nils-herrmann/clean_scirepeval huggingface dataset:
Collaborative GAN Sampling

Generative adversarial networks (GANs) have shown great promise in generating complex data such as images. A standard practice in GANs is to discard the discriminator after training and use only the generator for sampling. However, this loses valuable information of real data distribution learned by the discriminator. In this work, we propose a collaborative sampling scheme between the generator and discriminator for improved data generation. Guided by the discriminator, our approach refines generated samples through gradient-based optimization, shifting the generator distribution closer to the real data distribution. Additionally, we present a practical discriminator shaping method that can further improve the sample refinement process. Orthogonal to existing GAN variants, our proposed method offers a new degree of freedom in GAN sampling. We demonstrate its efficacy through experiments on synthetic data and image generation tasks.
Intonation issues in HMM-based speech synthesis for Vietnamese

In an HMM-based Text-To-Speech system, contextual features, including phonetic and prosodic factors have a significant influence to the spectrum, F0 and duration of the synthetic voice. This paper proposes prosodic features aiming at improving the naturalness of an HMM-based TTS system (VTed) for a tonal language, Vietnamese. The ToBI (Tones and Break Indices) features are used to learn two crucial prosodic cues i.e. intonation (boundary tones) and pause (break indices), concurrently with another set of features. The result of MOS test showed that the general quality of synthetic voice is rather good, 1.21 point lower than the natural voice. About 55% of the voice trained with ToBI boundary tone feature are perceived as similar to the voice trained without this feature, while a 10% difference in favour of the voice trained without this ToBI feature is observed. This may be linked with F0 contour lowering or raising regardless of lexical tones. This brought two main problems in the synthetic voice: discontinuity in spectrum and F0 or unexpected voice quality. This paper then concluded the need of much more work on intonation modeling that should take into account the Vietnamese tones. A new prosody model can be designed, which may consider the ToBI model, with respect to lexical tones and the syntactic structure of Vietnamese. Index Terms — Text-to-speech (TTS), speech synthesis, tonal language, Vietnamese, HMM-based speech synthesis, intonation, ToBI
Object modelling by registration of multiple range images

We study the problem of creating a complete model of a physical object. Although this may be possible using intensity images, we use here range images which directly provide access t o three dimensional information. T h e first problem that we need t o solve is t o find the transformation between the different views. Previous approaches have either assumed this transformation t o be known (which is extremely difficult for a complete model), or computed it with feature matching (which is not accurate enough for integration). In this paper, we propose a new approach which works on range d a t a directly, and registers successive views with enough overlapping area t o get an accurate transformation between views. This is performed by minimizing afunctional which does not require point t o point matches. We give the details of the registration method and modeling procedure, and illustrate them on real range images of complex objects. 1  Creating models of physical objects is a necessary component machine of biological vision modules. Such models can then be used in object recognition, pose estimation or inspection tasks. If the object of interest has been precisely designed, then such a model exists in the form of a CAD model. In many applications, however, it is either not possible or not practical to have access to such CAD models, and we need to build models from the physical object. Some researchers bypass the problem by using a model which consist,s of multiple views (, [a]), but t,liis is not, always enough. If one needs a complete model of an object, the following steps are necessary: 1. data acquisition, 2. registration between views, 3. integration of views. By view we mean the 3D surface information of the object from specific point of view. While the integration process is very dependent on the representation scheme used, the precondition for performing integration consists of knowing the transformation between the data from different views. The goal of registrat,ion is to find such a transformat.ion, which is also known as t,he covrespon,den,ce problem. This problem has been a t the core of many previous research efforts: Bhanu [a] developed an object modeling system for object recognition by rotating object through known angles to acquire multiple views. Chien et al.  and Ahuja and Veen-stra [l] used orthogonal views to construct octree object ' models. With these methods, …
Twin Clouds: An Architecture for Secure Cloud Computing (Extended Abstract)

Cloud computing promises a more cost effective enabling technology to outsource storage and computations. Existing approaches for secure outsourcing of data and arbitrary computations are either based on a single tamper-proof hardware, or based on recently proposed fully homomorphic encryption. The hardware based solutions are not scaleable, and fully homomorphic encryption is currently only of theoretical interest and very inefficient. In this paper we propose an architecture for secure outsourcing of data and arbitrary computations to an untrusted commodity cloud. In our approach, the user communicates with a trusted cloud (either a private cloud or built from multiple secure hardware modules) which encrypts and verifies the data stored and operations performed in the untrusted commodity cloud. We split the computations such that the trusted cloud is mostly used for security-critical operations in the less time-critical setup phase, whereas queries to the outsourced data are processed in parallel by the fast commodity cloud on encrypted data.
Reducing the energy cost of human walking using an unpowered exoskeleton

