nils-herrmann/clean_scirepeval huggingface dataset:
A survey of vertical handover decision algorithms in Fourth Generation heterogeneous wireless networks

1389-1286/$ see front matter 2010 Elsevier B.V doi:10.1016/j.comnet.2010.02.006 * Corresponding author.  (Y. Ahmet S ek Vertical handover decision (VHD) algorithms are essential components of the architecture of the forthcoming Fourth Generation (4G) heterogeneous wireless networks. These algorithms need to be designed to provide the required Quality of Service (QoS) to a wide range of applications while allowing seamless roaming among a multitude of access network technologies. In this paper, we present a comprehensive survey of the VHD algorithms designed to satisfy these requirements. To offer a systematic comparison, we categorize the algorithms into four groups based on the main handover decision criterion used. Also, to evaluate tradeoffs between their complexity of implementation and efficiency, we discuss three representative VHD algorithms in each group.
A Compressive-sensing based Testing Vehicle for 3D TSV Pre-bond and Post-bond Testing Data

Online testing vehicle is required for 3D TSV pre-bond and post-bond testing due to high probability of TSV failures. It has become a challenge to deal with large sets of generated testing data with limited probing when transmitting the data out. In this paper, a lossless compressive-sensing based testing vehicle is developed for online testing of TSVs. By exploring sparsity of the testing data under constraint of failure bound of TSV, sparse-representation based encoding can be deployed by XOR and AND network on chip to deal with large volume of testing data. Experimental results (with benchmarks) have shown that 89.70% pre-bond data compression rate can be achieved under 0.5% probability of failures; and 88.18% post-bond data compression rate can be achieved with 5% probability of failures.
Bridging the accessibility gap in Open Educational Resources

Open Educational Resources (OER) are being fostered as a global movement for providing educational opportunities to all. However, people with disabilities are still excluded from full participation because of the lack of accessibility of OER websites, as well as of the resources themselves. This work presents a proposal for the design of OER websites that would enable equitable access for all users. This design aims to bridge the accessibility gap through the personalization of the whole OER environment to facilitate an accessible User Experience (UX) based on a user profile that includes the self-identification of disability status. This profile configures not only the “look and feel” of the interface but also the delivery of educational resources suitable for this user profile. To achieve this purpose, the design goes beyond compliance with the ISO/IEC 40500 W3C Web Content Accessibility Guidelines (WCAG) 2.0, since it includes the personalization of the accessible experience through usability considerations and adaptations of educational resources. The delivery of educational resources matches the user’s profile with regard to their sensory abilities, cognitive faculties and their requirements of functionality control, display layout and language. As a proof of concept, we have developed an OER website based on this design and have conducted a set of UX tests that include users with different disabilities. The test results confirm the feasibility and suitability of our design regarding accessible UX. Finally, the contribution of this document arises from the explicit recognition of the particular needs associated with the disability profiles to establish the response of the entire OER system which enables a truly inclusive experience by exempting the user from performing configuration tasks.
A Low-Latency Library in FPGA Hardware for High-Frequency Trading (HFT)

Current High-Frequency Trading (HFT) platforms are typically implemented in software on computers with high-performance network adapters. The high and unpredictable latency of these systems has led the trading world to explore alternative "hybrid" architectures with hardware acceleration. In this paper, we survey existing solutions and describe how FPGAs are being used in electronic trading to approach the goal of zero latency. We present an FPGA IP library which implements networking, I/O, memory interfaces and financial protocol parsers. The library provides pre-built infrastructure which accelerates the development and verification of new financial applications. We have developed an example financial application using the IP library on a custom 1U FPGA appliance. The application sustains 10Gb/s Ethernet line rate with a fixed end-to-end latency of 1μs - up to two orders of magnitude lower than comparable software implementations.
Content-based multimedia information retrieval: State of the art and challenges

Extending beyond the boundaries of science, art, and culture, content-based multimedia information retrieval provides new paradigms and methods for searching through the myriad variety of media all over the world. This survey reviews 100+ recent articles on content-based multimedia information retrieval and discusses their role in current research directions which include browsing and search paradigms, user studies, affective computing, learning, semantic queries, new features and media types, high performance indexing, and evaluation techniques. Based on the current state of the art, we discuss the major challenges for the future.
Future Relations between Humans and Artificial Intelligence: A Stakeholder Opinion Survey in Japan

