nils-herrmann/clean_scirepeval huggingface dataset:
Fog Computing: Mitigating Insider Data Theft Attacks in the Cloud

Cloud computing promises to significantly change the way we use computers and access and store our personal and business information. With these new computing and communications paradigms arise new data security challenges. Existing data protection mechanisms such as encryption have failed in preventing data theft attacks, especially those perpetrated by an insider to the cloud provider. We propose a different approach for securing data in the cloud using offensive decoy technology. We monitor data access in the cloud and detect abnormal data access patterns. When unauthorized access is suspected and then verified using challenge questions, we launch a disinformation attack by returning large amounts of decoy information to the attacker. This protects against the misuse of the user's real data. Experiments conducted in a local file setting provide evidence that this approach may provide unprecedented levels of user data security in a Cloud environment.
Learning to Create Jazz Melodies Using a Product of Experts

We describe a neural network architecture designed to learn the musical structure of jazz melodies over chord progressions, then to create new melodies over arbitrary chord progressions from the resulting connectome (representation of neural network structure). Our architecture consists of two sub-networks, the interval expert and the chord expert, each being LSTM (long short-term memory) recurrent networks. These two sub-networks jointly learn to predict a probability distribution over future notes conditioned on past notes in the melody. We describe a training procedure for the network and an implementation as part of the opensource Impro-Visor (Improvisation Advisor) application, and demonstrate our method by providing improvised melodies based on a variety of training sets.
An experimental card game for teaching software engineering processes

The typical software engineering course consists of lectures in which concepts and theories are conveyed, along with a small “toy” software engineering project which attempts to give students the opportunity to put this knowledge into practice. Although both of these components are essential, neither one provides students with adequate practical knowledge regarding the process of software engineering. Namely, lectures allow only passive learning and projects are so constrained by the time and scope requirements of the academic environment that they cannot be large enough to exhibit many of the phenomena occurring in realworld software engineering processes. To address this problem, we have developed Problems and Programmers, an educational card game that simulates the software engineering process and is designed to teach those process issues that are not sufficiently highlighted by lectures and projects. We describe how the game is designed, the mechanics of its gameplay, and the results of an experiment we conducted involving students playing the game.
Evaluation of the neurovascular bundle position at the palate with cone beam computed tomography: an observational study

The aim of this study was to investigate the neurovascular bundle (NVB) position with cone-beam computerized tomography (CBCT).



CBCT images of 345 patients were evaluated. The distance from the neurovascular bundle to the cemento-enamel junction (CEJ) was measured (DNB). The distance from mid-palatal suture to the alveolar crest was used to determine the palatal depth. Palatal junction angle (PA) was measured using the junction angle between the hard palate and alveolar crest. The relationships between the DNB and the palatal depth and between these two parameters and the PA were evaluated. Student's t-test was used to analyze the differences in DNB related to gender, and the correlation between the DNB while Pearson correlation analysis was used to determine the correlation between the DNB and age (p = 0.05). The relationship between the DNB and the palatal depth, and the relationship between these two parameters and the PA were also evaluated using Pearson correlation analysis.



Except at the canine and first premolar areas the DNB was positively correlated with the palatal depth. No significant relationship between the PA and DNB or with PVD was observed. The highest DNB was 14 mm at the first molar, and the lowest was 10.8 mm at the canine.



Care is needed while rotating flap and harvesting the subepithelial connective tissue graft at the canine area because the neurovascular bundle passes approximately 11 mm apically to CEJ at the canine region.
Neural personalized response generation as domain adaptation

One of the most crucial problem on training personalized response generation models for conversational robots is the lack of large scale personal conversation data. To address the problem, we propose a two-phase approach, namely initialization then adaptation, to first pre-train an optimized RNN encoder-decoder model (LTS model) in a large scale conversational data for general response generation and then fine-tune the model in a small scale personal conversation data to generate personalized responses. For evaluation, we propose a novel human aided method, which can be seen as a quasi-Turing test, to evaluate the performance of the personalized response generation models. Experimental results show that the proposed personalized response generation model outperforms the state-of-the-art approaches to language model personalization and persona-based neural conversation generation on the automatic evaluation, offline human judgment and the quasi-Turing test.
Biocontrol mechanisms of Trichoderma strains.

