nils-herrmann/clean_scirepeval huggingface dataset:
Weighted Similarity Schemes for High Scalability in User-Based Collaborative Filtering

Similarity-based algorithms, often referred to as memory-based collaborative filtering techniques, are one of the most successful methods in recommendation systems. When explicit ratings are available, similarity is usually defined using similarity functions, such as the Pearson correlation coefficient, cosine similarity or mean square difference. These metrics assume similarity is a symmetric criterion. Therefore, two users have equal impact on each other in recommending new items. In this paper, we introduce new weighting schemes that allow us to consider new features in finding similarities between users. These weighting schemes, first, transform symmetric similarity to asymmetric similarity by considering the number of ratings given by users on non-common items. Second, they take into account the habit effects of users are regarded on rating items by measuring the proximity of the number of repetitions for each rate on common rated items. Experiments on two datasets were implemented and compared to other similarity measures. The results show that adding weighted schemes to traditional similarity measures These authors contributed equally to this work as the first author. P. Pirasteh · D. Hwang Department of Computer Engineering, Yeungnam University, Gyeongsan, Republic of Korea P. Pirasteh e-mail: parivash63@gmail.com D. Hwang e-mail: dosamhwang@gmail.com J. E. Jung ( ) Department of Computer Engineering, Chung-Ang University, Seoul, Republic of Korea e-mail: ontology.society@gmail.com significantly improve the results obtained from traditional similarity measures.
Forecasting short-term taxi demand using boosting-GCRF

It will be most efficient to frame operation strategies before actual taxi demand is revealed. But this is challenging due to limited knowledge of the taxi demand distribution in immediate future and is more prone to prediction errors. In this study, we develop the boosting Gaussian conditional random field (boosting-GCRF) model to accurately forecast the short-term taxi demand distribution using historical time-series demand over the study area. Comprehensive numerical experiments are conducted to evaluate the performance of boosting-GCRF as compared to five other benchmark algorithms. The results suggest that the boosting-GCRF is superior with the best modified mean absolute percentage error being 10.4%. The approach is observed to be robust based on its prediction performance on anomaly taxi demand data. In addition, the density functions generated by the boosting-GCRF model are found to well capture the actual distribution of the short-term taxi demand.
Overview of the English Slot Filling Track at the TAC 2014 Knowledge Base Population Evaluation

We overview the English Slot Filling (SF) track of the TAC2014 Knowledge Base Population (KBP) evaluation. The goal of this KBP track is to promote research in the extraction of binary relations between named and numeric entities from free text. The main changes this year include: (a) the inclusion of ambiguous queries, i.e., queries that point to multiple real-life entities with the same name; (b) accepting outputs created through inference; and (c) a simplification of the task and of the input format by removing references to the knowledge base for the entities included in queries. The SF track attracted 31 registered teams, out of which 18 teams submitted at least one run. The highest score this year was 36.72 F1, with a median of 19.80 F1.
Multi-source and multicore automotive ECUs - OS protection mechanisms and scheduling

As the demand for computing power is quickly increasing in the automotive domain, car manufacturers and tier-one suppliers are gradually introducing multicore ECUs in their electronic architectures. Additionally, these multicore ECUs offer new features such as higher levels of parallelism which ease the respect of the safety requirements such as the ISO 26262 and the implementation of other automotive use-cases. These new features involve also more complexity in the design, development and verification of the software applications. Hence, OEMs and suppliers will require new tools and methodologies for deployment and validation. In this paper, we review the operating system protection mechanisms (e.g., memory, timing), needed for multi-source software in a safety critical context, with a clear focus on AUTOSAR OS which is the upcoming defacto standard for automotive ECUs. Then, we identify the main use-cases for automotive multicore ECUs and present solutions for the scheduling in a context where there are hundreds of software components and only a few OS tasks are allowed. Finally, experiments aim to assess the load level that can be reached on realistic case-studies.
Internet addiction among Norwegian adults: a stratified probability sample study.

