nils-herrmann/clean_scirepeval huggingface dataset:
Pancultural self-enhancement.

The culture movement challenged the universality of the self-enhancement motive by proposing that the motive is pervasive in individualistic cultures (the West) but absent in collectivistic cultures (the East). The present research posited that Westerners and Easterners use different tactics to achieve the same goal: positive self-regard. Study 1 tested participants from differing cultural backgrounds (the United States vs. Japan), and Study 2 tested participants of differing self-construals (independent vs. interdependent). Americans and independents self-enhanced on individualistic attributes, whereas Japanese and interdependents self-enhanced on collectivistic attributes. Independents regarded individualistic attributes, whereas interdependents regarded collectivistic attributes, as personally important. Attribute importance mediated self-enhancement. Regardless of cultural background or self-construal, people self-enhance on personally important dimensions. Self-enhancement is a universal human motive.
Neuromorphic sensory systems

Biology provides examples of efficient machines which greatly outperform conventional technology. Designers in neuromorphic engineering aim to construct electronic systems with the same efficient style of computation. This task requires a melding of novel engineering principles with knowledge gleaned from neuroscience. We discuss recent progress in realizing neuromorphic sensory systems which mimic the biological retina and cochlea, and subsequent sensor processing. The main trends are the increasing number of sensors and sensory systems that communicate through asynchronous digital signals analogous to neural spikes; the improved performance and usability of these sensors; and novel sensory processing methods which capitalize on the timing of spikes from these sensors. Experiments using these sensors can impact how we think the brain processes sensory information.
Trust and distrust on the web: User experiences and website characteristics

Corresponding author at: University of Basel, Department of Psychology, Center for Cognitive Psychology and Methodology, Missionsstrasse 62a, CH 4055 Basel, Switzerland.  E-mail address: mirjam.seckler@unibas.ch (M. Seckler). Mirjam Seckler ⇑, Silvia Heinz, Seamus Forde, Alexandre N. Tuch, Klaus Opwis
C R ] 2 1 O ct 2 01 7 Generic Black-Box End-to-End Attack Against RNNs and Other API Call Based Malware Classifiers

Deep neural networks (DNNs) are used to solve complex classification problems, for which other machine learning classifiers, such as SVM, fall short. Recurrent neural networks (RNNs) have been used for tasks that involves sequential inputs, such as speech to text. In the cyber security domain, RNNs based on API calls have been used effectively to classify previously un-encountered malware. In this paper, we present a blackbox attack against RNNs, focusing on finding adversarial API call sequences that would be misclassified by a RNN without affecting the malware functionality. We also show that this attack is effective against many classifiers, due-to the transferability principle between RNN variants, feed-forward DNNs and traditional machine learning classifiers such as SVM. Finally, we implemented GADGET, a software framework to convert any malware binary to a binary undetected by an API calls based malware classifier, using the proposed attack, without access to the malware source code. We conclude by discussing possible defense mechanisms and countermeasures against the attack.
A note on the height of binary search trees

Let <italic>H<subscrpt>n</subscrpt></italic> be the height of a binary search tree with <italic>n</italic> nodes constructed by standard insertions from a random permutation of 1, … , <italic>n</italic>. It is shown that <italic>H<subscrpt>n</subscrpt></italic>/log <italic>n</italic> → <italic>c</italic> = 4.31107 … in probability as <italic>n</italic> → ∞, where <italic>c</italic> is the unique solution of <italic>c</italic> log((2<italic>e</italic>)/<italic>c</italic>) = 1, <italic>c</italic> ≥ 2. Also, for all <italic>p</italic> > 0, lim<italic><subscrpt>n</italic>→∞</subscrpt><italic>E</italic>(<italic>H<supscrpt>p</supscrpt><subscrpt>n</subscrpt></italic>)/ log<italic><supscrpt>p</supscrpt>n</italic> = <italic>c<supscrpt>p</supscrpt></italic>. Finally, it is proved that <italic>S<subscrpt>n</subscrpt></italic>/log <italic>n</italic> → <italic>c</italic><supscrpt>*</supscrpt> = 0.3733 … , in probability, where <italic>c</italic><supscrpt>*</supscrpt> is defined by <italic>c</italic> log((2<italic>e</italic>)/<italic>c</italic>) = 1, <italic>c</italic> ≤ 1, and <italic>S<subscrpt>n</subscrpt></italic> is the saturation level of the same tree, that is, the number of full levels in the tree.
Parsing Natural Language Conversations using Contextual Cues

