nils-herrmann/clean_scirepeval huggingface dataset:
Qualitative Methods in Empirical Studies of Software Engineering

ÐWhile empirical studies in software engineering are beginning to gain recognition in the research community, this subarea is also entering a new level of maturity by beginning to address the human aspects of software development. This added focus has added a new layer of complexity to an already challenging area of research. Along with new research questions, new research methods are needed to study nontechnical aspects of software engineering. In many other disciplines, qualitative research methods have been developed and are commonly used to handle the complexity of issues involving human behavior. This paper presents several qualitative methods for data collection and analysis and describes them in terms of how they might be incorporated into empirical studies of software engineering, in particular how they might be combined with quantitative methods. To illustrate this use of qualitative methods, examples from real software engineering studies are used throughout. Index TermsÐQualitative methods, data collection, data analysis, experimental design, empirical software engineering, participant observation, interviewing.
Learning Distributions over Logical Forms for Referring Expression Generation

We present a new approach to referring expression generation, casting it as a density estimation problem where the goal is to learn distributions over logical expressions identifying sets of objects in the world. Despite an extremely large space of possible expressions, we demonstrate effective learning of a globally normalized log-linear distribution. This learning is enabled by a new, multi-stage approximate inference technique that uses a pruning model to construct only the most likely logical forms. We train and evaluate the approach on a new corpus of references to sets of visual objects. Experiments show the approach is able to learn accurate models, which generate over 87% of the expressions people used. Additionally, on the previously studied special case of single object reference, we show a 35% relative error reduction over previous state of the art.
Frequent term-based text clustering

Text clustering methods can be used to structure large sets of text or hypertext documents. The well-known methods of text clustering, however, do not really address the special problems of text clustering: very high dimensionality of the data, very large size of the databases and understandability of the cluster description. In this paper, we introduce a novel approach which uses frequent item (term) sets for text clustering. Such frequent sets can be efficiently discovered using algorithms for association rule mining. To cluster based on frequent term sets, we measure the mutual overlap of frequent sets with respect to the sets of supporting documents. We present two algorithms for frequent term-based text clustering, FTC which creates flat clusterings and HFTC for hierarchical clustering. An experimental evaluation on classical text documents as well as on web documents demonstrates that the proposed algorithms obtain clusterings of comparable quality significantly more efficiently than state-of-the- art text clustering algorithms. Furthermore, our methods provide an understandable description of the discovered clusters by their frequent term sets.
Looks and Lies: The Role of Physical Attractiveness in Online Dating Self-Presentation and Deception

This study examines the role of online daters’ physical attractiveness in their profile selfpresentation and, in particular, their use of deception. Sixty-nine online daters identified the deceptions in their online dating profiles and had their photograph taken in the lab. Independent judges rated the online daters’ physical attractiveness. Results show that the lower online daters’ attractiveness, the more likely they were to enhance their profile photographs and lie about their physical descriptors (height, weight, age). The association between attractiveness and deception did not extend to profile elements unrelated to their physical appearance (e.g., income, occupation), suggesting that their deceptions were limited and strategic. Results are discussed in terms of (a) evolutionary theories about the importance of physical attractiveness in the dating realm and (b) the technological affordances that allow online daters to engage in selective self-presentation.
Applying a Security Requirements Engineering Process

Nowadays, security solutions are mainly focused on providing security defences, instead of solving one of the main reasons for security problems that refers to an appropriate Information Systems (IS) design. In fact, requirements engineering often neglects enough attention to security concerns. In this paper it will be presented a case study of our proposal, called SREP (Security Requirements Engineering Process), which is a standard-centred process and a reuse-based approach which deals with the security requirements at the earlier stages of software development in a systematic and intuitive way by providing a security resources repository and by integrating the Common Criteria into the software development lifecycle. In brief, a case study is shown in this paper demonstrating how the security requirements for a security critical IS can be obtained in a guided and systematic way by applying SREP.
Modeling, control, and validation of an electro-hydraulic steer-by-wire system for articulated vehicle applications