Most Norwegians are Internet users. We conducted a stratified probability sample study (Norway, 2007, age-group 16-74 years, N= 3,399, response rate 35.3%, 87.1% Internet users) to assess the prevalence of Internet addiction and at-risk Internet use by the Young Diagnostic Questionnaire (YDQ). The prevalence of Internet addiction (YDQ score 5-8) was 1.0% and an additional 5.2% were at-risk Internet users (YDQ score 3-4). Internet addiction and at-risk Internet use was strongly dependent on gender and age with highest prevalences among young males (16-29 years 4.1% and 19.0%, 30-39 years 3.3% and 10.7%). Logistic regression showed that male gender, young age, university level education, and an unsatisfactory financial situation were factors positively associated with "problematic Internet use" (at-risk and addicted use combined). Time spent on the Internet and prevalence of self-reported sleeping disorders, depression, and other psychological impairments increased linearly with YDQ score. Problematic Internet use clearly affects the lives of many people.
An Algorithm to Find Overlapping Community Structure in Networks

Recent years have seen the development of many graph clustering algorithms, which can identify community structure in networks. The vast majority of these only find disjoint communities, but in many real-world networks communities overlap to some extent. We present a new algorithm for discovering overlapping communities in networks, by extending Girvan and Newman’s well-known algorithm based on the betweenness centrality measure. Like the original algorithm, ours performs hierarchical clustering — partitioning a network into any desired number of clusters — but allows them to overlap. Experiments confirm good performance on randomly generated networks based on a known overlapping community structure, and interesting results have also been obtained on a range of real-world networks.
Nonexistence of Certain Spherical Designs of Odd Strengths and Cardinalities

A sphericalτ -design onSn−1 is a finite set such that, for all polynomials f of degree at most τ , the average of over the set is equal to the average of f over the sphere Sn−1. In this paper we obtain some necessary conditions for the existence of designs of odd strengths and cardinalities. This gives nonexistence results in many cases. Asymptotically, we derive a bound which is better than the corresponding estimation ensured by the Delsarte– Goethals–Seidel bound. We consider in detail the strengths τ = 3 andτ = 5 and obtain further nonexistence results in these cases. When the nonexistence argument does not work, we obtain bounds on the minimum distance of such designs.
A Case for Malleable Thread-Level Linear Algebra Libraries: The LU Factorization With Partial Pivoting

We propose two novel techniques for overcoming load-imbalance encountered when implementing so-called look-ahead mechanisms in relevant dense matrix factorizations for the solution of linear systems. Both techniques target the scenario where two thread teams are created/activated during the factorization, with each team in charge of performing an independent task/branch of execution. The first technique promotes worker sharing (WS) between the two tasks, allowing the threads of the task that completes first to be reallocated for use by the costlier task. The second technique allows a fast task to alert the slower task of completion, enforcing the early termination (ET) of the second task, and a smooth transition of the factorization procedure into the next iteration. The two mechanisms are instantiated via a new malleable thread-level implementation of the basic linear algebra subprograms, and their benefits are illustrated via an implementation of the LU factorization with partial pivoting enhanced with look-ahead. Concretely, our experimental results on an Intel-Xeon system with 12 cores show the benefits of combining WS+ET, reporting competitive performance in comparison with a task-parallel runtime-based solution.
Execute This! Analyzing Unsafe and Malicious Dynamic Code Loading in Android Applications

The design of the Android system allows applications to load additional code from external sources at runtime. On the one hand, malware can use this capability to add malicious functionality after it has been inspected by an application store or anti-virus engine at installation time. On the other hand, developers of benign applications can inadvertently introduce vulnerabilities. In this paper, we systematically analyze the security implications of the ability to load additional code in Android. We developed a static analysis tool to automatically detect attempts to load external code using static analysis techniques, and we performed a large-scale study of 1,632 popular applications from the Google Play store, showing that loading external code in an insecure way is a problem in as much as 9.25% of those applications and even 16% of the top 50 free applications. We also show how malware can use code-loading techniques to avoid detection by exploiting a conceptual weakness in current Android malware protection. Finally, we propose modifications to the Android framework that enforce integrity checks on code to mitigate the threats imposed by the ability to load external code.
WiFi localization and navigation for autonomous indoor mobile robots

Building upon previous work that demonstrates the effectiveness of WiFi localization information per se, in this paper we contribute a mobile robot that autonomously navigates in indoor environments using WiFi sensory data. We model the world as a WiFi signature map with geometric constraints and introduce a continuous perceptual model of the environment generated from the discrete graph-based WiFi signal strength sampling. We contribute our WiFi localization algorithm which continuously uses the perceptual model to update the robot location in conjunction with its odometry data. We then briefly introduce a navigation approach that robustly uses the WiFi location estimates. We present the results of our exhaustive tests of the WiFi localization independently and in conjunction with the navigation of our custom-built mobile robot in extensive long autonomous runs.
Learning a similarity metric discriminatively, with application to face verification

