nils-herrmann/clean_scirepeval huggingface dataset:
How should I explain? A comparison of different explanation types for recommender systems

Recommender systems help users locate possible items of interest more quickly by filtering and ranking them in a personalized way. Some of these systems provide the end user not only with such a personalized item list but also with an explanation which describes why a specific item is recommended and why the system supposes that the user will like it. Besides helping the user understand the output and rationale of the system, the provision of such explanations can also improve the general acceptance, perceived quality, or effectiveness of the system. In recent years, the question of how to automatically generate and present system-side explanations has attracted increased interest in research. Today some basic explanation facilities are already incorporated in e-commerce Web sites such as Amazon.com. In this work, we continue this line of recent research and address the question of how explanations can be communicated to the user in a more effective way. In particular, we present the results of a user study in which users of a recommender system were provided with different types of explanation. We experimented with ten different explanation types and measured their effects in different dimensions. The explanation types used in the study include both known visualizations from the literature as well as two novel interfaces based on tag clouds. Our study reveals that the content-based tag cloud explanations are particularly helpful to increase the user-perceived level of transparency and to increase user satisfaction even though they demand higher cognitive effort from the user. Based on these insights and observations, we derive a set of possible guidelines for designing or selecting suitable explanations for recommender systems.
Combline tunable bandpass filter using RF-MEMS switched capacitor bank

This paper presents the design and implementation of an evanescent tunable combline filter based on electronic tuning with the use of RF-MEMS capacitor banks. The use of MEMS tuning circuit results in the compact implementation of the proposed filter with high-Q and near to zero DC power consumption. The proposed filter consist of combline resonators with tuning disks that are loaded with RF-MEMS capacitor banks. A two-pole filter is designed and measured based on the proposed tuning concept. The filter operates at 2.5 GHz with a bandwidth of 22 MHz. Measurement results demonstrate a tuning range of 110 MHz while the quality factor is above 374 (1300–374 over the tuning range).
Application of honey-bee mating optimization algorithm on clustering

Cluster analysis is one of attractive data mining technique that use in many fields. One popular class of data clustering algorithms is the center based clustering algorithm. K-means used as a popular clustering method due to its simplicity and high speed in clustering large datasets. However, K-means has two shortcomings: dependency on the initial state and convergence to local optima and global solutions of large problems cannot found with reasonable amount of computation effort. In order to overcome local optima problem lots of studies done in clustering. Over the last decade, modeling the behavior of social insects, such as ants and bees, for the purpose of search and problem solving has been the context of the emerging area of swarm intelligence. Honey-bees are among the most closely studied social insects. Honey-bee mating may also be considered as a typical swarm-based approach to optimization, in which the search algorithm is inspired by the process of marriage in real honey-bee. Honey-bee has been used to model agent-based systems. In this paper, we proposed application of honeybee mating optimization in clustering (HBMK-means). We compared HBMK-means with other heuristics algorithm in clustering, such as GA, SA, TS, and ACO, by implementing them on several well-known datasets. Our finding shows that the proposed algorithm works than the best one.
Musical training influences linguistic abilities in 8-year-old children: more evidence for brain plasticity.

We conducted a longitudinal study with 32 nonmusician children over 9 months to determine 1) whether functional differences between musician and nonmusician children reflect specific predispositions for music or result from musical training and 2) whether musical training improves nonmusical brain functions such as reading and linguistic pitch processing. Event-related brain potentials were recorded while 8-year-old children performed tasks designed to test the hypothesis that musical training improves pitch processing not only in music but also in speech. Following the first testing sessions nonmusician children were pseudorandomly assigned to music or to painting training for 6 months and were tested again after training using the same tests. After musical (but not painting) training, children showed enhanced reading and pitch discrimination abilities in speech. Remarkably, 6 months of musical training thus suffices to significantly improve behavior and to influence the development of neural processes as reflected in specific pattern of brain waves. These results reveal positive transfer from music to speech and highlight the influence of musical training. Finally, they demonstrate brain plasticity in showing that relatively short periods of training have strong consequences on the functional organization of the children's brain.
Domain ontology based semantic search for efficient information retrieval through automatic query expansion