With efficiencies derived from evolution, growth and learning, humans are very well-tuned for locomotion. Metabolic energy used during walking can be partly replaced by power input from an exoskeleton, but is it possible to reduce metabolic rate without providing an additional energy source? This would require an improvement in the efficiency of the human–machine system as a whole, and would be remarkable given the apparent optimality of human gait. Here we show that the metabolic rate of human walking can be reduced by an unpowered ankle exoskeleton. We built a lightweight elastic device that acts in parallel with the user's calf muscles, off-loading muscle force and thereby reducing the metabolic energy consumed in contractions. The device uses a mechanical clutch to hold a spring as it is stretched and relaxed by ankle movements when the foot is on the ground, helping to fulfil one function of the calf muscles and Achilles tendon. Unlike muscles, however, the clutch sustains force passively. The exoskeleton consumes no chemical or electrical energy and delivers no net positive mechanical work, yet reduces the metabolic cost of walking by 7.2 ± 2.6% for healthy human users under natural conditions, comparable to savings with powered devices. Improving upon walking economy in this way is analogous to altering the structure of the body such that it is more energy-effective at walking. While strong natural pressures have already shaped human locomotion, improvements in efficiency are still possible. Much remains to be learned about this seemingly simple behaviour.
ZOZZLE: Fast and Precise In-Browser JavaScript Malware Detection

JavaScript malware-based attacks account for a large fraction of successful mass-scale exploitation happening today. Attackers like JavaScript-based attacks because they can be mounted against an unsuspecting user visiting a seemingly innocent web page. While several techniques for addressing these types of exploits have been proposed, in-browser adoption has been slow, in part because of the performance overhead these methods incur. In this paper, we propose ZOZZLE, a low-overhead solution for detecting and preventing JavaScript malware that is fast enough to be deployed in the browser. Our approach uses Bayesian classification of hierarchical features of the JavaScript abstract syntax tree to identify syntax elements that are highly predictive of malware. Our experimental evaluation shows that ZOZZLE is able to detect JavaScript malware through mostly static code analysis effectively. ZOZZLE has an extremely low false positive rate of 0.0003%, which is less than one in a quarter million. Despite this high accuracy, the ZOZZLE classifier is fast, with a throughput of over one megabyte of JavaScript code per second.
Machine Learning DDoS Detection for Consumer Internet of Things Devices

An increasing number of Internet of Things (IoT) devices are connecting to the Internet, yet many of these devices are fundamentally insecure, exposing the Internet to a variety of attacks. Botnets such as Mirai have used insecure consumer IoT devices to conduct distributed denial of service (DDoS) attacks on critical Internet infrastructure. This motivates the development of new techniques to automatically detect consumer IoT attack traffic. In this paper, we demonstrate that using IoT-specific network behaviors (e.g., limited number of endpoints and regular time intervals between packets) to inform feature selection can result in high accuracy DDoS detection in IoT network traffic with a variety of machine learning algorithms, including neural networks. These results indicate that home gateway routers or other network middleboxes could automatically detect local IoT device sources of DDoS attacks using low-cost machine learning algorithms and traffic data that is flow-based and protocol-agnostic.
An Efficient Architecture and Algorithm for Resource Provisioning in Fog Computing

Cloud computing is a model of sharing computing resources over any communication network by using virtualization. Virtualization allows a server to be sliced in virtual machines. Each virtual machine has its own operating system/applications that rapidly adjust resource allocation. Cloud computing offers many benefits, one of them is elastic resource allocation. To fulfill the requirements of clients, cloud environment should be flexible in nature and can be achieve by efficient resource allocation. Resource allocation is the process of assigning available resources to clients over the internet and plays vital role in Infrastructure-as-aService (IaaS) model of cloud computing. Elastic resource allocation is required to optimize the allocation of resources, minimizing the response time and maximizing the throughput to improve the performance of cloud computing. Sufficient solutions have been proposed for cloud computing to improve the performance but for fog computing still efficient solution have to be found. Fog computing is the virtualized intermediate layer between clients and cloud. It is a highly virtualized technology which is similar to cloud and provide data, computation, storage, and networking services between end users and cloud servers. This paper presents an efficient architecture and algorithm for resources provisioning in fog computing environment by using virtualization technique.
Can Structural Models Price Default Risk ? Evidence from Bond and Credit Derivative Markets ∗

