nils-herrmann/clean_scirepeval huggingface dataset:
PHOTOGROUP : DECENTRALIZED WEB APPLICATION USING ETHEREUM BLOCKCHAIN

1,2,3,4,5 Department Of Computer Engineering,  platforms such as Instagram, Facebook, Google+ etc, have created the boon in our humanitarian society[1]. Along with these social networking platforms there comes a great responsibility of handling user privacy as well as user data. In most of these websites, data is stored on the centralized system called as the server.  The whole system crash down if the server goes down. One of the solutions for this problem is to use a decentralized system. Decentralized applications works on Blockchain. A Blockchain is a group of blocks connected sequentially to each other. The blockchains are designed so that transactions remain immutable i.e. unchanged hence provides security. The data can be distributed and no one can tampered that data. This paper presents a decentralized social media photo sharing web application which is based on blockchain technology where the user would be able to view, like, comment, share photos shared by different users.
TopicMF: Simultaneously Exploiting Ratings and Reviews for Recommendation

Although users’ preference is semantically reflected in the free-form review texts, this wealth of information was not fully exploited for learning recommender models. Specifically, almost all existing recommendation algorithms only exploit rating scores in order to find users’ preference, but ignore the review texts accompanied with rating information. In this paper, we propose a novel matrix factorization model (called TopicMF) which simultaneously considers the ratings and accompanied review texts. Experimental results on 22 real-world datasets show the superiority of our model over the state-of-the-art models, demonstrating its effectiveness for recommendation tasks.
Detect-SLAM: Making Object Detection and SLAM Mutually Beneficial

Although significant progress has been made in SLAM and object detection in recent years, there are still a series of challenges for both tasks, e.g., SLAM in dynamic environments and detecting objects in complex environments. To address these challenges, we present a novel robotic vision system, which integrates SLAM with a deep neural networkbased object detector to make the two functions mutually beneficial. The proposed system facilitates a robot to accomplish tasks reliably and efficiently in an unknown and dynamic environment. Experimental results show that compare to the state-of-the-art robotic vision systems, the proposed system has three advantages: i) it greatly improves the accuracy and robustness of SLAM in dynamic environments by removing unreliable features from moving objects leveraging the object detector, ii) it builds an instance-level semantic map of the environment in an online fashion using the synergy of the two functions for further semantic applications; and iii) it improves the object detector so that it can detect/recognize objects effectively under more challenging conditions such as unusual viewpoints, poor lighting condition, and motion blur, by leveraging the object map.
Integration of network intrusion detection systems and honeypot networks for cloud security

With an aim of provisioning fast, reliable and low cost services to the users, the cloud-computing technology has progressed leaps and bounds. But, adjacent to its development is ever increasing ability of malicious users to compromise its security from outside as well as inside. The Network Intrusion Detection System (NIDS) techniques has gone a long way in detection of known and unknown attacks. The methods of detection of intrusion and deployment of NIDS in cloud environment are dependent on the type of services being rendered by the cloud. It is also important that the cloud administrator is able to determine the malicious intensions of the attackers and various methods of attack. In this paper, we carry out the integration of NIDS module and Honeypot Networks in Cloud environment with objective to mitigate the known and unknown attacks. We also propose method to generate and update signatures from information derived from the proposed integrated model. Using sandboxing environment, we perform dynamic malware analysis of binaries to derive conclusive evidence of malicious attacks.
Domain Transfer for Deep Natural Language Generation from Abstract Meaning Representations

Stochastic natural language generation systems that are trained from labelled datasets are often domainspecific in their annotation and in their mapping from semantic input representations to lexical-syntactic outputs. As a result, learnt models fail to generalize across domains, heavily restricting their usability beyond single applications. In this article, we focus on the problem of domain adaptation for natural language generation. We show how linguistic knowledge from a source domain, for which labelled data is available, can be adapted to a target domain by reusing training data across domains. As a key to this, we propose to employ abstract meaning representations as a common semantic representation across domains. We model natural language generation as a long short-term memory recurrent neural network encoderdecoder, in which one recurrent neural network learns a latent representation of a semantic input, and a second recurrent neural network learns to decode it to a sequence of words. We show that the learnt representations can be transferred across domains and can be leveraged effectively to improve training on new unseen domains. Experiments in three different domains and with six datasets demonstrate that the lexical-syntactic constructions learnt in one domain can be transferred to new domains and achieve up to 75-100% of the performance of in-domain training. This is based on objective metrics such as BLEU and semantic error rate and a subjective human rating study. Training a policy from prior knowledge from a different domain is consistently better than pure in-domain training by up to 10%.
Male or Female? Brains are Intersex

