nils-herrmann/clean_scirepeval huggingface dataset:
Phone-aware LSTM-RNN for voice conversion

This paper investigates a new voice conversion technique using phone-aware Long Short-Term Memory Recurrent Neural Networks (LSTM-RNNs). Most existing voice conversion methods, including Joint Density Gaussian Mixture Models (JDGMMs), Deep Neural Networks (DNNs) and Bidirectional Long Short-Term Memory Recurrent Neural Networks (BLSTM-RNNs), only take acoustic information of speech as features to train models. We propose to incorporate linguistic information to build voice conversion system by using monophones generated by a speech recognizer as linguistic features. The monophones and spectral features are combined together to train LSTM-RNN based voice conversion models, reinforcing the context-dependency modelling of LSTM-RNNs. The results of the 1st voice conversion challenge shows our system achieves significantly higher performance than baseline (GMM method) and was found among the most competitive scores in similarity test. Meanwhile, the experimental results show phone-aware LSTM-RNN method obtains lower Mel-cepstral distortion and higher MOS scores than the baseline LSTM-RNNs.
Applying Radio Environment Maps to Cognitive Wireless Regional Area Networks

The IEEE 802.22 wireless regional area network (WRAN) is the first worldwide commercial application of cognitive radio (CR) networks refarming the TV broadcast bands. According to US FCC's recent public notice, WRAN products are scheduled to be available for the market by February, 2009. This paper first presents a brief review of the IEEE 802.22 WRAN standardization, and then introduces the radio environment map (REM) as an innovative cost-efficient approach to developing and managing WRAN systems. REMs can provide powerful infrastructure support to the functionality of the WRAN cognitive engine (CE). The data model of the REM is presented together with an extensive discussion on how to exploit the REM for a variety of applications in WRAN systems, focusing on the REM-enabled case- and knowledge-based learning algorithms (REM-CKL) for WRAN CEs. Furthermore, REM-based radio scenario-driven testing (REM-SDT) is also presented as a viable approach to evaluating the performance of CEs. Future research topics are discussed in the final section.
Identification and path following control of an AR.Drone quadrotor

This paper describes the process of identification and closed-loop control of an Parrot AR.Drone Unmanned Aerial Vehicle (UAV) as well as a path following application based on IMC position controllers. The research issue is to achieve position control of the AR.Drone quadrotor movement via its on-board sensory equipment and external webcam video stream. Firstly, transfer functions are detailed for pitch and altitude movements and a comparison is made between implemented PID and IMC controller performance for both simulation and practice. Furthermore, using IMC controllers, a path following application exhibits controller behavior from a practical point of view. It is concluded that the dynamic model and the controllers implemented on the quadrotor can serve as a reliable basis for more advanced applications.
Fake Review Detection : Classification and Analysis of Real and Pseudo Reviews

In recent years, fake review detection has attracted significant attention from both businesses and the research community. For reviews to reflect genuine user experiences and opinions, detecting fake reviews is an important problem. Supervised learning has been one of the main approaches for solving the problem. However, obtaining labeled fake reviews for training is difficult because it is very hard if not impossible to reliably label fake reviews manually. Existing research has used several types of pseudo fake reviews for training. Perhaps, the most interesting type is the pseudo fake reviews generated using the Amazon Mechanical Turk (AMT) crowdsourcing tool. Using AMT crafted fake reviews,  reported an accuracy of 89.6% using only word n-gram features. This high accuracy is quite surprising and very encouraging. However, although fake, the AMT generated reviews are not real fake reviews on a commercial website. The Turkers (AMT authors) are not likely to have the same psychological state of mind while writing such reviews as that of the authors of real fake reviews who have real businesses to promote or to demote. Our experiments attest this hypothesis. Next, it is naturally interesting to compare fake review detection accuracies on pseudo AMT data and real-life data to see whether different states of mind can result in different writings and consequently different classification accuracies. For real review data, we use filtered (fake) and unfiltered (non-fake) reviews from Yelp.com (which are closest to ground truth labels) to perform a comprehensive set of classification experiments also employing only n-gram features. We find that fake review detection on Yelp’s real-life data only gives 67.8% accuracy, but this accuracy still indicates that n-gram features are indeed useful. We then propose a novel and principled method to discover the precise difference between the two types of review data using the information theoretic measure KL-divergence and its asymmetric property. This reveals some very interesting psycholinguistic phenomena about forced and natural fake reviewers. To improve classification on the real Yelp review data, we propose an additional set of behavioral features about reviewers and their reviews for learning, which dramatically improves the classification result on real-life opinion spam data.
Robot global path planning overview and a variation of ant colony system algorithm

