nils-herrmann/clean_scirepeval huggingface dataset:
Multilinear Hyperplane Hashing

Hashing has become an increasingly popular technique for fast nearest neighbor search. Despite its successful progress in classic pointto-point search, there are few studies regarding point-to-hyperplane search, which has strong practical capabilities of scaling up applications like active learning with SVMs. Existing hyperplane hashing methods enable the fast search based on randomly generated hash codes, but still suffer from a low collision probability and thus usually require long codes for a satisfying performance. To overcome this problem, this paper proposes a multilinear hyperplane hashing that generates a hash bit using multiple linear projections. Our theoretical analysis shows that with an even number of random linear projections, the multilinear hash function possesses strong locality sensitivity to hyperplane queries. To leverage its sensitivity to the angle distance, we further introduce an angular quantization based learning framework for compact multilinear hashing, which considerably boosts the search performance with less hash bits. Experiments with applications to large-scale (up to one million) active learning on two datasets demonstrate the overall superiority of the proposed approach.
Extracting data records from the web using tag path clustering

Fully automatic methods that extract lists of objects from the Web have been studied extensively. Record extraction, the first step of this object extraction process, identifies a set of Web page segments, each of which represents an individual object (e.g., a product). State-of-the-art methods suffice for simple search, but they often fail to handle more complicated or noisy Web page structures due to a key limitation -- their greedy manner of identifying a list of records through pairwise comparison (i.e., similarity match) of consecutive segments. This paper introduces a new method for record extraction that captures a list of objects in a more robust way based on a holistic analysis of a Web page. The method focuses on how a distinct tag path appears repeatedly in the DOM tree of the Web document. Instead of comparing a pair of individual segments, it compares a pair of tag path occurrence patterns (called visual signals) to estimate how likely these two tag paths represent the same list of objects. The paper introduces a similarity measure that captures how closely the visual signals appear and interleave. Clustering of tag paths is then performed based on this similarity measure, and sets of tag paths that form the structure of data records are extracted. Experiments show that this method achieves higher accuracy than previous methods.
Dejavu: an accurate energy-efficient outdoor localization system

We present Dejavu, a system that uses standard cell-phone sensors to provide accurate and energy-efficient outdoor localization suitable for car navigation. Our analysis shows that different road landmarks have a unique signature on cell-phone sensors; For example, going inside tunnels, moving over bumps, going up a bridge, and even potholes all affect the inertial sensors on the phone in a unique pattern. Dejavu employs a dead-reckoning localization approach and leverages these road landmarks, among other automatically discovered abundant virtual landmarks, to reset the accumulated error and achieve accurate localization. To maintain a low energy profile, Dejavu uses only energy-efficient sensors or sensors that are already running for other purposes.
 We present the design of Dejavu and how it leverages crowd-sourcing to automatically learn virtual landmarks and their locations. Our evaluation results from implementation on different android devices in both city and highway driving show that Dejavu can localize cell phones to within 8.4 m median error in city roads and 16.6 m on highways. Moreover, compared to GPS and other state-of-the-art systems, Dejavu can extend the battery lifetime by 347%, achieving even better localization results than GPS in the more challenging in-city driving conditions.
Dielectric Elastomer Spring Roll Actuators for a Portable Force Feedback Device

In this paper, we present a new portable force feedback device for surgery simulations. Dielectric elastomer spring roll linear actua tors for this device were manufactured, and characterized via pas sive tensile tests and active isometric tests. The actuators exhibited a maximum force of 7.2 N, and a maximum elongation of 31%. Due to the high driving voltage, electrical safety issues were also considered. The results showed that sufficient electrical safety can be provided to the user. Two prototypes were built, which practi cally showed functionalities of the actuator and the proposed force feedback concept with actuators connected between the fingers.
Ontological technologies for user modelling

This paper brings together research from two different fields – user modelling and web ontologies – in attempt to demonstrate how recent semantic trends in web development can be combined with the modern technologies of user modelling. Over the last several years, a number of user-adaptive systems have been exploiting ontologies for the purposes of semantics representation, automatic knowledge acquisition, domain and user model visualisation and creation of interoperable and reusable architectural solutions. Before discussing these projects, we first overview the underlying user modelling and ontological technologies. As an example of the project employing ontology-based user modelling, we present an experiment design for translation of overlay student models for relative domains by means of ontology mapping.
Voyant: generating structured feedback on visual designs using a crowd of non-experts

