nils-herrmann/clean_scirepeval huggingface dataset:
Industrial Big Data Analysis in Smart Factory: Current Status and Research Strategies

Under the background of cyber-physical systems and Industry 4.0, intelligent manufacturing has become an orientation and produced a revolutionary change. Compared with the traditional manufacturing environments, the intelligent manufacturing has the characteristics as highly correlated, deep integration, dynamic integration, and huge volume of data. Accordingly, it still faces various challenges. In this paper, we summarize and analyze the current research status in both domestic and aboard, including industrial big data collection, modeling of the intelligent product lines based on ontology, the predictive diagnosis based on industrial big data, group learning of product line equipment and the product line reconfiguration of intelligent manufacturing. Based on the research status and the problems, we propose the research strategies, including acquisition schemes of industrial big data under the environment of intelligent, ontology modeling and deduction method based intelligent product lines, predictive diagnostic methods on production lines based on deep neural network, deep learning among devices based on cloud supplements and 3-D self-organized reconfiguration mechanism based on the supplements of cloud. In our view, this paper will accelerate the implementation of smart factory.
Adaptive Grey-Box Fuzz-Testing with Thompson Sampling

Fuzz testing, or "fuzzing," refers to a widely deployed class of techniques for testing programs by generating a set of inputs for the express purpose of finding bugs and identifying security flaws. Grey-box fuzzing, the most popular fuzzing strategy, combines light program instrumentation with a data driven process to generate new program inputs. In this work, we present a machine learning approach that builds on AFL, the preeminent grey-box fuzzer, by adaptively learning a probability distribution over its mutation operators on a program-specific basis. These operators, which are selected uniformly at random in AFL and mutational fuzzers in general, dictate how new inputs are generated, a core part of the fuzzer's efficacy. Our main contributions are two-fold: First, we show that a sampling distribution over mutation operators estimated from training programs can significantly improve performance of AFL. Second, we introduce a Thompson Sampling, bandit-based optimization approach that fine-tunes the mutator distribution adaptively, during the course of fuzzing an individual program and outperforms offline training. A set of experiments across complex programs demonstrates that tuning the mutational operator distribution generates sets of inputs that yield significantly higher code coverage and finds more crashes faster and more reliably than both baseline versions of AFL as well as other AFL-based learning approaches.
Modelling Radiological Language with Bidirectional Long Short-Term Memory Networks

Motivated by the need to automate medical information extraction from free-text radiological reports, we present a bi-directional long short-term memory (BiLSTM) neural network architecture for modelling radiological language. The model has been used to address two NLP tasks: medical named-entity recognition (NER) and negation detection. We investigate whether learning several types of word embeddings improves BiLSTM’s performance on those tasks. Using a large dataset of chest x-ray reports, we compare the proposed model to a baseline dictionary-based NER system and a negation detection system that leverages the hand-crafted rules of the NegEx algorithm and the grammatical relations obtained from the Stanford Dependency Parser. Compared to these more traditional rule-based systems, we argue that BiLSTM offers a strong alternative for both our tasks.
Personality Based Gamification: How Different Personalities perceive Gamification

Personality is known to moderate task performance, learning styles, and gaming preferences. With the inclusion of game elements into non-game contexts such as education and workplaces, it is essential to develop models that will help explain and predict the influence of personality in environments that are, unlike the existing research, both ludic and utilitarian at the same time. In this study which is the first in a series of studies, we apply game elements to an undergraduate information systems course and examine the students perception of playfulness (n=102). Students completed a five factor personality test and answered questions about their preference for specific game elements and their overall playfulness from the gamification applied. T-tests and Partial Least Squares (PLS) analysis were performed comparing low and high measures of each personality trait revealing a higher preference level for badges by introverts and higher levels of preference from progression by agreeable personalities. Several significant differences in the overall playfulness were found in the relations of the path model and are presented. The significance of these results comes from the understanding that playfulness from game elements is not always granted and depends among other factors on personality.
A neighborhood graph based approach to regional co-location pattern discovery: a summary of results