We present a method for training a similarity metric from data. The method can be used for recognition or verification applications where the number of categories is very large and not known during training, and where the number of training samples for a single category is very small. The idea is to learn a function that maps input patterns into a target space such that the L/sub 1/ norm in the target space approximates the "semantic" distance in the input space. The method is applied to a face verification task. The learning process minimizes a discriminative loss function that drives the similarity metric to be small for pairs of faces from the same person, and large for pairs from different persons. The mapping from raw to the target space is a convolutional network whose architecture is designed for robustness to geometric distortions. The system is tested on the Purdue/AR face database which has a very high degree of variability in the pose, lighting, expression, position, and artificial occlusions such as dark glasses and obscuring scarves.
Design of Intelligent Ambulance and Traffic Control

This paper represents the unique feature which is very useful to ambulance drivers to take an alternate route in case of congestion. The various performance evaluation criteria are average waiting time, average distance traveled by vehicles, switching frequency of green light at a junction, efficient emergency mode operation and satisfactory operation of SMS using GSM Mobile. The performance of the Intelligent Traffic Light Controller is compared with the Fixed Mode Traffic Light Controller. It is observed that the proposed Intelligent Traffic Light Controller is more efficient than the conventional controller in respect of less waiting time, more distance traveled by average vehicles and efficient operation during emergency mode and GSM interface. Moreover, the designed system has simple architecture, fast response time, user friendliness and scope for further expansion.
Scalable Multi-party Computation for zk-SNARK Parameters in the Random Beacon Model

Zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARKs) have emerged as a valuable tool for verifiable computation and privacy preserving protocols. Currently practical schemes require a common reference string (CRS) to be constructed in a one-time setup for each statement. Ben-Sasson, Chiesa, Green, Tromer and Virza  devised a multi-party protocol to securely compute such a CRS, and an adaptation of this protocol was used to construct the CRS for the Zcash cryptocurrency . The scalability of these protocols is obstructed by the need for a “precommitment round” which forces participants to be defined in advance and requires them to secure their secret randomness throughout the duration of the protocol. Our primary contribution is a more scalable multi-party computation (MPC) protocol, secure in the random beacon model, which omits the precommitment round. We show that security holds even if an adversary has limited influence on the beacon. Next, we apply our main result to obtain a two-round protocol for computing an extended version of the CRS of Groth’s SNARK . We show that knowledge soundness is maintained in the generic group model when using this CRS. We also contribute a more secure pairing-friendly elliptic curve construction and implementation, tuned for use in zk-SNARKs, in light of recent optimizations [13] to the Number Field Sieve algorithm which reduced the security estimates of existing pairing-friendly curves used in zk-SNARK applications.
Ensemble forecasting of species distributions.

Concern over implications of climate change for biodiversity has led to the use of bioclimatic models to forecast the range shifts of species under future climate-change scenarios. Recent studies have demonstrated that projections by alternative models can be so variable as to compromise their usefulness for guiding policy decisions. Here, we advocate the use of multiple models within an ensemble forecasting framework and describe alternative approaches to the analysis of bioclimatic ensembles, including bounding box, consensus and probabilistic techniques. We argue that, although improved accuracy can be delivered through the traditional tasks of trying to build better models with improved data, more robust forecasts can also be achieved if ensemble forecasts are produced and analysed appropriately.
A Low-THD Class-D Audio Amplifier With Dual-Level Dual-Phase Carrier Pulsewidth Modulation

In this paper, a class-D audio amplifier which combines the advantages of the phase-shifted carrier pulsewidth modulation (PWM) and the multiple-level carrier PWM is proposed with a dual-level dual-phase carrier (DLDPC) PWM. The proposed closed-loop amplifier includes a second-order integrator and a DLDPC triangular wave generator. Two sets of 180° out-of-phase triangular waves are used as carriers, and each set has its respective offset voltage level with nonoverlapping amplitude. By performing the double Fourier analysis, it can be found that the linearity can be enhanced and the distortion can be reduced with the proposed modulation. Experimental results show that the proposed fully differential DLDPC PWM class-D audio amplifier features a total harmonic distortion lower than 0.01% with an output voltage swing of ±5 V.
Exposure of Children and Adolescents to Alcohol Marketing on Social Media Websites

In 2011, online marketing became the largest marketing channel in the UK, overtaking television for the first time. This study aimed to describe the exposure of children and young adults to alcohol marketing on social media websites in the UK.



We used commercially available data on the three most used social media websites among young people in the UK, from December 2010 to May 2011. We analysed by age (6-14 years; 15-24 years) and gender the reach (proportion of internet users who used the site in each month) and impressions (number of individual pages viewed on the site in each month) for Facebook, YouTube and Twitter. We further analysed case studies of five alcohol brands to assess the marketer-generated brand content available on Facebook, YouTube and Twitter in February and March 2012.



