nils-herrmann/clean_scirepeval huggingface dataset:
Image Fusion in Hyperspectral Image Classification using Genetic Algorithm

Hyper spectral remote sensors collect image data for a large number of narrow, adjacent spectral bands. Every pixel in hyperspectral image involves a continuous spectrum that is used to classify the objects with great detail and precision. This paper presents hyperspectral image classification mechanism using genetic algorithm with empirical mode decomposition and image fusion used in preprocessing stage. 2-D Empirical mode decomposition method is used to remove any noisy components in each band of the hyperspectral data. After filtering, image fusion is performed on the hyperspectral bands to selectively merge the maximum possible features from the source images to form a single image. This fused image is classified using genetic algorithm. Different indices, such as K-means (KMI), Davies-Bouldin Index (DBI), and Xie-Beni Index (XBI) are used as objective functions. This method increases classification accuracy of hyperspectral image.
A Two-Stream Mutual Attention Network for Semi-supervised Biomedical Segmentation with Noisy Labels

Learning-based methods suffer from a deficiency of clean annotations, especially in biomedical segmentation. Although many semi-supervised methods have been proposed to provide extra training data, automatically generated labels are usually too noisy to retrain models effectively. In this paper, we propose a Two-Stream Mutual Attention Network (TSMAN) that weakens the influence of back-propagated gradients caused by incorrect labels, thereby rendering the network robust to unclean data. The proposed TSMAN consists of two sub-networks that are connected by three types of attention models in different layers. The target of each attention model is to indicate potentially incorrect gradients in a certain layer for both sub-networks by analyzing their inferred features using the same input. In order to achieve this purpose, the attention models are designed based on the propagation analysis of noisy gradients at different layers. This allows the attention models to effectively discover incorrect labels and weaken their influence during the parameter updating process. By exchanging multi-level features within the twostream architecture, the effects of noisy labels in each subnetwork are reduced by decreasing the updating gradients. Furthermore, a hierarchical distillation is developed to provide more reliable pseudo labels for unlabelded data, which further boosts the performance of our retrained TSMAN. The experiments using both the HVSMR 2016 and BRATS 2015 benchmarks demonstrate that our semi-supervised learning framework surpasses the state-of-the-art fully-supervised results.
Logo Image Based Approach for Phishing Detection

Phishing is a cyber attack which involves a fake website mimicking the some real legitimate website. The website makes the user believe the website being authentic and thus online user provides their sensitive information like password, PIN, Social Security Number, and Credit Card Information etc. Due to involvement of such high sensitivity information, these websites are a huge threat to online users and detection and blocking of such website become crucial. In this thesis, we propose a new phishing detection method to protect the internet users from such attacks. In particular, given a website, our proposed method will be able to detect between a phishing website and a legitimate website just by the screenshot of the logo image of it. Due to the usage of screenshot for extracting the logo, any hidden logo will not be able to spoof the algorithm into considering the website as phishing as happened in existing methods. In first study focus was on dataset gathering and then the logo image is extracted. This logo image is uploaded to Google image search engine using automated script which returns the URLs associated with that image. Since the relationship between logo and domain name is exclusive it is reasonable to treat the logo image as identity of original URL. Hence the phishing website will not have the same relation to the logo image as such and will not get returned as URL by Google when search for that logo image. Further, Alexa page rank is also used to strengthen the detection accuracy.
A comparison of security requirements engineering methods

This paper presents a conceptual framework for security engineering, with a strong focus on security requirements elicitation and analysis. This conceptual framework establishes a clear-cut vocabulary and makes explicit the interrelations between the different concepts and notions used in security engineering. Further, we apply our conceptual framework to compare and evaluate current security requirements engineering approaches, such as the Common Criteria, Secure Tropos, SREP, MSRA, as well as methods based on UML and problem frames. We review these methods and assess them according to different criteria, such as the general approach and scope of the method, its validation, and quality assurance capabilities. Finally, we discuss how these methods are related to the conceptual framework and to one another.
Leveraging Parents : The Impact of High-Frequency Information on Student Achievement ∗

We partner text-messaging technology with school information systems to automate the gathering and provision of high-frequency information on students’ academic progress to parents. In an experiment across 22 schools, we use this technology to send weekly automated messages to parents about their child’s missed assignments, grades, and class absences. We pre-specified five primary outcomes. The intervention reduces course failures by 38%, increases class attendance by 17% and increases retention. The positive effects are particularly large for students with below-average GPA and students in high school, which persisted into a second year. There are no effects on state test scores, though the exams, which were new and zero stakes for students, were subsequently discontinued; students used substantially less than the expected amount of time to complete them. In contrast, we find significant improvements on in-class exam scores. Our results show this technology can improve student performance relatively cheaply and at scale.  We thank Dr. Ron Duerring, Jon Duffy and Kanawha County Schools, as well as Spencer Kier, Alex Farivar, Jeremy Lupoli, Sam Elhag and Zach Posner. Bergman has previously received compensation from the learning management system company to advise them with respect to their school-to-parent information technologies. †Teachers College, Columbia University, 525 W. 120th Street New York, New York 10027. Email addresses: bergman@tc.columbia.edu and ewc2130@tc.columbia.edu.
Deterministic Policy Gradient Algorithms