Regional co-location patterns (RCPs) represent collections of feature types frequently located together in certain localities. For example, RCP < (Bar, Alcohol -- Crimes), Downtown >suggests that a co-location pattern involving alcohol-related crimes and bars is often localized to downtown regions. Given a set of Boolean feature types, their geo-located instances, a spatial neighbor relation, and a prevalence threshold, the RCP discovery problem finds all prevalent RCPs (pairs of co-locations and their prevalence localities). RCP discovery is important in many societal applications, including public safety, public health, climate science and ecology. The RCP discovery problem involves three major challenges: (a) an exponential number of subsets of feature types, (b) an exponential number of candidate localities and (c) a tradeoff between accurately modeling pattern locality and achieving computational efficiency. Related work does not provide computationally efficient methods to discover all interesting RCPs with their natural prevalence localities. To address these limitations, this paper proposes a neighborhood graph based approach that discovers all interesting RCPs and is aware of a pattern's prevalence localities. We identify partitions based on the pattern instances and neighbor graph. We introduce two new interest measures, a regional participation ratio and a regional participation index to quantify the strength of RCPs. We present two new algorithms, Pattern Space (PS) enumeration and Maximal Locality (ML) enumeration and show that they are correct and complete. Experiments using real crime datasets show that ML pruning outperforms PS enumeration.
Closed-Loop Targeted Memory Reactivation during Sleep Improves Spatial Navigation

Sounds associated with newly learned information that are replayed during non-rapid eye movement (NREM) sleep can improve recall in simple tasks. The mechanism for this improvement is presumed to be reactivation of the newly learned memory during sleep when consolidation takes place. We have developed an EEG-based closed-loop system to precisely deliver sensory stimulation at the time of down-state to up-state transitions during NREM sleep. Here, we demonstrate that applying this technology to participants performing a realistic navigation task in virtual reality results in a significant improvement in navigation efficiency after sleep that is accompanied by increases in the spectral power especially in the fast (12-15 Hz) sleep spindle band. Our results show promise for the application of sleep-based interventions to drive improvement in real-world tasks.
Hypertrophy of labia minora: experience with 163 reductions.

Our purpose was to describe the surgical procedure, its results, and its complications and to determine whether patients are satisfied with surgical reduction of labia minora in cases of hypertrophy.



The records of 163 patients who underwent reduction of the labia minora during a 9-year period were reviewed. The ages of the patients ranged from 12 to 67 years (median, 26). Motives for requesting surgery were aesthetic concerns in 87% of the cases, discomfort in clothing in 64%, discomfort with exercise in 26%, and entry dyspareunia in 43%. Anatomic results were assessed 1 month postoperatively. Patient satisfaction was assessed by means of a mailed questionnaire.



No surgery-related significant complications were noticed. Anatomic results were satisfactory for 151 patients (93%). Ninety-eight completed questionnaires were returned. Eighty-one patients (83%) found that the results after surgery were satisfactory. Eighty-seven (89%) were satisfied with the aesthetic result, and 91 (93%) approved the functional outcome. Four patients (4%) would not undergo the same procedure again.



Labia minora reduction is a simple surgical procedure associated with a high degree of patient satisfaction.
Automated microsoft office macro malware detection using machine learning

Macro malware in Microsoft (MS) Office files has long persisted as a cybersecurity threat. Though it ebbed after its initial rampages around the turn of the century, it has reemerged as threat. Attackers are taking a persuasive approach and using document engineering, aided by improved data mining methods, to make MS Office file malware appear legitimate. Recent attacks have targeted specific corporations with malicious documents containing unusually relevant information. This development undermines the ability of users to distinguish between malicious and legitimate MS Office files and intensifies the need for automating macro malware detection. This study proposes a method of classifying MS Office files containing macros as malicious or benign using the K-Nearest Neighbors machine learning algorithm, feature selection, and TFIDF where p-code opcode n-grams (translated VBA macro code) compose the file features. This study achieves a 96.3% file classification accuracy on a sample set of 40 malicious and 118 benign MS Office files containing macros, and it demonstrates the effectiveness of this approach as a potential defense against macro malware. Finally, it discusses the challenges automated macro malware detection faces and possible solutions.
Concurrency - state models and Java programs

When writing can change your life, when writing can enrich you by offering much money, why don't you try it? Are you still very confused of where getting the ideas? Do you still have no idea with what you are going to write? Now, you will need reading. A good writer is a good reader at once. You can define how you write depending on what books to read. This concurrency state models and java programs can help you to solve the problem. It can be one of the right sources to develop your writing skill.
Learning Long Term Dependencies via Fourier Recurrent Units