Concern about the potential benefits and risks of artificial intelligence (AI) has given impetus to the establishment of AI research. AI requires responsible research and innovation (RRI) with the collaboration of various stakeholders, including the public, from the early stages. However, stakeholders may have different opinions about AI and human-machine relations. We aim to discuss these viewpoints by conducting a questionnaire survey, and to suggest perspectives on future collaborations among stakeholders.
The Role of Gender Stereotypes in Perceptions of Entrepreneurs and Intentions to Become an Entrepreneur

In this study we examine the role of socially constructed gender stereotypes in entrepreneurship and their influence on men and women’s entrepreneurial intentions. Data on characteristics of males, females, and entrepreneurs were collected from young adults in three countries. As hypothesized, entrepreneurs were perceived to have predominantly masculine characteristics. Additional results revealed that although both men and women perceive entrepreneurs to have characteristics similar to those of males (masculine genderrole stereotype), only women also perceived entrepreneurs and females as having similar characteristics (feminine gender-role stereotype). Further, though men and women did not differ in their entrepreneurial intentions, those who perceived themselves as more similar to males (high on male gender identification) had higher entrepreneurial intentions than those who saw themselves as less similar to males (low male gender identification). No such difference was found for people who saw themselves as more or less similar to females (female gender identification). The results were consistent across the three countries. Practical implications and directions for future research are discussed.
Generative and Discriminative Text Classification with Recurrent Neural Networks

We empirically characterize the performance of discriminative and generative LSTM models for text classification. We find that although RNN-based generative models are more powerful than their bag-of-words ancestors (e.g., they account for conditional dependencies across words in a document), they have higher asymptotic error rates than discriminatively trained RNN models. However we also find that generative models approach their asymptotic error rate more rapidly than their discriminative counterparts—the same pattern that Ng & Jordan (2001) proved holds for linear classification models that make more naı̈ve conditional independence assumptions. Building on this finding, we hypothesize that RNNbased generative classification models will be more robust to shifts in the data distribution. This hypothesis is confirmed in a series of experiments in zero-shot and continual learning settings that show that generative models substantially outperform discriminative models.
GUITAR: Piecing Together Android App GUIs from Memory Images

An Android app's graphical user interface (GUI) displays rich semantic and contextual information about the smartphone's owner and app's execution. Such information provides vital clues to the investigation of crimes in both cyber and physical spaces. In real-world digital forensics however, once an electronic device becomes evidence most manual interactions with it are prohibited by criminal investigation protocols. Hence investigators must resort to "image-and-analyze" memory forensics (instead of browsing through the subject phone) to recover the apps' GUIs. Unfortunately, GUI reconstruction is still largely impossible with state-of-the-art memory forensics techniques, which tend to focus only on individual in-memory data structures. An Android GUI, however, displays diverse visual elements each built from numerous data structure instances. Furthermore, whenever an app is sent to the background, its GUI structure will be explicitly deallocated and disintegrated by the Android framework. In this paper, we present GUITAR, an app-independent technique which automatically reassembles and redraws all apps' GUIs from the multitude of GUI data elements found in a smartphone's memory image. To do so, GUITAR involves the reconstruction of (1) GUI tree topology, (2) drawing operation mapping, and (3) runtime environment for redrawing. Our evaluation shows that GUITAR is highly accurate (80-95% similar to original screenshots) at reconstructing GUIs from memory images taken from a variety of Android apps on popular phones. Moreover, GUITAR is robust in reconstructing meaningful GUIs even when facing GUI data loss.
Power LDMOS with novel STI profile for improved Rsp, BVdss, and reliability

The profile of shallow trench isolation (STI) is designed to improve LDMOS specific on-resistance (Rsp), BVDSS, safe operating area (SOA), and hot carrier lifetimes (HCL) in an integrated BiCMOS power technology. Silicon etch, liner oxidation and CMP processes are tuned to improve the tradeoffs in a power technology showing significant improvement to both p-channel and n-channel Rsp compared to devices fabricated with the STI profile inherited from the original submicron CMOS platform. Extensive TCAD and experiments were carried out to gain insight into the physical mechanisms and further improve device performance after STI process optimization. The final process and device structures yield SOAs that are limited only by thermal constraints up to rated voltages
Driver behavior recognition based on deep convolutional neural networks

Traffic safety is a severe problem around the world. Many road accidents are normally related with the driver's unsafe driving behavior, e.g. eating while driving. In this work, we propose a vision-based solution to recognize the driver's behavior based on convolutional neural networks. Specifically, given an image, skin-like regions are extracted by Gaussian Mixture Model, which are passed to a deep convolutional neural networks model, namely R*CNN, to generate action labels. The skin-like regions are able to provide abundant semantic information with sufficient discriminative capability. Also, R*CNN is able to select the most informative regions from candidates to facilitate the final action recognition. We tested the proposed methods on Southeast University Driving-posture Dataset and achieve mean Average Precision(mAP) of 97.76% on the dataset which prove the proposed method is effective in drivers's action recognition.
Detecting Driver Drowsiness Based on Sensors: A Review