Using a set of structural models, we evaluate the price of default protection for a sample of US corporations. In contrast to previous evidence from corporate bond data, CDS premia are not systematically underestimated. In fact, one of our studied models has little difficulty on average in predicting their level. For robustness, we perform the same exercise for bond spreads by the same issuers on the same trading date. As expected, bond spreads relative to the Treasury curve are systematically underestimated. This is not the case when the swap curve is used as a benchmark, suggesting that previously documented underestimation results may be sensitive to the choice of risk free rate. ∗We are indebted to seminar participants at Wilfrid Laurier University, Carnegie Mellon University, the Bank of Canada, McGill University and Queen’s University for helpful discussions. In addition, we are grateful to participants of the 2005 EFA in Moscow, the 2005 NFA in Vancouver, the 2006 CFP Vallendar conference and the 2006 Moody’s NYU Credit Risk Conference. We are particularly grateful to Stephen Schaefer for comments. †Faculty of Management, McGill University, 1001 Sherbrooke Street West, Montreal QC, H3A 1G5 Canada. Tel +1 514 398-3186, Fax +1 514 398-3876, Email jan.ericsson@mcgill.ca. ‡Stockholm School of Economics, Department of Finance, Box 6501, S-113 83 Stockholm, Sweden. Tel: +46 8 736 9143, fax +46 8 312327.
Vertex Sparsification in Trees

Given an unweighted tree T = (V,E) with terminals K ⊂ V , we show how to obtain a 2-quality vertex flow and cut sparsifier H with VH = K. We prove that our result is essentially tight by providing a 2− o(1) lower-bound on the quality of any cut sparsifier for stars. In addition we give improved results for quasi-bipartite graphs. First, we show how to obtain a 2-quality flow sparsifier with VH = K for such graphs. We then consider the other extreme and construct exact sparsifiers of size O(2), when the input graph is unweighted.
A case study for scientific I/O: improving the FLASH astrophysics code

The FLASH code is a computational science tool for simulating and studying thermonuclear reactions. The program periodically outputs large checkpoint files (to resume a calculation from a particular point in time) and smaller plot files (for visualization and analysis). Initial experiments on BlueGene/P spent excessive time in input/output (I/O), making it difficult to do actual science. Our investigation of time spent in I/O revealed several locations in the I/O software stack where we could make improvements. Fixing data corruption in the MPI-IO library allowed us to use collective I/O, yielding an order of magnitude improvement. Restructuring the data layout provided a more efficient I/O access pattern and yielded another doubling of performance, but broke format assumptions made by other tools in the application workflow. Using new nonblocking APIs in the ParallelNetCDF library allowed us to keep high performance and maintain backward compatibility. The I/O research community has studied a host of optimizations and strategies. Sometimes the challenge for applications is knowing how to apply these new techniques to production codes. In this case study, we offer a demonstration of how computational scientists, with a detailed understanding of their application, and the I/O community, with a wide array of approaches from which to choose, can magnify each other’s efforts and achieve tremendous application productivity gains. Computational Science & Discovery 5 (2012) 015001 www.iop.org/journals/csd  (2012) 015001 R Latham et al
Entropy-based algorithms for best basis selection

Adapted waveform analysis uses a library of orthonormal bases and an efficiency functional to match a basis to a given signal or family of signals. It permits efficient compression of a variety of signals such as sound and images. The predefined libraries of modulated waveforms include orthogonal wavelet-packets, and localized trigonometric functions, have reasonably well controlled time-frequency localization properties. The idea is to build out of the library functions an orthonormal basis relative to which the given signal or collection of signals has the lowest information cost. The method relies heavily on the remarkable orthogonality properties of the new libraries: all expansions in a given library conserve energy, hence are comparable. Several cost functionals are useful; one of the most attractive is Shannon entropy, which has a geometric interpretation in this context.
Analysis of electromigration for Cu pillar bump in flip chip package

A demand for small form factor in IC packaging has lead to a reduced bump size and an increased current density. The high current density accompanying with Joule heat induces an electromigration failure. In this study, we investigated the effects of under bump metallization (UBM) on the electromigration failure. Three types of UBM such as Cu 5µm, Cu 10µm and Cu 5µm/Ni 2µm were compared with 60µm high Cu pillar bump(CPB). The current density was fixed at 5.09·104A/cm2 and the temperature ranged from 130°C to 170°C. Mean time to failure data (MTTF) were obtained from Weibull distribution analysis. MTTF of CPB was longer than the others and the MTTF order was as follows; CPB > Cu/Ni > Cu 10µm > Cu 5µm. As the temperature increased, MTTF decreased regardless of bump structures. This result implied that electromigration reliability of CPB was far better than other solder bumps and Cu/Ni UBM structure was more resistant to electromigration than single Cu UBM.
Do Deep Convolutional Nets Really Need to be Deep (Or Even Convolutional)?

