nils-herrmann/clean_scirepeval huggingface dataset:
Thwarting Fake OSN Accounts by Predicting their Victims

Traditional defense mechanisms for fighting against automated fake accounts in online social networks are victim-agnostic. Even though victims of fake accounts play an important role in the viability of subsequent attacks, there is no work on utilizing this insight to improve the status quo. In this position paper, we take the first step and propose to incorporate predictions about victims of unknown fakes into the workflows of existing defense mechanisms. In particular, we investigated how such an integration could lead to more robust fake account defense mechanisms. We also used real-world datasets from Facebook and Tuenti to evaluate the feasibility of predicting victims of fake accounts using supervised machine learning.
A Novel Anomaly Detection Scheme Based on Principal Component Classifier

This paper proposes a novel scheme that uses robust principal component classifier in intrusion detection problems where the training data may be unsupervised. Assuming that anomalies can be treated as outliers, an intrusion predictive model is constructed from the major and minor principal components of the normal instances. A measure of the difference of an anomaly from the normal instance is the distance in the principal component space. The distance based on the major components that account for 50% of the total variation and the minor components whose eigenvalues less than 0.20 is shown to work well. The experiments with KDD Cup 1999 data demonstrate that the proposed method achieves 98.94% in recall and 97.89% in precision with the false alarm rate 0.92% and outperforms the nearest neighbor method, density-based local outliers (LOF) approach, and the outlier detection algorithm based on Canberra metric.
Computer generated images vs. digital photographs: A synergetic feature and classifier combination approach

The development of powerful and low-cost hardware devices allied with great advances on content editing and authoring tools have promoted the creation of computer generated images (CG) to a degree of unrivaled realism. Differentiating a photo-realistic computer generated image from a real photograph (PG) can be a difficult task to naked eyes. Digital forensics techniques can play a significant role in this task. As a matter of fact, important research has been made by the scientific community in this regard. Most of the approaches focus on single image features aiming at detecting differences between real and computer generated images. However, with the current technology advances, there is no universal image characterization technique that completely solves this problem. In our work, we (1) present a complete study of several CG versus PG approaches; (2) create a large and heterogeneous dataset to be used as a training and validation database; (3) implement representative methods of the literature; and (4) devise automatic ways for combining the best approaches. We compared the implemented methods using the same validation environment showing their pros and cons with a common benchmark protocol. We collected approximately 4850 photographs and 4850 CGs with large diversity of image content and quality. We implemented a total of 13 methods. Results show that this set of methods can achieve up to 93% of accuracy when used without any form of machine learning fusion. The same methods, when combined through the implemented fusion schemes, can achieve an accuracy rate of 97%, representing a reduction of 57% of the classification error over the best individual result.
Designing collaborative healthcare technology for the acute care workflow

Preventable medical errors in hospitals are the third leading cause of death in the United States. Many of these are caused by poor situational awareness, especially in acute care resuscitation scenarios. While a number of checklists and technological interventions have been developed to reduce cognitive load and improve situational awareness, these tools often do not fit the clinical workflow. To better understand the challenges faced by clinicians in acute care codes, we conducted a qualitative study with interprofessional clinicians at three regional hospitals. Our key findings are: Current documentation processes are inadequate (with information recorded on paper towels); reference guides can serve as fixation points, reducing rather than enhancing situational awareness; the physical environment imposes significant constraints on workflow; homegrown solutions may be used often to solve unstandardized processes; simulation scenarios do not match real-world practice. We present a number of considerations for collaborative healthcare technology design and discuss the implications of our findings on current work for the development of more effective interventions for acute care resuscitation scenarios.
Volumetric and Multi-view CNNs for Object Classification on 3D Data

3D shape models are becoming widely available and easier to capture, making available 3D information crucial for progress in object classification. Current state-of-theart methods rely on CNNs to address this problem. Recently, we witness two types of CNNs being developed: CNNs based upon volumetric representations versus CNNs based upon multi-view representations. Empirical results from these two types of CNNs exhibit a large gap, indicating that existing volumetric CNN architectures and approaches are unable to fully exploit the power of 3D representations. In this paper, we aim to improve both volumetric CNNs and multi-view CNNs according to extensive analysis of existing approaches. To this end, we introduce two distinct network architectures of volumetric CNNs. In addition, we examine multi-view CNNs, where we introduce multiresolution filtering in 3D. Overall, we are able to outperform current state-of-the-art methods for both volumetric CNNs and multi-view CNNs. We provide extensive experiments designed to evaluate underlying design choices, thus providing a better understanding of the space of methods available for object classification on 3D data.
Hitting the memory wall: implications of the obvious

