying the fabrication process. The incident energy excites a collective resonant mode of the entire sphere (an electric dipole resonance), thereby inducing currents in each of the rings of the structure. The presence of the conductor elements outside of the feed plane is critical towards achieving the excellent bandwidth behavior observed here. The fabricated antennas have a normalized size $ka=0.54$ (where $k$ is the wavenumber and $a$ is the radius of the sphere) and exhibit high radiative efficiencies ($>$ 90%) and bandwidth performance near the fundamental limit for their size.
Partially observable Markov decision processes for spoken dialog systems

In a spoken dialog system, determining which action a machine should take in a given situation is a difficult problem because automatic speech recognition is unreliable and hence the state of the conversation can never be known with certainty. Much of the research in spoken dialog systems centres on mitigating this uncertainty and recent work has focussed on three largely disparate techniques: parallel dialog state hypotheses, local use of confidence scores, and automated planning. While in isolation each of these approaches can improve action selection, taken together they currently lack a unified statistical framework that admits global optimization. In this paper we cast a spoken dialog system as a partially observable Markov decision process (POMDP). We show how this formulation unifies and extends existing techniques to form a single principled framework. A number of illustrations are used to show qualitatively the potential benefits of POMDPs compared to existing techniques, and empirical results from dialog simulations are presented which demonstrate significant quantitative gains. Finally, some of the key challenges to advancing this method – in particular scalability – are briefly outlined.
Polisis: Automated Analysis and Presentation of Privacy Policies Using Deep Learning

Privacy policies are the primary channel through which companies inform users about their data collection and sharing practices. These policies are often long and difficult to comprehend. Short notices based on information extracted from privacy policies have been shown to be useful but face a significant scalability hurdle, given the number of policies and their evolution over time. Companies, users, researchers, and regulators still lack usable and scalable tools to cope with the breadth and depth of privacy policies. To address these hurdles, we propose an automated framework for privacy policy analysis (Polisis). It enables scalable, dynamic, and multi-dimensional queries on natural language privacy policies. At the core of Polisis is a privacy-centric language model, built with 130K privacy policies, and a novel hierarchy of neuralnetwork classifiers that accounts for both high-level aspects and fine-grained details of privacy practices. We demonstrate Polisis’ modularity and utility with two applications supporting structured and free-form querying. The structured querying application is the automated assignment of privacy icons from privacy policies. With Polisis, we can achieve an accuracy of 88.4% on this task. The second application, PriBot, is the first freeform question-answering system for privacy policies. We show that PriBot can produce a correct answer among its top-3 results for 82% of the test questions. Using an MTurk user study with 700 participants, we show that at least one of PriBot’s top-3 answers is relevant to users for 89% of the test questions.
Smarter universities: A vision for the fast changing digital era

In this paper we analyze the current situation of education in universities, with particular reference to the European scenario. Specifically, we observe that recent evolutions, such as pervasive networking and other enabling technologies, have been dramatically changing human life, knowledge acquisition, and the way works are performed and people learn. In this societal change, universities must maintain their leading role. Historically, they set trends primarily in education but now they are called to drive the change in other aspects too, such as management, safety, and environment protection. The availability of newer and newer technology reflects on how the relevant processes should be performed in the current fast changing digital era. This leads to the adoption of a variety of smart solutions in university environments to enhance the quality of life and to improve the performances of both teachers and students. Nevertheless, we argue that being smart is not enough for a modern university. In fact, universities should better become smarter. By “smarter university” we mean a place where knowledge is shared between employees, teachers, students, and all stakeholders in a seamless way. In this paper we propose, and discuss a smarter university model, derived from the one designed for the development of
Processing of Topological BIM Queries using Boundary Representation Based Methods