Prior research showed that shallow feed-forward nets sometimes can learn the complex functions previously learned by deep nets using a similar number of parameters as the deep models they mimic. We investigate if shallow models can learn to mimic the functions learned by deep convolutional models. We experiment with models with a varying number of convolutional layers trained to mimic a state-of-the-art ensemble of CIFAR-10 models. We are unable to train shallow models to have comparable accuracy to deep convolutional models. The student models do not have to be as deep as the teacher models, but they need multiple convolutional layers to learn functions of high accuracy.
The history of RFID

Radio frequency identification (RFID) is an integral part of our life, which increases productivity and convenience. It is the term coined for short-range radio technology used to communicate mainly digital information between a stationary location and a movable object or between movable objects. This RFID system uses the principle of modulated backscatter where it can transfer the data from the tag to the reader. The tag generally reads its internal memory of stored data and changes the loading on the tag antenna in a coded manner corresponding to the stored data. RFID is a technology, which spans systems engineering, software development, encryption etc., and thus there are many engineers involved in the development and application of RFID and at present the shortage of technical and business people trained in RFID is hampering the growth of the industry.
Factorial LDA: Sparse Multi-Dimensional Text Models

Latent variable models can be enriched with a multi-dimensional structure to consider the many latent factors in a text corpus, such as topic, author perspective and sentiment. We introduce factorial LDA, a multi-dimensional model in which a document is influenced by K different factors, and each word token depends on a K-dimensional vector of latent variables. Our model incorporates structured word priors and learns a sparse product of factors. Experiments on research abstracts show that our model can learn latent factors such as research topic, scientific discipline, and focus (methods vs. applications). Our modeling improvements reduce test perplexity and improve human interpretability of the discovered factors.
IMPLEMENTATION OF COMBINATIONAL CIRCUITS USING TERNARY MULTIPLEXER

IJCER | Mar-Apr 2012 | Vol. 2 | Issue No.2 |457-463 Page 457  This is the paper presenting a novel method for defining, analyzing and implementing the basic combinational circuitry with less number of ternary multiplexers. Multilplexer is used as basic building block to realize all the combinational and sequential circuitry providing complete, concise, implementation-free description of the ternary function involved. This shows the potential of VHDL modeling and simulation which can be applied to Ternary switching circuits for verifying its functionality and timing specifications. This is the method which is used in analyzing the complex ternary functions and reduction of gate count Keyword:Gate count, Multivalued logic, Reabilityunreability model ,VHDL, Ternary switching levels.
Performance analysis of left/right hand movement classification from EEG signal by intelligent algorithms

Brain Computer interfaces (BCI) has immense potentials to improve human lifestyle including that of the disabled. BCI has possible applications in the next generation human-computer, human-robot and prosthetic/assistive devices for rehabilitation. The dataset used for this study has been obtained from the BCI competition-II 2003 databank provided by the University of Technology, Graz. After pre-processing of the signals from their electrodes (C3 & C4), the wavelet coefficients, Power Spectral Density of the alpha and the central beta band and the average power of the respective bands have been employed as features for classification. This paper presents a comparative study of different classification methods including linear discriminant analysis (LDA), Quadratic discriminant analysis (QDA), k-nearest neighbor (KNN) algorithm, linear support vector machine (SVM), radial basis function (RBF) SVM and naive Bayesian classifiers algorithms in differentiating the raw EEG data obtained, into their associative left/right hand movements. Performance of left/right hand classification is studied using both original features and reduced features. The feature reduction here has been performed using Principal component Analysis (PCA). It is as observed that RBF kernelised SVM classifier indicates the highest performance accuracy of 82.14% with both original and reduced feature set. However, experimental results further envisage that all the other classification techniques provide better classification accuracy for reduced data set in comparison to the original data. It is also noted that the KNN classifier improves the classification accuracy by 5% when reduced features are used instead of the original.
Grounded Theory in Software Engineering Research: A Critical Review and Guidelines

Grounded Theory (GT) has proved an extremely useful research approach in several fields including medical sociology, nursing, education and management theory. However, GT is a complex method based on an inductive paradigm that is fundamentally different from the traditional hypothetico-deductive research model. As there are at least three variants of GT, some ostensibly GT research suffers from method slurring, where researchers adopt an arbitrary subset of GT practices that are not recognizable as GT. In this paper, we describe the variants of GT and identify the core set of GT practices. We then analyze the use of grounded theory in software engineering. We carefully and systematically selected 98 articles that mention GT, of which 52 explicitly claim to use GT, with the other 46 using GT techniques only. Only 16 articles provide detailed accounts of their research procedures. We offer guidelines to improve the quality of both conducting and reporting GT studies. The latter is an important extension since current GT guidelines in software engineering do not cover the reporting process, despite good reporting being necessary for evaluating a study and informing subsequent research.