In this work, we focus on semantic parsing of natural language conversations. Most existing methods for semantic parsing are based on understanding the semantics of a single sentence at a time. However, understanding conversations also requires an understanding of conversational context and discourse structure across sentences. We formulate semantic parsing of conversations as a structured prediction task, incorporating structural features that model the ‘flow of discourse’ across sequences of utterances. We create a dataset for semantic parsing of conversations, consisting of 113 real-life sequences of interactions of human users with an automated email assistant. The data contains 4759 natural language statements paired with annotated logical forms. Our approach yields significant gains in performance over traditional semantic parsing.
Deep Speaker Feature Learning for Text-independent Speaker Verification

Recently deep neural networks (DNNs) have been used to learn speaker features. However, the quality of the learned features is not sufficiently good, so a complex back-end model, either neural or probabilistic, has to be used to address the residual uncertainty when applied to speaker verification. This paper presents a convolutional time-delay deep neural network structure (CT-DNN) for speaker feature learning. Our experimental results on the Fisher database demonstrated that this CT-DNN can produce high-quality speaker features: even with a single feature (0.3 seconds including the context), the EER can be as low as 7.68%. This effectively confirmed that the speaker trait is largely a deterministic short-time property rather than a longtime distributional pattern, and therefore can be extracted from just dozens of frames.
Variable-Length Word Encodings for Neural Translation Models

Recent work in neural machine translation has shown promising performance, but the most effective architectures do not scale naturally to large vocabulary sizes. We propose and compare three variable-length encoding schemes that represent a large vocabulary corpus using a much smaller vocabulary with no loss in information. Common words are unaffected by our encoding, but rare words are encoded using a sequence of two pseudo-words. Our method is simple and effective: it requires no complete dictionaries, learning procedures, increased training time, changes to the model, or new parameters. Compared to a baseline that replaces all rare words with an unknown word symbol, our best variable-length encoding strategy improves WMT English-French translation performance by up to 1.7 BLEU.
ERIM: Secure and Efficient In-process Isolation with Memory Protection Keys

Isolating sensitive data and state can increase the security and robustness of many applications. Examples include protecting cryptographic keys against exploits like OpenSSL’s Heartbleed bug or protecting a language runtime from native libraries written in unsafe languages. When runtime references across isolation boundaries occur relatively infrequently, then page-based hardware isolation can be used, because the cost of kernelor hypervisor-mediated domain switching is tolerable. However, some applications, such as isolating cryptographic session keys in a network-facing application or isolating frequently invoked native libraries in managed runtimes, require very frequent domain switching. In such applications, the overhead of kernelor hypervisormediated domain switching is prohibitive. In this paper, we present ERIM, a novel technique that provides hardware-enforced isolation with low overhead, even at high switching rates (ERIM’s average overhead is less than 1% for 100,000 switches per second). The key idea is to combine memory protection keys (MPKs), a feature recently added to Intel CPUs that allows protection domain switches in userspace, with binary inspection to prevent circumvention. We show that ERIM can be applied with little effort to new and existing applications, doesn’t require compiler changes, can run on a stock Linux kernel, and has low runtime overhead even at high domain switching rates.
A Four-level Focus+Context Approach to Interactive Visual Analysis of Temporal Features in Large Scientific Data

In this paper we present a new approach to the interactive visual analysis of time-dependent scientific data – both from measurements as well as from computational simulation – by visualizing a scalar function over time for each of tenthousands or even millions of sample points. In order to cope with overdrawing and cluttering, we introduce a new four-level method of focus+context visualization. Based on a setting of coordinated, multiple views (with linking and brushing), we integrate three different kinds of focus and also the context in every single view. Per data item we use three values (from the unit interval each) to represent to which degree the data item is part of the respective focus level. We present a color compositing scheme which is capable of expressing all three values in a meaningful way, taking semantics and their relations amongst each other (in the context of our multiple linked view setup) into account. Furthermore, we present additional image-based postprocessing methods to enhance the visualization of large sets of function graphs, including a texture-based technique based on line integral convolution (LIC). We also propose advanced brushing techniques which are specific to the timedependent nature of the data (in order to brush patterns over time more efficiently). We demonstrate the usefulness of the new approach in the context of medical perfusion data.
Developing Secure Websites Using Feature Driven Development (FDD): A Case Study

secure software. In order to find the real-life issues, this case study was initiated to investigate whether the existing FDD can withstand requirements change and software security altogether. The case study was performed in controlled environment – in a course called Application Development—a four credit hours course at UTM. The course began by splitting up the class to seven software development groups and two groups were chosen to implement the existing process of FDD. After students were given an introduction to FDD, they started to adapt the processes to their proposed system. Then students were introduced to the basic concepts on how to make software systems secure. Though, they were still new to security and FDD, however, this study produced a lot of interest among the students. The students seemed to enjoy the challenge of creating secure system using FDD model.
Sleep Interference Effects of Pathological Electronic Media Use during Adolescence