To achieve semantic search, a search engine is needed which can interpret the meaning of a user's query and the relations among the concepts that a document contains with respect to a particular domain. We are presenting the skeleton of such a system based on ontology. In this system, a user enters a query from which the meaningful concepts are extracted; using these concepts and domain ontology, query expansion is performed. For all the terms (expanded and initial query terms), SPARQL query is built and then it is fired on the knowledge base that finds appropriate RDF triples in knowledge Base. Web documents relevant to the requested concepts and individuals specified in these triples are then retrieved. Finally, the retrieved documents are ranked according to their relevance to the user's query and then are sent to the user. If a user wants to find specific information; can search with another module of our system that works without query expansion. The approach of query expansion makes use of query concepts as well as synonyms of these concepts and the new terms relate with the original query terms within a threshold.
White-Box Traceable Ciphertext-Policy Attribute-Based Encryption Supporting Any Monotone Access Structures

In a ciphertext-policy attribute-based encryption (CP-ABE) system, decryption keys are defined over attributes shared by multiple users. Given a decryption key, it may not be always possible to trace to the original key owner. As a decryption privilege could be possessed by multiple users who own the same set of attributes, malicious users might be tempted to leak their decryption privileges to some third parties, for financial gain as an example, without the risk of being caught. This problem severely limits the applications of CP-ABE. Several traceable CP-ABE (T-CP-ABE) systems have been proposed to address this problem, but the expressiveness of policies in those systems is limited where only and gate with wildcard is currently supported. In this paper we propose a new T-CP-ABE system that supports policies expressed in any monotone access structures. Also, the proposed system is as efficient and secure as one of the best (non-traceable) CP-ABE systems currently available, that is, this work adds traceability to an existing expressive, efficient, and secure CP-ABE scheme without weakening its security or setting any particular trade-off on its performance.
Exploring Semantic Inter-Class Relationships (SIR) for Zero-Shot Action Recognition

Automatically recognizing a large number of action categories from videos is of significant importance for video understanding. Most existing works focused on the design of more discriminative feature representation, and have achieved promising results when the positive samples are enough. However, very limited efforts were spent on recognizing a novel action without any positive exemplars, which is often the case in the real settings due to the large amount of action classes and the users’ queries dramatic variations. To address this issue, we propose to perform action recognition when no positive exemplars of that class are provided, which is often known as the zero-shot learning. Different from other zero-shot learning approaches, which exploit attributes as the intermediate layer for the knowledge transfer, our main contribution is SIR, which directly leverages the semantic inter-class relationships between the known and unknown actions followed by label transfer learning. The inter-class semantic relationships are automatically measured by continuous word vectors, which learned by the skip-gram model using the large-scale text corpus. Extensive experiments on the UCF101 dataset validate the superiority of our method over fully-supervised approaches using few positive exemplars.
Hough-Transform and Extended RANSAC Algorithms for Automatic Detection of 3 D Building Roof Planes from Lidar Data

Airborne laser scanner technique is broadly the most appropriate way to acquire rapidly and with high density 3D data over a city. Once the 3D Lidar data are available, the next task is the automatic data processing, with major aim to construct 3D building models. Among the numerous automatic reconstruction methods, the techniques allowing the detection of 3D building roof planes are of crucial importance. Three main methods arise from the literature: region growing, Hough-transform and Random Sample Consensus (RANSAC) paradigm. Since region growing algorithms are sometimes not very transparent and not homogenously applied, this paper focuses only on the Hough-transform and the RANSAC algorithm. Their principles, their pseudocode rarely detailed in the related literature as well as their complete analyses are presented in this paper. An analytic comparison of both algorithms, in terms of processing time and sensitivity to cloud characteristics, shows that despite the limitation encountered in both methods, RANSAC algorithm is still more efficient than the first one. Under other advantages, its processing time is negligible even when the input data size is very large. On the other hand, Hough-transform is very sensitive to the segmentation parameters values. Therefore, RANSAC algorithm has been chosen and extended to exceed its limitations. Its major limitation is that it searches to detect the best mathematical plane among 3D building point cloud even if this plane does not always represent a roof plane. So the proposed extension allows harmonizing the mathematical aspect of the algorithm with the geometry of a roof. At last, it is shown that the extended approach provides very satisfying results, even in the case of very weak point density and for different levels of building complexity. Therefore, once the roof planes are successfully detected, the automatic building modelling can be carried out.
Ultra-dense networks in millimeter-wave frequencies

