nils-herrmann/clean_scirepeval huggingface dataset:
Developing Toward Generality: Overcoming Catastrophic Forgetting with Developmental Compression

General intelligence is the exhibition of intelligent behavior across multiple problems in a variety of settings, however intelligence is defined and measured. Endemic in approaches to realize such intelligence in machines is catastrophic forgetting, in which sequential learning corrupts knowledge obtained earlier in the sequence or in which tasks antagonistically compete for system resources. Methods for obviating catastrophic forgetting have either sought to identify and preserve features of the system necessary to solve one problem when learning to solve another, or enforce modularity such that minimally overlapping sub-functions contain task-specific knowledge. While successful in some domains, both approaches scale poorly because they require larger architectures as the number of training instances grows, causing different parts of the system to specialize for separate subsets of the data. Presented here is a method called developmental compression that addresses catastrophic forgetting in the neural networks of embodied agents. It exploits the mild impacts of developmental mutations to lessen adverse changes to previously evolved capabilities and ‘compresses’ specialized neural networks into a single generalized one. In the absence of domain knowledge, developmental compression produces systems that avoid overt specialization, alleviating the need to engineer a bespoke system for every task permutation, and does so in a way that suggests better scalability than existing approaches. This method is validated on a robot control problem and may be extended to other machine learning domains in the future.
Route prediction using trip observations and map matching

This paper uses location data traces (from GPS, Mobile Signals etc.) of past trips of vehicles to develop algorithm for predicting the end-to-end route of a vehicle. Focus is on overall route prediction rather than predicting road segments in short term. Researches in past for route prediction makes use of raw location data traces data decomposed into trips for such route predictions. This paper introduces an additional step to convert trips composed of location data traces points to trips of road network edges. This requires the algorithm to make use of road networks. We show that efficiency in storage and time complexity can be achieved without sacrificing the accuracy by doing so. Moreover, its well-known that location traces data has inherent inaccuracies due to hardware limitations of devices. Most of the researches don't handle it. This paper presents the results of route prediction algorithms under inaccuracies in data.
Relationship among family environment, self-control, friendship quality, and adolescents’ smartphone addiction in South Korea: Findings from nationwide data

Many studies have examined the negative impact on smartphone addiction in adolescents. Recent concerns have focused on predictors of smartphone addiction. This study aimed to investigate the association of adolescents' smartphone addiction with family environment (specifically, domestic violence and parental addiction). We further investigated whether self-control and friendship quality, as predictors of smartphone addiction, may reduce the observed risk.



We used the 2013 national survey on internet usage and utilization data from the National Information Agency of Korea. Information on exposure and covariates included self-reported experience of domestic violence and parental addiction, sociodemographic variables, and other variables potentially related to smartphone addiction. Smartphone addiction was estimated using a smartphone addiction proneness scale, a standardized measure developed by national institutions in Korea.



Adolescents who had experienced domestic violence (OR = 1.74; 95% CI: 1.23-2.45) and parental addiction (OR = 2.01; 95% CI: 1.24-3.27) were found to be at an increased risk for smartphone addiction after controlling for all potential variables. Furthermore, on classifying adolescents according to their level of self-control and friendship quality the association between domestic violence and parental addiction, and smartphone addiction was found to be significant in the group with adolescents with lower levels of self-control (OR = 2.87; 95% CI: 1.68-4.90 and OR = 1.95; 95% CI: 1.34-2.83) and friendship quality (OR = 2.33; 95% CI: 1.41-3.85 and OR = 1.83; 95% CI: 1.26-2.64).



Our findings suggest that family dysfunction was significantly associated with smartphone addiction. We also observed that self-control and friendship quality act as protective factors against adolescents' smartphone addiction.
Combining Memory-Based and Model-Based Collaborative Filtering in Recommender System

Collaborative filtering (CF) technique has been proved to be one of the most successful techniques in recommender systems. Two types of algorithms for collaborative filtering have been researched: memory-based CF and model-based CF. Memory-based approaches identify the similarity between two users by comparing their ratings on a set of items and have suffered from two fundamental problems: sparsity and scalability. Alternatively, the model-based approaches have been proposed to alleviate these problems, but these approaches tend to limit the range of users. This paper presents an approach that combines the advantages of these two kinds of approaches by joining the two methods. Firstly, it employs memory-based CF to fill the vacant ratings of the user-item matrix. Then, it uses the item-based CF as model-based to form the nearest neighbors of every item. At last, it produces prediction of the target user to the target item at real time. The collaborative filtering recommendation method combining memory-based CF and model-based CF can provide better recommendation than traditional collaborative filtering.
Treatment of exogenous ochronosis with a Q-switched alexandrite (755 nm) laser.

