nils-herrmann/clean_scirepeval huggingface dataset:
Application-Managed Flash

In flash storage, an FTL is a complex piece of code that resides completely inside the storage device and is provided by the manufacturer. Its principal virtue is providing interoperability with conventional HDDs. However, this virtue is also its biggest impediment in reaching the full performance of the underlying flash storage. We propose to refactor the flash storage architecture so that it relies on a new block I/O interface which does not permit overwriting of data without intervening erasures.We demonstrate how high-level applications, in particular file systems, can deal with this restriction efficiently by employing append-only segments. This refactoring dramatically reduces flash management overhead and improves performance of applications, such as file systems and databases, by permitting them to directly manage flash storage. Our experiments on a machine with the new block I/O interface show that DRAM in the flash controller is reduced by 128X and the performance of the file system improves by 80% over conventional SSDs.
Learner Engagement Measurement and Classification in 1:1 Learning

We explore the feasibility of measuring learner engagement and classifying the engagement level based on machine learning applied on data from 2D/3D camera sensors and eye trackers in a 1:1 learning setting. Our results are based on nine pilot sessions held in a local high school where we recorded features related to student engagement while consuming educational content. We label the collected data as Engaged or NotEngaged while observing videos of the students and their screens. Based on the collected data, perceptual user features (e.g., body posture, facial points, and gaze) are extracted. We use feature selection and classification methods to produce classifiers that can detect whether a student is engaged or not. Accuracies of up to 85-95% are achieved on the collected dataset. We believe our work pioneers in the successful classification of student engagement based on perceptual user features in a 1:1 authentic learning setting.
Accurate Identification of Ontology Alignments at Different Granularity Levels

As more and more ontologies are defined with different terms, ontology matching plays a crucial role in addressing the semantic heterogeneity problem in many different disciplines. Many efforts have been made to discover correspondences among terms in different ontologies. Most studies directly match two ontologies by utilizing terminological and structural methods that rely on ontologies themselves only. However, the decentralized characteristic of ontologies raises the uncertainty in ontology matching. To address this problem, we propose a four-stage ontology matching framework (FOMF) to enhance ontology matching performance. It is built upon the commonly accepted claim that an external comprehensive knowledge base can be used as a semantic bridge between domain ontologies for ontology matching. First, FOMF semantically maps domain ontologies to a knowledge base and then produces different types of alignments, including equivalence, subclass, sameas, and instance alignments. Similarities between two domain ontologies are next employed to enhance the equivalence and sameas alignments discovery. Finally, based on acquired alignments, inferred alignments are deduced to guarantee the completeness of matching results. Our experimental results show the superiority of the proposed method over the existing ones.
Assessment of degree of risk from sources of microbial contamination in cleanrooms ; 3 : Overall application

The European Commission and the Food and Drug Administration in the USA suggest that risk management and assessment methods should be used to identify and control sources of microbial contamination1,2. A risk management method has been described by Whyte3,4 that is based on the Hazard Analysis and Critical Control Point (HACCP) system but reinterpreted for use in cleanrooms, and called Risk Management of Contamination (RMC). This method is also described in the PHSS Technical Monograph No 145 and has the following steps.
Random Forests and VGG-NET: An Algorithm for the ISIC 2017 Skin Lesion Classification Challenge

This manuscript briefly describes an algorithm developed for the ISIC 2017 Skin Lesion Classification Competition. In this task, participants are asked to complete two independent binary image classification tasks that involve three unique diagnoses of skin lesions (melanoma, nevus, and seborrheic keratosis). In the first binary classification task, participants are asked to distinguish between (a) melanoma and (b) nevus and seborrheic keratosis. In the second binary classification task, participants are asked to distinguish between (a) seborrheic keratosis and (b) nevus and melanoma. The other phases of the competition are not considered. Our proposed algorithm consists of three steps: preprocessing, classification using VGG-NET  and Random Forests , and calculation of a final score.
Effects of quiz-style information presentation on user understanding

This paper proposes quiz-style information presentation for interactive systems as a means to improve user understanding in educational tasks. Since the nature of quizzes can highly motivate users to stay voluntarily engaged in the interaction and keep their attention on receiving information, it is expected that information presented as quizzes can be better understood by users. To verify the effectiveness of the approach, we implemented read-out and quiz systems and performed comparison experiments using human subjects. In the task of memorizing biographical facts, the results showed that user understanding for the quiz system was significantly better than that for the read-out system, and that the subjects were more willing to use the quiz system despite the long duration of the quizzes. This indicates that quiz-style information presentation promotes engagement in the interaction with the system, leading to the improved user understanding.
High speed network traffic analysis with commodity multi-core systems

