nils-herrmann/clean_scirepeval huggingface dataset:
IT Governance Practices in Small and Medium-Sized Enterprises: Recommendations from an Empirical Study

Much has been learned through IT governance research about the nature of ITrelated decisions, the location of decision rights for these decisions, and governance mechanisms applied to facilitate associated decision processes in large organisations. Our knowledge about IT governance structures in small and medium-sized enterprises (SME), on the other hand, is quite limited. Adopting a qualitative and inductive approach, this study examines the nature and influence of IT governance in SMEs through interviews with executives from three SMEs. Our results demonstrate that IT decision authority was centralized in all three SMEs but that senior management involvement in governance procedures and communication practices about governance policies were observed to explain differences in these organisations’ IT use. We propose recommendations based on the findings of this
Video-based lane estimation and tracking for driver assistance: survey, system, and evaluation

Driver-assistance systems that monitor driver intent, warn drivers of lane departures, or assist in vehicle guidance are all being actively considered. It is therefore important to take a critical look at key aspects of these systems, one of which is lane-position tracking. It is for these driver-assistance objectives that motivate the development of the novel "video-based lane estimation and tracking" (VioLET) system. The system is designed using steerable filters for robust and accurate lane-marking detection. Steerable filters provide an efficient method for detecting circular-reflector markings, solid-line markings, and segmented-line markings under varying lighting and road conditions. They help in providing robustness to complex shadowing, lighting changes from overpasses and tunnels, and road-surface variations. They are efficient for lane-marking extraction because by computing only three separable convolutions, we can extract a wide variety of lane markings. Curvature detection is made more robust by incorporating both visual cues (lane markings and lane texture) and vehicle-state information. The experiment design and evaluation of the VioLET system is shown using multiple quantitative metrics over a wide variety of test conditions on a large test path using a unique instrumented vehicle. A justification for the choice of metrics based on a previous study with human-factors applications as well as extensive ground-truth testing from different times of day, road conditions, weather, and driving scenarios is also presented. In order to design the VioLET system, an up-to-date and comprehensive analysis of the current state of the art in lane-detection research was first performed. In doing so, a comparison of a wide variety of methods, pointing out the similarities and differences between methods as well as when and where various methods are most useful, is presented
A Framework for Interaction-driven User Modeling of Mobile News Reading Behaviour

The news you read is, of course, a highly individual choice and one for which substantial and successful news recommendation techniques have been developed. But as well as what news you read, the way you choose and read that news is also known to be highly individual. We propose a framework for extending the user profile of news readers with features of these interactions. The extensions are dynamic through monitoring an individual's reading and browsing activity. They include factors learned from the user's interaction log and also factors inferred from category level definitions contained in the framework. We report a study in which users' interaction logs with a news app are used to generate user profiles that are verified with self-reported questionnaire data about reading habits. We discuss the implications of our user modeling approach in news personalisation for both recommendation and user interface personalisation for news apps.
Male-to-female transsexualism: technique, results and 3-year follow-up in 50 patients.

To evaluate the functional and cosmetic results of male-to-female gender-transforming surgery.



Between May 2001 and April 2008 we performed 50 male-to-female gender-transforming surgeries. All patients had been cross-dressing, living as women, and receiving estrogen and progesterone for at least 12 months, which was sufficient for breast development and atrophy of the testes and prostate to occur. This hormonal therapy was suspended 1 month before the operation.



The mean operative time was 190 min and the mean depth of the vagina was 10 cm. On follow-up, the most common complication (10%) was shrinkage of the neovagina, which could be corrected by a second surgical intervention. Of the 50 patients, 45 (90%) were satisfied with the esthetic results; 42 patients (84%) reported having regular sexual intercourse, 2 of whom had pain during intercourse. Of the 50 patients, 35 (70%) reported achieving clitoral orgasm.



Male-to-female gender-transforming surgery can assure satisfactory cosmetic and functional results, with a reduced intra- and postoperative morbidity. Nevertheless the experience of the surgeon and the center remains central to obtaining optimal results.
Efficient revocation and threshold pairing based cryptosystems

Boneh, Ding, Tsudik and Wong recently proposed a way for obtaining fast revocation of RSA keys. Their method consists in using security mediators that keep a piece of each user's private key in such a way that every decrytion or signature operation requires the help of the mediator for the user. Revocation is achieved by instructing the mediator to stop helping the user to sign or decrypt messages. This security architecture, called SEM, gave rise to an identity based mediated RSA scheme (IB-mRSA) that combines the advantages of fast revocation and identity based public keys. We show that, in opposition to what was stated in , this revocation method can be applied to several existing public key encryption and signature schemes (all those for which a secure practical threshold adaptation exists) including the Boneh-Franklin identity based encryption scheme and a pairing based digital signature schemes. We first describe a threshold adaptation of the Boneh-Franklin identity based encryption scheme and, then, we compare the mediated versions of these schemes with IB-mRSA from security and efficiency points of view.
An Intelligent System for Detection of User Behavior in Internet Banking

