nils-herrmann/clean_scirepeval huggingface dataset:
Hiding Behind the Shoulders of Giants: Abusing Crawlers for Indirect Web Attacks

It could be argued that without search engines, the web would have never grown to the size that it has today. To achieve maximum coverage and provide relevant results, search engines employ large armies of autonomous crawlers that continuously scour the web, following links, indexing content, and collecting features that are then used to calculate the ranking of each page. In this paper, we describe how autonomous crawlers can be abused by attackers to exploit vulnerabilities on thirdparty websites while hiding the true origin of the attacks. Moreover, we show how certain vulnerabilities on websites that are currently deemed unimportant, can be abused in a way that would allow an attacker to arbitrarily boost the rankings of malicious websites in the search results of popular search engines. Motivated by the potentials of these vulnerabilities, we propose a series of preventive and defensive countermeasures that website owners and search engines can adopt to minimize, or altogether eliminate, the effects of crawler-abusing attacks.
On the effect of endpoints on dynamic time warping

While there exist a plethora of classification algorithms for most data types, there is an increasing acceptance that the unique properties of time series mean that the combination of nearest neighbor classifiers and Dynamic Time Warping (DTW) is very competitive across a host of domains, from medicine to astronomy to environmental sensors. While there has been significant progress in improving the efficiency and effectiveness of DTW in recent years, in this work we demonstrate that an underappreciated issue can significantly degrade the accuracy of DTW in real-world deployments. This issue has probably escaped the attention of the very active time series research community because of its reliance on static highly contrived benchmark datasets, rather than real world dynamic datasets where the problem tends to manifest itself. In essence, the issue is that DTW’s eponymous invariance to warping is only true for the main “body” of the two time series being compared. However, for the “head” and “tail” of the time series, the DTW algorithm affords no warping invariance. The effect of this is that tiny differences at the beginning or end of the time series (which may be either consequential or simply the result of poor “cropping”) will tend to contribute disproportionally to the estimated similarity, producing incorrect classifications. In this work, we show that this effect is real, and reduces the performance of the algorithm. We further show that we can fix the issue with a subtle redesign of the DTW algorithm, and that we can learn an appropriate setting for the extra parameter we introduced. We further demonstrate that our generalization is amiable to all the optimizations that make DTW tractable for large datasets.
Grid-based DBSCAN for clustering extended objects in radar data

The online observation using high-resolution radar of a scene containing extended objects imposes new requirements on a robust and fast clustering algorithm. This paper presents an algorithm based on the most cited and common clustering algorithm: DBSCAN . The algorithm is modified to deal with the non-equidistant sampling density and clutter of radar data while maintaining all its prior advantages. Furthermore, it uses varying sampling resolution to perform an optimized separation of objects at the same time it is robust against clutter. The algorithm is independent of difficult to estimate input parameters such as the number or shape of available objects. The algorithm outperforms DBSCAN in terms of speed by using the knowledge of the sampling density of the sensor (increase of app. 40-70%). The algorithm obtains an even better result than DBSCAN by including the Doppler and amplitude information (unitless distance criteria).
Laughbot: Detecting Humor in Spoken Language with Language and Audio Cues

We propose detecting and responding to humor in spoken dialogue by extracting language and audio cues and subsequently feeding these features into a combined recurrent neural network (RNN) and logistic regression model. In this paper, we parse Switchboard phone conversations to build a corpus of punchlines and unfunny lines where punchlines precede laughter tokens in Switchboard transcripts. We create a combined RNN and logistic regression model that uses both acoustic and language cues to predict whether a conversational agent should respond to an utterance with laughter. Our model achieves an F1-score of 63.2 and accuracy of 73.9. This model outperforms our logistic language model (F1-score 56.6) and RNN acoustic model (59.4) as well as the final RNN model of D. Bertero, 2016 (52.9). Using our final model, we create a “laughbot” that audibly responds to a user with laughter when their utterance is classified as a punchline. A conversational agent outfitted with a humorrecognition system such as the one we present in this paper would be valuable as these agents gain utility in everyday life. Chatbots; spoken natural language processing; deep learning; machine learning
Novel use of electronic whiteboard in the operating room increases surgical team compliance with pre-incision safety practices.

Despite evidence that use of a checklist during the pre-incision time out improves patient morbidity and mortality, compliance with performing the required elements of the checklist has been low. In an effort to improve compliance, a standardized time out interactive Electronic Checklist System [iECS] was implemented in all hospital operating room (OR) suites at 1 institution. The purpose of this 12-month prospective observational study was to assess whether an iECS in the OR improves and sustains improved surgical team compliance with the pre-incision time out.