Exogenous ochronosis is a cutaneous disorder characterized by blue-black or slate-gray hyperpigmentation resulting from the prolonged use of certain topical agents, most commonly hydroquinones. It is notoriously difficult to treat.



To report the effectiveness of a quality-switched (QS) 755-nm alexandrite laser in treating hydroquinone-induced exogenous ochronosis.



Hydroquinone-induced exogenous ochronosis in two patients was treated with a QS alexandrite laser. The first patient received six treatments (average fluence=7.8 J/cm(2)) at 2-month intervals. The second patient received four treatments (average fluence=6.9 J/cm(2)) at 4-month intervals. Biopsies of lesional skin were obtained before and after laser treatment for histologic evaluation.



Significant lightening of the pigmented skin areas was achieved in both patients without scarring or textural changes. Decreased dermal pigmentation was observed on histologic examination of treated skin specimens.



The QS alexandrite laser can effectively treat exogenous ochronosis without untoward side effects.
Detection of Subconscious Face Recognition Using Consumer-Grade Brain-Computer Interfaces

We test the possibility of tapping the subconscious mind for face recognition using consumer-grade BCIs. To this end, we performed an experiment whereby subjects were presented with photographs of famous persons with the expectation that about 20% of them would be (consciously) recognized; and since the photos are of famous persons, we expected that subjects would have seen before some of the 80% they didn’t (consciously) recognize. Further, we expected that their subconscious would have recognized some of those in the 80% pool that they had seen before. An exit questionnaire and a set of criteria allowed us to label recognitions as conscious, false, no recognitions, or subconscious recognitions. We analyzed a number of event related potentials training and testing a support vector machine. We found that our method is capable of differentiating between no recognitions and subconscious recognitions with promising accuracy levels, suggesting that tapping the subconscious mind for face recognition is feasible.
Antecedents of Information and System Quality: An Empirical Examination Within the Context of Data Warehousing

Understanding the successful adoption of information technology is largely based upon understanding the linkages among quality, satisfaction, and usage. Although the satisfaction and usage constructs have been well studied in the information systems literature, there has been only limited attention to information and system quality over the past decade. To address this shortcoming, we developed a model consisting of nine fundamental determinants of quality in an information technology context, four under the rubric of information quality (the output of an information system) and five that describe system quality (the infonnation processing system required to produce the output). We then empirically examined the aptness of our model using a sample of 465 data warehouse users from seven different organizations that employed report-based, query-based, and analytical business intelligence tools. The results suggest that our determinants are indeed predictive of overall information and system quality in data warehouse environments, and that our model strikes a balance between comprehensiveness and parsimony. We conclude with a discussion of the implications for both theory and the development and implementation of information technology applications in practice.  21, No. 4, pp. 199-235.
Conducted EMI noise mitigation in DC-DC converters using active filtering method

Electromagnetic interference (EMI) noise mitigation is an important issue that should be addressed and emphasized when designing DC/DC converters. These later, are known to be the primary culprit of the EMI noise generation in most of the electronic systems, mainly due to the switching action of the MOSFET circuitries. Passive input EMI LC filters have been the intuitive solution for EMI noise mitigation; hence they have been integrated in almost every DC/DC converters. However, their size, weight and cost can cause a significant constraint in some applications. To overcome these constraints, an input active EMI filter is proposed. The active filter is based on the noise current phase shift and the injection of this noise current back to the DC input bus. However, the combination of the input active and the passive filters shows a substantial attenuation of the conducted emissions as compared to the passive filter only, which in turn contributes to the reduction of the size and weight of the input passive EMI filter. The proposed combination provides a design solution for compliance engineers where the PCB real-estate is an issue. Experimental results to demonstrate the performance and the effectiveness of the input active EMI filter in DC/DC converters are presented.
Central sensitization in carpal tunnel syndrome with extraterritorial spread of sensory symptoms