It is a known fact that training recurrent neural networks for tasks that have long term dependencies is challenging. One of the main reasons is the vanishing or exploding gradient problem, which prevents gradient information from propagating to early layers. In this paper we propose a simple recurrent architecture, the Fourier Recurrent Unit (FRU), that stabilizes the gradients that arise in its training while giving us stronger expressive power. Specifically, FRU summarizes the hidden states h along the temporal dimension with Fourier basis functions. This allows gradients to easily reach any layer due to FRU’s residual learning structure and the global support of trigonometric functions. We show that FRU has gradient lower and upper bounds independent of temporal dimension. We also show the strong expressivity of sparse Fourier basis, from which FRU obtains its strong expressive power. Our experimental study also demonstrates that with fewer parameters the proposed architecture outperforms other recurrent architectures on many tasks.
Improvement of Intrusion Detection System in Data Mining using Neural Network

Many researchers have argued that Artificial Neural Networks (ANNs) can improve the performance of intrusion detection systems (IDS). One of the central areas in network intrusion detection is how to build effective systems that are able to distinguish normal from intrusive traffic. In this paper four different algorithms are used namely as Multilayer Perception, Radial Base Function, Logistic Regression and Voted Perception. All these neural based algorithms are implemented in WEKA data mining tool to evaluate the performance. For experimental work, NSL KDD dataset is used. Each neural based algorithm is tested with conducted dataset. It is shown that the Multilayer Perception neural network algorithm is providing more accurate results than other algorithms.  Data Mining, Intrusion Detection System, Neural network classifier algorithm, NSL KDD dataset
Fusion of IMU and Vision for Absolute Scale Estimation in Monocular SLAM

The fusion of inertial and visual data is widely used to improve an object’s pose estimation. However, this type of fusion is rarely used to estimate further unknowns in the visual framework. In this paper we present and compare two different approaches to estimate the unknown scale parameter in a monocular SLAM framework. Directly linked to the scale is the estimation of the object’s absolute velocity and position in 3D. The first approach is a spline fitting task adapted from Jung and Taylor and the second is an extended Kalman filter. Both methods have been simulated offline on arbitrary camera paths to analyze their behavior and the quality of the resulting scale estimation. We then embedded an online multi rate extended Kalman filter in the Parallel Tracking and Mapping (PTAM) algorithm of Klein and Murray together with an inertial sensor. In this inertial / monocular SLAM framework, we show a real time, robust and fast converging scale estimation. Our approach does not depend on known patterns in the vision part nor a complex temporal synchronization between the visual and inertial sensor. The research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement n. 231855 (sFly). Gabriel Nützi is currently a Master student at the ETH Zurich. Stephan Weiss is currently PhD student at the ETH Zurich. Davide Scaramuzza is currently senior researcher and team leader at the ETH Zurich. Roland Siegwart is full professor at the ETH Zurich and head of the Autonomous Systems Lab. G. Nützi · S. Weiss · D. Scaramuzza · R. Siegwart ETH Autonomous Systems Laboratory, 8092, Zurich, Switzerland, www.asl.ethz.ch E-mail: nuetzi@student.ethz.ch, stephan.weiss@mavt.ethz.ch, davide.scaramuzza@ieee.org,
Achieving higher magnification in context

The difficulty of accessing information details while preserving context has generated many different focus-in-context techniques. A common limitation of focus-in-context techniques is their ability to work well at high magnification. We present a set of improvements that will make high magnification in context more feasible. We demonstrate new distortion functions that effectively integrate high magnification within its context. Finally, we show how lenses can be used on top of other lenses, effectively multiplying their magnification power in the same manner that a magnifying glass applied on top of another causes multiplicative magnification. The combined effect is to change feasible detail-in-context magnification factors from less than 8 to more than 40.
A Comparative Study of online P 2 P Lending in the USA and China

Peer-to-Peer (P2P) lending provides online users an innovative loaning and investment vehicle without the intermediation of financial institutions. However, the research on online P2P lending is still scarce. In this study we review relevant literature and conduct a comparative study of online P2P lending practices in the USA and China. We find that two categories of credit information, “hard” and “soft” information, may have profound influences on lending outcomes in both countries, but lenders in China is more reliable on “soft” information. This study provides valuable insights for future research and practices and enriches the understanding of online P2P lending across countries.
Language games for autonomous robots

extraordinarily difficult and poses challenges for both integration and grounding. Here, I propose a unifying idea that meets both challenges: language games. A language game is a sequence of verbal interactions between two agents situated in a specific environment. Language games both integrate the various activities required for dialogue and ground unknown words or phrases in a specific context, which helps constrain possible meanings. Over the past five years, I have been working with a team to develop and test language games on progressively more sophisticated systems, from relatively simple camera-based systems to humanoid robots. The results of our work show that language games are a useful way to both understand and design human–robot interaction.
Reverse Nested Miller Compensation Using Current Buffers in a Three-Stage LDO