We all know that the rate of improvement in microprocessor speed exceeds the rate of improvement in D R A M memory speed, each is improving exponentially, but the exponent for microprocessors is substantially larger than that for DRAMs. The difference between diverging exponentials also grows exponential ly; so, al though the disparity between processor and memory speed is already an issue, downst ream someplace it will be a much bigger one. How big and how soon? The answers to these questions are what the authors had failed to appreciate.
Genetic Algorithm Solution of the Knapsack Problem Used in Finding Full Issues in the Holy Quran Based on the Number (19)

The Holy Quran is the biggest Miracle of Muslims everywhere and at every time; therefore, it is valid for every time and place. Actually, researches and studies into the Holy Quran that aim to uncover new miracles within are considered as a kind of worship for Muslims researchers since it facilitates the Islamic mission and clarifies the vague picture of Islam throughout the world. From this perspective, the researchers have selected the vague miracle of the number 19 in the Holy Quran to examine through this study. In this study, the researchers describe a special algorithm that facilitates the retrieval process of the full-issues according to the conditions of number 19 miracle in the Holy Quran. This algorithm is derived from the genetic algorithm that is used to solve the knapsack problem, since it is so similar to the case of this study. However, in the results of this study many full-issues have appeared that insure the truth of the number 19 miracle in the Holy Quran and thus support the miracle of the whole Holy Quran.
Mobile-edge CoMputing for VehiCular networks

1556-6072/17©2017ieee ieee vehicular technology magazine | June 2017 Cloud-based vehicular networks are a promising paradigm to improve vehicular services through distributing computation tasks between remote clouds and local vehicular terminals. To further reduce the latency and the transmission cost of the computation off-loading, we propose a cloud-based mobileedge computing (MEC) off-loading framework in vehicular networks. In this framework, we study the effectiveness of the computation transfer strategies with vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication modes. Considering the time consumption of the computation task execution and the mobility of the vehicles, we present an efficient predictive combination-mode relegation scheme, where the tasks are adaptively off-loaded to the MEC servers through direct uploading or predictive relay transmissions. Illustrative results indicate that our proposed scheme greatly reduces the cost of computation and improves task transmission efficiency.
Domain Specific Sentence Level Mood Extraction from Malayalam Text

Natural Language Processing (NLP) is a field which studies the interactions between computers and natural languages. NLP is used to enable computers to attain the capability of manipulating natural languages with a level of expertise equivalent to humans. There exists a wide range of applications for NLP, of which sentiment analysis(SA) plays a major role. In sentimental analysis, the emotional polarity of a given text is analysed, and classified as positive, negative or neutral. A more difficult task is to refine the classification into different moods such as happy, sad, angry etc. Analysing a natural language for mood extraction is not at all an easy task for a computer. Even after achieving capabilities of massive amount of computation within a matter of seconds, understanding the sentiments embodied in phrases and sentences of textual information remains one of the toughest tasks for a computer till date. This paper focuses on tagging the appropriate mood in Malayalam text. Tagging is used to specify whether a sentence indicates a sad, happy or angry mood of the person involved or if the sentence contains just facts, devoid of emotions. This research work is heavily dependent on the language since the structures vary from language to language. Mood extraction and tagging has been successfully implemented for English and other European languages. For the south Indian language Malayalam, no significant work has yet been done on mood extraction. We will be focusing on domain-specific sentence-level mood extraction from Malayalam text. The extraction process involves parts-of-speech tagging of the input sentence, extracting the patterns from the input sentence which will contribute to the mood of the sentence, such as the adjective, adverb etc., and finally scoring the sentence on an emotive scale by calculating the semantic orientation of the sentence using the extracted patterns. Sentiment classification is becoming a promising topic with the rise of social media such as blogs, social networking sites, where people express their views on various topics. Mood extraction enables computers to automate the activities performed by human for making decisions based on the moods of opinions expressed in Malayalam text.
Robust and Flexible Power-Proportional Storage (CMU-PDL-10-106)