Extraterritorial spread of sensory symptoms is frequent in carpal tunnel syndrome (CTS). Animal models suggest that this phenomenon may depend on central sensitization. We sought to obtain psychophysical evidence of sensitization in CTS with extraterritorial symptoms spread. We recruited 100 unilateral CTS patients. After selection to rule out concomitant upper-limb causes of pain, 48 patients were included. The hand symptoms distribution was graded with a diagram into median and extramedian pattern. Patients were asked on proximal pain. Quantitative sensory testing (QST) was performed in the territory of injured median nerve and in extramedian territories to document signs of sensitization (hyperalgesia, allodynia, wind-up). Extramedian pattern and proximal pain were found in 33.3% and 37.5% of patients, respectively. The QST profile associated with extramedian pattern includes: (1) thermal and mechanic hyperalgesia in the territory of the injured median nerve and in those of the uninjured ulnar and radial nerves and (2) enhanced wind-up. No signs of sensitization were found in patients with the median distribution and those with proximal symptoms. Different mechanisms may underlie hand extramedian and proximal spread of symptoms, respectively. Extramedian spread of symptoms in the hand may be secondary to spinal sensitization but peripheral and supraspinal mechanisms may contribute. Proximal spread may represent referred pain. Central sensitization may be secondary to abnormal activity in the median nerve afferents or the consequence of a predisposing trait. Our data may explain the persistence of sensory symptoms after median nerve surgical release and the presence of non-anatomical sensory patterns in neuropathic pain.
The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions

Training of neural networks for automated diagnosis of pigmented skin lesions is hampered by the small size and lack of diversity of available datasets of dermatoscopic images. We tackle this problem by releasing the HAM10000 ("Human Against Machine with 10000 training images") dataset. We collected dermatoscopic images from different populations acquired and stored by different modalities. Given this diversity we had to apply different acquisition and cleaning methods and developed semi-automatic workflows utilizing specifically trained neural networks. The final dataset consists of 10015 dermatoscopic images which are released as a training set for academic machine learning purposes and are publicly available through the ISIC archive. This benchmark dataset can be used for machine learning and for comparisons with human experts. Cases include a representative collection of all important diagnostic categories in the realm of pigmented lesions. More than 50% of lesions have been confirmed by pathology, while the ground truth for the rest of the cases was either follow-up, expert consensus, or confirmation by in-vivo confocal microscopy.
Interleaved-Boost Converter With High Voltage Gain

This paper presents an interleaved-boost converter, magnetically coupled to a voltage-doubler circuit, which provides a voltage gain far higher than that of the conventional boost topology. Besides, this converter has low-voltage stress across the switches, natural-voltage balancing between output capacitors, low-input current ripple, and magnetic components operating with the double of switching frequency. These features make this converter suitable to applications where a large voltage step-up is demanded, such as grid-connected systems based on battery storage, renewable energies, and uninterruptible power system applications. Operation principle, main equations, theoretical waveforms, control strategy, dynamic modeling, and digital implementation are provided. Experimental results are also presented validating the proposed topology.
A Review on Corpus Annotation for Arabic Sentiment Analysis

Mining publicly available data for meaning and value is an important research direction within social media analysis. To automatically analyze collected textual data, a manual effort is needed for a successful machine learning algorithm to effectively classify text. This pertains to annotating the text adding labels to each data entry. Arabic is one of the languages that are growing rapidly in the research of sentiment analysis, despite limited resources and scares annotated corpora. In this paper, we review the annotation process carried out by those papers. A total of 27 papers were reviewed between the years of 2010 and 2016.
An ERP study of syntactic processing in English and nonsense sentences

The timecourse of the interaction between syntactic and semantic information during sentence processing in monolingual native English speakers was investigated using event-related potentials (ERPs). To examine the effects of semantic information on syntactic processing, the results for normal English sentences were compared to those for semantically impoverished nonsense (Jabberwocky) sentences. Within each sentence type condition, half of the sentences contained a syntactic violation. Violations elicited a larger amplitude N1 and more negative ERPs around 200 ms after the onset of the critical word relative to the grammatical condition. Although these effects were observed in both sentence types, they were anteriorly distributed for English sentences only. Moreover, the P600 elicited by the syntactic violation was attenuated in processing Jabberwocky as compared to English sentences. These results suggest that semantic and syntactic information are integrated during the earlier stages of syntactic processing indexed by the anterior negativities, and that these interactions continue in the later stages of processing indexed by the P600.
Neural Networks and Financial Trading and the Efficient Markets Hypothesis