Demands for very high system capacity and end-user data rates of the order of 10 Gb/s can be met in localized environments by Ultra-Dense Networks (UDN), characterized as networks with very short inter-site distances capable of ensuring low interference levels during communications. UDNs are expected to operate in the millimeter-wave band, where wide bandwidth signals needed for such high data rates can be designed, and will rely on high-gain beamforming to mitigate path loss and ensure low interference. The dense deployment of infrastructure nodes will make traditional wire-based backhaul provisioning challenging. Wireless self-backhauling over multiple hops is proposed to enhance flexibility in deployment. A description of the architecture and a concept based on separation of mobility, radio resource coordination among multiple nodes, and data plane handling, as well as on integration with wide-area networks, is introduced. A simulation of a multi-node office environment is used to demonstrate the performance of wireless self-backhauling at various loads.
People's Perceptions of Personalized Ads

Advertising is key to the business model of many online services. Personalization aims to make ads more relevant for users and more effective for advertisers. However, relatively few studies into user attitudes towards personalized ads are available. We present a San Francisco Bay Area survey (N=296) and in-depth interviews (N=24) with teens and adults. People are divided and often either (strongly) agreed or disagreed about utility or invasiveness of personalized ads and associated data collection. Mobile ads were reported to be less relevant than those on desktop. Participants explained ad personalization based on their personal previous behaviors and guesses about demographic targeting. We describe both metrics improvements as well as opportunities for improving online advertising by focusing on positive ad interactions reported by our participants, such as personalization focused not just on product categories but specific brands and styles, awareness of life events, and situations in which ads were useful or even inspirational.
The Eyelink Toolbox: eye tracking with MATLAB and the Psychophysics Toolbox.

The Eyelink Toolbox software supports the measurement of eye movements. The toolbox provides an interface between a high-level interpreted language (MATLAB), a visual display programming toolbox (Psychophysics Toolbox), and a video-based eyetracker (Eyelink). The Eyelink Toolbox enables experimenters to measure eye movements while simultaneously executing the stimulus presentation routines provided by the Psychophysics Toolbox. Example programs are included with the toolbox distribution. Information on the Eyelink Toolbox can be found at http://psychtoolbox.org/.
MITRE: Seven Systems for Semantic Similarity in Tweets

This paper describes MITRE’s participation in the Paraphrase and Semantic Similarity in Twitter task (SemEval-2015 Task 1). This effort placed first in Semantic Similarity and second in Paraphrase Identification with scores of Pearson’s r of 61.9%, F1 of 66.7%, and maxF1 of 72.4%. We detail the approaches we explored including mixtures of string matching metrics, alignments using tweet-specific distributed word representations, recurrent neural networks for modeling similarity with those alignments, and distance measurements on pooled latent semantic features. Logistic regression is used to tie the systems together into the ensembles submitted for evaluation.
Learning to Represent Programs with Graphs

Learning tasks on source code (i.e., formal languages) have been considered recently, but most work has tried to transfer natural language methods and does not capitalize on the unique opportunities offered by code’s known sematics. For example, long-range dependencies induced by using the same variable or function in distant locations are often not considered. We propose to use graphs to represent both the syntactic and semantic structure of code and use graph-based deep learning methods to learn to reason over program structures. In this work, we present how to construct graphs from source code and how to scale Gated Graph Neural Networks training to such large graphs. We evaluate our method on two tasks: VARNAMING, in which a network attempts to predict the name of a variable given its usage, and VARMISUSE, in which the network learns to reason about selecting the correct variable that should be used at a given program location. Our comparison to methods that use less structured program representations shows the advantages of modeling known structure, and suggests that our models learn to infer meaningful names and to solve the VARMISUSE task in many cases. Additionally, our testing showed that VARMISUSE identifies a number of bugs in mature open-source projects.
The Soft Robotics Toolkit: Strategies for Overcoming Obstacles to the Wide Dissemination of Soft-Robotic Hardware

The Soft Robotics Toolkit (SRT) is an open-access website containing detailed information about the design, fabrication, and characterization of soft-robotic components and systems . Soft robotics is a growing field of research concerned with the development of electromechanical technology composed of compliant materials or structures. The SRT website hosts design files, multimedia fabrication instructions, and software tutorials submitted by an international community of soft-robotics researchers and designers. In this article, we describe the development of the SRT and some challenges in developing widely disseminated robotic-hardware resources. Our attempts to overcome these challenges in the development of the toolkit are discussed by focusing on strategies that have been used to engage participants ranging from K-12 grade students to robotics research groups. A series of design competitions encouraged people to use and contribute to the toolkit. New fabrication methods requiring only low-cost and accessible materials were developed to lower the entry barriers to soft robotics and instructional materials and outreach activities were used to engage new audiences. We hope that our experiences in developing and scaling the toolkit may serve as guidance for other open robotic-hardware projects.
Demodulation Techniques for Self-Oscillating Eddy-Current Displacement Sensor Interfaces: A Review

