nils-herrmann/clean_scirepeval huggingface dataset:
An Empirical Study of Click Fraud in Mobile Advertising Networks

Smartphone advertisement is increasingly used among many applications and allows developers to obtain revenue through in-app advertising. Our study aims at identifying potential security risks of a type of mobile advertisement where advertisers are charged for their advertisements only when a user clicks (or touches) on the advertisements in their applications. In the Android platform, we design an automated click generation attack and empirically evaluate eight popular advertising networks by performing real attacks on them. Our experimental results show that six advertising networks (75%) out of eight (Millennial Media, App Lovin, Ad Fit, Mdot M, Rev Mob and Cauly Ads) are vulnerable to our attacks. We also discuss how to develop effective defense mechanisms to mitigate such automated click fraud attacks.
Policy Optimization via Importance Sampling

Policy optimization is an effective reinforcement learning approach to solve continuous control tasks. Recent achievements have shown that alternating online and offline optimization is a successful choice for efficient trajectory reuse. However, deciding when to stop optimizing and collect new trajectories is non-trivial, as it requires to account for the variance of the objective function estimate. In this paper, we propose a novel, model-free, policy search algorithm, POIS, applicable in both action-based and parameter-based settings. We first derive a high-confidence bound for importance sampling estimation; then we define a surrogate objective function, which is optimized offline whenever a new batch of trajectories is collected. Finally, the algorithm is tested on a selection of continuous control tasks, with both linear and deep policies, and compared with state-of-the-art policy optimization methods.
Designing a Smart Museum: When Cultural Heritage Joins IoT

The adoption of the Internet of Things (IoT) paradigm constitutes the basic building block to progress towards unified ICT platforms for a variety of applications within the large framework of the smart cities. Unfortunately, designing a general architecture for IoT is still a very complex challenge, since in such system may be involved several devices, link layer technologies and services. Cultural Heritage represents a worldwide resource of inestimable value and it gains more and more importance when embedded into the digital ecosystem of a smart city. In this paper we focus specifically to design a IoT architecture that is able to support the designing of a smart museum, a static cultural space that becomes intelligent thanks to the definition of an innovative model of sensors and services. Furthermore, the paper will present and discuss a real case of study, placed in a temporary art exhibition of sculptures in the Maschio Angioino Castle, located in Naples, Italy.
Pancreas Segmentation in Abdominal CT Scan: A Coarse-to-Fine Approach

Deep neural networks have been widely adopted for automatic organ segmentation from CT-scanned images. However, the segmentation accuracy on some small organs (e.g., the pancreas) is sometimes below satisfaction, arguably because deep networks are easily distracted by the complex and variable background region which occupies a large fraction of the input volume. In this paper, we propose a coarse-to-fine approach to deal with this problem. We train two deep neural networks using different regions of the input volume. The first one, the coarse-scaled model, takes the entire volume as its input. It is used for roughly locating the spatial position of the pancreas. The second one, the fine-scaled model, only sees a small input region covering the pancreas, thus eliminating the background noise and providing more accurate segmentation especially around the boundary areas. At the testing stage, we first use the coarse-scaled model to roughly locate the pancreas, then adopt the fine-scaled model to refine the initial segmentation in an iterative manner to obtain increasingly better segmentation. We evaluate our algorithm on the NIH pancreas segmentation dataset with 82 volumes, and outperform the state-of-theart [18] by more than 4%, measured by the Dice-Sørensen Coefficient (DSC). In addition, we report 62.43% DSC for our worst case, significantly better than 34.11% reported in .
A Novel OFDM Chirp Waveform Scheme for Use of Multiple Transmitters in SAR

In this letter, we present a new waveform technique for the use of multiple transmitters in synthetic aperture radar (SAR) data acquisition. This approach is based on the principle of the orthogonal-frequency-division-multiplexing technique. Unlike multiple subband approaches, the proposed scheme allows the generation of multiple orthogonal waveforms on common spectral support and thereby enables to exploit the full bandwidth for each waveform. This letter introduces the modulation and the demodulation processing in regard to typical spaceborne SAR receive signals. The proposed processing techniques are verified by a simulation for the case of pointlike targets.
A study of sex differences in fingerprint ridge density in a North Indian young adult population.