X-by-wire control systems in automotive applications refer to systems where the input device used by the operator is connected to the actuation power subsystem by electrical wires, as opposed to being connected by mechanical or hydraulic means. The "X" in the X-by-wire is replaced by "steer", "throttle", and "brake" to represent the steer-by-wire, throttle-by-wire, and brake-by-wire systems. Common to all of these subsystems is that the operator control input device (i.e., steering column, acceleration pedal, and brake pedal) is not connected to the actuation devices mechanically. Rather, it is connected to an embedded computer which, in turn, sends the control signals to the actuation devices. Current state of art steering systems used in articulated vehicles are hydro-mechanical type systems, i.e., the steering column motion is transmitted and amplified by the main hydraulic circuit by hydro-mechanical means. This paper presents a new steer-by-wire (SBW) system which we designed, modeled, analyzed, and tested on wheel type loader construction equipment. The simulation results and tests conducted on a prototype development vehicle (a medium size wheel type loader) show very good agreement. The control algorithm is modeled using graphical modeling tools similar to Simulink and StateFlow. A real-time control algorithm is implemented on a Motorola 68332 microprocessor-based embedded controller. The operational performance of the steer-by-wire system has been convincingly demonstrated.
Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks

Data diversity is critical to success when training deep learning models. Medical imaging data sets are often imbalanced as pathologic findings are generally rare, which introduces significant challenges when training deep learning models. In this work, we propose a method to generate synthetic abnormal MRI images with brain tumors by training a generative adversarial network using two publicly available data sets of brain MRI. We demonstrate two unique benefits that the synthetic images provide. First, we illustrate improved performance on tumor segmentation by leveraging the synthetic images as a form of data augmentation. Second, we demonstrate the value of generative models as an anonymization tool, achieving comparable tumor segmentation results when trained on the synthetic data versus when trained on real subject data. Together, these results offer a potential solution to two of the largest challenges facing machine learning in medical imaging, namely the small incidence of pathological findings, and the restrictions around sharing of patient data.
A Bandwidth-efficient Architecture for Media Processing

Media applications are characterized by large amounts of available parallelism, little data reuse, and a high computation to memory access ratio. While these characteristics are poorly matched to conventional microprocessor architectures, they are a good fit for modern VLSI technology with its high arithmetic capacity but limited global bandwidth. The stream programming model, in which an application is coded as streams of data records passing through computation kernels, exposes both parallelism and locality in media applications that can be exploited by VLSI architectures. The Imagine architecture supports the stream programming model by providing a bandwidth hierarchy tailored to the demands of media applications. Compared to a conventional scalar processor, Imagine reduces the global register and memory bandwidth required by typical applications by factors of 13 and 21 respectively. This bandwidth efficiency enables a single chip Imagine processor to achieve a peak performance of 16.2GFLOPS (single-precision floating point) and sustained performance of up to 8.5GFLOPS on media processing kernels.
Adaptive relevance feedback in information retrieval

Relevance Feedback has proven very effective for improving retrieval accuracy. A difficult yet important problem in all relevance feedback methods is how to optimally balance the original query and feedback information. In the current feedback methods, the balance parameter is usually set to a fixed value across all the queries and collections. However, due to the difference in queries and feedback documents, this balance parameter should be optimized for each query and each set of feedback documents.
 In this paper, we present a learning approach to adaptively predict the optimal balance coefficient for each query and each collection. We propose three heuristics to characterize the balance between query and feedback information. Taking these three heuristics as a road map, we explore a number of features and combine them using a regression approach to predict the balance coefficient. Our experiments show that the proposed adaptive relevance feedback is more robust and effective than the regular fixed-coefficient feedback.
A Lightweight YOLOv2: A Binarized CNN with A Parallel Support Vector Regression for an FPGA

A frame object detection problem consists of two problems: one is a regression problem to spatially separated bounding boxes, the second is the associated classification of the objects within realtime frame rate. It is widely used in the embedded systems, such as robotics, autonomous driving, security, and drones - all of which require high-performance and low-power consumption. This paper implements the YOLO (You only look once) object detector on an FPGA, which is faster and has a higher accuracy. It is based on the convolutional deep neural network (CNN), and it is a dominant part both the performance and the area. However, the object detector based on the CNN consists of a bounding box prediction (regression) and a class estimation (classification). Thus, the conventional all binarized CNN fails to recognize in most cases. In the paper, we propose a lightweight YOLOv2, which consists of the binarized CNN for a feature extraction and the parallel support vector regression (SVR) for both a classification and a localization. To our knowledge, this is the first time binarized CNN»s have been successfully used in object detection. We implement a pipelined based architecture for the lightweight YOLOv2 on the Xilinx Inc. zcu102 board, which has the Xilinx Inc. Zynq Ultrascale+ MPSoC. The implemented object detector archived 40.81 frames per second (FPS). Compared with the ARM Cortex-A57, it was 177.4 times faster, it dissipated 1.1 times more power, and its performance per power efficiency was 158.9 times better. Also, compared with the nVidia Pascall embedded GPU, it was 27.5 times faster, it dissipated 1.5 times lower power, and its performance per power efficiency was 42.9 times better. Thus, our method is suitable for the frame object detector for an embedded vision system.
Creation and Interaction with Large-scale Domain-Specific Knowledge Bases