In this paper we consider deterministic policy gradient algorithms for reinforcement learning with continuous actions. The deterministic policy gradient has a particularly appealing form: it is the expected gradient of the action-value function. This simple form means that the deterministic policy gradient can be estimated much more efficiently than the usual stochastic policy gradient. To ensure adequate exploration, we introduce an off-policy actor-critic algorithm that learns a deterministic target policy from an exploratory behaviour policy. We demonstrate that deterministic policy gradient algorithms can significantly outperform their stochastic counterparts in high-dimensional action spaces.
Breast cancer relapse prognosis by classic and modern structures of machine learning algorithms

According to medical reports, cancers are big problems in the world society. In this paper we are supposed to predict breast cancer recurrence by multi-layer perceptron with two different outputs, a deep neural network as a feature extraction and multi-layer perceptron as a classifier, rough neural network with two different outputs, and finally, support vector machine. Then, we compare the results achieved by each method. It can be understood that rough neural network with two outputs leads to the highest accuracy and the lowest variance among other structures.
Remanufacturing as a Marketing Strategy

T profitability of remanufacturing systems for different cost, technology, and logistics structures has been extensively investigated in the literature. We provide an alternative and somewhat complementary approach that considers demand-related issues, such as the existence of green segments, original equipment manufacturer competition, and product life-cycle effects. The profitability of a remanufacturing system strongly depends on these issues as well as on their interactions. For a monopolist, we show that there exist thresholds on the remanufacturing cost savings, the green segment size, market growth rate, and consumer valuations for the remanufactured products, above which remanufacturing is profitable. More important, we show that under competition remanufacturing can become an effective marketing strategy, which allows the manufacturer to defend its market share via price discrimination.
Wireless wearable pulse oximeter for health monitoring using ZigBee wireless sensor network

In this work, a portable real-time wireless health monitoring system is developed. The system is used for remote monitoring of patients' heart rate and oxygen saturation in blood. The system was designed and implemented using ZigBee wireless technologies. All pulse oximetry data are transferred within a group of wireless personal area network (WPAN) to database computer server. The sensor modules were designed for low power operation with a program that can adjust power management depending on scenarios of power source and current power operation. The sensor unit consists of (1) two types of LEDs and photodiode packed in Velcro strip that is facing to a patient's fingertip; (2) Microcontroller unit for interfacing with ZigBee module, processing pulse oximetry data and storing some data before sending to base PC; (3) ZigBee module for communicating the data of pulse oximetry, ZigBee module gets all commands from microcontroller unit and it has a complete ZigBee stack inside and (4) Base node for receiving and storing the data before sending to PC.
Surpassing Human-Level Face Verification Performance on LFW with GaussianFace

Face verification remains a challenging problem in very complex conditions with large variations such as pose, illumination, expression, and occlusions. This problem is exacerbated when we rely unrealistically on a single training data source, which is often insufficient to cover the intrinsically complex face variations. This paper proposes a principled multi-task learning approach based on Discriminative Gaussian Process Latent Variable Model (DGPLVM), named GaussianFace, for face verification. In contrast to relying unrealistically on a single training data source, our model exploits additional data from multiple source-domains to improve the generalization performance of face verification in an unknown target-domain. Importantly, our model can adapt automatically to complex data distributions, and therefore can well capture complex face variations inherent in multiple sources. To enhance discriminative power, we introduced a more efficient equivalent form of Kernel Fisher Discriminant Analysis to DGPLVM. To speed up the process of inference and prediction, we exploited the low rank approximation method. Extensive experiments demonstrated the effectiveness of the proposed model in learning from diverse data sources and generalizing to unseen domains. Specifically, the accuracy of our algorithm achieved an impressive accuracy rate of 98.52% on the well-known and challenging Labeled Faces in the Wild (LFW) benchmark. For the first time, the human-level performance in face verification (97.53%) on LFW is surpassed.
Aesthetics of interaction: a literature synthesis