In recent years, driver drowsiness has been one of the major causes of road accidents and can lead to severe physical injuries, deaths and significant economic losses. Statistics indicate the need of a reliable driver drowsiness detection system which could alert the driver before a mishap happens. Researchers have attempted to determine driver drowsiness using the following measures: (1) vehicle-based measures; (2) behavioral measures and (3) physiological measures. A detailed review on these measures will provide insight on the present systems, issues associated with them and the enhancements that need to be done to make a robust system. In this paper, we review these three measures as to the sensors used and discuss the advantages and limitations of each. The various ways through which drowsiness has been experimentally manipulated is also discussed. We conclude that by designing a hybrid drowsiness detection system that combines non-intrusive physiological measures with other measures one would accurately determine the drowsiness level of a driver. A number of road accidents might then be avoided if an alert is sent to a driver that is deemed drowsy.
Frequency split metal artifact reduction (FSMAR) in computed tomography.

The problem of metal artifact reduction (MAR) is almost as old as the clinical use of computed tomography itself. When metal implants are present in the field of measurement, severe artifacts degrade the image quality and the diagnostic value of CT images. Up to now, no generally accepted solution to this issue has been found. In this work, a method based on a new MAR concept is presented: frequency split metal artifact reduction (FSMAR). It ensures efficient reduction of metal artifacts at high image quality with enhanced preservation of details close to metal implants.



FSMAR combines a raw data inpainting-based MAR method with an image-based frequency split approach. Many typical methods for metal artifact reduction are inpainting-based MAR methods and simply replace unreliable parts of the projection data, for example, by linear interpolation. Frequency split approaches were used in CT, for example, by combining two reconstruction methods in order to reduce cone-beam artifacts. FSMAR combines the high frequencies of an uncorrected image, where all available data were used for the reconstruction with the more reliable low frequencies of an image which was corrected with an inpainting-based MAR method. The algorithm is tested in combination with normalized metal artifact reduction (NMAR) and with a standard inpainting-based MAR approach. NMAR is a more sophisticated inpainting-based MAR method, which introduces less new artifacts which may result from interpolation errors. A quantitative evaluation was performed using the examples of a simulation of the XCAT phantom and a scan of a spine phantom. Further evaluation includes patients with different types of metal implants: hip prostheses, dental fillings, neurocoil, and spine fixation, which were scanned with a modern clinical dual source CT scanner.



FSMAR ensures sharp edges and a preservation of anatomical details which is in many cases better than after applying an inpainting-based MAR method only. In contrast to other MAR methods, FSMAR yields images without the usual blurring close to implants.



FSMAR should be used together with NMAR, a combination which ensures an accurate correction of both high and low frequencies. The algorithm is computationally inexpensive compared to iterative methods and methods with complex inpainting schemes. No parameters were chosen manually; it is ready for an application in clinical routine.
An Improved Equivalent Circuit Model of a Single-Sided Linear Induction Motor

The derivation of the equivalent circuit for a single-sided linear induction motor (SLIM) is not straightforward, particularly if it includes longitudinal end effects from the cut-open primary magnetic path, transversal edge effects from the differing widths between the primary lamination and secondary sheet, and half-filled primary slots. This paper proposes an improved series equivalent circuit for this machine. The longitudinal end effects are estimated using three different impedances representing the normal, forward, and backward flux density waves in the air gap, whose two boundary conditions are deduced by introducing the conception of magnetic barrier surface. The transversal edge effects are accounted for by correction coefficient K̅t and air-gap flux density correction coefficient K̅̅b. Using the series circuit, the performance of the SLIM was assessed in a similar manner to a rotating induction machine. A 4-kW SLIM prototype was tested, which validated the simulation technique.
Generalization Properties and Implicit Regularization for Multiple Passes SGM

We study the generalization properties of stochastic gradient methods for learning with convex loss functions and linearly parameterized functions. We show that, in the absence of penalizations or constraints, the stability and approximation properties of the algorithm can be controlled by tuning either the step-size or the number of passes over the data. In this view, these parameters can be seen to control a form of implicit regularization. Numerical results complement the theoretical findings.
Forward Reasoning and Dependency-Directed Backtracking in a System for Computer-Aided Circuit Analysis