The genus Trichoderma comprises a great number of fungal strains that act as biological control agents, the antagonistic properties of which are based on the activation of multiple mechanisms. Trichoderma strains exert biocontrol against fungal phytopathogens either indirectly, by competing for nutrients and space, modifying the environmental conditions, or promoting plant growth and plant defensive mechanisms and antibiosis, or directly, by mechanisms such as mycoparasitism. These indirect and direct mechanisms may act coordinately and their importance in the biocontrol process depends on the Trichoderma strain, the antagonized fungus, the crop plant, and the environmental conditions, including nutrient availability, pH, temperature, and iron concentration. Activation of each mechanism implies the production of specific compounds and metabolites, such as plant growth factors, hydrolytic enzymes, siderophores, antibiotics, and carbon and nitrogen permeases. These metabolites can be either overproduced or combined with appropriate biocontrol strains in order to obtain new formulations for use in more efficient control of plant diseases and postharvest applications.
A capacitance-to-digital converter for displacement sensing with 17b resolution and 20μs conversion time

In precision mechatronic systems, such as wafer steppers, the position of critical mechanical components must be dynamically stabilized with sub-nanometer precision. This can be achieved by a servo loop consisting of a displacement sensor and an actuator. Compared to optical interferometers, capacitive displacement sensors offer smaller size and lower cost. However, mechanical tolerances limit their electrode spacing to about 10μm , while the targeted resolution is below 100pmrms. This requires a capacitance-to-digital converter (CDC) with more than 17b resolution. Furthermore, its latency must be low enough (20μs) to avoid compromising servo-loop stability. Lastly, it should be stable enough to maintain measurement accuracy during the intervals between system calibrations.
Tribeca: A Stream Database Manager for Network Traffic Analysis

High speed computer and telephone networks carry large amounts of data and signalling traffic. The engineers who build and maintain these networks use a combination of hardware and software tools to monitor the stream of network traffic. Some of these tools operate directly on the live network; others record data on magnetic tape for later offline analysis by software. Most analysis tasks require tens to hundreds of gigabytes of data. Traffic analysis applications include protocol performance analysis, conformance testing, error monitoring and fraud detection.
Cohn topology-based 1:8 power divider for S-band array antenna feeding network

In this paper, a design of 1:8 power divider is proposed for S-band array antenna feeding network. The proposed power divider which is designed based on Cohn topology is implemented on an RO4003C dielectric substrate which has the relative permittivity and the thickness of 3.55 and 1.524mm, respectively. Seven pairs of 1:2 power divider are used to construct the design of 1:8 power divider. Some parameters such as insertion, isolation and voltage standing wave ratio (VSWR) are used to analyze the performance of power divider in the frequency range of 2–4GHz. The result shows that the 1: 2 power divider has the values of insertion loss, isolation and VSWR of greater than 3.08dB, greater than 24dB, and less than 1.1, respectively. Meanwhile, the 1:8 power divider has the insertion value of greater than 9.26dB, isolation value of greater than 23dB, and VSWR value of less than 1.5. The characterization result has shown an adequate performance for S-band array antenna feeding network of radar system.
A novel modified flower pollination algorithm for global optimization

The flower pollination algorithm (FPA) is a relatively new natural bio-inspired optimization algorithm that mimics the real-life processes of the flower pollination. Indeed, this algorithm is based globally on two main rules: the global pollination (biotic and cross-pollination) and the local pollination (abiotic and self-pollination). The random permutation between these latter allows to keep a permanent balance between intensification and diversification. However, this procedure causes an involuntary orientation toward a bad solution (local optima). In addition, FPA illustrates an inadequacy in terms of intensification and diversification of new solutions; this has become clear when the complexity of the treated problem is increased. Further, FPA has also another insufficiency, which is its slow convergence rate caused in principle by its weak intensification. In this paper, to overcome these weaknesses, we have introduced some modifications on the basic FPA algorithmic structure based on the two following improvements: (1) Generating a set of global orientations (toward global or local pollination) for all members of the population. Indeed, each element (global orientation) in this set is composed of a fixed number (equal to the population size) of sub-random orientation. Thus, the number of elements is fixed by the designer, which enhances significantly the diversification characteristic. (2) Constructing a set of best solution vectors relating to all generated global orientations. In fact, this set is compared at each iteration to a fixed number of actual solution vectors to select the best among them based on their fitness values. The proposed algorithm called novel modified FPA (NMFPA) with its novel algorithmic structure offers to researchers the opportunity to: (1) use it in their comparison study (e.g., with others FPA proposed variants) and (2) develop other new methods or techniques based on its novel integrated mechanisms. To demonstrate the performance of this new FPA variant, a set of 28 benchmark functions defined in IEEE-CEC’13 and a 15 real-world numerical optimization problems proposed in the IEEE-CEC’11 are employed. Compared with FPA, two its famous variants and other state-of-the-art evolutionary algorithms, NMFPA shows overall better performance.
Avoiding and treating dermal filler complications.

