nils-herrmann/clean_scirepeval huggingface dataset:
Operators' adaptation to imperfect automation - Impact of miss-prone alarm systems on attention allocation and performance

Operators in complex environments are often supported by alarm systems that indicate when to shift attention to certain tasks. As alarms are not perfectly reliable, operators have to select appropriate strategies of attention allocation to compensate for unreliability and to maintain overall performance. This study explores how humans adapt to differing alarm reliabilities. Within a multi-task simulation consisting of a monitoring task and two other concurrent tasks, participants were assigned to one of five groups. In the manual control group none of the tasks was supported by an alarm system, whereas the four experimental groups were supported in the monitoring task by a miss-prone alarm system differing in reliability, i.e. 68.75%, 75%, 87.5%, 93.75%. Compared to the manual control group, all experimental groups benefited from the support by alarms, with best performance for the highest reliability condition. However, for the lowest reliability group the benefit was associated with an increased attentional effort, a more demanding attention allocation strategy, and a declined relative performance in a concurrent task. Results are discussed in the context of recent automation research. & 2014 Elsevier Ltd. All rights reserved.
SOFIA: An automated security oracle for black-box testing of SQL-injection vulnerabilities

Security testing is a pivotal activity in engineering secure software. It consists of two phases: generating attack inputs to test the system, and assessing whether test executions expose any vulnerabilities. The latter phase is known as the security oracle problem. 
 In this work, we present SOFIA, a Security Oracle for SQL-Injection Vulnerabilities. SOFIA is programming-language and source-code independent, and can be used with various attack generation tools. Moreover, because it does not rely on known attacks for learning, SOFIA is meant to also detect types of SQLi attacks that might be unknown at learning time. The oracle challenge is recast as a one-class classification problem where we learn to characterise legitimate SQL statements to accurately distinguish them from SQLi attack statements. 
 We have carried out an experimental validation on six applications, among which two are large and widely-used. SOFIA was used to detect real SQLi vulnerabilities with inputs generated by three attack generation tools. The obtained results show that SOFIA is computationally fast and achieves a recall rate of 100% (i.e., missing no attacks) with a low false positive rate (0.6%).
Clustering Web pages based on their structure

Several techniques have been recently proposed to automatically generate Web wrappers, i.e., programs that extract data from HTML pages, and transform them into a more structured format, typically in XML. These techniques automatically induce a wrapper from a set of sample pages that share a common HTML template. An open issue, however, is how to collect suitable classes of sample pages to feed the wrapper inducer. Presently, the pages are chosen manually. In this paper, we tackle the problem of automatically discovering the main classes of pages offered by a site by exploring only a small yet representative portion of it. We propose a model to describe abstract structural features of HTML pages. Based on this model, we have developed an algorithm that accepts the URL of an entry point to a target Web site, visits a limited yet representative number of pages, and produces an accurate clustering of pages based on their structure. We have developed a prototype, which has been used to perform experiments on real-life Web sites.
Digital multiphase buck converter with current balance/phase shedding control

This paper presents a 2-phase interleaved buck converter which both has current balance and phase shedding control. These methods have been incorporated into high current multiphase converters in the past. But in this paper, it operates under low current situation which meets the portable device specification. A voltage mode digital controller in FPGA implementation is described. Experimental results are obtained for a 1 MHz, 1V/0.8A 2-phase voltage mode buck regulator.
Discovering states and transformations in image collections

Objects in visual scenes come in a rich variety of transformed states. A few classes of transformation have been heavily studied in computer vision: mostly simple, parametric changes in color and geometry. However, transformations in the physical world occur in many more flavors, and they come with semantic meaning: e.g., bending, folding, aging, etc. The transformations an object can undergo tell us about its physical and functional properties. In this paper, we introduce a dataset of objects, scenes, and materials, each of which is found in a variety of transformed states. Given a novel collection of images, we show how to explain the collection in terms of the states and transformations it depicts. Our system works by generalizing across object classes: states and transformations learned on one set of objects are used to interpret the image collection for an entirely new object class.
Influence Maximization for Complementary Goods: Why Parties Fail to Cooperate?

