nils-herrmann/clean_scirepeval huggingface dataset:
Deep Learning in Software Engineering

Recent years, deep learning is increasingly prevalent in the field of Software Engineering (SE). However, many open issues still remain to be investigated. How do researchers integrate deep learning into SE problems? Which SE phases are facilitated by deep learning? Do practitioners benefit from deep learning? The answers help practitioners and researchers develop practical deep learning models for SE tasks. To answer these questions, we conduct a bibliography analysis on 98 research papers in SE that use deep learning techniques. We find that 41 SE tasks in all SE phases have been facilitated by deep learning integrated solutions. In which, 84.7% papers only use standard deep learning models and their variants to solve SE problems. The practicability becomes a concern in utilizing deep learning techniques. How to improve the effectiveness, efficiency, understandability, and testability of deep learning based solutions may attract more SE researchers in the future.  Driven by the success of deep learning in data mining and pattern recognition, recent years have witnessed an increasing trend for industrial practitioners and academic researchers to integrate deep learning into SE tasks [1]-[3]. For typical SE tasks, deep learning helps SE participators extract requirements from natural language text , generate source code [2], predict defects in software [3], etc. As an initial statistics of research papers in SE in this study, deep learning has achieved competitive performance against previous algorithms on about 40 SE tasks. There are at least 98 research papers published or accepted in 66 venues, integrating deep learning into SE tasks. Despite the encouraging amount of papers and venues, there exists little overview analysis on deep learning in SE, e.g., the common way to integrate deep learning into SE, the SE phases facilitated by deep learning, the interests of SE practitioners on deep learning, etc. Understanding these questions is important. On the one hand, it helps practitioners and researchers get an overview understanding of deep learning in SE. On the other hand, practitioners and researchers can develop more practical deep learning models according to the analysis. For this purpose, this study conducts a bibliography analysis on research papers in the field of SE that use deep learning techniques. In contrast to literature reviews,
Lares: An Architecture for Secure Active Monitoring Using Virtualization

Host-based security tools such as anti-virus and intrusion detection systems are not adequately protected on today's computers. Malware is often designed to immediately disable any security tools upon installation, rendering them useless. While current research has focused on moving these vulnerable security tools into an isolated virtual machine, this approach cripples security tools by preventing them from doing active monitoring. This paper describes an architecture that takes a hybrid approach, giving security tools the ability to do active monitoring while still benefiting from the increased security of an isolated virtual machine. We discuss the architecture and a prototype implementation that can process hooks from a virtual machine running Windows XP on Xen. We conclude with a security analysis and show the performance of a single hook to be 28 musecs in the best case.
SCPKI: A Smart Contract-based PKI and Identity System

The Public Key Infrastructure (PKI) in use today on the Internet to secure communications has several drawbacks arising from its centralised and non-transparent design. In the past there has been instances of certificate authorities publishing rogue certificates for targeted attacks, and this has been difficult to immediately detect as certificate authorities are not transparent about the certificates they issue. Furthermore, the centralised selection of trusted certificate authorities by operating system and browser vendors means that it is not practical to untrust certificate authorities that have issued rogue certificates, as this would disrupt the TLS process for many other hosts.
 SCPKI is an alternative PKI system based on a decentralised and transparent design using a web-of-trust model and a smart contract on the Ethereum blockchain, to make it easily possible for rogue certificates to be detected when they are published. The web-of-trust model is designed such that an entity or authority in the system can verify (or vouch for) fine-grained attributes of another entity's identity (such as company name or domain name), as an alternative to the centralised certificate authority identity verification model.
The Parallel Iterative Closest Point Algorithm

This paper describes a parallel implementation developed to improve the time performance of the Iterative Closest Point Algorithm. Within each iteration, the correspondence calculations are distributed among the processor resources. At the end of each iteration, the results of the correspondence determination are communicated back to a central processor and the current transformation is calculated. A number of additional techniques were developed that served to improve upon this basic scheme. Calculating the partial sums within each distributed resource made it unnecessary to transmit the correspondence values back to the central processor, which reduced the communication overhead, and improved time performance. Randomly distributing the points among the processor resources resulted in a better load balancing, which further improved time performance. We also found that thinning the image by randomly removing a certain percentage of the points did not improve the performance, when viewed as the progression of mse with time. The method was implemented and tested on a 22 node Beowulf class cluster. For a large image, linear performance improvements were obtained for up to 16 processors, while they held for up to 8 processors with a smaller image.
A vision-based approach for autonomous landing