All fillers are associated with the risk of both early and late complications. Early side effects such as swelling, redness, and bruising occur after intradermal or subdermal injections. The patient has to be aware of and accept these risks. Adverse events that last longer than 2 weeks can be attributable to technical shortcomings (e.g., too superficial an implantation of a long-lasting filler substance). Such adverse events can be treated with intradermal 5-fluorouracil, steroid injections, vascular lasers, or intense pulsed light, and later with dermabrasion or shaving. Late adverse events also include immunologic phenomena such as late-onset allergy and nonallergic foreign body granuloma. Both react well to intralesional steroid injections, which often have to be repeated to establish the right dose. Surgical excisions shall remain the last option and are indicated for hard lumps in the lips and visible hard nodules or hard granuloma in the subcutaneous fat.
Identifying adverse drug events from patient social media: A case study for diabetes

Patient social media sites have emerged as major platforms for discussion of treatments and drug side effects, making them a promising source for listening to patients' voices in adverse drug event reporting. However, extracting patient reports from social media continues to be a challenge in health informatics research. In light of the need for more robust extraction methods, the authors developed a novel information extraction framework for identifying adverse drug events from patient social media. They also conducted a case study on a major diabetes patient social media platform to evaluate their framework's performance. Their approach achieves an f-measure of 86 percent in recognizing discussion of medical events and treatments, an f-measure of 69 percent for identifying adverse drug events, and an f-measure of 84 percent in patient report extraction. Their proposed methods significantly outperformed prior work in extracting patient reports of adverse drug events in health social media.
The Impact of a Product-Harm Crisis on Marketing Effectiveness

P crises are among a firm’s worst nightmares. A firm may experience (i) a loss in baseline sales, (ii) a reduced own effectiveness for its marketing instruments, (iii) an increased cross sensitivity to rival firms’ marketing-mix activities, and (iv) a decreased cross impact of its marketing-mix instruments on the sales of competing, unaffected brands. We find that this quadruple jeopardy materialized in a case study of an Australian product-harm crisis faced by Kraft peanut butter. We arrive at this conclusion by using a timevarying error-correction model that quantifies the consequences of this crisis on base sales, and on ownand cross-brand shortand long-term effectiveness. The proposed modeling approach allows managers to make more informed decisions on how to regain the brands’ pre-crisis performance levels.
Adversarial support vector machine learning

Many learning tasks such as spam filtering and credit card fraud detection face an active adversary that tries to avoid detection. For learning problems that deal with an active adversary, it is important to model the adversary's attack strategy and develop robust learning models to mitigate the attack. These are the two objectives of this paper. We consider two attack models: a free-range attack model that permits arbitrary data corruption and a restrained attack model that anticipates more realistic attacks that a reasonable adversary would devise under penalties. We then develop optimal SVM learning strategies against the two attack models. The learning algorithms minimize the hinge loss while assuming the adversary is modifying data to maximize the loss. Experiments are performed on both artificial and real data sets. We demonstrate that optimal solutions may be overly pessimistic when the actual attacks are much weaker than expected. More important, we demonstrate that it is possible to develop a much more resilient SVM learning model while making loose assumptions on the data corruption models. When derived under the restrained attack model, our optimal SVM learning strategy provides more robust overall performance under a wide range of attack parameters.
A collaborative representation based projections method for feature extraction

In graph embedding based methods, we usually need to manually choose the nearest neighbors and then compute the edge weights using the nearest neighbors via L2 norm (e.g. LLE). It is difficult and unstable to manually choose the nearest neighbors in high dimensional space. So how to automatically construct a graph is very important. In this paper, first, we give a L2-graph like L1-graph. L2-graph calculates the edge weights using the total samples, avoiding manually choosing the nearest neighbors; second, a L2-graph based feature extraction method is presented, called collaborative representation based projections (CRP). Like SPP, CRP aims to preserve the collaborative representation based reconstruction relationship of data. CRP utilizes a L2 norm graph to characterize the local compactness information. CRP maximizes the ratio between the total separability information and the local compactness information to seek the optimal projection matrix. CRP is much faster than SPP since CRP calculates the objective function with L2 norm while SPP calculate the objective function with L1 norm. Experimental results on FERET, AR, Yale face databases and the PolyU finger-knuckle-print database demonstrate that CRP works well in feature extraction and leads to a good recognition performance. & 2014 Elsevier Ltd. All rights reserved.
Markov Random Field model for single image defogging