Adolescents increasingly use electronic media as a night-time activity, amid concerns about the potential negative impact on sleep and daytime functioning. The present study examined electronic media use and pathological media use in relation to sleep activity in a normative sample of Australian adolescents. A total of 1,287 high school students aged 12–18 years (50 % female) were recruited from seven secondary schools in South Australia. Adolescents completed a questionnaire assessing electronic media use, pathological media use, and sleep factors. Adolescents reported non-optimal sleep duration on weekday (71 %) and weekend nights (53 %). One in five adolescents reported nightly bedtime delay as a consequence of electronic media use. Adolescent pathological media users reported significantly more sleep problems than their non-pathological peers. These data contribute to current knowledge of how electronic media use may negatively affect adolescent sleep patterns, particularly in regard to sleep displacement and sleep-onset latency effects. Further research is needed in light of the increasing accessibility and uptake of portable electronic media devices, as well as the growing use of media as a sleeping aid, among young people.
Image smoothing via L0 gradient minimization

We present a new image editing method, particularly effective for sharpening major edges by increasing the steepness of transition while eliminating a manageable degree of low-amplitude structures. The seemingly contradictive effect is achieved in an optimization framework making use of L0 gradient minimization, which can globally control how many non-zero gradients are resulted in to approximate prominent structure in a sparsity-control manner. Unlike other edge-preserving smoothing approaches, our method does not depend on local features, but instead globally locates important edges. It, as a fundamental tool, finds many applications and is particularly beneficial to edge extraction, clip-art JPEG artifact removal, and non-photorealistic effect generation.
Human Action Recognition Using LBP-TOP as Sparse Spatio-Temporal Feature Descriptor

In this paper we apply the Local Binary Pattern on Three Orthogonal Planes (LBP-TOP) descriptor to the field of human action recognition. A video sequence is described as a collection of spatial-temporal words after the detection of space-time interest points and the description of the area around them. Our contribution has been in the description part, showing LBP-TOP to be a promising descriptor for human action classification purposes. We have also developed several extensions to the descriptor to enhance its performance in human action recognition, showing the method to be computationally efficient.
DevOps: A Definition and Perceived Adoption Impediments

As the interest in DevOps continues to grow, there is an increasing need for software organizations to understand how to adopt it successfully. This study has as objective to clarify the concept and provide insight into existing challenges of adopting DevOps. First, the existing literature is reviewed. A definition of DevOps is then formed based on the literature by breaking down the concept into its defining characteristics. We interview 13 subjects in a software company adopting DevOps and, finally, we present 11 impediments for the company’s DevOps adoption that were identified based on the interviews.
Identification of High-Level Concept Clones in Source Code

Source code duplication occurs frequently within large software systems. Pieces of source code, functions, and data types are often duplicated in part, or in whole, for a variety of reasons. Programmers may simply be reusing a piece of code via copy and paste or they may be “reinventing the wheel”. Previous research on the detection of clones is mainly focused on identifying pieces of code with similar (or nearly similar) structure. Our approach is to examine the source code text (comments and identifiers) and identify implementations of similar high-level concepts (e.g., abstract data types). The approach uses an information retrieval technique (i.e., latent semantic indexing) to statically analyze the software system and determine semantic similarities between source code documents (i.e., functions, files, or code segments). These similarity measures are used to drive the clone detection process. The intention of our approach is to enhance and augment existing clone detection methods that are based on structural analysis. This synergistic use of methods will improve the quality of clone detection. A set of experiments is presented that demonstrate the usage of semantic similarity measure to identify clones within a version of NCSA Mosaic.
NON-LOCAL UNSUPERVISED VARIATIONAL IMAGE SEGMENTATION MODELS

New image denoising models based on non-local image information have been recently introduced in the literature. These so-called ”non-local” denoising models provide excellent results because these models can denoise smooth regions or/and textured regions simultaneously, unlike standard denoising models. Standard variational models s.a. Total Variation-based models are defined to work in a small local neighborhood, which is enough to denoise smooth regions. However, textures are not local in nature and requires semi-local/non-local information to be denoised efficiently. Several papers have introduced non-local filters and non-local variational models for image denoising. Yet, few studies have been done to develop unsupervised image segmentation models based on non-local information. This will be the goal of this paper. We define and study three unsupervised non-local segmentation models. These models will be based on the continuous global minimization approach for image segmentation recently introduced in . The energy of [10, 6] is a first order energy composed of the weighted Total Variation norm and a linear term. The first proposed non-local segmentation model will extend the Total Variation regularization term of [10, 6] to the non-local Total Variation energy. We will see that the non-local energy can segment fine and small structures better than the standard Total Variation energy. The second model will extend the data-based term of [10, 6] to a non-local term using the Chan-Vese model. The proposed non-local Chan-Vese model will overcome the main limitation of the original model, that does not work with local intensity inhomogeneities. Finally, the third model will also extend the data-based term of [10, 6] to a non-local term using the Mumford-Shah energy. The original Mumford-Shah energy is designed to work for piecewise smooth images only. We suggest to extend it to textures, defining a non-local Mumford-Shah model that works with real-world images. Numerical minimization schemes presented in this paper are based on continuous and discrete (graph cut) approaches. Experimental results will illustrate the improvements provided by the three proposed non-local unsupervised segmentation models.
A multisided generalization of Bézier surfaces