Building Information Models (BIM) are comprehensive digital representations of buildings, which provide a large set of information originating from the different disciplines involved in the design, construction and operation processes. Moreover, accessing the data needed for a specific downstream application scenario is a challenging task in large-scale BIM projects. Several researchers recently proposed using formal query languages for specifying the desired information in a concise, well-defined manner. One of the main limitations of the languages introduced so far, however, is the inadequate treatment of geometric information. This is a significant drawback, as buildings are inherently spatial objects and qualitative spatial relationships accordingly play an important role in the analysis and verification of building models. In addition, the filters needed in specific data exchange scenarios for selecting the information required can be built by spatial objects and their relations. The lack of spatial functionality in BIM query languages is filled by the Query Language for Building Information Models (QL4BIM) which provides metric, directional and topological operators for defining filter expressions with qualitative spatial semantics. This paper focuses on the topological operators provided by the language. In particular, it presents a new implementation method based on the boundary representation of the operands which outperforms the previously presented octree-based approaches. The paper discusses the developed algorithms in detail and presents extensive performance tests.
Learning Discrete Hashing Towards Efficient Fashion Recommendation

In our daily life, how to match clothing well is always a troublesome problem especially when we are shopping online to select a pair of matched pieces of clothing from tens of thousands available selections. To help common customers overcome selection issues, recent studies in the recommender system area have started to infer the fashion matching results automatically. The traditional fashion recommendation is normally achieved by considering visual similarity of clothing items or/and item co-purchase history from existing shopping transactions. Due to the high complexity of visual features and the lack of historical item purchase records, most of the existing work is unlikely to make an efficient and accurate recommendation. To address the problem, in this paper, we propose a new model called Discrete Supervised Fashion Coordinates Hashing. Its main objective is to learn meaningful yet compact high-level features of clothing items, which are represented as binary hash codes. In detail, this learning process is supervised by a clothing matching matrix, which is initially constructed based on limited known matching pairs and subsequently on the self-augmented ones. The proposed model jointly learns the intrinsic matching patterns from the matching matrix and the binary representations from the clothing items’ images, where the visual feature of each clothing item is discretized into a fixed-length binary vector. The binary representation learning significantly reduces the memory cost and accelerates the recommendation speed. The experiments compared with several state-of-the-art approaches have evidenced the superior performance of the proposed approach on efficient fashion recommendation.
Navigation in a small world

The small-world phenomenon — the principle that most of us are linked by short chains of acquaintances — was first investigated as a question in sociology and is a feature of a range of networks arising in nature and technology. Experimental study of the phenomenon revealed that it has two fundamental components: first, such short chains are ubiquitous, and second, individuals operating with purely local information are very adept at finding these chains. The first issue has been analysed, and here I investigate the second by modelling how individuals can find short chains in a large social network.
Automatic Question Generation from Queries

With increasing popularity of communitybased question answering services such as Yahoo! Answers, huge amounts of user generated questions are available. In this paper, we propose using these data along with search engine query logs to create a question generation shared task that aims to automatically generate questions given a query. At least two benefits could result from such a task. First, it can be used to rank possible candidate questions; therefore, it would enable search of question answer archives. Second, it could be used as a more informative and richer way of query expansion to guide or aid search.
Examining Alignment of Frames Using Actor-Network Constructs: The Implementation of an IT Project

IT projects often cannot succeed without bridging different stakeholders’ perspectives on system design and function during implementation. These perspectives often start as competing technology frames consisting of different beliefs, interests, technology evaluation routines and artifact characteristics. For successful implementation of IT, these competing frames usually need to be stabilized into a dominant frame which is widely accepted. Prior research has offered few clues on the mechanisms that are at work in aligning competing frames. This paper uses a case study to propose mechanisms for aligning frames—namely, two actor-network theory constructs of “black-boxing” and “obligatory points of passage”—that aid in translating the competing frames into a truce frame. The examined properties of these mechanisms are described and analyzed, thereby extending the literature on technology frames by providing evidence of generic aligning mechanisms.
Planar monopole antennas on substrates fabricated through an additive manufacturing process

This paper introduces a new method for creating a flexible antenna using additive manufacturing for the construction of the substrate. Two substrates were created using a 3D multi-material polymer printer. These substrates were composed using different ratios of the two materials supported by the printer. Planar monopole antennas with a bevel were placed on top of these substrates to form flexible antennas. This paper demonstrates a quick way to create antennas that can be used on non-rigid structures.
Activities of garlic oil, garlic powder, and their diallyl constituents against Helicobacter pylori.