Multi-core systems are the current dominant trend in computer processors. However, kernel network layers often do not fully exploit multi-core architectures. This is due to issues such as legacy code, resource competition of the RX-queues in network interfaces, as well as unnecessary memory copies between the OS layers. The result is that packet capture, the core operation in every network monitoring application, may even experience performance penalties when adapted to multi-core architectures. This work presents common pitfalls of network monitoring applications when used with multi-core systems, and presents solutions to these issues. We describe the design and implementation of a novel multi-core aware packet capture kernel module that enables monitoring applications to scale with the number of cores. We showcase that we can achieve high packet capture performance on modern commodity hardware.
Perceived aggressiveness predicts fighting performance in mixed-martial-arts fighters.

Accurate assessment of competitive ability is a critical component of contest behavior in animals, and it could be just as important in human competition, particularly in human ancestral populations. Here, we tested the role that facial perception plays in this assessment by investigating the association between both perceived aggressiveness and perceived fighting ability in fighters' faces and their actual fighting success. Perceived aggressiveness was positively associated with the proportion of fights won, after we controlled for the effect of weight, which also independently predicted perceived aggression. In contrast, perception of fighting ability was confounded by weight, and an association between perceived fighting ability and actual fighting success was restricted to heavyweight fighters. Shape regressions revealed that aggressive-looking faces are generally wider and have a broader chin, more prominent eyebrows, and a larger nose than less aggressive-looking faces. Our results indicate that perception of aggressiveness and fighting ability might cue different aspects of success in male-male physical confrontation.
PFID: Pittsburgh fast-food image dataset

We introduce the first visual dataset of fast foods with a total of 4,545 still images, 606 stereo pairs, 303 360° videos for structure from motion, and 27 privacy-preserving videos of eating events of volunteers. This work was motivated by research on fast food recognition for dietary assessment. The data was collected by obtaining three instances of 101 foods from 11 popular fast food chains, and capturing images and videos in both restaurant conditions and a controlled lab setting. We benchmark the dataset using two standard approaches, color histogram and bag of SIFT features in conjunction with a discriminative classifier. Our dataset and the benchmarks are designed to stimulate research in this area and will be released freely to the research community.
Unified Collaborative and Content-Based Web Service Recommendation

The last decade has witnessed a tremendous growth of web services as a major technology for sharing data, computing resources, and programs on the web. With increasing adoption and presence of web services, designing novel approaches for efficient and effective web service recommendation has become of paramount importance. Most existing web service discovery and recommendation approaches focus on either perishing UDDI registries, or keyword-dominant web service search engines, which possess many limitations such as poor recommendation performance and heavy dependence on correct and complex queries from users. It would be desirable for a system to recommend web services that align with users' interests without requiring the users to explicitly specify queries. Recent research efforts on web service recommendation center on two prominent approaches: collaborative filtering and content-based recommendation. Unfortunately, both approaches have some drawbacks, which restrict their applicability in web service recommendation. In this paper, we propose a novel approach that unifies collaborative filtering and content-based recommendations. In particular, our approach considers simultaneously both rating data (e.g., QoS) and semantic content data (e.g., functionalities) of web services using a probabilistic generative model. In our model, unobservable user preferences are represented by introducing a set of latent variables, which can be statistically estimated. To verify the proposed approach, we conduct experiments using 3,693 real-world web services. The experimental results show that our approach outperforms the state-of-the-art methods on recommendation performance.
Poststroke fatigue: a 2-year follow-up study of stroke patients in Sweden.

Fatigue is common among stroke patients. This study determined the prevalence of fatigue among long-term survivors after stroke and what impact fatigue had on various aspects of daily life and on survival.



This study was based on Riks-Stroke, a hospital-based national register for quality assessment of acute stroke events in Sweden. During the first 6 months of 1997, 8194 patients were registered in Riks-Stroke, and 5189 were still alive 2 years after the stroke. They were followed up by a mail questionnaire, to which 4023 (79%) responded. Patients who reported that they always felt depressed were excluded.



To the question, "Do you feel tired?" 366 (10.0%) of the patients answered that they always felt tired, and an additional 1070 (29.2%) were often tired. Patients who always felt tired were on average older than the rest of the study population (74.5 versus 71.5 years, P<0.001); therefore, all subsequent analyses were age adjusted. Fatigue was an independent predictor for having to move into an institutional setting after stroke. Fatigue was also an independent predictor for being dependent in primary activities of daily living functions. Three years after stroke, patients with fatigue also had a higher case fatality rate.