Power-proportional cluster-based storage is an important component of an overall cloud computing infrastructure. With it, substantial subsets of nodes in the storage cluster can be turned off to save power during periods of low utilization. Rabbit is a distributed file system that arranges its data-layout to provide ideal power-proportionality down to very low minimum number of powered-up nodes (enough to store a primary replica of available datasets). Rabbit addresses the node failure rates of large-scale clusters with data layouts that minimize the number of nodes that must be powered-up if a primary fails. Rabbit also allows different datasets to use different subsets of nodes as a building block for interference avoidance when the infrastructure is shared by multiple tenants. Experiments with a Rabbit prototype demonstrate its power-proportionality, and simulation experiments demonstrate its properties at scale.  We thank the members and companies of the PDL Consortium (including APC, Data Domain, EMC, Facebook, Google, Hewlett-Packard Labs, Hitachi, IBM, Intel, LSI, Microsoft, NEC Laboratories, NetApp, Oracle, Seagate, Sun, Symantec, VMWare, and Yahoo! Labs) for their interest, insights, feedback, and support. This research was sponsored in part by NSF grants #CNS-0326453, by Intel Labs Pittsburgh, by DoE award DE-FC02-06ER25767, and by CyLab under ARO grant DAAD19-02-1-0389. Intel enabled the experiments with funding and generous hardware donations. We would also like to thank Intel corporation for providing funding and equipment, and IBM research for their generous support.
A new high compression compressor for large multiplier

A new high compression compressor is proposed in this paper. This compressor has 7 inputs, 2 output, 2 carry-ins from adjacent two cells and 2 carry-outs to the next two cells. It achieves higher compression ratio than 4:2 compressor, 5:2 compressor and 6:2 compressor. Simulation shows that a 64x64 bit multiplier using this proposed 7:2 compressor is not only 16% faster than multiplier built with 3:2 compressors, but also outperforms multiplier built with other commonly used compressors.
Mining Offensive Language on Social Media

English. The present research deals with the automatic annotation and classification of vulgar ad offensive speech on social media. In this paper we will test the effectiveness of the computational treatment of the taboo contents shared on the web, the output is a corpus of 31,749 Facebook comments which has been automatically annotated through a lexicon-based method for the automatic identification and classification of taboo expressions. Italiano. La presente ricerca affronta il tema dell’annotazione e della classificazione automatica dei contenuti volgari e offensivi espressi nei social media. Lo scopo del nostro lavoro consiste nel testare l’efficacia del trattamento computazionale dei contenuti tabù condivisi in rete. L’output che forniamo un corpus di 31,749 commenti generati dagli utenti di Facebook e annotato automaticamente attraverso un metodo basato sul lessico per l’identificazione e la classificazione delle
Speeding up k-means Clustering by Bootstrap Averaging

K-means clustering is one of the most popular clustering algorithms used in data mining. However, clustering is a time consuming task, particularly w ith the large data sets found in data mining. In this p aper we show how bootstrap averaging with k-means can produce results comparable to clustering all of the data but in much less time. The approach of bootstr ap (sampling with replacement) averaging consists of running k-means clustering to convergence on small bootstrap samples of the training data and averagin g similar cluster centroids to obtain a single model. We show why our approach should take less computation time and empirically illustrate its benefits. We sh ow that the performance of our approach is a monotonic function of the size of the bootstrap sample. Howev er, knowing the size of the bootstrap sample that yield s as good results as clustering the entire data set rema ins an open and important question.
Designing a mobile game to thwarts malicious IT threats: A phishing threat avoidance perspective

Phishing is an online identity theft, which aims to steal sensitive information such as username, password and online banking details from victims. To prevent this, phishing education needs to be considered. Game based education is becoming more and more popular. This paper introduces a mobile game prototype for the android platform based on a story, which simplifies and exaggerates real life. The elements of a game design framework for avoiding phishing attacks were used to address the game design issues and game design principles were used as a set of guidelines for structuring and presenting information. The overall mobile game design was aimed to enhance the user’s avoidance behaviour through motivation to protect themselves against phishing threats. The prototype mobile game design was presented on MIT App Inventor Emulator.
Practical Matrix Completion and Corruption Recovery Using Proximal Alternating Robust Subspace Minimization

Low-rank matrix completion is a problem of immense practical importance. Recent works on the subject often use nuclear norm as a convex surrogate of the rank function. Despite its solid theoretical foundation, the convex version of the problem often fails to work satisfactorily in real-life applications. Real data often suffer from very few observations, with support not meeting the randomness requirements, ubiquitous presence of noise and potentially gross corruptions, sometimes with these simultaneously occurring. This paper proposes a Proximal Alternating Robust Subspace Minimization method to tackle the three problems. The proximal alternating scheme explicitly exploits the rank constraint on the completed matrix and uses the $$\ell _0$$ ℓ 0 pseudo-norm directly in the corruption recovery step. We show that the proposed method for the non-convex and non-smooth model converges to a stationary point. Although it is not guaranteed to find the global optimal solution, in practice we find that our algorithm can typically arrive at a good local minimizer when it is supplied with a reasonably good starting point based on convex optimization. Extensive experiments with challenging synthetic and real data demonstrate that our algorithm succeeds in a much larger range of practical problems where convex optimization fails, and it also outperforms various state-of-the-art algorithms.
Induction of pluripotent stem cells from fibroblast cultures