The ability to create and interact with large-scale domainspecific knowledge bases from unstructured/semi-structured data is the foundation for many industry-focused cognitive systems. We will demonstrate the Content Services system that provides cloud services for creating and querying highquality domain-specific knowledge bases by analyzing and integrating multiple (un/semi)structured content sources. We will showcase an instantiation of the system for a financial domain. We will also demonstrate both cross-lingual natural language queries and programmatic API calls for interacting with this knowledge base.
Structural basis of PAM-dependent target DNA recognition by the Cas9 endonuclease

The CRISPR-associated protein Cas9 is an RNA-guided endonuclease that cleaves double-stranded DNA bearing sequences complementary to a 20-nucleotide segment in the guide RNA. Cas9 has emerged as a versatile molecular tool for genome editing and gene expression control. RNA-guided DNA recognition and cleavage strictly require the presence of a protospacer adjacent motif (PAM) in the target DNA. Here we report a crystal structure of Streptococcus pyogenes Cas9 in complex with a single-molecule guide RNA and a target DNA containing a canonical 5′-NGG-3′ PAM. The structure reveals that the PAM motif resides in a base-paired DNA duplex. The non-complementary strand GG dinucleotide is read out via major-groove interactions with conserved arginine residues from the carboxy-terminal domain of Cas9. Interactions with the minor groove of the PAM duplex and the phosphodiester group at the +1 position in the target DNA strand contribute to local strand separation immediately upstream of the PAM. These observations suggest a mechanism for PAM-dependent target DNA melting and RNA–DNA hybrid formation. Furthermore, this study establishes a framework for the rational engineering of Cas9 enzymes with novel PAM specificities.
Interactive Bayesian Hierarchical Clustering

Clustering is a powerful tool in data analysis, but it is often difficult to find a grouping that aligns with a user’s needs. To address this, several methods incorporate constraints obtained from users into clustering algorithms, but unfortunately do not apply to hierarchical clustering. We design an interactive Bayesian algorithm that incorporates user interaction into hierarchical clustering while still utilizing the geometry of the data by sampling a constrained posterior distribution over hierarchies. We also suggest several ways to intelligently query a user. The algorithm, along with the querying schemes, shows promising results on real data.
Bypassing synthesis: PLS for face recognition with pose, low-resolution and sketch

This paper presents a novel way to perform multi-modal face recognition. We use Partial Least Squares (PLS) to linearly map images in different modalities to a common linear subspace in which they are highly correlated. PLS has been previously used effectively for feature selection in face recognition. We show both theoretically and experimentally that PLS can be used effectively across modalities. We also formulate a generic intermediate subspace comparison framework for multi-modal recognition. Surprisingly, we achieve high performance using only pixel intensities as features. We experimentally demonstrate the highest published recognition rates on the pose variations in the PIE data set, and also show that PLS can be used to compare sketches to photos, and to compare images taken at different resolutions.
Merging Occupancy Grid Maps From Multiple Robots

Mapping can potentially be speeded up in a significant way by using multiple robots exploring different parts of the environment. But the core question of multirobot mapping is how to integrate the data of the different robots into a single global map. A significant amount of research exists in the area of multirobot mapping that deals with techniques to estimate the relative robots poses at the start or during the mapping process. With map merging, the robots in contrast individually build local maps without any knowledge about their relative positions. The goal is then to identify regions of overlap at which the local maps can be joined together. A concrete approach to this idea is presented in form of a special similarity metric and a stochastic search algorithm. Given two maps m and m', the search algorithm transforms m' by rotations and translations to find a maximum overlap between m and m'. In doing so, the heuristic similarity metric guides the search algorithm toward optimal solutions. Results from experiments with up to six robots are presented based on simulated as well as real-world map data
Constructing Abstraction Hierarchies Using a Skill-Symbol Loop

We describe a framework for building abstraction hierarchies whereby an agent alternates skill- and representation-construction phases to construct a sequence of increasingly abstract Markov decision processes. Our formulation builds on recent results showing that the appropriate abstract representation of a problem is specified by the agent's skills. We describe how such a hierarchy can be used for fast planning, and illustrate the construction of an appropriate hierarchy for the Taxi domain.
Unweaving leaving : the use of models in the management of employee turnover