Feedback on designs is critical for helping users iterate toward effective solutions. This paper presents Voyant, a novel system giving users access to a non-expert crowd to receive perception-oriented feedback on their designs from a selected audience. Based on a formative study, the system generates the elements seen in a design, the order in which elements are noticed, impressions formed when the design is first viewed, and interpretation of the design relative to guidelines in the domain and the user's stated goals. An evaluation of the system was conducted with users and their designs. Users reported the feedback about impressions and interpretation of their goals was most helpful, though the other feedback types were also valued. Users found the coordinated views in Voyant useful for analyzing relations between the crowd's perception of a design and the visual elements within it. The cost of generating the feedback was considered a reasonable tradeoff for not having to organize critiques or interrupt peers.
Broadband radial waveguide power amplifier using a spatial power combining technique

A broadband radial waveguide power amplifier has been designed and fabricated using a spatial power dividing/combining technique. A simple electromagnetic model of this power-dividing/combining structure has been developed. Analysis based on equivalent circuits gives the design formula for perfect power-dividing/ combining circuits. The measured small-signal gain of the eight-device power amplifier is 12 –16.5 dB over a broadband from 7 to 15 GHz. The measured maximum output power at 1-dB compression is 28.6 dBm at 10 GHz, with a power-combining efficiency of about 91%. Furthermore, the performance degradation of this power amplifier because of device failures has also been measured.
Linguistic Considerations in Automatic Question Generation

As students read expository text, comprehension is improved by pausing to answer questions that reinforce the material. We describe an automatic question generator that uses semantic pattern recognition to create questions of varying depth and type for self-study or tutoring. Throughout, we explore how linguistic considerations inform system design. In the described system, semantic role labels of source sentences are used in a domain-independent manner to generate both questions and answers related to the source sentence. Evaluation results show a 44% reduction in the error rate relative to the best prior systems, averaging over all metrics, and up to 61% reduction in the error rate on grammaticality judgments.
Customer loyalty in e-commerce : an exploration of its antecedents and consequences

This paper investigates the antecedents and consequences of customer loyalty in an online business-to-consumer (B2C) context. We identify eight factors (the 8Cs—customization, contact interactivity, care, community, convenience, cultivation, choice, and character) that potentially impact e-loyalty and develop scales to measure these factors. Data collected from 1,211 online customers demonstrate that all these factors, except convenience, impact e-loyalty. The data also reveal that e-loyalty has an impact on two customer-related outcomes: word-ofmouth promotion and willingness to pay more.
A tutorial overview of anomaly detection in hyperspectral images

In this paper, a tutorial overview on anomaly detection for hyperspectral electro-optical systems is presented. This tutorial is focused on those techniques that aim to detect small man-made anomalies typically found in defense and surveillance applications. Since a variety of methods have been proposed for detecting such targets, this tutorial places emphasis on the techniques that are either mathematically more tractable or easier to interpret physically. These methods are not only more suitable for a tutorial publication, but also an essential to a study of anomaly detection. Previous surveys on this subject have focused mainly on anomaly detectors developed in a statistical framework and have been based on well-known background statistical models. However, the most recent research trends seem to move away from the statistical framework and to focus more on deterministic and geometric concepts. This work also takes into consideration these latest trends, providing a wide theoretical review without disregarding practical recommendations about algorithm implementation. The main open research topics are addressed as well, the foremost being algorithm optimization, which is required for embodying anomaly detectors in real-time systems.
Dynamic Business Model Framework for Value Webs

This paper develops a new framework for explaining the dynamic aspects of business models in value webs. As companies move from research to roll-out and maturity three forces cause changes in business models. The technological forces are most important in the first phase, regulation in the second phase, and markets in the third. The forces cause change through influence on the technology, services, finances, and organizational network of the firm. As a result, partners in value webs will differ across these phases. A case study of NTT DoCoMo’s i-mode illustrates the framework.
Comparison of the communication protocols DLMS/COSEM, SML and IEC 61850 for smart metering applications

Communication technology plays an increasingly important role in the growing automated metering infrastructure (AMI) market. This paper presents a thorough analysis and comparison of four application layer protocols in the smart metering context. The inspected protocols are DLMS/COSEM, the Smart Message Language (SML), and the MMS and SOAP mappings of IEC 61850. The focus of this paper is on their use over TCP/IP. The protocols are first compared with respect to qualitative criteria such as the ability to transmit clock synchronization information. Afterwards the message size of meter reading requests and responses and the different binary encodings of the protocols are compared.
Go and no-go learning in reward and punishment: Interactions between affect and effect