Monocular vision is frequently used in Micro Air Vehicles for many tasks such autonomous navigation, tracking, search and autonomous landing. To address this problem and in the context of autonomous landing of a MAV on a platform, we use a template-based matching in an image pyramid scheme in combination with an edge detector. Thus, the landing zone is localised via image processing in a frame-to-frame basis. Images are captured by the MAV's onboard camera of the MAV and processed with a multi-scale image processing strategy to detect the landing zone at different scales. We assessed our approach in real-time experiments using a Parrot Bebop 2.0 in outdoors and at different heights.
Core Vector Machines: Fast SVM Training on Very Large Data Sets

Standard SVM training has O(m3) time andO(m2) space complexities, where m is the training set size. It is thus computationally infeasible on very larg e data sets. By observing that practical SVM implementations onlyapproximatethe optimal solution by an iterative strategy, we scale up kernel methods by exploiting such “approximateness” in t h s paper. We first show that many kernel methods can be equivalently formulated as minimum en closing ball (MEB) problems in computational geometry. Then, by adopting an efficient appr oximate MEB algorithm, we obtain provably approximately optimal solutions with the idea of c re sets. Our proposed Core Vector Machine (CVM) algorithm can be used with nonlinear kernels a nd has a time complexity that is linear in m and a space complexity that is independent of m. Experiments on large toy and realworld data sets demonstrate that the CVM is as accurate as exi sting SVM implementations, but is much faster and can handle much larger data sets than existin g scale-up methods. For example, CVM with the Gaussian kernel produces superior results on th e KDDCUP-99 intrusion detection data, which has about five million training patterns, in only 1.4 seconds on a 3.2GHz Pentium–4 PC.
Physical Attractiveness and the “ Nice Guy Paradox ” : Do Nice Guys Really Finish Last ? 1

The nice guy stereotype asserts that, although women often say that they wish to date kind, sensitive men, when actually given a choice, women will reject nice men in favor of men with other salient characteristics, such as physical attractiveness. To explore this stereotype, two studies were conducted. In Study 1, 48 college women were randomly assigned into experimental conditions in which they read a script that depicted 2 men competing for a date with a woman. The niceness of 1 target man’s responses was manipulated across conditions. In Study 2, 194 college women were randomly assigned to conditions in which both the target man’s responses and his physical attractiveness were manipulated. Overall results indicated that both niceness and physical attractiveness were positive factors in women’s choices and desirability ratings of the target men. Niceness appeared to be the most salient factor when it came to desirability for more serious relationships, whereas physical attractiveness appeared more important in terms of desirability for more casual, sexual relationships.
Allan Variance Analysis on Error Characters of MEMS Inertial Sensors for an FPGA-based GPS / INS System

The combination of GPS/INS provides an ideal navigation system of full capability of continuously outputting position, velocity, and attitude of the host platform. However, the accuracy of INS degrades with time when GPS signals are blocked in environments such as tunnels, dense urban canyons and indoors. To dampen down the error growth, the INS sensor errors should be properly estimated and compensated before the inertial data are involved in the navigation computation. Therefore appropriate modelling of the INS sensor errors is a necessity. Allan Variance (AV) is a simple and efficient method for verifying and modelling these errors by representing the root mean square (RMS) random drift error as a function of averaging time. The AV can be used to determine the characteristics of different random processes. This paper applies the AV to analyse and model different types of random errors residing in the measurements of MEMS inertial sensors. The derived error model will be further applied to a low-cost GPS/MEMS-INS system once the correctness of the model is verified. The paper gives the detail of the AV analysis as well as presents the test results.
Exploiting Explicit Paths for Multi-hop Reading Comprehension

We focus on the task of multi-hop reading comprehension where a system is required to reason over a chain of multiple facts, distributed across multiple passages, to answer a question. Inspired by graph-based reasoning, we present a path-based reasoning approach for textual reading comprehension. It operates by generating potential paths across multiple passages, extracting implicit relations along this path, and composing them to encode each path. The proposed model achieves a 2.3% gain on the WikiHop Dev set as compared to previous state-of-the-art and, as a side-effect, is also able to explain its reasoning through explicit paths of sentences.
ContextVP: Fully Context-Aware Video Prediction