In this paper we introduce a class of surface patch representations, called S-patches, that unify and generalize triangular and tensor product Bézier surfaces by allowing patches to be defined over any convex polygonal domain; hence, S-patches may have any number of boundary curves. Other properties of S-patches are geometrically meaningful control points, separate control over positions and derivatives along boundary curves, and a geometric construction algorithm based on de Casteljau's algorithm. Of special interest are the regular S-patches, that is, S-patches defined on regular domain polygons. Also presented is an algorithm for smoothly joining together these surfaces with Ck continuity.
Detection of Road Cracks with Multiple Images

Extracting the defects of the road pavement in images is difficult and, most of the time, one image is used alone. The difficulties of this task are: illumination changes, objects on the road, artefacts due to the dynamic acquisition. In this work, we try to solve some of these problems by using acquisitions from different points of view. In consequence, we present a new methodology based on these steps : the detection of defects in each image, the matching of the images and the merging of the different extractions. We show the increase in performances and more particularly how the false detections are reduced.
Computer vision based detection and localization of potholes in asphalt pavement images

Asphalt pavement distresses have significant importance in roads and highways. This paper addresses the detection and localization of one of the key pavement distresses, the potholes using computer vision. Different kinds of pothole and non-pothole images from asphalt pavement are considered for experimentation. Considering the appearance-shape based nature of the potholes, Histograms of oriented gradients (HOG) features are computed for the input images. Features are trained and classified using Naïve Bayes classifier resulting in labeling of the input as pothole or non-pothole image. To locate the pothole in the detected pothole images, normalized graph cut segmentation scheme is employed. Proposed scheme is tested on a dataset having broad range of pavement images. Experimentation results showed 90 % accuracy for the detection of pothole images and high recall for the localization of pothole in the detected images.
A Survey on the Vehicle Routing Problem and Its Variants

In this paper, we have conducted a literature review on the recent developments and publications involving the vehicle routing problem and its variants, namely vehicle routing problem with time windows (VRPTW) and the capacitated vehicle routing problem (CVRP) and also their variants. The VRP is classified as an NP-hard problem. Hence, the use of exact optimization methods may be difficult to solve these problems in acceptable CPU times, when the problem involves real-world data sets that are very large. The vehicle routing problem comes under combinatorial problem. Hence, to get solutions in determining routes which are realistic and very close to the optimal solution, we use heuristics and meta-heuristics. In this paper we discuss the various exact methods and the heuristics and meta-heuristics used to solve the VRP and its variants.
A Linear Algorithm for Tracing Magnet Position and Orientation by Using Three-Axis Magnetic Sensors

For medical diagnoses and treatments, it is often desirable to wirelessly trace an object that moves inside the human body. A magnetic tracing technique suggested for such applications uses a small magnet as the excitation source, which does not require the power supply and connection wire. It provides good tracing accuracy and can be easily implemented. As the magnet moves, it establishes around the human body a static magnetic field, whose intensity is related to the magnet's 3-D position and 2-D orientation parameters. With magnetic sensors, these magnetic intensities can be detected in some predetermined spatial points, and the position and orientation parameters can be computed. Typically, a nonlinear optimization algorithm is applied to such a problem, but a linear algorithm is preferable for faster, more reliable computation, and lower complexity. In this paper, we propose a linear algorithm to determine the 5-D magnet's position and orientation parameters. With the data from five (or more) three-axis magnetic sensors, this algorithm results in a solution by the matrix and algebra computations. We applied this linear algorithm on the real localization system, and the results of simulations and real experiments show that satisfactory tracing accuracy can be achieved by using a sensor array with enough three-axis magnetic sensors.
Vision-based human motion analysis: An overview

Markerless vision-based human motion analysis has the potential to provide an inexpensive, non-obtrusive solution for the estimation of body poses. The significant research effort in this domain has been motivated by the fact that many application areas, including surveillance, Human–Computer Interaction and automatic annotation, will benefit from a robust solution. In this paper, we discuss the characteristics of human motion analysis. We divide the analysis into a modeling and an estimation phase. Modeling is the construction of the likelihood function, estimation is concerned with finding the most likely pose given the likelihood surface. We discuss model-free approaches separately. This taxonomy allows us to highlight trends in the domain and to point out limitations of the current state of the art.