The underlying assumption in popular and scientific publications on sex differences in the brain is that human brains can take one of two forms "male" or "female," and that the differences between these two forms underlie differences between men and women in personality, cognition, emotion, and behavior. Documented sex differences in brain structure are typically taken to support this dimorphic view of the brain. However, neuroanatomical data reveal that sex interacts with other factors in utero and throughout life to determine the structure of the brain, and that because these interactions are complex, the result is a multi-morphic, rather than a dimorphic, brain. More specifically, here I argue that human brains are composed of an ever-changing heterogeneous mosaic of "male" and "female" brain characteristics (rather than being all "male" or all "female") that cannot be aligned on a continuum between a "male brain" and a "female brain." I further suggest that sex differences in the direction of change in the brain mosaic following specific environmental events lead to sex differences in neuropsychiatric disorders.
Design and Implementation of Digital dining in Restaurants using Android

With the increase in the number of restaurants and population of restaurant-goers, a need to enhance the working of hospitality industry is felt. This research work aims for this betterment of hospitality industry by incorporating technology. A recent survey on the utilisation of technology in hospitality industries showcased that various applications based on wireless technologies are already in use enabling partial automation of the food ordering process. In this paper, we discuss about the design and implementation of digital dining in restaurants using android technology. This system is a basic dynamic database utility system which fetches all information from a centralized database. The tablet at the customer table contains the android application with all the restaurant and menu details. The customer tablet, kitchen display and the cashier counter connects directly with each other through Wi-Fi. This wireless application is user-friendly, improves efficiency and accuracy for restaurants by saving time, reduces human errors and provides customer feedback. This system successfully overcomes the drawbacks in earlier automated food ordering systems and is less expensive as it requires a onetime investment for gadgets.
Instant Object Detection in Lidar Point Clouds

In this letter, we present a new approach for object classification in continuously streamed Lidar point clouds collected from urban areas. The input of our framework is raw 3-D point cloud sequences captured by a Velodyne HDL-64 Lidar, and we aim to extract all vehicles and pedestrians in the neighborhood of the moving sensor. We propose a complete pipeline developed especially for distinguishing outdoor 3-D urban objects. First, we segment the point cloud into regions of ground, short objects (i.e., low foreground), and tall objects (high foreground). Then, using our novel two-layer grid structure, we perform efficient connected component analysis on the foreground regions, for producing distinct groups of points, which represent different urban objects. Next, we create depth images from the object candidates, and apply an appearance-based preliminary classification by a convolutional neural network. Finally, we refine the classification with contextual features considering the possible expected scene topologies. We tested our algorithm in real Lidar measurements, containing 1485 objects captured from different urban scenarios.
Unsupervised Semantic Role Labellin

We present an unsupervised method for labelling the arguments of verbs with their semantic roles. Our bootstrapping algorithm makes initial unambiguous role assignments, and then iteratively updates the probability model on which future assignments are based. A novel aspect of our approach is the use of verb, slot, and noun class information as the basis for backing off in our probability model. We achieve 50–65% reduction in the error rate over an informed baseline, indicating the potential of our approach for a task that has heretofore relied on large amounts of manually generated training data.
Ship Underkeel Clearance in Waves

A method is described for combining squat, heel and wave-induced motion calculations into an overall underkeel clearance (UKC) assessment, based on the “dynamic draft” at all the corners of the ship. Particular attention is given to predicting ship motions in shallow water. The effects of resonant roll, wave spreading and extreme motions are discussed. A general UKC assessment is proposed, valid for calm water or swell conditions. The principles are applicable to long-term UKC assessment for dredging optimization, as well as assessing specific transits through port approach channels, based on real-time or forecast wave conditions.
A Chatbot for Automatic Processing of Learner Concerns in an Online Learning Platform

In this article, we present a chatbot model that can automatically respond to learners’ concerns on an online training platform. The proposed chatbot model is based on an adaptation of the similarity of Dice to understand the concerns of learners. The first phase of this approach allows selecting the preestablished concerns that the teacher has in a knowledge base which are closest to those posed by the learner. The second phase consists of selecting among these k most appropriate concerns based on a measure of similarity built on the concept of domain keywords. The experimentation of the prototype of this chatbot makes it possible to find the adequate answers. In the case, where the question refers to a question from the teacher, the learner is asked if the question identified is the one he was referring to. If he answers in the affirmative, the instructions associated with his request are sent to him. If not, the learner’s concern is sent to the human tutor. The hybridization of this chatbot with the human agent comes to enrich the initial knowledge base of the chatbot. The results obtained with the concept based on the keywords of the domain are encouraging. The learner’s comprehension rate is above 50% when applying the concept of domain keywords while the measure of Dice is below 50%. Metadata; ontologies; semantic similarity; natural language; semantic web; chatbot
NB-WiFi: IEEE 802.11 and Bluetooth Low Energy Combined for Efficient Support of IoT