Fatigue is frequent and often severe, even late after stroke. It is associated with profound deterioration of several aspects of everyday life and with higher case fatality, but it usually receives little attention by healthcare professionals. Intervention studies are needed.
A method for classifying medical images using transfer learning: A pilot study on histopathology of breast cancer

The advance of deep learning has made huge changes in computer vision and produced various off-the-shelf trained models. Particularly, Convolutional Neural Network (CNN) has been widely used to build image classification model which allow researchers transfer the pre-trained learning model for other classifications. We propose a transfer learning method to detect breast cancer using histopathology images based on Google's Inception v3 model which were initially trained for the classification of non-medical images. The pilot study shows the feasibility of transfer learning in the detection of breast cancer with AUC of 0.93.
Arm pose copying for humanoid robots

Learning by imitation is becoming increasingly important for teaching humanoid robots new skills. The simplest form of imitation is behavior copying in which the robot is minimizing the difference between its perceived motion and that of the imitated agent. One problem that must be solved even in this simplest of all imitation tasks is calculating the learner's pose corresponding to the perceived pose of the agent it is imitating. This paper presents a general framework for solving this problem in closed form for the arms of a generalized humanoid robot of which most available humanoids are special cases. The paper also reports the evaluation of the proposed system for real and simulated robots.
An Incremental Parser for Abstract Meaning Representation

Meaning Representation (AMR) is a semantic representation for naturalMeaning Representation (AMR) is a semantic representation for natural language that embeds annotations related to traditional tasks such as named entity recognition, semantic role labeling, word sense disambiguation and co-reference resolution. We describe a transition-based parser for AMR that parses sentences leftto-right, in linear time. We further propose a test-suite that assesses specific subtasks that are helpful in comparing AMR parsers, and show that our parser is competitive with the state of the art on the LDC2015E86 dataset and that it outperforms state-of-the-art parsers for recovering named entities and handling polarity.
Model-based Q-learning for humanoid robots

This paper is proposal with regarding reinforcement learning and robotics. It contains our study so far about reinforcement learning problem and Q-learning — one of the methods to solve it. The method is tested both by running on a simulation and on NAO robot. Both are written in high programming language. Since the testing are also done on NAO robot. This paper also includes our understanding about NAO robot and Robotic Operating System (ROS), and our approach to apply Q-learning on NAO robot. In the end, we have been successfully tested Q-learning method and apply it to NAO robot in real-world environment.
Genetic algorithm-based solver for very large multiple jigsaw puzzles of unknown dimensions and piece orientation

In this paper we propose the first genetic algorithm (GA)-based solver for jigsaw puzzles of unknown puzzle dimensions and unknown piece location and orientation. Our solver uses a novel crossover technique, and sets a new state-of-the-art in terms of the puzzle sizes solved and the accuracy obtained. The results are significantly improved, even when compared to previous solvers assuming known puzzle dimensions. Moreover, the solver successfully contends with a mixed bag of multiple puzzle pieces, assembling simultaneously all puzzles.
Verifying Anaconda's expert rating by competing against Chinook: experiments in co-evolving a neural checkers player

Since the early days of arti5cial intelligence, there has been interest in having a computer teach itself how to play a game of skill, like checkers, at a level that is competitive with human experts. To be truly noteworthy, such e7orts should minimize the amount of human intervention in the learning process. Recently, co-evolution has been used to evolve a neural network (called Anaconda) that, when coupled with a minimax search, can evaluate checker-boards and play to the level of a human expert, as indicated by its rating of 2045 on an international web site for playing checkers. The neural network uses only the location, type, and number of pieces on the board as input. No other features that would require human expertise are included. Experiments were conducted to verify the neural network’s expert rating by competing it in 10 games against a “novice-level” version of Chinook, a world-champion checkers program. The neural network had 2 wins, 4 losses, and 4 draws in the 10-game match. Based on an estimated rating of Chinook at the novice level, the results corroborate Anaconda’s expert rating.
Design of Kernels in Convolutional Neural Networks for Image Classification

Despite the effectiveness of convolutional neural networks (CNNs) for image classification, our understanding of the effect of shape of convolution kernels on learned representations is limited. In this work, we explore and employ the relationship between shape of kernels which define receptive fields (RFs) in CNNs for learning of feature representations and image classification. For this purpose, we present a feature visualization method for visualization of pixel-wise classification score maps of learned features. Motivated by our experimental results, and observations reported in the literature for modeling of visual systems, we propose a novel design of shape of kernels for learning of representations in CNNs. In the experimental results, the proposed models achieved an outstanding performance in the classification task, comparing to a base CNN models that introduces more parameters and computational time. Additionally, we examined the region of interest (ROI) of different models in the classification task and analyzed the robustness of the proposed method to occluded images. Our results indicate the effectiveness of the proposed approach.
Persecutory delusions and catastrophic worry in psychosis: developing the understanding of delusion distress and persistence.