The efficient markets hypothesis asserts that the price of an asset reflects all of the information that can be obtained from past prices of the asset. A direct corollary of this hypothesis is that stock prices follow a random walk, and that any profits derived from timing the market are due entirely to chance. In the absence of any ability to predict the market, the most appropriate strategy—according to proponents of the efficient markets hypothesis—is to buy and hold. In this paper we describe a methodology by which neural networks can be trained indirectly, using a genetic algorithm based weight optimisation procedure, to determine buy and sell points for financial commodities traded on a stock exchange. In order to test the significance of the returns achieved using this methodology, we compare the returns on four financial price series with returns achieved on random walk data derived from each of these series using a bootstrapping procedure. These bootstrapped samples contain exactly the same distribution of daily returns as the original series, but lack any serial dependence present in the original. Our results indicate that on some price series the return achieved is significantly greater than that which can be achieved on the bootstrapped samples. This lends support to the claim that some financial time series are not entirely random, and that—contrary to the predictions of the efficient markets hypothesis—a trading strategy based solely on historical price data can be used to achieve returns better than those achieved using a buy-and-hold strategy.
Recommending News Based on Hybrid User Profile, Popularity, Trends, and Location

Reading the news is a favorite hobby for many people anywhere in the world. With the popularity of the Internet and social media, users are constantly provided, or even bombarded, with the latest news around the world. With numerous sources of news, it has become a real challenge for users to follow the news that they are interested. Previous work used user profile to recommend personalized news; and used RSS feeds and latest tweets to provide popular, trendy news. In this work we combine these two methods with three enhancements. First, to personalize news recommendation we used a hybrid approach, which involved the analyses of click through, user tweets, and user Twitter friends list to build user profile, this method significantly improves the accuracy of user profile. Second, to address the importance of temporal dynamics, we add a unique new feature of location preference to the news recommendation system. Third, we allow users to choose the ratio of popular news vs. trendy news they desire. The resulting system is then evaluated based on user satisfaction and accuracy. The results show that the average user satisfaction increases from 8.6 to 9.4 when location preference is added, while the accuracy of the recommendation system is around 92–95%. We believe that the proposed system is a successful example of incorporating temporal dynamics to recommendation systems; the combination of using hybrid user profile, popularity, trends and location would have significant impact on other recommendation systems in the future.
A framework of fuzzy information fusion for the segmentation of brain tumor tissues on MR images

A framework of fuzzy information fusion is proposed in this paper to automatically segment tumor areas of human brain from multispectral magnetic resonance imaging (MRI) such as T1-weighted, T2-weighted and proton density (PD) images. A priori knowledge about tumors described by radiology experts for different types of MRI are very helpful to guide a automatic and a precise segmentation. However, the terminology used by radiology experts are variable in term of image signal. In order to benefit of these descriptions, we propose to modellize them by fuzzy models. One fuzzy model is built for one type of MRI sequence. The segmentation is finally based on a fusion of different fuzzy information obtained from different types of MRI images. Our algorithm consists of four stages: the registration of multispectral MR images, the creation of fuzzy models describing the characteristics of tumor, the fusion based on fuzzy fusion operators and the adjustment by fuzzy region growing based on fuzzy connecting. The comparison between the obtained results and the hand-tracings of a radiology expert shows that the proposed algorithm is efficient. An average probability of correct detection 96% and an average probability of false detection 5% are obtained through studies of four patients.
Sentiment analysis on social media using morphological sentence pattern model

Social media became popular than ever as people are willing to share their emotions and opinions or to participate in social networking. Accordingly, the understanding of social media usage became important. The sentiment analysis is emerged as one of useful methods to analyze emotional stats expressed in textual data including social media data. However, this method still presents some limitations, particularly with an accuracy issue. For example, our previous sentiment analysis used a probability model and needed to adopt human-coded train-sets to maintain an acceptable accuracy level (89%). To overcome and improve this weakness, we propose an automated sentiment analysis in this paper by using the morphological sentence pattern model. We found that this new approach presented in this paper allowed us to achieve a higher level of accuracy (91.2%). The movie reviews were used for this analysis from IMDb, Rotten Tomatoes, Metacritic, YouTube and Twitter.
Comparison of human and computer performance across face recognition experiments

a r t i c l e i n f o Since 2005, human and computer performance has been systematically compared as part of face recognition competitions, with results being reported for both still and video imagery. The key results from these competitions are reviewed. To analyze performance across studies, the cross-modal performance analysis (CMPA) framework is introduced. The CMPA framework is applied to experiments that were part of face a recognition competition. The analysis shows that for matching frontal faces in still images, algorithms are consistently superior to humans. For video and difficult still face pairs, humans are superior. Finally, based on the CMPA framework and a face performance index, we outline a challenge problem for developing algorithms that are superior to humans for the general face recognition problem.