This paper offers a review of the literature on labour turnover in organizations. Initially, the importance of the subject area is established, as analyses of turnover are outlined and critiqued.This leads toadiscussionof thevariousways inwhich turnover and its consequences are measured. The potentially critical impact of turnover behaviour on organizational effectiveness is presented as justification for the need to model turnover, as a precursor to prediction and prevention. Key models from the literature of labour turnover are presented and critiqued.
At least nine ways to play : approaching gamer mentalities

Do digital games and play mean the same things for different people? This article presents the results of a three-year study in which we sought for new ways to approach digital games cultures and playing practices. First, we present the research process in brief and emphasise the importance of merging different kinds of methods and materials in the study of games cultures. Second, we introduce a gaming mentality heuristics that is not dedicated to a certain domain or genre of games, addressing light casual and light social gaming motivations as well as more dedicated ones in a joint framework. Our analysis reveals that, in contrast to common belief, the majority of digital gaming takes place between ‘casual relaxing’ and ‘committed entertaining’, where the multiplicity of experiences, feelings, and understandings that people have about their playing and digital games is wide-ranging. Digital gaming is thus found to be a multi-faceted social and cultural phenomenon which can be understood, practiced and used in various ways.
Authentic Third-party Data Publication

Integrity critical databases, such as financial information, used in high-value decisions, are frequently published over the internet. Publishers of such data must satisfy the integrity, authenticity, and non-repudiation requirements of end clients. Providing this protection over public data networks is costly. This is partly because building and running secure systems is hard. In practice, large systems can not be verified to be secure and are frequently penetrated. The negative consequences of a system intrusion at the data publisher can be severe. The problem is further complicated by data and server replication to satisfy availability and scalability requirements. We aim to reduce the trust required of the publisher of large, infrequently updated databases. To do this, we separate the roles of owner and publisher. With a few trusted digital signatures on the part of the owner, an untrusted publisher can use techniques based on Merkle hash trees, to provide authenticity and non-repudiation of the answer to a database query. We do not require a key to be held in an on-line system, thus reducing the impact of system penetrations. By allowing untrusted publishers, our solution moves towards more scalable publication of large databases.
1-Gb/s Transmission Over a Phosphorescent White LED by Using Rate-Adaptive Discrete Multitone Modulation

Light-emitting diodes (LEDs), which will be increasingly used in lighting technology, will also allow for distribution of broadband optical wireless signals. Visible-light communication (VLC) using white LEDs offers several advantages over the RF-based wireless systems, i.e., license-free spectrum, low power consumption, and higher privacy. Mostly, optical wireless can provide much higher data rates. In this paper, we demonstrate a VLC system based on a white LED for indoor broadband wireless access. After investigating the nonlinear effects of the LED and the power amplifier, a data rate of 1 Gb/s has been achieved at the standard illuminance level, by using an optimized discrete multitone modulation technique and adaptive bit- and power-loading algorithms. The bit-error ratio of the received data was $1.5\cdot 10^{-3}$, which is within the limit of common forward error correction (FEC) coding. These results twice the highest capacity that had been previously obtained.
SgxPectre Attacks: Stealing Intel Secrets from SGX Enclaves via Speculative Execution

This paper presents SgxPectre Attacks that exploit the recently disclosed CPU bugs to subvert the confidentiality and integrity of SGX enclaves. Particularly, we show that when branch prediction of the enclave code can be influenced by programs outside the enclave, the control flow of the enclave program can be temporarily altered to execute instructions that lead to observable cache-state changes. An adversary observing such changes can learn secrets inside the enclave memory or its internal registers, thus completely defeating the confidentiality guarantee offered by SGX. To demonstrate the practicality of our SgxPectre Attacks, we have systematically explored the possible attack vectors of branch target injection, approaches to win the race condition during enclave’s speculative execution, and techniques to automatically search for code patterns required for launching the attacks. Our study suggests that any enclave program could be vulnerable to SgxPectre Attacks since the desired code patterns are available in most SGX runtimes (e.g., Intel SGX SDK, Rust-SGX, and Graphene-SGX). Most importantly, we have applied SgxPectre Attacks to steal seal keys and attestation keys from Intel signed quoting enclaves. The seal key can be used to decrypt sealed storage outside the enclaves and forge valid sealed data; the attestation key can be used to forge attestation signatures. For these reasons, SgxPectre Attacks practically defeat SGX’s security protection. This paper also systematically evaluates Intel’s existing countermeasures against SgxPectre Attacks and discusses the security implications.