We consider the problem where companies provide different types of products and want to promote their products through viral marketing simultaneously. Most previous works assume products are purely competitive. Different from them, our work considers that each product has a pairwise relationship which can be from strongly competitive to strongly complementary to each other's product. The problem is to maximize the spread size with the presence of different opponents with different relationships on the network. We propose Interacting Influence Maximization (IIM) game to model such problems by extending the model of the Competitive Influence Maximization (CIM) game studied by previous works, which considers purely competitive relationship. As for the theoretical approach, we prove that the Nash equilibrium of highly complementary products of different companies may still be very inefficient due to the selfishness of companies. We do so by introducing a well-known concept in game theory, called Price of Stability (PoS) of the extensive-form game. We prove that in any k selfish players symmetric complementary IIM game, the overall spread of the products can be reduced to as less as 1/k of the optimal spread. Since companies may fail to cooperate with one another, we propose different competitive objective functions that companies may consider and deal with separately. We propose a scalable strategy for maximizing influence differences, called TOPBOSS that is guaranteed to beat the first player in a single-round two-player second-move game. In the experiment, we first propose a learning method to learn the ILT model, which we propose for IIM game, from both synthetic and real data to validate the effectiveness of ILT. We then exhibit that the performance of several heuristic strategies in the traditional influence maximization problem can be improved by acquiring the knowledge of the existence of competitive/complementary products in the network. Finally, we compare the TOPBOSS with different heuristic algorithms in real data and demonstrate the merits of TOPBOSS.
Information theory and neural coding

Information theory quantifies how much information a neural response carries about the stimulus. This can be compared to the information transferred in particular models of the stimulus–response function and to maximum possible information transfer. Such comparisons are crucial because they validate assumptions present in any neurophysiological analysis. Here we review information-theory basics before demonstrating its use in neural coding. We show how to use information theory to validate simple stimulus–response models of neural coding of dynamic stimuli. Because these models require specification of spike timing precision, they can reveal which time scales contain information in neural coding. This approach shows that dynamic stimuli can be encoded efficiently by single neurons and that each spike contributes to information transmission. We argue, however, that the data obtained so far do not suggest a temporal code, in which the placement of spikes relative to each other yields additional information.
Benchmarks for Grid-Based Pathfinding

The study of algorithms on grids has been widespread in a number of research areas. Grids are easy to implement and offer fast memory access. Because of their simplicity, they are used even in commercial video games. But, the evaluation of work on grids has been inconsistent between different papers. Many research papers use different problem sets, making it difficult to compare results between papers. Furthermore, the performance characteristics of each test set are not necessarily obvious. This has motivated the creation of a standard test set of maps and problems on the maps that are open for all researchers to use. In addition to creating these sets, we use a variety of metrics to analyze the properties of the test sets. The goal is that these test sets will be useful to many researchers, making experimental results more comparable across papers, and improving the quality of research on grid-based domains.
DeepHTTP: Semantics-Structure Model with Attention for Anomalous HTTP Traffic Detection and Pattern Mining

In the Internet age, cyber-attacks occur frequently with complex types. Traffic generated by access activities can record website status and user request information, which brings a great opportunity for network attack detection. Among diverse network protocols, Hypertext Transfer Protocol (HTTP) is widely used in government, organizations and enterprises. In this work, we propose DeepHTTP, a semantics structure integration model utilizing Bidirectional Long Short-TermMemory (Bi-LSTM) with attention mechanism to model HTTP traffic as a natural language sequence. In addition to extracting traffic content information, we integrate structural information to enhance the generalization capabilities of the model. Moreover, the application of attention mechanism can assist in discovering critical parts of anomalous traffic and furthermining attack patterns. Additionally, we demonstrate how to incrementally update the data set and retrain model so that it can be adapted to new anomalous traffic. Extensive experimental evaluations over large traffic data have illustrated that DeepHTTP has outstanding performance in traffic detection and pattern discovery.
Implementation matters: a review of research on the influence of implementation on program outcomes and the factors affecting implementation.

The first purpose of this review was to assess the impact of implementation on program outcomes, and the second purpose was to identify factors affecting the implementation process. Results from over quantitative 500 studies offered strong empirical support to the conclusion that the level of implementation affects the outcomes obtained in promotion and prevention programs. Findings from 81 additional reports indicate there are at least 23 contextual factors that influence implementation. The implementation process is affected by variables related to communities, providers and innovations, and aspects of the prevention delivery system (i.e., organizational functioning) and the prevention support system (i.e., training and technical assistance). The collection of implementation data is an essential feature of program evaluations, and more information is needed on which and how various factors influence implementation in different community settings.
Inferring new indications for approved drugs via random walk on drug-disease heterogenous networks