Fingerprints have considerable value in morphological, biological, anthropological and forensic studies. Fingerprints collected from the crime scene and from the items of evidence of crime have been successfully used to identify suspects, victims or any other person who had touched the surface in question. The thickness of epidermal ridges varies between individuals; females are supposed to have finer ridges than males and therefore a greater ridge density. The present research is an attempt to distinguish sex from fingerprint ridge density in the radial, ulnar and lower areas of a fingerprint in a North Indian population. A total of 194 individuals (97 males and 97 females) aged between 18 and 25 years were included in the study and fingerprints were collected from each finger of the participants. Thus, a total of 1940 fingerprints were obtained and epidermal ridges were counted in the radial, ulnar, and lower areas of each fingerprint. The radial and ulnar areas are the 5 mm × 5 mm areas on the radial and ulnar side of the central core respectively while the lower area is designated as 5 mm × 5 mm area adjoining the flexion crease of the terminal phalanx on a fingerprint. The fingerprint ridge density in radial, ulnar and lower areas and between sexes was compared statistically using t-test. The results indicate that the females tend to have a significantly higher ridge density than males in the three areas analyzed in the study. The fingerprint ridge density in the ulnar and radial areas of the fingerprints is significantly higher than the lower area. The present study suggests that the fingerprint ridge density can be a relevant and useful morphological parameter in distinguishing sex of a latent fingerprint of unknown origin from the scene of crime. The findings can also be useful in identification of mutilated remains when a dismembered hand is brought for medico-legal examination.
DDD: A New Ensemble Approach for Dealing with Concept Drift

Online learning algorithms often have to operate in the presence of concept drifts. A recent study revealed that different diversity levels in an ensemble of learning machines are required in order to maintain high generalization on both old and new concepts. Inspired by this study and based on a further study of diversity with different strategies to deal with drifts, we propose a new online ensemble learning approach called Diversity for Dealing with Drifts (DDD). DDD maintains ensembles with different diversity levels and is able to attain better accuracy than other approaches. Furthermore, it is very robust, outperforming other drift handling approaches in terms of accuracy when there are false positive drift detections. In all the experimental comparisons we have carried out, DDD always performed at least as well as other drift handling approaches under various conditions, with very few exceptions.
UIMA Ruta: Rapid development of rule-based information extraction applications

Rule-based information extraction is an important approach for processing the increasingly available amount of unstructured data. The manual creation of rule-based applications is a time-consuming and tedious task, which requires qualified knowledge engineers. The costs of this process can be reduced by providing a suitable rule language and extensive tooling support.  The system was designed with focus on rapid development. The rule language and its matching paradigm facilitate the quick specification of comprehensible extraction knowledge. They support a compact representation while still providing a high level of expressiveness. These advantages are supplemented by the development environment UIMA Ruta Workbench. It provides, in addition to extensive editing support, essential assistance for explanation of rule execution, introspection, automatic validation, and rule induction. UIMA Ruta is a useful tool for academia and industry due to its open source license. We compare UIMA Ruta to related rule-based systems especially concerning the compactness of the rule representation, the expressiveness, and the provided tooling support. The competitiveness of the runtime performance is shown in relation to a popular and freelyavailable system. A selection of case studies implemented with UIMA Ruta illustrates the usefulness of the system in real-world scenarios.
Fast & Dynamic Image Restoration using Laplace equation Based Image Inpainting

Images tend to degenerate over time and are exposed to noises. These noises don’t only affect the visual outlook but also hampers the allied significance to these images. Different techniques are being put to use for de-noising such images. Image in-painting is one such phenomenon of denoising the images that involves approximating the de-noised form of the image. The current study aims at developing an in-painting system for restoration of lost art, reconstruction of destroyed images and removal of unnecessary objects. The motivation for the same has been driven from Partial differential equation based anisotropic diffusion model or image in-painting .The steady state heat equation or the Laplace equation has been used to model and approximate the de-noised data for noised region of the image. The Laplace equation has been used clubbed with the Dirichlet boundary conditions in order to fill in the degenerated or the noised region. The Dirichlet conditions provides a firm starting point for approximating the structural framework of the noised region in order to remove the inconsistencies using the colour intensities of the neighboring pixels as the boundary conditions. The colour intensities are modeled to be diffusive in nature. The experiments conducted using the proposed approach portray significant speedups and presents a practicable in-painting strategy.
GMove: Group-Level Mobility Modeling Using Geo-Tagged Social Media