Security and making trust is the first step toward development in both real and virtual societies. Internet-based development is inevitable. Increasing penetration of technology in the internet banking and its effectiveness in contributing to banking profitability and prosperity requires that satisfied customers turn into loyal customers. Currently, a large number of cyber attacks have been focused on online banking systems, and these attacks are considered as a significant security threat. Banks or customers might become the victim of the most complicated financial crime, namely internet fraud. This study has developed an intelligent system that enables detecting the user's abnormal behavior in online banking. Since the user's behavior is associated with uncertainty, the system has been developed based on the fuzzy theory, this enables it to identify user behaviors and categorize suspicious behaviors with various levels of intensity. The performance of the fuzzy expert system has been evaluated using a receiver operating characteristic curve, which provides the accuracy of 94%. This expert system is optimistic to be used for improving e-banking services security and quality.
Multilevel Converters for Large Electric Drives

Traditional two-level high-frequency pulse width modulation (PWM) inverters for motor drives have several problems associated with their high frequency switching which produces common-mode voltage and high voltage change (dV/dt) rates to the motor windings. Multilevel inverters solve these problems because their devices can switch at a much lower frequency. Two different multilevel topologies are identified for use as a converter for electric drives, a cascade inverter with separate dc sources and a back-to-back diode clamped converter. The cascade inverter is a natural fit for large automotive allelectric drives because of the high VA ratings possible and because it uses several levels of dc voltage sources which would be available from batteries or fuel cells. The back-to-back diode clamped converter is ideal where a source of ac voltage is available such as a hybrid electric vehicle. Simulation and experimental results show the superiority of these two converters over PWM based drives.
Photon counting and direct ToF camera prototype based on CMOS SPADs

This paper presents a camera prototype for 2D/3D image capture in low illumination conditions based on single-photon avalanche-diode (SPAD) image sensor for direct time-offlight (d-ToF). The imager is a 64×64 array with in-pixel TDC for high frame rate acquisition. Circuit design techniques are combined to ensure successful 3D image capturing under low sensitivity conditions and high level of uncorrelated noise such as dark count and background illumination. Among them an innovative time gated front-end for the SPAD detector, a reverse start-stop scheme and real-time image reconstruction at Ikfps are incorporated by the imager. To the best of our knowledge, this is the first ToF camera based on a SPAD sensor fabricated and proved for 3D image reconstruction in a standard CMOS process without any opto-flavor or high voltage option. It has a depth resolution of 1cm at an illumination power from less than 6nW/mm2 down to 0.1nW/mm2.
Efficient set joins on similarity predicates

In this paper we present an efficient, scalable and general algorithm for performing set joins on predicates involving various similarity measures like intersect size, Jaccard-coefficient, cosine similarity, and edit-distance. This expands the existing suite of algorithms for set joins on simpler predicates such as, set containment, equality and non-zero overlap. We start with a basic inverted index based probing method and add a sequence of optimizations that result in one to two orders of magnitude improvement in running time. The algorithm folds in a data partitioning strategy that can work efficiently with an index compressed to fit in any available amount of main memory. The optimizations used in our algorithm generalize to several weighted and unweighted measures of partial word overlap between sets.
A handle bar metaphor for virtual object manipulation with mid-air interaction

Commercial 3D scene acquisition systems such as the Microsoft Kinect sensor can reduce the cost barrier of realizing mid-air interaction. However, since it can only sense hand position but not hand orientation robustly, current mid-air interaction methods for 3D virtual object manipulation often require contextual and mode switching to perform translation, rotation, and scaling, thus preventing natural continuous gestural interactions. A novel handle bar metaphor is proposed as an effective visual control metaphor between the user's hand gestures and the corresponding virtual object manipulation operations. It mimics a familiar situation of handling objects that are skewered with a bimanual handle bar. The use of relative 3D motion of the two hands to design the mid-air interaction allows us to provide precise controllability despite the Kinect sensor's low image resolution. A comprehensive repertoire of 3D manipulation operations is proposed to manipulate single objects, perform fast constrained rotation, and pack/align multiple objects along a line. Three user studies were devised to demonstrate the efficacy and intuitiveness of the proposed interaction techniques on different virtual manipulation scenarios.
A client-side analysis of TLS usage in mobile apps