Since traditional drug research and development is often time-consuming and high-risk, there is an increasing interest in establishing new medical indications for approved drugs, referred to as drug repositioning, which provides a relatively low-cost and high-efficiency approach for drug discovery. With the explosive growth of large-scale biochemical and phenotypic data, drug repositioning holds great potential for precision medicine in the post-genomic era. It is urgent to develop rational and systematic approaches to predict new indications for approved drugs on a large scale. In this paper, we propose the two-pass random walks with restart on a heterogenous network, TP-NRWRH for short, to predict new indications for approved drugs. Rather than random walk on bipartite network, we integrated the drug-drug similarity network, disease-disease similarity network and known drug-disease association network into one heterogenous network, on which the two-pass random walks with restart is implemented. We have conducted performance evaluation on two datasets of drug-disease associations, and the results show that our method has higher performance than six existing methods. A case study on the Alzheimer’s disease showed that nine of top 10 predicted drugs have been approved or investigational for neurodegenerative diseases. The experimental results show that our method achieves state-of-the-art performance in predicting new indications for approved drugs. We proposed a two-pass random walk with restart on the drug-disease heterogeneous network, referred to as TP-NRWRH, to predict new indications for approved drugs. Performance evaluation on two independent datasets showed that TP-NRWRH achieved higher performance than six existing methods on 10-fold cross validations. The case study on the Alzheimer’s disease showed that nine of top 10 predicted drugs have been approved or are investigational for neurodegenerative diseases. The results show that our method achieves state-of-the-art performance in predicting new indications for approved drugs.
Efficient Gabor filter design for texture segmentation

Gabor filters have been successfully applied to a broad range of image processing tasks. The present paper considers the design of a single filter to segment a two-texture image. A new efficient algorithm for Gabor-filter design is presented, along with methods for estimating filter output statistics. The algorithm draws upon previous results that showed that the output of a Gabor-filtered texture is modeled well by a Rician distribution. A measure of the total output power is used to select the center frequency of the filter and is used to estimate the Rician statistics of the Gabor-filtered image. The method is further generalized to include the statistics of postfiltered outputs that are generated by a Gaussian filtering operation following the Gabor filter. The new method typically requires an order of magnitude less computation to design a filter than a previously proposed method. Experimental results demonstrate the efficacy of the method. Image segmentation Texture Gabor filters Rician statistics Texture segmentation Image statistics Texture analysis
Information-Centric Networking in next-generation communications scenarios

Next-generation networking environments, characterized by the overlapping of wireless networks of different types, are emerging as a new and extremely interesting scenario. Their high dynamicity and heterogeneity present significant challenges from the communications perspective, which call for the adoption of new paradigms based on opportunistic and Information-Centric Networking (ICN) approaches. Applications operating in next-generation environments have peculiar characteristics that could benefit from ICN-based middleware solutions. This paper presents ICeDiM, a middleware we designed for ICN communications in next-generation scenarios, which builds on top of the innovative concept of Application-level Dissemination Channels (ADCs) with tunable permeability levels. A thorough and in-depth experimental evaluation of ICeDiM in a next-generation environment realistically simulated using ICeONE, a modified version of the well-known ONE simulator, demonstrates that our approach can achieve very good performance levels in terms of delivery ratio and network resource consumption.
Labia Majora Hypertrophy

A case involving hypertrophy of the labia majora is described. A married 34-year-old mother of a 5-year-old daughter reported that from adolescence, her large labia majora and its protuberance have resembled male genitalia even when she is wearing a slip. For that reason, in the summer, she is ashamed of her appearance and thus has avoided swimming pools and beach areas. With the patient in supine position, the crural creases were marked. The marks were drawn 1 cm apart medially into the medial part of the labia majora parallel to the natural vulvar crease. The estimated amount of skin and fat to be removed was marked. In this case, more skin and fat needed to be removed from the left side than from the right side. The surgery was performed using sedation and local anesthesia as with outpatient surgery. To avoid excessive bleeding of this area, profuse infiltration was performed using 200 ml of anesthetic solution. Resection of the skin and fat in the area was performed with two hooks holding the area up to avoid introducing the scalpel too deeply. Only the superficial fat was removed. After meticulous hemostasia, two layers of 3/0 absorbable running sutures were placed, one in the deep fat and another in the subdermis, until the wound edges were approached. A 5/0 mononylon running suture closed the skin. These sutures provided strong support to all this very mobile area, avoiding dead spaces and bleeding because postoperative compressive bandages are difficult to hold in this region. The patient was instructed to wash the area four times a day. The surgery was ambulatory, with the patient returning to her province the day after surgery, then coming back for a control visit on postoperative day 10. On postoperative day 1, a moderate edema was observed but no hematoma. The stitches were removed on day 10 after surgery. The postoperative evolution of the case was uneventful. The sensitivity of the labia majora’s interior aspect was preserved. With legs open, the labia majora closed the entrance to the vagina without showing the labia minora. A moderate edema was observed 4 weeks after surgery, and 6 months after surgery, the patient’s external genitalia had reached a normal appearance. At 4 months after surgery, the scars were barely noticeable.
Exploring possible predictors and moderators of an executive function training for children with an autism spectrum disorder.