This paper presents a comprehensive study of demodulation techniques for high-frequency self-oscillating eddy-current displacement sensor (ECDS) interfaces. Increasing the excitation frequency is essential for lowering the skin depth in many demanding industrial applications, that require better resolution. However, a high excitation frequency poses design challenges in the readout electronics, and particularly in the demodulation functional block. We analyze noise, linearity, and stability design considerations in amplitude demodulators for nanometer and sub-nanometer ECDSs. A number of state-of-the-art amplitude demodulation techniques employed in high-frequency ECDSs are reviewed, and their pros and cons are evaluated.
Anomaly detection based on probability density function with Kullback-Leibler divergence

Anomaly detection is a popular problem in many fields. We investigate an anomaly detection method based on probability density function (PDF) of different status. The constructed PDF only require few training data based on Kullback–Leibler Divergence method and small signal assumption. The measurement matrix was deduced according to principal component analysis (PCA). And the statistical detection indicator was set up under iid Gaussian Noise background. The performance of the proposed anomaly detection method was tested with through wall human detection experiments. The results showed that the proposed method could detection human being for brick wall and gypsum wall, but had unremarkable results for concrete wall. & 2016 Elsevier B.V. All rights reserved.
Business Models in the Mobile Ecosystem

The mobile ecosystem is constantly changing. The roles of each actor are uncertain and the question how each actor cooperates with each other is of interest of researchers both in academia and industry. In this paper we examine the mobile ecosystem from a business perspective. We used five mobile companies as case studies, which were investigated through interviews and questionnaire surveys. The companies covered different roles in the ecosystem, including network operator, device manufacturer, and application developer. With our empirical data as a starting point, we analyze the revenue streams of different actors in the ecosystem. The results will contribute to an understanding of the business models and dependencies that characterize actors in the current mobile ecosystem.
Technology Acceptance Model 3 and a Research Agenda on Interventions

Prior research has provided valuable insights into how and why employees make a decision about the adoption and use of information technologies (ITs) in the workplace. From an organizational point of view, however, the more important issue is how managers make informed decisions about interventions that can lead to greater acceptance and effective utilization of IT. There is limited research in the IT implementation literature that deals with the role of interventions to aid such managerial decision making. Particularly, there is a need to understand how various interventions can influence the known determinants of IT adoption and use. To address this gap in the literature, we draw from the vast body of research on the technology acceptance model (TAM), particularly the work on the determinants of perceived usefulness and perceived ease of use, and: (i) develop a comprehensive nomological network (integrated model) of the determinants of individual level (IT) adoption and use; (ii) empirically test the proposed integrated model; and (iii) present a research agenda focused on potential preand postimplementation interventions that can enhance employees’ adoption and use of IT. Our findings and research agenda have important implications for managerial decision making on IT implementation in organizations.  Management Support, Organizational Support, Peer Support, Technology Acceptance Model (TAM), Technology Adoption, Training, User Acceptance, User Involvement, and User Participation.
Augmented reality in informal learning environments: A field experiment in a mathematics exhibition

Recent advances in mobile technologies (esp., smart phones and tablets with built-in cameras, GPS and Internet access) made augmented reality (AR ) applications available for the broad public. While many researchers have examined the af fordances and constraints of AR for teaching and learning, quantitative evidence for it s effectiveness is still scarce. To contribute to filling this research gap, we designed and condu cted a pretest-posttest crossover field experiment with 101 participants at a mathematics exh ibition to measure the effect of AR on acquiring and retaining mathematical knowledge in a n informal learning environment. We hypothesized that visitors acquire more knowledge f rom augmented exhibits than from exhibits without AR. The theoretical rationale for our h ypothesis is that AR allows for the efficient and effective implementation of a subset of the des ign principles defined in the cognitive theory of multimedia. The empirical results we obtaine d show that museum visitors performed better on knowledge acquisition and retention tests related to augmented exhibits than to nonaugmented exhibits and that they perceived AR as a valuable and desirable add-on for museum exhibitions.