Direct observational analyses of preprocedural time outs were performed on 80 cases 1 month before, and 1 and 9 months after implementation of the iECS, for a total of 240 observed cases. Three observers, who achieved high interrater reliability (kappa = 0.83), recorded a compliance score (yes, 1; no, 0) on each element of the time out. An element was scored as compliant if it was clearly verbalized by the surgical team.



Pre-intervention observations indicated that surgical staff verbally communicated the core elements of the time out procedure 49.7 ± 12.9% of the time. After implementation of the iECS, direct observation of 80 surgical cases at 1 and 9 months indicated that surgical staff verbally communicated the core elements of the time out procedure 81.6 ± 11.4% and 85.8 ± 6.8% of the time, respectively, resulting in a statistically significant (P < .0001) increase in time out procedural compliance.



Implementation of a standardized, iECS can dramatically increase compliance with preprocedural time outs in the OR, an important and necessary step in improving patient outcomes and reducing preventable complications and deaths.
A Frame Tracking Model for Memory-Enhanced Dialogue Systems

Recently, resources and tasks were proposed to go beyond state tracking in dialogue systems. An example is the frame tracking task, which requires recording multiple frames, one for each user goal set during the dialogue. This allows a user, for instance, to compare items corresponding to different goals. This paper proposes a model which takes as input the list of frames created so far during the dialogue, the current user utterance as well as the dialogue acts, slot types, and slot values associated with this utterance. The model then outputs the frame being referenced by each triple of dialogue act, slot type, and slot value. We show that on the recently published Frames dataset, this model significantly outperforms a previously proposed rule-based baseline. In addition, we propose an extensive analysis of the frame tracking task by dividing it into sub-tasks and assessing their difficulty with respect to our model.
Hubs in Space: Popular Nearest Neighbors in High-Dimensional Data

Different aspects of the curse of dimensionality are known t o present serious challenges to various machine-learning methods and tasks. This paper explores a n w spect of the dimensionality curse, referred to ashubness , that affects the distribution of k-occurrences: the number of times a point appears among the k nearest neighbors of other points in a data set. Through theo retical and empirical analysis involving synthetic and real data sets we show t at under commonly used assumptions this distribution becomes considerably skewed as dimensio nality increases, causing the emergence of hubs, that is, points with very highk-occurrences which effectively represent “popular” neare st neighbors. We examine the origins of this phenomenon, showi ng that it is an inherent property of data distributions in high-dimensional vector space, di scuss its interaction with dimensionality reduction, and explore its influence on a wide range of machin e-learning tasks directly or indirectly based on measuring distances, belonging to supervis ed, semi-supervised, and unsupervised learning families.
Employing Graph Databases as a Standardization Model towards Addressing Heterogeneity

Schema heterogeneity has been perceived as a major challenge towards data integration and exchange for more than two decades. The advent of big data and NoSQL data stores has further led to proliferation of data models thus exacerbating those challenges. It would be useful to have an approach that allows leveraging both schema-based and schemaless data stores. A graph model provides a solution towards unifying them under a common representation. We present an approach to transform schemas into a homogeneous graph representation so that they can be further integrated with data from other NoSQL stores in an automated and seamless manner. We demonstrate our approach over relational and RDF schemas but the framework is extensible to allow further integration of additional data stores. Through this paper, we contribute to the data complexity (variety) aspect of big data by bridging the gap between different data stores including schema-based and schemaagnostic. We utilize Neo4j as the graph database. We present a proof-of-concept that implements our approach along with an evaluation based on qualitative and quantitative metrics.
E-Voting with Blockchain: An E-Voting Protocol with Decentralisation and Voter Privacy

Technology has positive impacts on many aspects of our social life. Designing a 24 hour globally connected architecture enables ease of access to a variety of resources and services. Furthermore, technology like the Internet has been a fertile ground for innovation and creativity. One such disruptive innovation is blockchain – a keystone of cryptocurrencies. The blockchain technology is presented as a game changer for many of the existing and emerging technologies/services. With its immutability property and decentralised architecture, it is taking centre stage in many services as an equalisation factor to the current parity between consumers and large corporations/governments. One potential application of the blockchain is in e-voting schemes. The objective of such a scheme would be to provide a decentralised architecture to run and support a voting scheme that is open, fair, and independently verifiable. In this paper, we propose a potential new e-voting protocol that utilises the blockchain as a transparent ballot box. The protocol has been designed to adhere to fundamental e-voting properties as well as offer a degree of decentralisation and allow for the voter to change/update their vote (within the permissible voting period). This paper highlights the pros and cons of using blockchain for such a proposal from a practical point view in both development/deployment and usage contexts. Concluding the paper is a potential roadmap for blockchain technology to be able to support complex applications.
The human use of human beings - cybernetics and society

Bargaining with reading habit is no need. Reading is not kind of something sold that you can take or not. It is a thing that will change your life to life better. It is the thing that will give you many things around the world and this universe, in the real world and here after. As what will be given by this human use of human beings cybernetics and society, how can you bargain with the thing that has many benefits for you?
The incredible queen of green: Nutritive value and therapeutic potential of Moringa oleifera Lam.