In a recent theoretical account of persecutory delusions, it is suggested that anxiety and worry are important factors in paranoid experience [Freeman, D., Garety, P. A., Kuipers, E., Fowler, D., & Bebbington, P. E. (2002). A cognitive model of persecutory delusions. . In emotional disorders worry has been understood in terms of catastrophising. In the current study, the concept of catastrophising is applied for the first time with persecutory delusions. Thirty individuals with current persecutory delusions and 30 non-clinical controls participated in a cross-sectional study. The group with persecutory delusions was also followed up at 3 months to assess predictors of delusion persistence. At its most severe, 21% of individuals with persecutory delusions had clinical worry, 68% had levels of worry comparable with treatment seeking GAD patients. Further, high levels of anxiety, worry and catastrophising were associated with high levels of persecutory delusion distress and with the persistence of delusions over 3 months. If future research replicates these findings, worry reduction interventions for individuals with persecutory delusions may be warranted.
DATA MINING APPROACH FOR CLASSIFYING TWITTER ’ S USERS

Social networks are the most important communication channels in recent years, which popular among the different social groups. These networks affected the ideas and policies of individuals, groups and communities. Every day, millions of tweets on Twitter are being published. These tweets reflect opinions and beliefs of their publishers and affect others as well. Therefore, it is important to analyze these tweets and identify and classify trends of different users. This research aims to classify social network to anomaly groups such as: Terrorist and dissident; by analyzing tweets data on the Twitter; then identify an anonymous user’s affiliation to these groups. To address this problem, we first extract a set of features to characterize each group using different data mining techniques and store these features in the database. Text mining, sentiment analysis, and opinion mining techniques will be used to accomplish this extraction. The objective of data extraction is to measure the similarity of selected user tweets with respect to extracted features. It will enable to determine high percentage of similarity between the user tweets and group characteristics to expose his/her affiliation to this group.  Data mining, Social network, Twitter, Analysis,   Alqhtani and M. Rizwan Jameel Qureshi, Data Mining Approach for Classifying Twitter’s Users.  2017, pp. 42–53.
Bioethanol production from Gracilaria verrucosa, a red alga, in a biorefinery approach.

In this study, Gracilaria verrucosa, red seaweed has been used for production of agar and bioethanol. The algae harvested at various time durations resulted in extraction of ~27-33% agar. The leftover pulp was found to contain ~62-68% holocellulose, which on enzymatic hydrolysis yielded 0.87 g sugars/g cellulose. The enzymatic hydrolysate on fermentation with Saccharomyces cerevisiae produced ethanol with an ethanol yield of 0.43 g/g sugars. The mass balance evaluation of the complete process demonstrates that developing biorefinery approach for exploiting Gracilaria verrucosa, a red alga, could be commercially viable.
A Circuit-Compatible SPICE model for Enhancement Mode Carbon Nanotube Field Effect Transistors

This paper presents a circuit-compatible compact model for short channel length (5 nm~100 nm), quasi-ballistic single wall carbon nanotube field-effect transistors (CNFETs). For the first time, a universal circuit-compatible CNFET model was implemented with HSPICE. This model includes practical device non-idealities, e.g. the quantum confinement effects in both circumferential and channel length direction, the acoustical/optical phonon scattering in channel region and the resistive source/drain, as well as the real time dynamic response with a transcapacitance array. This model is valid for CNFET for a wide diameter range and various chiralities as long as the carbon nanotube (CNT) is semiconducting
Microservices validation: Mjolnirr platform case study

Microservice architecture is a cloud application design pattern that implies that the application is divided into a number of small independent services, each of which is responsible for implementing of a certain feature. The need for continuous integration of developed and/or modified microservices in the existing system requires a comprehensive validation of individual microservices and their co-operation as an ensemble with other microservices. In this paper, we would provide an analysis of existing methods of cloud applications testing and identify features that are specific to the microservice architecture. Based on this analysis, we will try to propose a validation methodology of the microservice systems.