As mobile applications become more pervasive, they provide us with a variety of online services that range from social networking to banking and credit card management. Since many of these services involve communicating and handling of private user information – and also due to increasing security demands from users – the use of TLS connections has become a necessity for today’s mobile applications. However, an improper use of TLS and failure to adhere to TLS security guidelines by app developers, exposes users to agents performing TLS interception thus giving them a false sense of security. Unfortunately, researchers and users alike lack of information and easy-to-deploy mechanisms to analyze how securely mobile apps implement TLS. Hence, in order to understand and assess the security of mobile app communications, it is crucial to study their use of the TLS protocol. In this poster we present a method to study the use of TLS in mobile apps using the data provided by the ICSI Haystack app , a mobile measurement platform that enables on-device analysis of mobile traffic without requiring root access. The unique vantage point provided by the Haystack platform enables a variety of measurements from the edge of the network with real user workload and the added bonus of having contextual information on the device to supplement the data collection.
What Makes a Great Software Engineer?

Good software engineers are essential to the creation of good software. However, most of what we know about software-engineering expertise are vague stereotypes, such as 'excellent communicators' and 'great teammates'. The lack of specificity in our understanding hinders researchers from reasoning about them, employers from identifying them, and young engineers from becoming them. Our understanding also lacks breadth: what are all the distinguishing attributes of great engineers (technical expertise and beyond)? We took a first step in addressing these gaps by interviewing 59 experienced engineers across 13 divisions at Microsoft, uncovering 53 attributes of great engineers. We explain the attributes and examine how the most salient of these impact projects and teams. We discuss implications of this knowledge on research and the hiring and training of engineers.
A Fuzzy Local Search Classifier for Intrusion Detection

In this paper, we propose a fuzzy local search (FLS) method for intrusion detection. The FLS system is a fuzzy classifier, whose knowledge base is modeled as a fuzzy rule such as "if-then" and improved by a local search metaheuristic. The proposed method is implemented and tested on the benchmark KDD'99 intrusion dataset. The results are encouraging and demonstrate the benefits of our approach.
CLICKS: Mining Subspace Clusters in Categorical Data via K-Partite Maximal Cliques

We present a novel algorithm called CLICKS, that finds clusters in categorical datasets based on a search for k-partite maximal cliques. Unlike previous methods, CLICKS mines subspace clusters. It uses a selective vertical method to guarantee complete search. CLICKS outperforms previous approaches by over an order of magnitude and scales better than any of the existing method for high-dimensional datasets. We demonstrate this improvement in an excerpt from our comprehensive performance studies.
Discriminative Decorrelation for Clustering and Classification

Object detection has over the past few years converged on using linear SVMs over HOG features. Training linear SVMs however is quite expensive, and can become intractable as the number of categories increase. In this work we revisit a much older technique, viz. Linear Discriminant Analysis, and show that LDA models can be trained almost trivially, and with little or no loss in performance. The covariance matrices we estimate capture properties of natural images. Whitening HOG features with these covariances thus removes naturally occuring correlations between the HOG features. We show that these whitened features (which we call WHO) are considerably better than the original HOG features for computing similarities, and prove their usefulness in clustering. Finally, we use our findings to produce an object detection system that is competitive on PASCAL VOC 2007 while being considerably easier to train and test.
Synchronization Detection and Recovery of Steganographic Messages with Adversarial Learning

As a means for secret communication, steganography aims at concealing a message within a medium such that the presence of the hidden message can hardly be detected. In computer vision tasks, adversarial training has become a competitive learning method to generate images. However, the generative tasks are confronted with great challenge on synthesizing images. This paper studies the mechanism of applying adversarial learning for discriminative tasks to learn the steganographic algorithm. We show that through unsupervised adversarial training, the adversarial model can produce robust steganographic solutions, which act like an encryption. Through supervised adversarial training, we can also train a robust steganalyzer, which is utilized to discriminate whether an image contains secret information. Our model is composed of three modules, i.e. a generator, a discriminator and a steganalyzer. All the three members are trained simultaneously. To formulate the algorithm, we use a game to represent the communication between the three parties. In this game, the generator and discriminator attempt to communicate with each other with secret messages hidden in an image. While the steganalyzer attempts to analyze whether there is a transmission of confidential information. Experimental results demonstrate the effectiveness of the proposed method on steganography tasks.
pVM: persistent virtual memory for efficient capacity scaling and object storage