This paper presents the results of a research that aims to develop an algorithm to solve robot path planning (RPP) problems in static environments. The problem is to find a global optimal path that satisfies the optimization criteria of shortest path with minimum computation time. A description of a variation of Ant Colony System (ACS) algorithm utilized for Robot Path Planning (RPP) purposes is presented. A representation of heuristic and visibility equation of state transition rules is proposed to sustain the function of Ant Colony System (ACS) for solving RPP problem of finding the optimal path. This algorithm was applied within a global static map that consists of feasible free space nodes. The performance of the algorithm in terms of computation time and number of iteration required to obtain an optimal path were evaluated by using a simulation approach. Subsequently, its performance was compared to the performance of Genetic Algorithm (GA) a well known and established RPP algorithm. The results obtained indicate that the developed algorithm performed much better than the GA. In addition, an overview of robot path planning (PP) algorithms in global static environment is also offered. Path Planning, Ant Colony System, Algorithm, Global Environment, Autonomous Robot.
SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters

Deep neural networks have enjoyed remarkable success for various vision tasks, however it remains challenging to apply CNNs to domains lacking a regular underlying structures such as 3D point clouds. Towards this we propose a novel convolutional architecture, termed SpiderCNN, to efficiently extract geometric features from point clouds. SpiderCNN is comprised of units called SpiderConv, which extend convolutional operations from regular grids to irregular point sets that can be embedded in R, by parametrizing a family of convolutional filters. We design the filter as a product of a simple step function that captures local geodesic information and a Taylor polynomial that ensures the expressiveness. SpiderCNN inherits the multi-scale hierarchical architecture from classical CNNs, which allows it to extract semantic deep features. Experiments on ModelNet40[4] demonstrate that SpiderCNN achieves state-of-the-art accuracy 92.4% on standard benchmarks, and shows competitive performance on segmentation task.
Super-resolution image reconstruction: a technical overview

MAY 2003  21 1053-5888/03/$17.00©2003IEEE In most electronic imaging applications, images with high resolution (HR) are desired and often required. HR means that pixel density within an image is high, and therefore an HR image can offer more details that may be critical in various applications. For example, HR medical images are very helpful for a doctor to make a correct diagnosis. It may be easy to distinguish an object from similar ones using HR satellite images, and the performance of pattern recognition in computer vision can be improved if an HR image is provided. Since the 1970s, charge-coupled device (CCD) and CMOS image sensors have been widely used to capture digital images. Although these sensors are suitable for most imaging applications, the current resolution level and consumer price will not satisfy the future demand. For example, people want an inexpensive HR digital camera/camcorder or see the price gradually reduce, and scientists often need a very HR level close to that of an analog 35 mm film that has no visible artifacts when an image is magnified. Thus, finding a way to increase the current resolution level is needed. The most direct solution to increase spatial resolution is to reduce the pixel size (i.e., increase the number of pixels per unit area) by sensor manufacturing techniques. As the pixel size decreases, however, the amount of light available also decreases. It generates shot noise that degrades the image quality severely. To reduce the pixel size without suffering the effects of shot noise, therefore, there exists the limitation of the pixel size reduction, and the optimally limited pixel size is estimated at about 40 μm2 for a 0.35 μm CMOS process. The current image sensor technology has almost reached this level. Another approach for enhancing the spatial resolution is to increase the chip size, which leads to an increase in capacitance . Since large capacitance makes it difficult to speed up a charge transfer rate, this approach is not considered effective. The high cost for high precision optics and image sensors is also an important concern in many commercial applications regarding HR imaging. Therefore, a new approach toward increasing spatial resolution is required to overcome these limitations of the sensors and optics manufacturing technology. One promising approach is to use signal processing techniques to obtain an HR image (or sequence) from observed multiple low-resolution (LR) images. Recently, such a resolution enhancement approach has been one of the most active research areas, and it is called super resolution (SR) (or HR) image reconstruction or simply resolution enhancement in the literature [1]-[61]. In this article, we use the term “SR image reconstruction” to refer to a signal processing approach toward resolution enhancement because the term “super” in “super ©DIGITAL VISION, LTD.
Robot Collisions: A Survey on Detection, Isolation, and Identification