Chronic Helicobacter pylori disease is reduced with Allium vegetable intake. This study was designed to assess the in vivo anti-H. pylori potential of a variety of garlic substances. The garlic materials all showed substantial but widely differing anti-H. pylori effects against all strains and isolates tested. The MICs (range, 8 to 32 microg/ml) and minimum bactericidal concentrations (MBCs) (range, 16 to 32 microg/ml) of undiluted garlic oil (GO) were smaller than those of garlic powder (GP) (MIC range, 250 to 500 microg/ml; MBC range, 250 to 500 microg/ml) but greater than the MIC of allicin (4. 0 microg/ml) (Table 2) present in GP. Allicin (MIC, 6 microg/ml; MBC, 6 microg/ml) was more potent than diallyl disulfide (MIC range, 100 to 200 microg/ml; MBC range, 100 to 200 microg/ml), its corresponding sulfide, but of a strength similar to that of diallyl tetrasulfide (MIC range, 3 to 6 microg/ml; MBC range, 3 to 6 microg/ml). Antimicrobial activity of the diallyl sulfides increased with the number of sulfur atoms. Time course viability studies and microscopy showed dose-dependent anti-H. pylori effects with undiluted GO, GP, allicin, and diallyl trisulfide after a lag phase of ca. 1 to 2 h. Substantial in vitro anti-H. pylori effects of pure GO and GP and their diallyl sulfur components exist, suggesting their potential for in vivo clinical use against H. pylori infections.
A review on the platform design, dynamic modeling and control of hybrid UAVs

This article presents a review on the platform design, dynamic modeling and control of hybrid Unmanned Aerial Vehicles (UAVs). For now, miniature UAVs which have experienced a tremendous development are dominated by two main types, i.e., fixed-wing UAV and Vertical Take-Off and Landing (VTOL) UAV, each of which, however, has its own inherent limitations on such as flexibility, payload, axnd endurance. Enhanced popularity and interest are recently gained by a newer type of UAVs, named hybrid UAV that integrates the beneficial features of both conventional ones. In this paper, a technical overview of the recent advances of the hybrid UAV is presented. More specifically, the hybrid UAV's platform design together with the associated technical details and features are introduced first. Next, the work on hybrid UAV's flight dynamics modeling is then categorized and explained. As for the flight control system design for the hybrid UAV, several flight control strategies implemented are discussed and compared in terms of theory, linearity and implementation.
A Dual-Polarized Pattern Reconfigurable Yagi Patch Antenna for Microbase Stations

A two-port pattern reconfigurable three-layered Yagi-Uda patch antenna with ±45° dual-polarization characteristic is presented. A driven patch (DP) and two large parasitic patches (LPPs) are printed on the top side of the middle layer, and the middle and bottom layers share a common metal ground. Two microstrip feedlines printed orthogonally on the bottom side of the bottom layer are used to feed the DP through two H-shaped slots etched in the ground. The LPPs are connected to/disconnected from the ground controlled by switches. By adjusting the connection states of the LPPs, one wide-beam mode and three narrow-beam modes can be obtained in both polarizations. A small parasitic patch is printed on the top side of the top layer to improve the pattern coherence of the two polarizations. This antenna is studied by both simulation and measurement. The measured common bandwidth of the four modes in both polarizations is 3.32–3.51 GHz, and the isolations between two polarizations in all the modes are higher than 20 dB. The low-profile antenna is very suitable for microbase-station applications.
X-band 1kW SSPA using 20-way Hybrid Radial combiner for accelerator

The X-band pulsed 1kW SSPA for XFEL has been designed and manufactured. Amplifier modules were combined by a 20-way Hybrid Radial combiner in order to reduce output loss. The manufactured 20-Way Hybrid Radial Combiner is capable of high output with a combining efficiency of 95%. The entire system is driven by Pulse and results in a Power Gain of 60dB with an Output Power of 1kW.
A study on cloud based Internet of Things: CloudIoT

The Next Revolution in the era of computing will be changing in comparison to traditional desktop. Many objects surrounds the human users will be on the network in one form or in another form in the Cloud Computing and Internet of Things framework. Cloud Computing and Internet of Things are two different technologies, these are into our daily life. Most of the surveys discussed the literature work on Internet of Things and Cloud separately. This paper presents the need for integration of Cloud and Internet of Things, an agent-oriented and Cloud assisted on Cloud IoT paradigm which based upon the layered reference architecture. Reference architecture for agent-oriented vision and Cloud-assisted is proposed, a Cloud based IoT paradigm applications scenario is described that have been presented in the literature, and Finally identified and discussed about open issues and future directions.
Introducing the QEST broker: Scaling the IoT by bridging MQTT and REST