Previously, a total of 121 children with an autism spectrum disorder (ASD) performed an adaptive working memory (WM)-training, an adaptive flexibility-training, or a non-adaptive control (mock)-training. Despite overall improvement, there were minor differences between the adaptive and mock-training conditions. Moreover, dropout was relatively high (26%). In the current study we explored potential predicting and moderating factors to clarify these findings. The effects of intelligence, autism traits, WM, flexibility, reward sensitivity and Theory of Mind on dropout, improvement during training, and improvement in everyday executive functioning (EF), ASD-like behavior, and Quality of Life (QoL) were studied. None of the predictors influenced dropout or training improvement. However, 1) more pre-training autism traits related to less improvement in EF and QoL, and 2) higher reward sensitivity was related to more improvement in QoL and ASD-like behavior. These findings suggest that these EF-training procedures may be beneficial for children with fewer autism traits and higher reward sensitivity. However, the exploratory nature of the analyses warrant further research before applying the findings clinically.
Metamaterial-Based Antennas

Metamaterials are materials typically engineered with novel or artificial structures to produce electromagnetic properties that are unusual or difficult to obtain in nature. Because of their promise to provide engineerable permittivity, permeability, and index of refraction, metamaterials have drawn broad interest and have led to possible utilization in many electromagnetic applications from the microwave to optical regime, especially for the radiated-wave devices. This paper presents a detailed review upon the most recent research efforts associated with those metamaterial-based small antennas. They are discussed and classified in several different categories such as the antennas based on dispersion engineering, metaresonators, and metamaterial loadings. Some practical difficulties or limitations for the development of metamaterial-based small antennas are pointed out and the possible approaches to resolve these problems are also illustrated. A wide variety of antenna examples are included to facilitate the understanding for general readers.
Parallel and Distributed Data Pipelining with KNIME

In recent years a new category of data analysis applications have evolved, known as data pipelining tools, which enable even nonexperts to perform complex analysis tasks on potentially huge amounts of data. Due to the complex and computing intensive analysis processes and methods used, it is often neither sufficient nor possible to simply rely on the increase of performance of single processors. Promising solutions to this problem are parallel and distributed approaches that can accelerate the analysis process. In this paper we discuss the parallel and distribution potential of pipelining tools by demonstrating several parallel and distributed implementations in the open source pipelining platform KNIME. We verify the practical applicability in a number of real world experiments.
Accuracy in Random Number Generation

The generation of continuous random variables on a digital computer encounters a problem of accuracy caused by approximations and discretization error. These in turn impose a bias on the simulation results. An ideal discrete approximation of a continuous distribution and a measure of error are proposed. Heuristic analysis of common methods for transforming uniform deviates to other continuous random variables is discussed. Comments and recommendations are made for the design of algorithms to reduce the bias and avoid overflow problems.
Annotating meta-discourse in academic lectures from different disciplines

The use of discourse structure was shown to be effective in various applications. Meta-discourse is often used as an expression to signal discourse structure. Previous work focused on using the meta-discourse structure in written texts, or spoken material in very clean conditions. This paper presents a metadiscourse annotated corpus in a more challenging educational context. The corpus comprises of academic lectures from two different disciplines: physics and economics. The schema used focuses on five categories:  Previewing, Reviewing and Enumerating. The annotation task is described in detail, including instructions and strategies used by expert annotators. Annotation results are reported in terms of inter-annotator agreement, self-reported confidence and number of occurrences. Results show that meta-discourse is frequently used in academic lectures and this is observed in the two selected disciplines. Further analysis of the corpus is conducted showing that some of these categories, namely Introduction and Previewing, are correlated with labelled topic boundaries, which is also consistent in both disciplines. This finding shows the potential for using meta-discourse information in topic segmentation task.