Understanding human mobility is of great importance to various applications, such as urban planning, traffic scheduling, and location prediction. While there has been fruitful research on modeling human mobility using tracking data (e.g., GPS traces), the recent growth of geo-tagged social media (GeoSM) brings new opportunities to this task because of its sheer size and multi-dimensional nature. Nevertheless, how to obtain quality mobility models from the highly sparse and complex GeoSM data remains a challenge that cannot be readily addressed by existing techniques. We propose GMove, a group-level mobility modeling method using GeoSM data. Our insight is that the GeoSM data usually contains multiple user groups, where the users within the same group share significant movement regularity. Meanwhile, user grouping and mobility modeling are two intertwined tasks: (1) better user grouping offers better within-group data consistency and thus leads to more reliable mobility models; and (2) better mobility models serve as useful guidance that helps infer the group a user belongs to. GMove thus alternates between user grouping and mobility modeling, and generates an ensemble of Hidden Markov Models (HMMs) to characterize group-level movement regularity. Furthermore, to reduce text sparsity of GeoSM data, GMove also features a text augmenter. The augmenter computes keyword correlations by examining their spatiotemporal distributions. With such correlations as auxiliary knowledge, it performs sampling-based augmentation to alleviate text sparsity and produce high-quality HMMs.
 Our extensive experiments on two real-life data sets demonstrate that GMove can effectively generate meaningful group-level mobility models. Moreover, with context-aware location prediction as an example application, we find that GMove significantly outperforms baseline mobility models in terms of prediction accuracy.
Automatic scraper of celebrity images from heterogeneous websites based on face recognition and sorting for profiling

Now days it has become trend to follow all the celebrities as we consider them as our role models. So instead of searching the images of various celebrities in different websites we can find them in a single website by sorting all the images. Reliable database of images is essential for any image recognition system. Through Internet we find all the required images. These images will serve as samples for automatic recognition system. With these images we do face detection, face recognition, face sorting using various techniques like local binary patterns, haar cascades. We make an overall analysis of the detector. Using opencv we detect and recognize images. Similarity matching is done to check how the images are related to each other. Collection of images is based on user defined templates, which are in web browser environment. With the help of this system one can give their requirement and the image of celebrity is displayed based on that.
Jointless structure and under-actuation mechanism for compact hand exoskeleton

It is important for a wearable robot to be compact and sufficiently light for use as an assistive device. Since human fingers are arranged in a row in dense space, the concept of traditional wearable robots using a rigid frame and a pin joint result in size and complexity problems. A structure without a conventional pin joint, called a jointless structure, has the potential to be used as a wearable robotic hand because the human skeleton and joint can replace the robot's conventional structure. Another way to reduce the weight of the system is to use under-actuation. Under-actuation enables adaptive grasping with less number of actuators for robotic hands. Differential mechanisms are widely used for multi-finger under-actuation; however, they require additional working space. We propose a design with a jointless structure and a novel under-actuation mechanism to reduce the size and weight of a hand exoskeleton. Using these concepts, we developed a prototype that weighs only 80 grams. To evaluate the prototype, fingertip force and blocked force are measured. Fingertip force is the force that can be applied by the finger of the hand exoskeleton on the object surface. The fingertip force is about 18 N when actuated by a tension force of 35 N from the motor. 18 N is sufficient for simple pinch motion in daily activities. Another factor related to performance of the under-actuation mechanism is blocked force, which is a force required to stop one finger while the other finger keeps on moving. It is measured to be 0.5 N, which is sufficiently small. With these experiments, the feasibility of the new hand exoskeleton has been shown.
Watching MOOCs together: investigating co-located MOOC study groups

Current MOOCs emphasize offering individualistic learning experiences, and most MOOC research is centered on this context. Recent research also suggests that MOOC students may prefer to study in groups. Social facilitation within the study groups may render the learning of difficult concepts a pleasing experience. We report a longitudinal study that investigates how co-located study groups watch and study MOOC videos together. The study was conducted with on-campus flipped classroom students. Our subjects reported an overall high satisfaction with the study group style. The research reveals that students like to stay synchronized in the group while watching MOOC videos. However, they have to find a balance between synchronization, video interaction and the amount of conversation. Watching MOOCs on a shared display addresses the need of synchronicity, and the distribution of control can increase the video interactivities.
Memory-augmented Chinese-Uyghur neural machine translation

Neural machine translation (NMT) has achieved notable performance recently. However, this approach has not been widely applied to the translation task between Chinese and Uyghur, partly due to the limited parallel data resource and the large proportion of rare words caused by the agglutinative nature of Uyghur. In this paper, we collect ∼200,000 sentence pairs and show that with this middle-scale database, an attention-based NMT can perform very well on Chinese- Uyghur/Uyghur-Chinese translation. To tackle rare words, we propose a novel memory structure to assist the NMT inference. Our experiments demonstrated that the memory-augmented NMT (M-NMT) outperforms both the vanilla NMT and the phrase-based statistical machine translation (SMT). Interestingly, the memory structure provides an elegant way for dealing with words that are out of vocabulary.
ItemRank: A Random-Walk Based Scoring Algorithm for Recommender Engines

