nils-herrmann/clean_scirepeval huggingface dataset:
Detecting documents forged by printing and copying

This paper describes a method to distinguish documents produced by laser printers, inkjet printers, and electrostatic copiers, three commonly used document creation devices. The proposed approach can distinguish between documents produced by these sources based on features extracted from the characters in the documents. Hence, it can also be used to detect tampered documents produced by a mixture of these sources. We analyze the characteristics associated with laser/inkjet printers and electrostatic copiers and determine the signatures created by the different physical and technical processes involved in each type of printing. Based on the analysis of these signatures, we computed the features of noise energy, contour roughness, and average gradient. To the best of our knowledge, this is the first work to distinguish documents produced by laser printer, inkjet printer, and copier based on features extracted from individual characters in the documents. Experimental results show that this method has an average accuracy of 90% and works with JPEG compression.
A Folded Reflectarray Antenna With a Planar SIW Slot Array Antenna as the Primary Source

An integrated design of a planar source-fed folded reflectarray antenna (FRA) with low profile is proposed to achieve high gain for Q-band millimeter wave high data rate wireless communication applications. A planar substrate integrated waveguide (SIW) slot array antenna is adopted as the primary source to illuminate the proposed FRA. Considerations of the off-focus loss and the asymmetry E- and H-plane beamwidths loss of the planar source are thoroughly investigated to improve the aperture efficiency of the reflectarray antenna. A prototype is implemented and the experimental results are in good agreement with the predictions, which exhibit a measured |S11| less than -10 dB over the frequency band of 41.6-44 GHz, a maximum boresight gain of 31.9 dBi at 44 GHz with the aperture efficiency of 49% and a -3 dB gain drop bandwidth of 41.8-44.9 GHz. By integrating with the planar feed, the proposed folded reflectarray antenna benefits from its low-profile, low cost, and the ability of coplanar integration with millimeter wave planar circuits while maintaining the good performance in terms of gain and efficiency.
From On-Premise Software to Cloud Services: The Impact of Cloud Computing on Enterprise Software Vendors' Business Models

Cloud computing is an emerging paradigm that allows users to conveniently access computing resources as pay-per-use services. Whereas cloud offerings such as Amazon’s Elastic Compute Cloud and Google Apps are rapidly gaining a large user base, enterprise software’s migration towards the cloud is still in its infancy. For software vendors the move towardscloud solutions implies profound changes in their value-creation logic. Not only are they forced to deliver fully web-enabled solutions and to replace their license model with service fees, they also need to build the competencies to host and manage business-critical applications for their customers. This motivates our research, which investigates cloud computing’s implications for enterprise software vendors’ business models. From multiple case studies covering traditional and pure cloud providers, we find that moving from on-premise software to cloud services affects all business model components, that is, the customer value proposition, resource base, value configuration, and financial flows. It thus underpins cloud computing’s disruptive nature in the enterprise software domain. By deriving two alternative business model configurations, SaaS and SaaS+PaaS, our research synthesizes the strategic choices for enterprise software vendors and provides guidelines for designing viable business models.
Design and development of non invasive glucose measurement system

One of the biggest health challenges of 21st century is diabetics due to its exponential increase in the diabetics patients in the age group of 20–79 years. To prevent the complication due to diabetics it is essential to monitor the blood glucose level continuously. Most of the regular glucose measurement systems are invasive in nature. Invasive methods cause pain, time consumption, high cost and potential risk of spreading infection diseases. Therefore there is a great demand to have reliable cost effective and comfortable non invasive system for the detection of blood glucose level continuously. The proposed method is based on the direct effect of glucose on the scattering properties of the organ. Glucose decreases the mismatch in refractive index between scatterers and their surrounding media, leading to a smaller scattering coefficient and, consequently, a shorter optical path. The reduction in scattering is due to an increase in glucose concentration. As a result, with the growing concentration of glucose, fewer photons are absorbed and the light intensity increases. In the present work, we have used PPG technique. An algorithm was developed from the PPG data for monitoring blood glucose. The result obtained from this technique was compared with ARKRAY, Glucocard tm01-mini and found good agreement.
Mondor's disease.