A novel frequency compensation scheme called reverse nested Miller compensation using current buffers (RNMCCB) for three-stage amplifiers is proposed. As opposed to previous reverse nested schemes, our work uses inverting gain stages for both the second and third stages. The outer compensation loop utilizes a current mirror as an inverting current buffer (CB), and the inner loop uses a common-gate amplifier as a CB, creating two left-half-plane (LHP) zeros. We introduce a simple and effective method of placing a resistor in series with a CB for accurate placement of LHP zeros. As a design example of the RNMCCB scheme, we propose a three-stage low dropout voltage regulator (LDO) in a 0.5-¿m CMOS process to supply 1.21 V to a load ranging from 1 ¿A to 100 mA. Our design goals were to simultaneously achieve very high current efficiency and very low transient output voltage variation. As such, we achieved a 99.95% current efficiency and a maximum load transient output voltage variation of ±48 mV with an output capacitor of 100 nF. Experimental results, in good agreement with theoretical analysis, validate the novel RNMCCB frequency compensation scheme.
Leveraging Unlabeled Whole-Slide-Images for Mitosis Detection

Mitosis count is an important biomarker for prognosis of various cancers. At present, pathologists typically perform manual counting on a few selected regions of interest in breast whole-slide-images (WSIs) of patient biopsies. This task is very time-consuming, tedious and subjective. Automated mitosis detection methods have made great advances in recent years. However, these methods require exhaustive labeling of a large number of selected regions of interest. This task is very expensive because expert pathologists are needed for reliable and accurate annotations. In this paper, we present a semi-supervised mitosis detection method which is designed to leverage a large number of unlabeled breast cancer WSIs. As a result, our method capitalizes on the growing number of digitized histology images, without relying on exhaustive annotations, subsequently improving mitosis detection. Our method first learns a mitosis detector from labeled data, uses this detector to mine additional mitosis samples from unlabeled WSIs, and then trains the final model using this larger and diverse set of mitosis samples. The use of unlabeled data improves F1-score by ∼5% compared to our best performing fullysupervised model on the TUPAC validation set. Our submission (single model) to TUPAC challenge ranks highly on the leaderboard with an F1-score of 0.64.
An analysis on the significance of ticket analytics and defect analysis from software quality perspective

Software even though intangible should undergo evolution to fit into the ever changing real world scenarios. Each issue faced by the development and service team directly reflects in the quality of the software product. According to the related work, very few research is going on in the field of ticket and its related incident; a part of corrective maintenance. In depth research on incident tickets should be viewed as critical since, it provides information related to the kind of maintenance activities that is performed in any timestamp. Therefore classifying and analyzing tickets becomes a critical task in managing the operations of the service since each incident will be having a service level agreement associated with it. Further, incident analysis is essential to identify the patterns associated. Due to the existence of huge population of software in each organization and millions of incidents get reported per software product every year, it is practically impossible to manually analyze all the tickets. This paper focuses on projecting the importance of ticket to maintain the quality of software products and also distinguish it from the defect associated with a software system. This paper projects the importance of identifying defects in software as well as handling the incident related tickets and resolving it when viewed from the perspective of quality. It also gives an overview of the scope defect analysis and ticket analytics provide to the researchers.
Pervasive social context: Taxonomy and survey

As pervasive computing meets social networks, there is a fast growing research field called pervasive social computing. Applications in this area exploit the richness of information arising out of people using sensor-equipped pervasive devices in their everyday life combined with intense use of different social networking services. We call this set of information pervasive social context. We provide a taxonomy to classify pervasive social context along the dimensions space, time, people, and information source (STiPI) as well as commenting on the type and reason for creating such context. A survey of recent research shows the applicability and usefulness of the taxonomy in classifying and assessing applications and systems in the area of pervasive social computing. Finally, we present some research challenges in this area and illustrate how they affect the systems being surveyed.
Clustered Collaborative Filtering Approach for Distributed Data Mining on Electronic Health Records

Distributed Data Mining (DDM) has become one of the promising areas of Data Mining. DDM techniques include classifier approach and agent-approach. Classifier approach plays a vital role in mining distributed data, having homogeneous and heterogeneous approaches depend on data sites. Homogeneous classifier approach involves ensemble learning, distributed association rule mining, meta-learning and knowledge probing. Heterogeneous classifier approach involves collective principle component analysis, distributed clustering, collective decision tree and collective bayesian learning model. In this paper, classifier approach for DDM is summarized and an architectural model based on clustered-collaborative filtering for Electronic Health Records (EHR) is proposed.