Robot assistants and professional coworkers are becoming a commodity in domestic and industrial settings. In order to enable robots to share their workspace with humans and physically interact with them, fast and reliable handling of possible collisions on the entire robot structure is needed, along with control strategies for safe robot reaction. The primary motivation is the prevention or limitation of possible human injury due to physical contacts. In this survey paper, based on our early work on the subject, we review, extend, compare, and evaluate experimentally model-based algorithms for real-time collision detection, isolation, and identification that use only proprioceptive sensors. This covers the context-independent phases of the collision event pipeline for robots interacting with the environment, as in physical human–robot interaction or manipulation tasks. The problem is addressed for rigid robots first and then extended to the presence of joint/transmission flexibility. The basic physically motivated solution has already been applied to numerous robotic systems worldwide, ranging from manipulators and humanoids to flying robots, and even to commercial products.
Diffeomorphic demons: Efficient non-parametric image registration

We propose an efficient non-parametric diffeomorphic image registration algorithm based on Thirion's demons algorithm. In the first part of this paper, we show that Thirion's demons algorithm can be seen as an optimization procedure on the entire space of displacement fields. We provide strong theoretical roots to the different variants of Thirion's demons algorithm. This analysis predicts a theoretical advantage for the symmetric forces variant of the demons algorithm. We show on controlled experiments that this advantage is confirmed in practice and yields a faster convergence. In the second part of this paper, we adapt the optimization procedure underlying the demons algorithm to a space of diffeomorphic transformations. In contrast to many diffeomorphic registration algorithms, our solution is computationally efficient since in practice it only replaces an addition of displacement fields by a few compositions. Our experiments show that in addition to being diffeomorphic, our algorithm provides results that are similar to the ones from the demons algorithm but with transformations that are much smoother and closer to the gold standard, available in controlled experiments, in terms of Jacobians.
CREDIT CARD FRAUD DETECTION BASED ON BEHAVIOR MINING

H Institute of Science & Technology, Ernakulam, Kerala, India shrly_shilu@yahoo.com  Globalization and increased use of the Internet for online shopping has resulted in a considerable proliferation of credit card transactions throughout the world.Higher acceptability and convenience of credit cards for purchases has not only given personal comfort to customers but also attracted a large number of attackers. As a result, credit card payment systems must be supported by efficient fraud detection capability for minimizing unwanted activities by adversaries. Most of the well known algorithms for fraud detection are based on supervised training Every cardholder has a certain shopping behavior, which establishes an activity profile for him. Existing FDS try to capture behavioral patterns as rules which are static .This becomes ineffective when cardholder develops new patterns of behavior Here, we propose a unsupervised method to dynamically profile behavior pattern of customer Then the incoming transactions are compared against the user profile to indicate the anomalies, based on which the corresponding warnings are outputted. A FP tree based pattern matching algorithm is used to evaluate how unusual the new transactions are.
The networked sensor tapestry (NeST): a privacy enhanced software architecture for interactive analysis of data in video-sensor networks

This paper details the architecture of a test-bed under development for secure sharing, capture, distributed processing, and archiving of surveillance data called the Networked Sensor Tapestry (NeST). The test-bed consists of core software modules including a centralized server, client interface library, a layered XML messaging scheme. Mobile hardware clients are interfaced to the NeST using a Tiny-OS based microcontroller with sensor data collected over a 1-wire data bus. Maintaining subject privacy in video and other sensor monitoring scenarios can be imperative for the successful deployment of surveillance networks. Subject privacy is integrated into the architecture and can (if desired) operate as a buffer to the server core, denying access to identity specific information to any or all modules or operators. We introduce 3 fundamental privacy concepts: The <i>privacy buffer</i>: is a core component of the NeST server and utilizes programmable plug-in <i>privacy filters</i> operating on incoming sensor data to prevent access to or transform data to remove personally identifiable information. These privacy filters are developed and specified using a <i>privacy grammar</i> that can connect multiple low-level data filters and features to create arbitrary data-dependent privacy definitions. The utility of the architecture is demonstrated with a connection to a variety of hardware/software clients including PDA based client hardware, remote sensor interface devices, software modules for sensor data inferencing, data visualization, sensor control and data archival applications.
Active-feedback frequency-compensation technique for low-power multistage amplifiers