Mondor's disease (MD) is a rare condition, which is considered a thrombophlebitis of the subcutaneous veins. It commonly occurs on the anterolateral thoracoabdominal wall, but it can also occur on the penis, groin, antecubital fossa and posterior cervical region. The clinical features are a sudden and typically asymptomatic onset of a cord-like induration, although some patients report a feeling of 'strain'. It is a self-limiting process that lasts a short period of time, which may be the reason why there are few reports about its diagnosis and treatment. Its pathogenesis has remained unclear, because of the lack of methods to reliably differentiate between veins and lymphatic vessels. Immunohistochemical staining for CD31 and D240 has been identified recently as the best method to distinguish small veins from lymphatic vessels, making it a valuable technique in diagnosing not only MD, but also many other diseases in which veins or lymphatic vessels are affected. MD has been associated with several systemic diseases such as breast cancer and hypercoagulability states, thus laboratory studies are recommended to exclude any possible systemic disorders. As this condition is usually a benign and self-limiting process, vigorous treatment is only recommended when the process is symptomatic or recurrent.
Maximum and minimum stock price forecasting of Brazilian power distribution companies based on artificial neural networks

Time series forecasting has been widely used to determine future prices of stocks, and the analysis and modeling of finance time series is an important task for guiding investors’ decisions and trades. Nonetheless, the prediction of prices by means of a time series is not trivial and it requires a thorough analysis of indexes, variables and other data. In addition, in a dynamic environment such as the stock market, the non-linearity of the time series is a pronounced characteristic, and this immediately affects the efficacy of stock price forecasts. Thus, this paper aims at proposing a methodology that forecasts the maximum and minimum day stock prices of three Brazilian power distribution companies, which are traded in the São Paulo Stock Exchange BM&FBovespa. When compared to the other papers already published in the literature, one of the main contributions and novelty of this paper is the forecast of the range of closing prices of Brazilian power distribution companies’ stocks. As a result of its application, investors may be able to define threshold values for their stock trades. Moreover, such a methodology may be of great interest to home brokers who do not possess ample knowledge to invest in such companies. The proposed methodology is based on the calculation of distinct features to be analysed by means of attribute selection, defining the most relevant attributes to predict the maximum and minimum day stock prices of each company. Then, the actual prediction was carried out by Artificial Neural Networks (ANNs), which had their performances evaluated by means of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) calculations. The proposed methodology for addressing the problem of prediction of maximum and minimum day stock prices for Brazilian distribution companies is effective. In addition, these results were only possible to be achieved due to the combined use of attribute selection by correlation analysis and ANNs.
Large-Scale Content-Based Matching of MIDI and Audio Files

MIDI files, when paired with corresponding audio recordings, can be used as ground truth for many music information retrieval tasks. We present a system which can efficiently match and align MIDI files to entries in a large corpus of audio content based solely on content, i.e., without using any metadata. The core of our approach is a convolutional network-based cross-modality hashing scheme which transforms feature matrices into sequences of vectors in a common Hamming space. Once represented in this way, we can efficiently perform large-scale dynamic time warping searches to match MIDI data to audio recordings. We evaluate our approach on the task of matching a huge corpus of MIDI files to the Million Song Dataset. 1. TRAINING DATA FOR MIR Central to the task of content-based Music Information Retrieval (MIR) is the curation of ground-truth data for tasks of interest (e.g. timestamped chord labels for automatic chord estimation, beat positions for beat tracking, prominent melody time series for melody extraction, etc.). The quantity and quality of this ground-truth is often instrumental in the success of MIR systems which utilize it as training data. Creating appropriate labels for a recording of a given song by hand typically requires person-hours on the order of the duration of the data, and so training data availability is a frequent bottleneck in content-based MIR tasks. MIDI files that are time-aligned to matching audio can provide ground-truth information [8,25] and can be utilized in score-informed source separation systems [9, 10]. A MIDI file can serve as a timed sequence of note annotations (a “piano roll”). It is much easier to estimate information such as beat locations, chord labels, or predominant melody from these representations than from an audio signal. A number of tools have been developed for inferring this kind of information from MIDI files [6, 7, 17, 19]. Halevy et al.  argue that some of the biggest successes in machine learning came about because “...a large training set of the input-output behavior that we seek to automate is available to us in the wild.” The motivation behind c Colin Raffel, Daniel P. W. Ellis. Licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).  Colin Raffel, Daniel P. W. Ellis. “LargeScale Content-Based Matching of MIDI and Audio Files”, 16th International Society for Music Information Retrieval Conference, 2015.
Audio-Visual Speech Recognition with a Hybrid CTC/Attention Architecture