Video prediction models based on convolutional networks, recurrent networks, and their combinations often result in blurry predictions. We identify an important contributing factor for imprecise predictions that has not been studied adequately in the literature: blind spots, i.e., lack of access to all relevant past information for accurately predicting the future. To address this issue, we introduce a fully contextaware architecture that captures the entire available past context for each pixel using Parallel Multi-Dimensional LSTM units and aggregates it using blending units. Our model outperforms a strong baseline network of 20 recurrent convolutional layers and yields state-of-the-art performance for next step prediction on three challenging real-world video datasets: Human 3.6M, Caltech Pedestrian, and UCF-101. Moreover, it does so with fewer parameters than several recently proposed models, and does not rely on deep convolutional networks, multi-scale architectures, separation of background and foreground modeling, motion flow learning, or adversarial training. These results highlight that full awareness of past context is of crucial importance for video prediction.
Time series forecasting using neural networks

Recent studies have shown the classification and prediction power of the Neural Networks. It has been demonstrated that a NN can approximate any continuous function. Neural networks have been successfully used for forecasting of financial data series. The classical methods used for time series prediction like Box-Jenkins or ARIMA assumes that there is a linear relationship between inputs and outputs. Neural Networks have the advantage that can approximate nonlinear functions. In this paper we compared the performances of different feed forward and recurrent neural networks and training algorithms for predicting the exchange rate EUR/RON and USD/RON. We used data series with daily exchange rates starting from 2005 until 2013.
JOTS - v40n2 - Augmented Reality Applications in Education

Technology is ever changing and ever growing. One of the newest developing technologies is augmented reality (AR), which can be applied to many different existing technologies, such as: computers, tablets, and smartphones. AR technology can also be utilized through wearable components, for example, glasses. Throughout this literature review on AR the following aspects are discussed at length: research explored, theoretical foundations, applications in education, challenges, reactions, and implications. Several different types of AR devices and applications are discussed at length, and an in-depth analysis is done on several studies that have implemented AR technology in an educational setting. This review focuses on how AR technology can be applied, the issues surrounding the use of this technology, viewpoints of those who have worked with AR applications; it also identifies multiple areas to be explored in future research.
The functions and dysfunctions of hierarchy

Functionalist accounts of hierarchy, longstanding in the social sciences, have gained recent prominence in studies of leadership, power, and status. This chapter takes a critical look at a core implication of the functionalist perspective – namely, that steeper hierarchies help groups and organizations perform better than do flatter structures. We review previous research relevant to this question, ranging from studies of communication structures in small groups to studies of compensation systems in large corporations. This review finds that in contrast to strong functionalist assertions, the effects of steeper hierarchies are highly mixed. Sometimes steeper hierarchies benefit groups and sometimes they harm groups. We thus propose five conditions that moderate the effects of hierarchy steepness: (1) the kinds of tasks on which the group is working; (2) whether the right individuals have been selected as leaders; (3) how the possession of power modifies leaders’ psychology; (4) whether the hierarchy facilitates or hampers intra-group coordination; and (5) whether the hierarchy affects group members’ motivation in positive or deleterious ways.
Individual and Corporate Social Responsibility

Individual and Corporate Social Responsibility Society’s demands for individual and corporate social responsibility as an alternative response to market and distributive failures are becoming increasingly prominent. We first draw on recent developments in the “psychology and economics” of prosocial behavior to shed light on this trend, which reflects a complex interplay of genuine altruism, social or self image concerns, and material incentives. We then link individual concerns to corporate social responsibility, contrasting three possible understandings of the term: the adoption of a more long-term perspective by firms, the delegated exercise of prosocial behavior on behalf of stakeholders, and insider-initiated corporate philanthropy. For both individuals and firms we discuss the benefits, costs and limits of socially responsible behavior as a means to further societal goals.
Linear Discriminant Generative Adversarial Networks

We develop a novel method for training of GANs for unsupervised and class conditional generation of images, called Linear Discriminant GAN (LD-GAN). The discriminator of an LD-GAN is trained to maximize the linear separability between distributions of hidden representations of generated and targeted samples, while the generator is updated based on the decision hyper-planes computed by performing LDA over the hidden representations. LD-GAN provides a concrete metric of separation capacity for the discriminator, and we experimentally show that it is possible to stabilize the training of LD-GAN simply by calibrating the update frequencies between generators and discriminators in the unsupervised case, without employment of normalization methods and constraints on weights. In the class conditional generation tasks, the proposed method shows improved training stability together with better generalization performance compared to WGAN (Arjovsky et al. 2017) that employs an auxiliary classifier.
Nonlinear multiresolution signal decomposition schemes. II. Morphological wavelets