Decision-making invokes two fundamental axes of control: affect or valence, spanning reward and punishment, and effect or action, spanning invigoration and inhibition. We studied the acquisition of instrumental responding in healthy human volunteers in a task in which we orthogonalized action requirements and outcome valence. Subjects were much more successful in learning active choices in rewarded conditions, and passive choices in punished conditions. Using computational reinforcement-learning models, we teased apart contributions from putatively instrumental and Pavlovian components in the generation of the observed asymmetry during learning. Moreover, using model-based fMRI, we showed that BOLD signals in striatum and substantia nigra/ventral tegmental area (SN/VTA) correlated with instrumentally learnt action values, but with opposite signs for go and no-go choices. Finally, we showed that successful instrumental learning depends on engagement of bilateral inferior frontal gyrus. Our behavioral and computational data showed that instrumental learning is contingent on overcoming inherent and plastic Pavlovian biases, while our neuronal data showed this learning is linked to unique patterns of brain activity in regions implicated in action and inhibition respectively.
Unity Power Factor Isolated Three-Phase Rectifier With Two Single-Phase Buck Rectifiers Based on the Scott Transformer

This paper proposes an isolated three-phase rectifier power-factor correction using two single-phase buck preregulators in continuous conduction mode. The use of the Scott transformer renders a simple and robust rectifier to operate with unity power factor. With only two active switches, the rectifier is able to generate symmetrical currents in the line and a regulated voltage output without any necessary synchronous switches. The proposed control technique with sinusoidal pulse width modulation uses a feedforward of the output inductor current and only one voltage control regulates the output. Complete simulation results under closed-loop operation are given and a 12-kW prototype has been implemented in the laboratory, which demonstrated to operate successfully with excellent performance, and thus can feasibly be implemented in higher power applications.
Detecting Android Malware on Network Level

As Android OS establishes itself as the primary platform on smartphones, a substantial increase in malware targeted at Android devices is being observed in the wild. While anti-virus software is available, and Android limits applications to user approved permissions, many users remain unaware of the threat posed by malware and of actual infections on their devices. In this paper we explore techniques to enable mobile network operators to detect Android malware and violations of user privacy through network traffic analysis.
Virtual reality in the treatment of spider phobia: a controlled study.

This study explored whether virtual reality (VR) exposure therapy was effective in the treatment of spider phobia. We compared a treatment condition vs. a waiting list condition in a between group design with 23 participants. Participants in the VR treatment group received an average of four one-hour exposure therapy sessions. VR exposure was effective in treating spider phobia compared to a control condition as measured with a Fear of Spiders questionnaire, a Behavioural Avoidance Test (BAT), and severity ratings made by the clinician and an independent assessor. Eighty-three percent of patients in the VR treatment group showed clinically significant improvement compared with 0% in the waiting list group, and no patients dropped out. This study shows that VR exposure can be effective in the treatment of phobias.
Patch Fed Planar Dielectric Slab Extended Hemi-Elliptical Lens Antenna

We present the results of a parametric study devised to allow us to optimally design a patch fed planar dielectric slab waveguide extended hemi-elliptical lens antenna. The lens antenna, 11lambda times 13lambda in the lens plane and 0.6lambda thick, constructed from polystyrene and weighing only 90 g is fabricated and characterized at 28.5 GHz for both single and multiple operating configurations. The lens when optimized for single beam operation achieves 18.5 dB measured gain (85% aperture efficiency), 40deg and 4.1deg half power beam width for E plane and H plane respectively and 10% impedance bandwidth for -10 dB return loss. While for optimized for multiple beam operation it is shown that the lens can accommodate up to 9 feeds and that beam symmetry can be maintained over a scan angle of 27deg with a gain of 14.9 to 17.7 dB, and first side lobe levels of -11 to -7 dB respectively. Over the frequency range 26 to 30 GHz the lens maintains a worst case return loss of -10 dB and port to port feed isolation of better than -25 dB. Further it is shown that residual leaked energy from the structure is less than -48 dBm at 1 cm, thus making a low profile enclosure possible. We also show that by simultaneous excitation of two adjacent ports we can obtain difference patterns with null depths of up to -36 dB.
A meta-analysis of the technology acceptance model