Clinical application of embryonic stem (ES) cells faces difficulties regarding use of embryos, as well as tissue rejection after implantation. One way to circumvent these issues is to generate pluripotent stem cells directly from somatic cells. Somatic cells can be reprogrammed to an embryonic-like state by the injection of a nucleus into an enucleated oocyte or by fusion with ES cells. However, little is known about the mechanisms underlying these processes. We have recently shown that the combination of four transcription factors can generate ES-like pluripotent stem cells directly from mouse fibroblast cultures. The cells, named induced pluripotent stem (iPS) cells, can be differentiated into three germ layers and committed to chimeric mice. Here we describe detailed methods and tips for the generation of iPS cells.
The Whale Optimization Algorithm

This paper proposes a novel nature-inspired meta-heuristic optimization algorithm, called Whale Optimization Algorithm (WOA), which mimics the social behavior of humpback whales. The algorithm is inspired by the bubble-net hunting strategy. WOA is tested with 29 mathematical optimization problems and 6 structural design problems. Optimization results prove that the WOA algorithm is very competitive compared to the state-of-art meta-heuristic algorithms as well as conventional methods. The source codes of the WOA algorithm are publicly available at http://www.alimirjalili.com/WOA.html
ConTagNet: Exploiting User Context for Image Tag Recommendation

In recent years, deep convolutional neural networks have shown great success in single-label image classification. However, images usually have multiple labels associated with them which may correspond to different objects or actions present in the image. In addition, a user assigns tags to a photo not merely based on the visual content but also the context in which the photo has been captured. Inspired by this, we propose a deep neural network which can predict multiple tags for an image based on the content as well as the context in which the image is captured. The proposed model can be trained end-to-end and solves a multi-label classification problem. We evaluate the model on a dataset of 1,965,232 images which is drawn from the YFCC100M dataset provided by the organizers of Yahoo-Flickr Grand Challenge. We observe a significant improvement in the prediction accuracy after integrating user-context and the proposed model performs very well in the Grand Challenge.
On-Line Economic Optimization of Energy Systems Using Weather Forecast Information

We establish an on-line optimization framework to exploit weather forecast information in the operation of energy systems. We argue that anticipating the weather conditions can lead to more proactive and cost-effective operations. The framework is based on the solution of a stochastic dynamic real-time optimization (D-RTO) problem incorporating forecasts generated from a state-of-the-art weather prediction model. The necessary uncertainty information is extracted from the weather model using an ensemble approach. The accuracy of the forecast trends and uncertainty bounds are validated using real meteorological data. We present a numerical simulation study in a building system to demonstrate the developments.
Tactile Sensing—From Humans to Humanoids

Starting from human ¿sense of touch,¿ this paper reviews the state of tactile sensing in robotics. The physiology, coding, and transferring tactile data and perceptual importance of the ¿sense of touch¿ in humans are discussed. Following this, a number of design hints derived for robotic tactile sensing are presented. Various technologies and transduction methods used to improve the touch sense capability of robots are presented. Tactile sensing, focused to fingertips and hands until past decade or so, has now been extended to whole body, even though many issues remain open. Trend and methods to develop tactile sensing arrays for various body sites are presented. Finally, various system issues that keep tactile sensing away from widespread utility are discussed.
Classification of dental diseases using CNN and transfer learning

Automated medical assistance system is in high demand with the advances in research in the machine learning area. In many such applications, availability of labeled medical dataset is a primary challenge and dataset of dental diseases is not an exception. An attempt towards accurate classification of dental diseases is addressed in this paper. Labeled dataset consisting of 251 Radio Visiography (RVG) x-ray images of 3 different classes is used for classification. Convolutional neural network (CNN) has become a most effective tool in machine learning which enables solving the problems like image recognition, segmentation, classification, etc., with high order of accuracy. It is found from literature that CNN performs well in natural image classification problems where large dataset is available. In this paper we experimented on the performance of CNN for diagnosis of small labeled dental dataset. In addition, transfer learning is used to improve the accuracy. Experimental results are presented for three different architectures of CNN. Overall accuracy achieved is very encouraging.