Recommender systems are an emerging technology that helps consumers to find interesting products. A recommender system makes personalized product suggestions by extracting knowledge from the previous users interactions. In this paper, we present ”ItemRank”, a random–walk based scoring algorithm, which can be used to rank products according to expected user preferences, in order to recommend top–rank items to potentially interested users. We tested our algorithm on a standard database, the MovieLens data set, which contains data collected from a popular recommender system on movies, that has been widely exploited as a benchmark for evaluating recently proposed approaches to recommender system (e.g. [Fouss et al., 2005; Sarwar et al., 2002]). We compared ItemRank with other state-of-the-art ranking techniques (in particular the algorithms described in [Fouss et al., 2005]). Our experiments show that ItemRank performs better than the other algorithms we compared to and, at the same time, it is less complex than other proposed algorithms with respect to memory usage and computational cost too.
Deadline-Constrained Cost Optimization Approaches for Workflow Scheduling in Clouds

Nowadays it is becoming more and more attractive to execute workflow applications in the cloud because it enables workflow applications to use computing resources on demand. Meanwhile, it also challenges traditional workflow scheduling algorithms that only concentrate on optimizing the execution time. This paper investigates how to minimize execution cost of a workflow in clouds under a deadline constraint and proposes a metaheuristic algorithm L-ACO as well as a simple heuristic ProLiS. ProLiS distributes the deadline to each task, proportionally to a novel definition of probabilistic upward rank, and follows a two-step list scheduling methodology: rank tasks and sequentially allocates each task a service which meets the sub-deadline and minimizes the cost. L-ACO employs ant colony optimization to carry out deadline-constrained cost optimization: the ant constructs an ordered task list according to the pheromone trail and probabilistic upward rank, and uses the same deadline distribution and service selection methods as ProLiS to build solutions. Moreover, the deadline is relaxed to guide the search of L-ACO towards constrained optimization. Experimental results show that compared with traditional algorithms, the performance of ProLiS is very competitive and L-ACO performs the best in terms of execution costs and success ratios of meeting deadlines.
Improved Deep Metric Learning with Multi-class N-pair Loss Objective

Deep metric learning has gained much popularity in recent years, following the success of deep learning. However, existing frameworks of deep metric learning based on contrastive loss and triplet loss often suffer from slow convergence, partially because they employ only one negative example while not interacting with the other negative classes in each update. In this paper, we propose to address this problem with a new metric learning objective called multi-class N -pair loss. The proposed objective function firstly generalizes triplet loss by allowing joint comparison among more than one negative examples – more specifically, N -1 negative examples – and secondly reduces the computational burden of evaluating deep embedding vectors via an efficient batch construction strategy using only N pairs of examples, instead of (N+1)×N . We demonstrate the superiority of our proposed loss to the triplet loss as well as other competing loss functions for a variety of tasks on several visual recognition benchmark, including fine-grained object recognition and verification, image clustering and retrieval, and face verification and identification.
#MeToo Alexa: How Conversational Systems Respond to Sexual Harassment

Conversational AI systems, such as Amazon’s Alexa, are rapidly developing from purely transactional systems to social chatbots, which can respond to a wide variety of user requests. In this article, we establish how current stateof-the-art conversational systems react to inappropriate requests, such as bullying and sexual harassment on the part of the user, by collecting and analysing the novel #MeTooAlexa corpus. Our results show that commercial systems mainly avoid answering, while rulebased chatbots show a variety of behaviours and often deflect. Data-driven systems, on the other hand, are often non-coherent, but also run the risk of being interpreted as flirtatious and sometimes react with counter-aggression. This includes our own system, trained on “clean” data, which suggests that inappropriate system behaviour is not caused by data bias.
Evolutionary computation: comments on the history and current state

Evolutionary computation has started to receive significant attention during the last decade, although the origins can be traced back to the late 1950’s. This article surveys the history as well as the current state of this rapidly growing field. We describe the purpose, the general structure, and the working principles of different approaches, including genetic algorithms (GA) [with links to genetic programming(GP) and classifier systems (CS)], evolution strategies(ES), and evolutionary programming (EP) by analysis and comparison of their most important constituents (i.e., representations, variation operators, reproduction, and selection mechanism). Finally, we give a brief overview on the manifold of application domains, although this necessarily must remain incomplete.
Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relations

We propose a novel data augmentation for labeled sentences called contextual augmentation. We assume an invariance that sentences are natural even if the words in the sentences are replaced with other words with paradigmatic relations. We stochastically replace words with other words that are predicted by a bi-directional language model at the word positions. Words predicted according to a context are numerous but appropriate for the augmentation of the original words. Furthermore, we retrofit a language model with a label-conditional architecture, which allows the model to augment sentences without breaking the label-compatibility. Through the experiments for six various different text classification tasks, we demonstrate that the proposed method improves classifiers based on the convolutional or recurrent neural networks.