A statistical meta-analysis of the technology acceptance model (TAM) as applied in various fields was conducted using 88 published studies that provided sufficient data to be credible. The results show TAM to be a valid and robust model that has been widely used, but which potentially has wider applicability. A moderator analysis involving user types and usage types was performed to investigate conditions under which TAM may have different effects. The study confirmed the value of using students as surrogates for professionals in some TAM studies, and perhaps more generally. It also revealed the power of meta-analysis as a rigorous alternative to qualitative and narrative literature review methods.
Improving Content-based and Hybrid Music Recommendation using Deep Learning

Existing content-based music recommendation systems typically employ a \textit{two-stage} approach. They first extract traditional audio content features such as Mel-frequency cepstral coefficients and then predict user preferences. However, these traditional features, originally not created for music recommendation, cannot capture all relevant information in the audio and thus put a cap on recommendation performance. Using a novel model based on deep belief network and probabilistic graphical model, we unify the two stages into an automated process that simultaneously learns features from audio content and makes personalized recommendations. Compared with existing deep learning based models, our model outperforms them in both the warm-start and cold-start stages without relying on collaborative filtering (CF). We then present an efficient hybrid method to seamlessly integrate the automatically learnt features and CF. Our hybrid method not only significantly improves the performance of CF but also outperforms the traditional feature mbased hybrid method.
Partitioning 3D Surface Meshes Using Watershed Segmentation

ÐThis paper describes a method for partitioning 3D surface meshes into useful segments. The proposed method generalizes morphological watersheds, an image segmentation technique, to 3D surfaces. This surface segmentation uses the total curvature of the surface as an indication of region boundaries. The surface is segmented into patches, where each patch has a relatively consistent curvature throughout, and is bounded by areas of higher, or drastically different, curvature. This algorithm has applications for a variety of important problems in visualization and geometrical modeling including 3D feature extraction, mesh reduction, texture mapping 3D surfaces, and computer aided design. Index TermsÐSurfaces, surface segmentation, watershed algorithm, curvature-based methods.
TWO STEP CREDIT RISK ASSESMENT MODEL FOR RETAIL BANK LOAN APPLICATIONS USING DECISION TREE DATA MINING TECHNIQUE

Data mining techniques are becoming very popular nowadays because of the wide availability of huge quantity of data and the need for transforming such data into knowledge. In today’s globalization, core banking model and cut throat competition making banks to struggling to gain a competitive edge over each other. The face to face interaction with customer is no more exists in the modern banking world. Banking systems collect huge amounts of data on day to day basis, be it customer information, transaction details like deposits and withdrawals, loans, risk profiles, credit card details, credit limit and collateral details related information. Thousands of decisions are taken in a bank on daily basis. In recent years the ability to generate, capture and store data has increased enormously. The information contained in this data can be very important. The wide availability of huge amounts of data and the need for transforming such data into knowledge encourage IT industry to use data mining. Lending is the primary business of the banks. Credit Risk Management is one of the most important and critical factor in banking world. Without proper credit risk management banks will face huge losses and lending becomes very tough for the banks. Data mining techniques are greatly used in the banking industry which helps them compete in the market and provide the right product to the right customer with less risk. Credit risks which account for the risk of loss and loan defaults are the major source of risk encountered by banking industry. Data mining techniques like classification and prediction can be applied to overcome this to a great extent. In this paper we introduce an effective prediction model for the bankers that help them predict the credible customers who have applied for loan. Decision Tree Induction Data Mining Algorithm is applied to predict the attributes relevant for credibility. A prototype of the model is described in this paper which can be used by the organizations in making the right decision to approve or reject the loan request of the customers.  Banking industry; Data Mining; Risk Management; Classification; Credit Scoring; Non-Performing Assets; Default Detection; Non-Performing Loans Decision Tree; Credit Risk Assessment; Classification; Prediction --------------------------------------------------------------------***----------------------------------------------------------
An autonomous labeling approach to support vector machines algorithms for network traffic anomaly detection

In the past years, several support vector machines (SVM) novelty detection approaches have been applied on the network intrusion detection field. The main advantage of these approaches is that they can characterize normal traffic even when trained with datasets containing not only normal traffic but also a number of attacks. Unfortunately, these algorithms seem to be accurate only when the normal traffic vastly outnumbers the number of attacks present in the dataset. A situation which can not be always hold This work presents an approach for autonomous labeling of normal traffic as a way of dealing with situations where class distribution does not present the imbalance required for SVM algorithms. In this case, the autonomous labeling process is made by SNORT, a misuse-based intrusion detection system. Experiments conducted on the 1998 DARPA dataset show that the use of the proposed autonomous labeling approach not only outperforms existing SVM alternatives but also, under some attack distributions, obtains improvements over SNORT itself.