Fog reduces contrast and thus the visibility of vehicles and obstacles for drivers. Each year, this causes traffic accidents. Fog is caused by a high concentration of very fine water droplets in the air. When light hits these droplets, it is scattered and this results in a dense white background, called the atmospheric veil. As pointed in , Advanced Driver Assistance Systems (ADAS) based on the display of defogged images from a camera may help the driver by improving objects visibility in the image and thus may lead to a decrease of fatality and injury rates. In the last few years, the problem of single image defogging has attracted attention in the image processing community. Being an ill-posed problem, several methods have been proposed. However, a few among of these methods are dedicated to the processing of road images. One of the first exception is the method in ,  where a planar constraint is introduced to improve the restoration of the road area, assuming an approximately flat road. The single image defogging problem being ill-posed, the choice of the Bayesian approach seems adequate to set this problem as an inference problem. A first Markov Random Field (MRF) approach of the problem has been proposed recently in . However, this method is not dedicated to road images. In this paper, we propose a novel MRF model of the single image defogging problem which applies to all kinds of images but can also easily be refined to obtain better results on road images using the planar constraint. A comparative study and quantitative evaluation with several state-of-the-art algorithms is presented. This evaluation demonstrates that the proposed MRF model allows to derive a new algorithm which produces better quality results, in particular in case of a noisy input image.
Linear Antenna Array Synthesis with Invasive Weed Optimization Algorithm

Linear antenna array design is one of the most important electromagnetic optimization problems of current interest. This article describes the application of a recently developed metaheuristic algorithm, known as the Invasive Weed Optimization (IWO), to optimize the spacing between the elements of the linear array to produce a radiation pattern with minimum side lobe level and null placement control. The results of the IWO algorithm have been shown to meet or beat the results obtained using other state-of-the-art metaheuristics like the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Memetic Algorithms (MA), and Tabu Search (TS) in a statistically meaningful way
ASAP: Prioritizing Attention via Time Series Smoothing

Time series visualization of streaming telemetry (i.e., charting of key metrics such as server load over time) is increasingly prevalent in modern data platforms and applications. However, many existing systems simply plot the raw data streams as they arrive, often obscuring large-scale trends due to small-scale noise. We propose an alternative: to better prioritize end users’ attention, smooth time series visualizations as much as possible to remove noise, while retaining large-scale structure to highlight significant deviations. We develop a new analytics operator called ASAP that automatically smooths streaming time series by adaptively optimizing the trade-off between noise reduction (i.e., variance) and trend retention (i.e., kurtosis). We introduce metrics to quantitatively assess the quality of smoothed plots and provide an efficient search strategy for optimizing these metrics that combines techniques from stream processing, user interface design, and signal processing via autocorrelation-based pruning, pixel-aware preaggregation, and ondemand refresh. We demonstrate that ASAP can improve users’ accuracy in identifying long-term deviations in time series by up to 38.4% while reducing response times by up to 44.3%. Moreover, ASAP delivers these results several orders of magnitude faster than alternative search strategies.
Alignment Based Precision Checking

Most organizations have process models describing how cases need to be handled. In fact, legislation and standardization (cf. the Sarbanes-Oxley Act, the Basel II Accord, and the ISO 9000 family of standards) are forcing organizations to document their processes. These processes are often not enforced by information systems. However, torrents of event data are recorded by today’s information systems. These recorded events reflect how processes are really executed. Often reality deviates from the modeled behavior. Therefore, measuring the extent process executions conform to a predefined process model is increasingly important. In this paper, we propose an approach to measure the precision of a process model with respect to an event log. Unlike earlier approaches, we first align model and log thus making our approach more robust, even in case of deviations. The approach has been implemented in the ProM 6 tool and evaluated using both artificial and real life cases.
Towards Accurate Predictions of Customer Purchasing Patterns

A range of algorithms was used to classify online retail customers of a UK company using historical transaction data. The predictive capabilities of the classifiers were assessed using linear regression, Lasso and regression trees. Unlike most related studies, classifications were based upon specific and marketing focused customer behaviours. Prediction accuracy on untrained customers was generally better than 80%. The models implemented (and compared) for classification were: Logistic Regression, Quadratic Discriminant Analysis, Linear SVM, RBF SVM, Gaussian Process, Decision Tree, Random Forest and Multi-layer Perceptron (Neural Network). Postcode data was then used to classify solely on demographics derived from the UK Land Registry and similar public data sources. Prediction accuracy remained better than 60%.
Mining Education Data to Predict Student's Retention: A comparative Study

The main objective of higher education is to provide quality education to students. One way to achieve highest level of quality in higher education system is by discovering knowledge for prediction regarding enrolment of students in a course. This paper presents a data mining project to generate predictive models for student retention management. Given new records of incoming students, these predictive models can produce short accurate prediction lists identifying students who tend to need the support from the student retention program most. This paper examines the quality of the predictive models generated by the machine learning algorithms. The results show that some of the machines learning algorithms are able to establish effective predictive models from the existing student