New technologies provide expanded opportunities for interaction design. The growing number of possible ways to interact, in turn, creates a new responsibility for designers: Besides the product's visual aesthetics, one has to make choices about the aesthetics of interaction. This issue recently gained interest in Human-Computer Interaction (HCI) research. Based on a review of 19 approaches, we provide an overview of today's state of the art. We focused on approaches that feature "qualities", "dimensions" or "parameters" to describe interaction. Those fell into two broad categories. One group of approaches dealt with detailed spatio-temporal attributes of interaction sequences (i.e., action-reaction) on a sensomotoric level (i.e., form). The other group addressed the feelings and meanings an interaction is enveloped in rather than the interaction itself (i.e., experience). Surprisingly, only two approaches addressed both levels simultaneously, making the explicit link between form and experience. We discuss these findings and its implications for future theory building.
New Metrics and Experimental Paradigms for Continual Learning

In order for a robotic agent to learn successfully in an uncontrolled environment, it must be able to immediately alter its behavior. Deep neural networks are the dominant approach for classification tasks in computer vision, but typical algorithms and architectures are incapable of immediately learning new tasks without catastrophically forgetting previously acquired knowledge. There has been renewed interest in solving this problem, but there are limitations to existing solutions, including poor performance compared to offline models, large memory footprints, and learning slowly. In this Abstract, we formalize the continual learning paradigm and propose new benchmarks for assessing continual learning agents.
The psycholinguistic and affective structure of words conveying pain

Despite the flourishing research on the relationships between affect and language, the characteristics of pain-related words, a specific type of negative words, have never been systematically investigated from a psycholinguistic and emotional perspective, despite their psychological relevance. This study offers psycholinguistic, affective, and pain-related norms for words expressing physical and social pain. This may provide a useful tool for the selection of stimulus materials in future studies on negative emotions and/or pain. We explored the relationships between psycholinguistic, affective, and pain-related properties of 512 Italian words (nouns, adjectives, and verbs) conveying physical and social pain by asking 1020 Italian participants to provide ratings of Familiarity, Age of Acquisition, Imageability, Concreteness, Context Availability, Valence, Arousal, Pain-Relatedness, Intensity, and Unpleasantness. We also collected data concerning Length, Written Frequency (Subtlex-IT), N-Size, Orthographic Levenshtein Distance 20, Neighbor Mean Frequency, and Neighbor Maximum Frequency of each word. Interestingly, the words expressing social pain were rated as more negative, arousing, pain-related, and conveying more intense and unpleasant experiences than the words conveying physical pain.
Role of Vascular Oxidative Stress in Obesity and Metabolic Syndrome

Obesity is associated with vascular diseases that are often attributed to vascular oxidative stress. We tested the hypothesis that vascular oxidative stress could induce obesity. We previously developed mice that overexpress p22phox in vascular smooth muscle, , which have increased vascular ROS production. At baseline, tg(sm/p22phox) mice have a modest increase in body weight. With high-fat feeding, tg(sm/p22phox) mice developed exaggerated obesity and increased fat mass. Body weight increased from 32.16 ± 2.34 g to 43.03 ± 1.44 g in tg(sm/p22phox) mice (vs. 30.81 ± 0.71 g to 37.89 ± 1.16 g in the WT mice). This was associated with development of glucose intolerance, reduced HDL cholesterol, and increased levels of leptin and MCP-1. Tg(sm/p22phox) mice displayed impaired spontaneous activity and increased mitochondrial ROS production and mitochondrial dysfunction in skeletal muscle. In mice with vascular smooth muscle-targeted deletion of p22phox (p22phox(loxp/loxp)/tg(smmhc/cre) mice), high-fat feeding did not induce weight gain or leptin resistance. These mice also had reduced T-cell infiltration of perivascular fat. In conclusion, these data indicate that vascular oxidative stress induces obesity and metabolic syndrome, accompanied by and likely due to exercise intolerance, vascular inflammation, and augmented adipogenesis. These data indicate that vascular ROS may play a causal role in the development of obesity and metabolic syndrome.
A CMOS Sub-l-V nanopower current and voltage reference with leakage compensation

In this paper, a CMOS sub-1-V nanopower reference is proposed, which is implemented without resistors and with only standard CMOS transistors. The proposed circuit has the most attractive merit that it can afford reference current and reference voltage simultaneously. Moreover, the leakage compensation technique is utilized, and thus it has very low temperature coefficient for a wide temperature range. The proposed circuit is verified by SPICE simulation with CMOS 0.18um process. The temperature coefficient of the reference voltage and reference current are 0.0037%/°C and 0.0091%/°C, respectively. Also, the power supply voltage can be as low as 0.85V and its power consumption is only 5.1nW.
Feature-preserving procedural texture