Facebook was the social media site with the highest reach, with an average monthly reach of 89% of males and 91% of females aged 15-24. YouTube had a similar average monthly reach while Twitter had a considerably lower usage in the age groups studied. All five of the alcohol brands studied maintained a Facebook page, Twitter page and YouTube channel, with varying levels of user engagement. Facebook pages could not be accessed by an under-18 user, but in most cases YouTube content and Twitter content could be accessed by those of all ages.



The rise in online marketing of alcohol and the high use of social media websites by young people suggests that this is an area requiring further monitoring and regulation.
ThingTalk : A Distributed Language for a Social Internet of Things

The Internet of Things has increased the number of connected devices by an order of magnitude. This increase comes with a corresponding increase in complexity, which has led to centralized architectures that do not respect the privacy of the users and incur high development and deployment costs. We propose the Open Thing Platform as a new distributed architecture which is built around ThingTalk, a new high level declarative programming language that can express the interaction between multiple physical devices and online services. The architecture abstracts away the details of the communication without sacrificing privacy. We experiment with ThingTalk in multiple domains and we show that several applications can be written in a few lines of code and with no need for any server infrastructure.
Gradient based image completion by solving the Poisson equation

This paper presents a novel gradient-based image completion algorithm for removing significant objects from natural images or photographs. Our method reconstructs the region of removal in two phases. Firstly, the gradient maps in the removed area are completed through a patch-based filling algorithm. After that, the image is reconstructed from the gradient maps by solving a Poisson equation. A new patch-matching criterion is developed in our approach to govern the completion of gradient maps. Both the gradient and the color information are incorporated in this new criterion, so a better image completion result is obtained. Several examples and comparisons are given at the end of the paper to demonstrate the performance of our gradient-based image completion approach.
Personalizing Similar Product Recommendations in Fashion E-commerce

In fashion e-commerce platforms, product discovery is one of the key components of a good user experience. There are numerous ways using which people find the products they desire. Similar product recommendations is one of the popular modes using which users find products that resonate with their intent. Generally these recommendations are not personalized to a specific user. Traditionally, collaborative filtering based approaches have been popular in the literature for recommending non-personalized products given a query product. Also, there has been focus on personalizing the product listing for a given user. In this paper, we marry these approaches so that users will be recommended with personalized similar products. Our experimental results on a large fashion ecommerce platform (Myntra) show that we can improve the key metrics by applying personalization on similar product recommendations.
CRED: A Deep Residual Network of Convolutional and Recurrent Units for Earthquake Signal Detection

Earthquake signal detection is at the core of observational seismology. A good detection algorithm should be sensitive to small and weak events with a variety of waveform shapes, robust to background noise and non-earthquake signals, and efficient for processing large data volumes. Here, we introduce the CnnRnn Earthquake Detector (CRED), a detector based on deep neural networks. The network uses a combination of convolutional layers and bi-directional long-short-term memory units in a residual structure. It learns the timefrequency characteristics of the dominant phases in an earthquake signal from three component data recorded on a single station. We train the network using 500,000 seismograms (250k associated with tectonic earthquakes and 250k identified as noise) recorded in Northern California and tested it with a F-score of 99.95. The robustness of the trained model with respect to the noise level and non-earthquake signals is shown by applying it to a set of semi-synthetic signals. The model is applied to one month of continuous data recorded at Central Arkansas to demonstrate its efficiency, generalization, and sensitivity. Our model is able to detect more than 700 microearthquakes as small as -1.3 ML induced during hydraulic fracturing far away than the training region. The performance of the model is compared with STA/LTA, template matching, and FAST algorithms. Our results indicate an efficient and reliable performance of CRED. This framework holds great promise in lowering the detection threshold while minimizing false positive detection rates.
TextRay: Mining Clinical Reports to Gain a Broad Understanding of Chest X-Rays

The chest X-ray (CXR) is by far the most commonly performed radiological examination for screening and diagnosis of many cardiac and pulmonary diseases. There is an immense world-wide shortage of physicians capable of providing rapid and accurate interpretation of this study. A radiologist-driven analysis of over two million CXR reports generated an ontology including the 40 most prevalent pathologies on CXR. By manually tagging a relatively small set of sentences, we were able to construct a training set of 959k studies. A deep learning model was trained to predict the findings given the patient frontal and lateral scans. For 12 of the findings we compare the model performance against a team of radiologists and show that in most cases the radiologists agree on average more with the algorithm than with each other.