Key features of the two dominating standards for the unlicensed bands, IEEE 802.11 and Bluetooth Wireless Technology, are combined to obtain a physical layer (PHY) with several desirable features for internet of things (IoT). The proposed PHY, which is referred to as Narrow-band WiFi (NB-WiFi) can be supported by an OFDM transceiver available in an IEEE 802.11 access point (AP). In addition, NB-WiFi supports concurrent use of low data rate IoT application and high data rate broadband using IEEE 802.11ax technology, based on a single IFFT#x002F;FFT in the AP. In the sensor node, Bluetooth Low Energy (BLE) hardware can be reused, making it suitable for dual mode implementation of BLE and NB-WiFi. The performance of the proposed PHY is simulated for an AWGN channel, and it achieves about 10dB improved sensitivity compared to a typical BLE receiver, due to the lower data rate.
Hidden Roles of CSR : Perceived Corporate Social Responsibility as a Preventive against Counterproductive Work Behaviors

The purpose of this study is to identify how the degree to which employees perceive that their organization is socially responsible influences their counterproductive behaviors at work. We developed a research model capturing the relationships between perceived corporate social responsibility (CSR), affective organizational commitment (AOC), and organization-, colleague-, and customer-directed counterproductive work behaviors (CWBs). Drawing on social identity and social bonding theories, we hypothesized that employees’ perceptions of CSR would enhance their levels of AOC and thereby hinder the three types of CWBs. Survey data from 252 frontline employees of six luxury hotels in South Korea were examined. We found that perceived CSR is indirectly and negatively related to the three types of CWBs through AOC. We discussed the theoretical contributions and practical implications of this study.
Paclitaxel plus bevacizumab versus paclitaxel alone for metastatic breast cancer.

In an open-label, randomized, phase 3 trial, we compared the efficacy and safety of paclitaxel with that of paclitaxel plus bevacizumab, a monoclonal antibody against vascular endothelial growth factor, as initial treatment for metastatic breast cancer.



We randomly assigned patients to receive 90 mg of paclitaxel per square meter of body-surface area on days 1, 8, and 15 every 4 weeks, either alone or with 10 mg of bevacizumab per kilogram of body weight on days 1 and 15. The primary end point was progression-free survival; overall survival was a secondary end point.



From December 2001 through May 2004, a total of 722 patients were enrolled. Paclitaxel plus bevacizumab significantly prolonged progression-free survival as compared with paclitaxel alone (median, 11.8 vs. 5.9 months; hazard ratio for progression, 0.60; P<0.001) and increased the objective response rate (36.9% vs. 21.2%, P<0.001). The overall survival rate, however, was similar in the two groups (median, 26.7 vs. 25.2 months; hazard ratio, 0.88; P=0.16). Grade 3 or 4 hypertension (14.8% vs. 0.0%, P<0.001), proteinuria (3.6% vs. 0.0%, P<0.001), headache (2.2% vs. 0.0%, P=0.008), and cerebrovascular ischemia (1.9% vs. 0.0%, P=0.02) were more frequent in patients receiving paclitaxel plus bevacizumab. Infection was more common in patients receiving paclitaxel plus bevacizumab (9.3% vs. 2.9%, P<0.001), but febrile neutropenia was uncommon (<1% overall).



Initial therapy of metastatic breast cancer with paclitaxel plus bevacizumab prolongs progression-free survival, but not overall survival, as compared with paclitaxel alone.
Machine Learning as an Adversarial Service: Learning Black-Box Adversarial Examples

Neural networks are known to be vulnerable to adversarial examples, inputs that have been intentionally perturbed to remain visually similar to the source input, but cause a misclassification. Until now, black-box attacks against neural networks have relied on transferability of adversarial examples. White-box attacks are used to generate adversarial examples on a substitute model and then transferred to the black-box target model. In this paper, we introduce a direct attack against black-box neural networks, that uses another attacker neural network to learn to craft adversarial examples. We show that our attack is capable of crafting adversarial examples that are indistinguishable from the source input and are misclassified with overwhelming probability reducing accuracy of the black-box neural network from 99.4% to 0.77% on the MNIST dataset, and from 91.4% to 6.8% on the CIFAR-10 dataset. Our attack can adapt and reduce the effectiveness of proposed defenses against adversarial examples, requires very little training data, and produces adversarial examples that can transfer to different machine learning models such as Random Forest, SVM, and K-Nearest Neighbor. To demonstrate the practicality of our attack, we launch a live attack against a target black-box model hosted online by Amazon: the crafted adversarial examples reduce its accuracy from 91.8% to 61.3%. Additionally, we show attacks proposed in the literature have unique, identifiable distributions. We use this information to train a classifier that is robust against such attacks.
Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation

In this paper, we study the problem of learning vision-based dynamic manipulation skills using a scalable reinforcement learning approach. We study this problem in the context of grasping, a longstanding challenge in robotic manipulation. In contrast to static learning behaviors that choose a grasp point and then execute the desired grasp, our method enables closed-loop vision-based control, whereby the robot continuously updates its grasp strategy based on the most recent observations to optimize long-horizon grasp success. To that end, we introduce QT-Opt, a scalable self-supervised vision-based reinforcement learning framework that can leverage over 580k real-world grasp attempts to train a deep neural network Q-function with over 1.2M parameters to perform closed-loop, real-world grasping that generalizes to 96% grasp success on unseen objects. Aside from attaining a very high success rate, our method exhibits behaviors that are quite distinct from more standard grasping systems: using only RGB visionbased perception from an over-the-shoulder camera, our method automatically learns regrasping strategies, probes objects to find the most effective grasps, learns to reposition objects and perform other non-prehensile pre-grasp manipulations, and responds dynamically to disturbances and
Partial fingerprint matching: Fusion of level 2 and level 3 features

Increase in identity frauds, crimes, security there is growing need of fingerprint technology in civilian and law enforcement applications. Partial fingerprints are of great interest which are either found at crime scenes or resulted from improper scanning. These fingerprints are poor in quality and the number of features present depends on size of fingerprint. Due to the lack of features such as core and delta, general fingerprint matching algorithms do not perform well for partial fingerprint matching. By using combination of level1 and level 2 features accuracy of partial matching cannot be increased. Therefore, we utilize extended features in combination with other feature set. Efficacious fusion methods for coalesce of different modality systems perform better for these types of prints. In this paper, we propose a method for partial fingerprint matching using score level fusion of minutiae based radon transform and pores based LBP extraction. To deal with broken ridges and fragmentary information, radon transform is used to get local information around minutiae. Finally, we evaluate the performance by comparing Equal Error Rate (ERR) of proposed method and existing method and proposed method reduces the error rate to 1.84%.
Methods and uncertainties in bioclimatic envelope modelling under climate change

Potential impacts of projected climate change on biodiversity are often assessed using single-species bioclimatic ‘envelope’ models. Such models are a special case of species distribution models in which the current geographical distribution of species is related to climatic variables so to enable projections of distributions under future climate change scenarios. This work reviews a number of critical methodological issues that may lead to uncertainty in predictions from bioclimatic modelling. Particular attention is paid to recent developments of bioclimatic modelling that address some of these issues as well as to the topics where more progress needs to be made. Developing and applying bioclimatic models in a informative way requires good understanding of a wide range of methodologies, including the choice of modelling technique, model validation, collinearity, autocorrelation, biased sampling of explanatory variables, scaling and impacts of nonclimatic factors. A key challenge for future research is integrating factors such as land cover, direct CO2 effects, biotic interactions and dispersal mechanisms into species-climate models. We conclude that, although bioclimatic envelope models have a number of important advantages, they need to be applied only when users of models have a thorough understanding of their limitations and uncertainties.
3D Anisotropic Hybrid Network: Transferring Convolutional Features from 2D Images to 3D Anisotropic Volumes

While deep convolutional neural networks (CNN) have been successfully applied for 2D image analysis, it is still challenging to apply them to 3D anisotropic volumes, especially when the within-slice resolution is much higher than the between-slice resolution and when the amount of 3D volumes is relatively small. On one hand, direct learning of CNN with 3D convolution kernels suffers from the lack of data and likely ends up with poor generalization; insufficient GPU memory limits the model size or representational power. On the other hand, applying 2D CNN with generalizable features to 2D slices ignores between-slice information. Coupling 2D network with LSTM to further handle the between-slice information is not optimal due to the difficulty in LSTM learning. To overcome the above challenges, we propose a 3D Anisotropic Hybrid Network (AHNet) that transfers convolutional features learned from 2D images to 3D anisotropic volumes. Such a transfer inherits the desired strong generalization capability for withinslice information while naturally exploiting between-slice information for more effective modelling. The focal loss is further utilized for more effective end-to-end learning. We experiment with the proposed 3D AH-Net on two different medical image analysis tasks, namely lesion detection from a Digital Breast Tomosynthesis volume, and liver and liver tumor segmentation from a Computed Tomography volume and obtain the state-of-the-art results.
L ESSONS FROM THE R ADEMACHER C OMPLEXITY FOR D EEP L EARNING

Understanding the generalization properties of deep learning models is critical for successful applications, especially in the regimes where the number of training samples is limited. We study the generalization properties of deep neural networks via the empirical Rademacher complexity and show that it is easier to control the complexity of convolutional networks compared to general fully connected networks. In particular, we justify the usage of small convolutional kernels in deep networks as they lead to a better generalization error. Moreover, we propose a representation based regularization method that allows to decrease the generalization error by controlling the coherence of the representation. Experiments on the MNIST dataset support these foundations.