In its original form, the wavelet transform is a linear tool. However, it has been increasingly recognized that nonlinear extensions are possible. A major impulse to the development of nonlinear wavelet transforms has been given by the introduction of the lifting scheme by Sweldens (1995, 1996, 1998). The aim of this paper, which is a sequel to a previous paper devoted exclusively to the pyramid transform, is to present an axiomatic framework encompassing most existing linear and nonlinear wavelet decompositions. Furthermore, it introduces some, thus far unknown, wavelets based on mathematical morphology, such as the morphological Haar wavelet, both in one and two dimensions. A general and flexible approach for the construction of nonlinear (morphological) wavelets is provided by the lifting scheme. This paper briefly discusses one example, the max-lifting scheme, which has the intriguing property that preserves local maxima in a signal over a range of scales, depending on how local or global these maxima are.
A Wideband Circularly Polarized Conformal Endoscopic Antenna System for High-Speed Data Transfer

In this paper, a conformal wideband circularly polarized (CP) antenna is presented for endoscopic capsule application over the 915-MHz Industrial, Scientific, and Medical (902–928 MHz) band. The thickness of the antenna is only 0.2 mm, which can be wrapped inside a capsule’s inner wall. By cutting meandered slots on the patch, using open-end slots on the ground, and utilizing two long arms, the proposed antenna obtains a significant size reduction. In the conformal form, the antenna volume measures only 66.7 mm3. A single-layer homogeneous muscle phantom box is used for the initial design and optimization with parametric studies. The effect of the internal components inside a capsule is discussed in analyzing the antenna’s performance and to realize a more practical scenario. In addition, a realistic human body model in a Remcom XFdtd simulation environment is considered to evaluate the antenna characteristics and CP purity, and to specify the specific absorption rate limit in different organs along the gastrointestinal tract. The performance of the proposed antenna is experimentally validated by using a minced pork muscle phantom and by using an American Society for Testing and Materials phantom immersed in a liquid solution. For measurements, a new technique applying a printed 3-D capsule is devised. From simulations and measurements, we found that the impedance bandwidth of the proposed antenna is more than 20% and with a maximum simulated axial ratio bandwidth of around 29.2% in homogeneous tissue. Finally, a wireless communication link at a data rate of 78 Mb/s is calculated by employing link-budget analysis.
Guiding Policies with Language via Meta-Learning

Behavioral skills or policies for autonomous agents are conventionally learned from reward functions, via reinforcement learning, or from demonstrations, via imitation learning. However, both modes of task specification have their disadvantages: reward functions require manual engineering, while demonstrations require a human expert to be able to actually perform the task in order to generate the demonstration. Instruction following from natural language instructions provides an appealing alternative: in the same way that we can specify goals to other humans simply by speaking or writing, we would like to be able to specify tasks for our machines. However, a single instruction may be insufficient to fully communicate our intent or, even if it is, may be insufficient for an autonomous agent to actually understand how to perform the desired task. In this work, we propose an interactive formulation of the task specification problem, where iterative language corrections are provided to an autonomous agent, guiding it in acquiring the desired skill. Our proposed language-guided policy learning algorithm can integrate an instruction and a sequence of corrections to acquire new skills very quickly. In our experiments, we show that this method can enable a policy to follow instructions and corrections for simulated navigation and manipulation tasks, substantially outperforming direct, non-interactive instruction following.
Compositional Obverter Communication Learning From Raw Visual Input

One of the distinguishing aspects of human language is its compositionality, which allows us to describe complex environments with limited vocabulary. Previously, it has been shown that neural network agents can learn to communicate in a highly structured, possibly compositional language based on disentangled input (e.g. handengineered features). Humans, however, do not learn to communicate based on well-summarized features. In this work, we train neural agents to simultaneously develop visual perception from raw image pixels, and learn to communicate with a sequence of discrete symbols. The agents play an image description game where the image contains factors such as colors and shapes. We train the agents using the obverter technique where an agent introspects to generate messages that maximize its own understanding. Through qualitative analysis, visualization and a zero-shot test, we show that the agents can develop, out of raw image pixels, a language with compositional properties, given a proper pressure from the environment.