An active-feedback frequency-compensation (AFFC) technique for low-power operational amplifiers is presented in this paper. With an active-feedback mechanism, a high-speed block separates the low-frequency high-gain path and high-frequency signal path such that high gain and wide bandwidth can be achieved simultaneously in the AFFC amplifier. The gain stage in the active-feedback network also reduces the size of the compensation capacitors such that the overall chip area of the amplifier becomes smaller and the slew rate is improved. Furthermore, the presence of a left-half-plane zero in the proposed AFFC topology improves the stability and settling behavior of the amplifier. Three-stage amplifiers based on AFFC and nested-Miller compensation (NMC) techniques have been implemented by a commercial 0.8m CMOS process. When driving a 120-pF capacitive load, the AFFC amplifier achieves over 100-dB dc gain, 4.5-MHz gain-bandwidth product (GBW) , 65 phase margin, and 1.5-V/ s average slew rate, while only dissipating 400W power at a 2-V supply. Compared to a three-stage NMC amplifier, the proposed AFFC amplifier provides improvement in both the GBW and slew rate by 11 times and reduces the chip area by 2.3 times without significant increase in the power consumption.
A Review on Comparative analysis of different clustering and Decision Tree for Synthesized Data Mining Algorithm

Web mining is the sub category or application of data mining techniques to extract knowledge from Web data. With the advent of new advancements in technology the rapid use of new algorithms has been increased in the market. A data mining is one of the fast growing research field which is used in a wide areas of applications. The data mining consists of classification algorithms, association algorithms and searching algorithms. Different classification and clustering algorithm are used for the synthetic datasets. In this paper various techniques that are based on clustering and decision tree for synthesized data mining are discussed.
Improvements to BM25 and Language Models Examined

Recent work on search engine ranking functions report improvements on BM25 and Language Models with Dirichlet Smoothing. In this investigation 9 recent ranking functions (BM25, BM25+, BM25T, BM25-adpt, BM25L, TF1°Δ°p×ID, LM-DS, LM-PYP, and LM-PYP-TFIDF) are compared by training on the INEX 2009 Wikipedia collection and testing on INEX 2010 and 9 TREC collections. We find that once trained (using particle swarm optimization) there is very little difference in performance between these functions, that relevance feedback is effective, that stemming is effective, and that it remains unclear which function is best over-all.
Leveraging Peer Centrality in the Designof Socially-Informed Peer-to-Peer Systems

Social applications mine user social graphs to improve performance in search, provide recommendations, allow resource sharing and increase data privacy. When such applications are implemented on a peer-to-peer (P2P) architecture, the social graph is distributed on the P2P system: the traversal of the social graph translates into a socially-informed routing in the peer-to-peer layer. In this work we introduce the model of a projection graph that is the result of decentralizing a social graph onto a peer-to-peer network. We focus on three social network metrics: degree, node betweenness and edge betweenness centrality and analytically formulate the relation between metrics in the social graph and in the projection graph. We demonstrate with an application scenario on real social networks the usability of the projection graph properties in designing social search applications and unstructured P2P overlays that exhibit improved performance and reduced overhead over baseline scenarios.
Research Methods in Computer Science: The Challenges and Issues

Research methods are essential parts in conducting any research project. Although they have been theorized and summarized based on best practices, every field of science requires an adaptation of the overall approaches to perform research activities. In addition, any specific research needs a particular adjustment to the generalized approach and specializing them to suit the project in hand. However, unlike most well-established science disciplines, computing research is not supported by well-defined, globally accepted methods. This is because of its infancy and ambiguity in its definition, on one hand, and its extensive coverage and overlap with other fields, on the other hand. This article discusses the research methods in science and engineering in general and in computing in particular. It shows that despite several special parameters that make research in computing rather unique, it still follows the same steps that any other scientific research would do. The article also shows the particularities that researchers need to consider when they conduct research in this field.
Hierarchical Complex Activity Representation and Recognition Using Topic Model and Classifier Level Fusion

Human activity recognition is an important area of ubiquitous computing. Most current researches in activity recognition mainly focus on simple activities, e.g., sitting, running, walking, and standing. Compared with simple activities, complex activities are more complicated with high-level semantics, e.g., working, commuting, and having a meal. This paper presents a hierarchical model to recognize complex activities as mixtures of simple activities and multiple actions. We generate the components of complex activities using a clustering algorithm, represent and recognize complex activities by applying a topic model on these components. It is a data-driven method that can retain effective information for representing and recognizing complex activities. In addition, acceleration and physiological signals are fused in classifier level to ensure the overall performance of complex activity recognition. The results of experiments show that our method has ability to represent and recognize complex activities effectively.