Moringa oleifera Lam. (synonym: Moringa pterygosperma Gaertn.) (M. oleifera) known in 82 countries by 210 different names is well known by the name of the miracle tree. It is one of the extensively cultivated and highly valued members of Moringaceae, a monogeneric family, comprising of thirteen perennial angiosperm shrubs and trees[1-3]. Moringa tree is endemic to the Himalayan foothills of Pakistan, Afghanistan, Bangladesh and India, and is cultivated throughout tropics. It is recognized by a mixture of vernacular names, among of them, drumstick tree, horseradish tree, ben oil tree and malunggay are the most commonly reported in the history of this plant[4]. In Pakistan, Sohanjna is the vernacular name of M. oleifera[5,6]. It yields low quality timber, as it is a softwood tree, but it is belived for centuries that this plant possesses a number of industrial, traditional and medicinal benefits[7]. Fertilizer (seed cake), green manure (leaves), blue dye (wood), fencing (living trees), domestic cleaning agent (crushed leaves), alley cropping, animal feed (leaves and seed cake), medicine (all plant parts), foliar nutrient (juice expressed from the leaves), gum (tree trunks), biogas (leaves), biopesticide, ornamental plantings, water purifier (powdered seeds), honey (flower nectar) are different uses of this plant reported in literature[2,6,8-20]. M. oleifera is a good source of aminoacids and contains a number of important minerals, β-carotene, various phenolics and vitamins[21,22]. M. oleifera is also an important vegetable food article of trade, particularly in Pakistan, Hawaii, Philippines, Africa and India which has a huge deliberation as the natural nutrition[1,23]. In South Asia, various plant parts, including leaves, bark, root, gum, flowers, pods, seeds and seed oil are used for the variety of infectious and inflammatory disorders along with hepatorenal, gastrointestinal, hematological and cardiovascular diseases[22,24-26]. Various therapeutic potentials are also credited to different parts of ARTICLE INFO
Nudging guideline-concordant antibiotic prescribing: a randomized clinical trial.

"Nudges" that influence decision making through subtle cognitive mechanisms have been shown to be highly effective in a wide range of applications, but there have been few experiments to improve clinical practice.



To investigate the use of a behavioral "nudge" based on the principle of public commitment in encouraging the judicious use of antibiotics for acute respiratory infections (ARIs).



Randomized clinical trial in 5 outpatient primary care clinics. A total of 954 adults had ARI visits during the study timeframe: 449 patients were treated by clinicians randomized to the posted commitment letter (335 in the baseline period, 114 in the intervention period); 505 patients were treated by clinicians randomized to standard practice control (384 baseline, 121 intervention).



The intervention consisted of displaying poster-sized commitment letters in examination rooms for 12 weeks. These letters, featuring clinician photographs and signatures, stated their commitment to avoid inappropriate antibiotic prescribing for ARIs.



Antibiotic prescribing rates for antibiotic-inappropriate ARI diagnoses in baseline and intervention periods, adjusted for patient age, sex, and insurance status.



Baseline rates were 43.5% and 42.8% for control and poster, respectively. During the intervention period, inappropriate prescribing rates increased to 52.7% for controls but decreased to 33.7% in the posted commitment letter condition. Controlling for baseline prescribing rates, we found that the posted commitment letter resulted in a 19.7 absolute percentage reduction in inappropriate antibiotic prescribing rate relative to control (P = .02). There was no evidence of diagnostic coding shift, and rates of appropriate antibiotic prescriptions did not diminish over time.



Displaying poster-sized commitment letters in examination rooms decreased inappropriate antibiotic prescribing for ARIs. The effect of this simple, low-cost intervention is comparable in magnitude to costlier, more intensive quality-improvement efforts.
EdgeConnect: Generative Image Inpainting with Adversarial Edge Learning

Over the last few years, deep learning techniques have yielded significant improvements in image inpainting. However, many of these techniques fail to reconstruct reasonable structures as they are commonly over-smoothed and/or blurry. This paper develops a new approach for image inpainting that does a better job of reproducing filled regions exhibiting fine details. We propose a two-stage adversarial model EdgeConnect that comprises of an edge generator followed by an image completion network. The edge generator hallucinates edges of the missing region (both regular and irregular) of the image, and the image completion network fills in the missing regions using hallucinated edges as a priori. We evaluate our model end-to-end over the publicly available datasets CelebA, Places2, and Paris StreetView, and show that it outperforms current state-ofthe-art techniques quantitatively and qualitatively.
Local multidimensional scaling