We present a rule-based system for computer-aided circuit analysis. The set of rules, called EL, is written in a rule language called ARS. Rules are implemented by ARS as pattern-directed invocation demons monitoring an associative data base. Deductions are performed in an antecedent manner, giving EL's analysis a catch-as-catch-can flavour suggestive of the behavior of expert circuit analyzers. We call this style of circuit analysis propagation of constraints. The system threads deduced fact~ with justifications which mention the antecedent facts attd the rule used. These justifications may be examined by the user to gain in right into the operation of the set of rules as they apply to a problem. ~q~e same justifications are used by the system to determine the currently active dat~-base context Jor reasoning in hypothetical situations. They are also used by the system in the at~alysis of failures to reduce the search space. This leads to effective control of combinatorial search which we call dependency-directed backtracking.
Regression testing in the presence of non-code changes

Regression testing is an important activity performed to validate modified software, and one of its key tasks is regression test selection (RTS) -- selecting a subset of existing test cases to run on the modified software. Most existing RTS techniques focus on changes made to code components and completely ignore non-code elements, such as configuration files and databases, which can also change and affect the system behavior. To address this issue, we present a new RTS technique that performs accurate test selection in the presence of changes to non-code components. To do this, our technique computes traceability between test cases and the external data accessed by an application, and uses this information to perform RTS in the presence of changes to non-code elements. We present our technique, a prototype implementation of our technique, and a set of preliminary empirical results that illustrate the feasibility, effectiveness, and potential usefulness of our approach.
Regression approaches to voice quality controll based on one-to-many eigenvoice conversion

This paper proposes techniques for flexibly controlling voice quality of converted speech from a particular source speaker based on one-to-many eigenvoice conversion (EVC). EVC realizes a voice quality control based on the manipulation of a small number of parameters, i.e., weights for eigenvectors, of an eigenvoice Gaussian mixture model (EV-GMM), which is trained with multiple parallel data sets consisting of a single source speaker and many pre-stored target speakers. However, it is difficult to control intuitively the desired voice quality with those parametersbecause each eigenvector doesn’t usually represent a specific physical meaning. In order to cope with this problem, we propose regression approaches to the EVC-based voice quality controller. The tractable voice quality control of the converted speech is achieved with a low-dimensionalvoice quality control vector capturing specific voice characteristics. We conducted experimental verifications of each of the proposed approaches.
Microsaccades uncover the orientation of covert attention

Fixational eye movements are subdivided into tremor, drift, and microsaccades. All three types of miniature eye movements generate small random displacements of the retinal image when viewing a stationary scene. Here we investigate the modulation of microsaccades by shifts of covert attention in a classical spatial cueing paradigm. First, we replicate the suppression of microsaccades with a minimum rate about 150 ms after cue onset. Second, as a new finding we observe microsaccadic enhancement with a maximum rate about 350 ms after presentation of the cue. Third, we find a modulation of the orientation towards the cue direction. These multiple influences of visual attention on microsaccades accentuate their role for visual information processing. Furthermore, our results suggest that microsaccades can be used to map the orientation of visual attention in psychophysical experiments.
Does Attendance Matter ? An Examination of Student Attitudes , Participation , Performance and Attendance

Non attendance of lectures and tutorials appears to be a growing trend. The literature suggests many possible reasons including students’ changing lifestyle, attitudes, teaching and technology. This paper looks at the reasons for non attendance of students in the Faculty of Commerce at the University of Wollongong and identifies relationships between attendance, participation and performance. The results indicate that there are valid reasons for non attendance that are both in the control of learners and teachers. There are also clear benefits for students to be gained in attendance; however, changes in the way we learn, teach, assess and use technology are recommended if we wish to reverse the trend.  Jour na l o f Un ive rs i t y Teach ing and Lear n ing Pr ac t i ce Does Attendance Matter? An Examination of Student Attitudes, Participation, Performance and Attendance
Usability Analysis of Visual Programming Environments: A 'Cognitive Dimensions' Framework

The cognitive dimensions framework is a broad-brush evaluation technique for interactive devices and for non-interactive notations. It sets out a small vocabulary of terms designed to capture the cognitively-relevant aspects of structure, and shows how they can be traded off against each other. The purpose of this paper is to propose the framework as an evaluation technique for visual programming environments. We apply it to two commercially-available dataflow languages (with further examples from other systems) and conclude that it is effective and insightful; other HCI-based evaluation techniques focus on different aspects and would make good complements. Insofar as the examples we used are representative, current VPLs are successful in achieving a good ‘closeness of match’, but designers need to consider the ‘viscosity’ (resistance to local change) and the ‘secondary notation’ (possibility of conveying extra meaning by choice of layout, colour, etc.).