This paper presents how to synthesize a texture in a procedural way that preserves the features of the input exemplar. The exemplar is analyzed in both spatial and frequency domains to be decomposed into feature and non-feature parts. Then, the non-feature parts are reproduced as a procedural noise, whereas the features are independently synthesized. They are combined to output a non-repetitive texture that also preserves the exemplar’s features. The proposed method allows the user to control the extent of extracted features and also enables a texture to edited quite effectively.
Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning

Recommender systems in e-learning domain play an important role in assisting the learners to find useful and relevant learning materials that meet their learning needs. Personalized intelligent agents and recommender systems have been widely accepted as solutions towards overcoming information retrieval challenges by learners arising from information overload. Use of ontology for knowledge representation in knowledge-based recommender systems for e-learning has become an interesting research area. In knowledge-based recommendation for e-learning resources, ontology is used to represent knowledge about the learner and learning resources. Although a number of review studies have been carried out in the area of recommender systems, there are still gaps and deficiencies in the comprehensive literature review and survey in the specific area of ontology-based recommendation for e-learning. In this paper, we present a review of literature on ontology-based recommenders for e-learning. First, we analyze and classify the journal papers that were published from 2005 to 2014 in the field of ontology-based recommendation for e-learning. Secondly, we categorize the different recommendation techniques used by ontology-based e-learning recommenders. Thirdly, we categorize the knowledge representation technique, ontology type and ontology representation language used by ontology-based recommender systems, as well as types of learning resources recommended by e-learning recommenders. Lastly, we discuss the future trends of this recommendation approach in the context of e-learning. This study shows that use of ontology for knowledge representation in e-learning recommender systems can improve the quality of recommendations. It was also evident that hybridization of knowledge-based recommendation with other recommendation techniques can enhance the effectiveness of e-learning recommenders.
Early Detection of Signs of Anorexia and Depression Over Social Media using Effective Machine Learning Frameworks

The CLEF eRisk 2018 challenge focuses on early detection of signs of depression or anorexia using posts or comments over social media. The eRisk lab has organized two tasks this year and released two different corpora for the individual tasks. The corpora are developed using the posts and comments over Reddit, a popular social media. The machine learning group at Ramakrishna Mission Vivekananda Educational and Research Institute (RKMVERI), India has participated in this challenge and individually submitted five results to accomplish the objectives of these two tasks. The paper presents different machine learning techniques and analyze their performance for early risk prediction of anorexia or depression. The techniques involve various classifiers and feature engineering schemes. The simple bag of words model has been used to perform ada boost, random forest, logistic regression and support vector machine classifiers to identify documents related to anorexia or depression in the individual corpora. We have also extracted the terms related to anorexia or depression using metamap, a tool to extract biomedical concepts. Theerefore, the classifiers have been implemented using bag of words features and metamap features individually and subsequently combining these features. The performance of the recurrent neural network is also reported using GloVe and Fasttext word embeddings. Glove and Fasttext are pre-trained word vectors developed using specific corpora e.g., Wikipedia. The experimental analysis on the training set shows that the ada boost classifier using bag of words model outperforms the other methods for task1 and it achieves best score on the test set in terms of precision over all the runs in the challenge. Support vector machine classifier using bag of words model outperforms the other methods in terms of fmeasure for task2. The results on the test set submitted to the challenge suggest that these framework achieve reasonably good performance.
Class II correction with the Twin Force Bite Corrector.

JCO/APRIL 2004 M distalization in Class II cases has been accomplished with various functional appliances, including fixed interarch appliances, such as the Herbst* and Jasper Jumper,** and fixed intra-arch appliances. The Twin Force Bite Corrector (TFBC)*** is a new fixed intermaxillary appliance with a built-in constant force for Class II correction. This article presents two patients who were part of a long-term prospective study currently in progress at the University of Connecticut Department of Orthodontics. Each patient was treated with the TFBC to correct a skeletal Class II malocclusion due to a retrognathic mandible.
Robust end-of-utterance detection for real-time speech recognition applications

In this paper we propose a sub-band energy based end-ofutterance algorithm that is capable of detecting the time instant when the user has stopped speaking. The proposed algorithm finds the time instant at which many enough sub-band spectral energy trajectories fall and stay for a pre-defined fixed time below adaptive thresholds, i.e. a non-speech period is detected after the end of the utterance. With the proposed algorithm a practical speech recognition system can give timely feedback for the user, thereby making the behaviour of the speech recognition system more predictable and similar across different usage environments and noise conditions. The proposed algorithm is shown to be more accurate and noise robust than the previously proposed approaches. Experiments with both isolated command word recognition and continuous digit recognition in various noise conditions verify the viability of the proposed approach with an average proper endof-utterance detection rate of around 94% in both cases, representing 43% error rate reduction over the most competitive previously published method.