In a visualization task, every nonlinear projection method needs to make a compromise between trustworthiness and continuity. In a trustworthy projection the visualized proximities hold in the original data as well, whereas a continuous projection visualizes all proximities of the original data. We show experimentally that one of the multidimensional scaling methods, curvilinear components analysis, is good at maximizing trustworthiness. We then extend it to focus on local proximities both in the input and output space, and to explicitly make a user-tunable parameterized compromise between trustworthiness and continuity. The new method compares favorably to alternative nonlinear projection methods.
Diagnostic Performance of a Smartphone‐Based Photoplethysmographic Application for Atrial Fibrillation Screening in a Primary Care Setting

Diagnosing atrial fibrillation (AF) before ischemic stroke occurs is a priority for stroke prevention in AF. Smartphone camera-based photoplethysmographic (PPG) pulse waveform measurement discriminates between different heart rhythms, but its ability to diagnose AF in real-world situations has not been adequately investigated. We sought to assess the diagnostic performance of a standalone smartphone PPG application, Cardiio Rhythm, for AF screening in primary care setting.



Patients with hypertension, with diabetes mellitus, and/or aged ≥65 years were recruited. A single-lead ECG was recorded by using the AliveCor heart monitor with tracings reviewed subsequently by 2 cardiologists to provide the reference standard. PPG measurements were performed by using the Cardiio Rhythm smartphone application. AF was diagnosed in 28 (2.76%) of 1013 participants. The diagnostic sensitivity of the Cardiio Rhythm for AF detection was 92.9% (95% CI] 77-99%) and was higher than that of the AliveCor automated algorithm (71.4% [95% CI 51-87%]). The specificities of Cardiio Rhythm and the AliveCor automated algorithm were comparable (97.7% [95% CI: 97-99%] versus 99.4% [95% CI 99-100%]). The positive predictive value of the Cardiio Rhythm was lower than that of the AliveCor automated algorithm (53.1% [95% CI 38-67%] versus 76.9% [95% CI 56-91%]); both had a very high negative predictive value (99.8% [95% CI 99-100%] versus 99.2% [95% CI 98-100%]).



The Cardiio Rhythm smartphone PPG application provides an accurate and reliable means to detect AF in patients at risk of developing AF and has the potential to enable population-based screening for AF.
Attachment theory as a framework for explaining engagement with Facebook

Research on the relation between personality and styles of engagement with social media is surprisingly limited and has generated mixed results. The present research applied attachment theory to illuminate individual differences in styles of Facebook engagement. Two studies (N = 583) supported a mediational model explaining various forms of active Facebook use as stemming from attachment anxiety, which predisposes individuals to sensitivity about social feedback, thereby leading them to engage in attentionseeking social media behavior. These results held while controlling for extraversion, neuroticism, and self-esteem. Attachment avoidance predicted restrained Facebook use, primarily due to its association with (low) extraversion. These findings resolve inconsistencies in previous research and demonstrate that attachment theory is a particularly useful framework through which to study the influence of personality on social-media behavior.
Theory of edge detection.

A theory of edge detection is presented. The analysis proceeds in two parts. (1) Intensity changes, which occur in a natural image over a wide range of scales, are detected separately at different scales. An appropriate filter for this purpose at a given scale is found to be the second derivative of a Gaussian, and it is shown that, provided some simple conditions are satisfied, these primary filters need not be orientation-dependent. Thus, intensity changes at a given scale are best detected by finding the zero values of delta 2G(x,y)*I(x,y) for image I, where G(x,y) is a two-dimensional Gaussian distribution and delta 2 is the Laplacian. The intensity changes thus discovered in each of the channels are then represented by oriented primitives called zero-crossing segments, and evidence is given that this representation is complete. (2) Intensity changes in images arise from surface discontinuities or from reflectance or illumination boundaries, and these all have the property that they are spatially. Because of this, the zero-crossing segments from the different channels are not independent, and rules are deduced for combining them into a description of the image. This description is called the raw primal sketch. The theory explains several basic psychophysical findings, and the operation of forming oriented zero-crossing segments from the output of centre-surround delta 2G filters acting on the image forms the basis for a physiological model of simple cells (see Marr & Ullman 1979).
Model Extraction for ARINC 653 Based Avionics Software

One of the most exciting and promising approaches to ensure the correctness of critical systems is software model checking, which considers real code, written with standard programming languages like C. One general technique to implement this approach is producing a reduced model of the software in order to employ existing and efficient tools, like spin. This paper presents the application of the technique to avionics software constructed on top of an application interface (api) compliant with the arinc 653 specification (apex), which is widely employed by the manufacturers in the avionics industry. The paper uses modelling techniques previously developed by the authors. However, these techniques are now extended to deal with new problems, like real-time aspects and scheduling. The paper also contains a novel testing method to ensure the correctness of the key part in the model: the execution engine that implements apex services. This testing method uses spin to execute official test suites.