Next-generation byte-addressable nonvolatile memories (NVMs), such as phase change memory (PCM) and Memristors, promise fast data storage, and more importantly, address DRAM scalability issues. State-of-the-art OS mechanisms for NVMs have focused on improving the block-based virtual file system (VFS) to manage both persistence and the memory capacity scaling needs of applications. However, using the VFS for capacity scaling has several limitations, such as the lack of automatic memory capacity scaling across DRAM and NVM, inefficient use of the processor cache and TLB, and high page access costs. These limitations reduce application performance and also impact applications that use NVM for persistent object storage with flat namespaces, such as photo stores, NoSQL databases, and others.
 To address such limitations, we propose persistent virtual memory (pVM), a system software abstraction that provides applications with (1) automatic OS-level memory capacity scaling, (2) flexible memory placement policies across NVM, and (3) fast object storage. pVM extends the OS virtual memory (VM) instead of building on the VFS and abstracts NVM as a NUMA node with support for NVM-based memory placement mechanisms. pVM inherits benefits from the cache and TLB-efficient VM subsystem and augments these further by distinguishing between persistent and nonpersistent capacity use of NVM. Additionally, pVM achieves fast persistent storage by further extending the VM subsystem with consistent and durable OS-level persistent metadata. Our evaluation of pVM with memory capacity-intensive applications shows a 2.5x speedup and up to 80% lower TLB and cache misses compared to VFS-based systems. pVM's object store provides 2x higher throughput compared to the block-based approach of the state-of-the art solution and up to a 4x reduction in the time spent in the OS.
Poetic Machine: Computational Creativity for Automatic Poetry Generation in Bengali

The paper reports an initial study on computational poetry generation for Bengali. Bengali is a morpho-syntactically rich language and partially phonemic. The poetry generation task has been defined as a follow-up rhythmic sequence generation based on user input. The design process involves rhythm understanding from the given input and follow-up rhyme generation by leveraging syllable/phonetic mapping and natural language generation techniques. A syllabification engine based on grapheme-tophoneme mapping has been developed in order to understand the given input rhyme. A Support Vector Machine-based classifier then predicts the follow-up syllable/phonetic pattern for the generation and candidate words are chosen automatically, based on the syllable pattern. The final rhythmic poetical follow-up sentence is generated through n-gram matching with weight-based aggregation. The quality of the automatically generated rhymes has been evaluated according to three criteria: poeticness, grammaticality, and meaningfulness.
Alternative seating for young children with Autism Spectrum Disorder: effects on classroom behavior.

A single subject, withdrawal design was used to investigate the effects of therapy balls as seating on engagement and in-seat behavior of young children with Autism Spectrum Disorder (ASD). In addition, social validity was assessed to evaluate teachers' opinions regarding the intervention. During baseline and withdrawal (A phases) participants used their typical classroom seating device (chair, bench or carpet square). During the intervention (B phases) participants sat on therapy balls. Results indicated substantial improvements in engagement and in-seat behavior when participants were seated on therapy balls. Social validity findings indicated that the teachers' preferred the therapy balls. This study suggests therapy balls as classroom seating may facilitate engagement and in-seat behavior and create opportunities to provide effective instruction.
A Microfluidically Reconfigurable Dual-Band Slot Antenna With a Frequency Coverage Ratio of 3:1

Reconfiguration of filled and empty microchannels is used to realize a switchable dual-band slot antenna whose both bands can be controlled independently. Reactive loading effect of Galinstan bridges is used to shift down each slot antenna's frequency of operation. Using five microchannels results in a 5-bit reconfigurable antenna. Any combination of frequencies within the ranges of 1.8-3.1 GHz (first band) and 3.2-5.4 GHz (second band) can be obtained. This translates into a discrete tuning ratio of ~ 1.7:1 for both bands and overall ratio of 3:1 (1.8-5.4 GHz) for the antenna. The antenna circuit board is realized using common printed circuit board (PCB) techniques, and the microchannels' polydimethylsiloxane (PDMS) substrate is fabricated using 3-D printing technologies. The circuit board and the PDMS substrates are bonded to each other using a thin layer of spin-coated PDMS. The performance of the antenna is measured, and close agreement between the simulation and measurement is observed.
Towards Brazilian Portuguese automatic text simplification systems

In this paper we investigate the main linguistic phenomena that can make texts complex and how they could be simplified. We focus on a corpus analysis of simple account texts available on the web for Brazilian Portuguese and propose simplification strategies for this language. This study illustrates the need for text simplification to facilitate accessibility to information by poor literacy readers and potentially by people with other cognitive disabilities. It also highlights characteristics of simplification for Portuguese, which may differ from other languages. Such study consists of the first step towards building Brazilian Portuguese text simplification systems. One of the scenarios in which these systems could be used is that of reading electronic texts produced, e.g., by the Brazilian government or by relevant news agencies.
Culture and Institutions

A growing body of empirical work measuring different types of cultural traits has shown that culture matters for a variety of economic outcomes. This paper focuses on one specific aspect of the relevance of culture: its relationship to institutions. We review work with a theoretical, empirical, and historical bent to assess the presence of a two-way causal effect between culture and institutions. 1 We thank Benjamin Friedman and Andrei Shleifer for useful conversations and Janet Currie, Steven Durlauf, and six anonymous referees for excellent comments.