In the “Internet of Things” (IoT) vision the physical world blends with virtual one, while machine-to-machine interaction improve our daily life. Clearly, how these virtual objects are exposed to us is critical, so that their user interface must be designed to support the easiness of usage that is driven by the users' needs, which is different from what machines requires. These two requirements must be solved, and an integrated solution should emerge, if we want to bring the IoT to the 50 billions network that is predicted to became in the next years. We believe that these requirements cannot be met by the same communication protocol, and so we propose a new kind of broker, named QEST that can bridge the two worlds, represented by their state-of-the-art protocols: MQTT and REST. In this paper, we demonstrate that our approach allows rapid development of user-facing IoT systems, while grating machines all the performance they need.
Deep joint demosaicking and denoising

Demosaicking and denoising are the key first stages of the digital imaging pipeline but they are also a severely ill-posed problem that infers three color values per pixel from a single noisy measurement. Earlier methods rely on hand-crafted filters or priors and still exhibit disturbing visual artifacts in hard cases such as moiré or thin edges. We introduce a new data-driven approach for these challenges: we train a deep neural network on a large corpus of images instead of using hand-tuned filters. While deep learning has shown great success, its naive application using existing training datasets does not give satisfactory results for our problem because these datasets lack hard cases. To create a better training set, we present metrics to identify difficult patches and techniques for mining community photographs for such patches. Our experiments show that this network and training procedure outperform state-of-the-art both on noisy and noise-free data. Furthermore, our algorithm is an order of magnitude faster than the previous best performing techniques.
Angels and Devils of Digital Social Norm Enforcement: A Theory about Aggressive versus Civilized Online Comments

We develop a theory that explains when commenters choose to be aggressive versus civilized in social media depending on their personal social norm context. In particular, we enrich traditional social norm theory by introducing the concept of moral legitimacy. This concept suggests that justifications, particularly those that put social norm violators outside of moral boundaries, cause aggression. Using the diversity of 45,982 comments of a real-world online firestorm, our results confirm that social norm contexts matter strongly for online behavior. The developed theory challenges existing speculations about online aggression and helps to develop strategies to encourage enlightened, civilized discourse on the Internet.
What Goes Around, Comes Around: Experimental Evidence on Exposed Lies

We experimentally investigate the optimal way to handle the uncovering of a noble lie, that is, a lie that supposedly is in the best interest of a given community. For this purpose, we analyze a public good game with feedback to group members on the average contributions of the other group members. The computer program inflates the feedback and shows higher than real average contributions to the high contributors. As shown by earlier studies, the partial feedback inflation increases the total payoff of the public good as it avoids the feeling of being a sucker for above average contributors. The lie is then uncovered and we continue with different feedback modes on contributions, some inflated, some true. We find that players respond similarly to both feedback modes. However, with true feedback, initial contributions in the second stage are significantly higher than with inflated feedback.
A Methodology for assessing Agile Software Development Approaches

Agile methods provide an organization or a team the flexibility to adopt a selected subset of principles and practices based on their culture, their values, and the types of systems that they develop. More specifically, every organization or team implements a customized agile method, tailored to better accommodate its needs. However, the extent to which a customized method supports the organizational objectives, or rather the ‘goodness’ of that method is questionable. Existing agile assessment approaches focus on a comparative analysis, or are limited in scope and application. In this research, we propose a structured, systematic and comprehensive approach to assess the ‘goodness’ of agile methods. We examine an agile method based on (1) its adequacy, (2) the capability of the organization to support the adopted principles and practices specified by the method, and (3) the method’s effectiveness. We propose the Objectives, Principles and Practices (OPP) Framework to guide our assessment. The Framework identifies (1) objectives of the agile philosophy, (2) principles that support the objectives, (3) practices that are reflective of the principles, (4) the linkages between the objectives, principles and practices, and (5) indicators for each practice to assess the effectiveness of the practice and the extent to which the organization supports its implementation. In this document, we discuss our solution approach, preliminary results, and future work.