Recent works in speech recognition rely either on connectionist temporal classification (CTC) or sequence-to-sequence models for character-level recognition. CTC assumes conditional independence of individual characters, whereas attention-based models can provide nonsequential alignments. Therefore, we could use a CTC loss in combination with an attention-based model in order to force monotonic alignments and at the same time get rid of the conditional independence assumption. In this paper, we use the recently proposed hybrid CTC/attention architecture for audio-visual recognition of speech in-the-wild. To the best of our knowledge, this is the first time that such a hybrid architecture architecture is used for audio-visual recognition of speech. We use the LRS2 database and show that the proposed audio-visual model leads to an 1.3% absolute decrease in word error rate over the audio-only model and achieves the new state-of-the-art performance on LRS2 database (7% word error rate). We also observe that the audio-visual model significantly outperforms the audio-based model (up to 32.9% absolute improvement in word error rate) for several different types of noise as the signal-to-noise ratio decreases.
Actor-Critic Reinforcement Learning with Energy-Based Policies

We consider reinforcement learning in Markov decision processes with high dimensional state and action spaces. We parametrize policies using energy-based models (particularly restricted Boltzmann machines), and train them using policy gradient learning. Our approach builds upon Sallans and Hinton (2004), who parameterized value functions using energy-based models, trained using a non-linear variant of temporal-difference (TD) learning. Unfortunately, non-linear TD is known to diverge in theory and practice. We introduce the first sound and efficient algorithm for training energy-based policies, based on an actorcritic architecture. Our algorithm is computationally efficient, converges close to a local optimum, and outperforms Sallans and Hinton (2004) in several high dimensional domains.
A Hybrid Password Authentication Scheme Based on Shape and Text

Textual-based password authentication scheme tends to be more vulnerable to attacks such as shouldersurfing and hidden camera. To overcome the vulnerabilities of traditional methods, visual or graphical password schemes have been developed as possible alternative solutions to text-based password schemes. Because simply adopting graphical password authentication also has some drawbacks, schemes using graphic and text have been developed. In this paper, we propose a hybrid password authentication scheme based on shape and text. It uses shapes of strokes on the grid as the origin passwords and allows users to login with text passwords via traditional input devices. The method provides strong resistant to hidden-camera and shoulder-surfing. Moreover, the scheme has high scalability and flexibility to enhance the authentication process security. The analysis of the security level of this approach is also discussed.
Watson: Beyond Jeopardy!

May 2012  This paper presents a vision for applying the Watson technology to health care and describes the steps needed to adapt and improve performance in a new domain. Specifically, it elaborates upon a vision for an evidence-based clinical decision support system, based on the DeepQA technology, that affords exploration of a broad range of hypotheses and their associated evidence, as well as uncovers missing information that can be used in mixed-initiative dialog. It describes the research challenges, the adaptation approach, and finally reports results on the first steps we have taken toward this goal.
A Differential-Fed UWB Antenna Element With Unidirectional Radiation

A new differential-fed broadband antenna element with unidirectional radiation is proposed. This antenna is composed of a folded bowtie, a center-fed loop, and a box-shaped reflector. A pair of differential feeds is developed to excite the antenna and provide an ultrawideband (UWB) impedance matching. The box-shaped reflector is used for the reduction of the gain fluctuation across the operating frequency band. An antenna prototype for UWB applications is fabricated and measured, exhibiting an impedance bandwidth of 132% with standing wave ratio ≤ 2 from 2.48 to 12.12 GHz, over which the gain varies between 7.2 and 14.1 dBi at boresight. The proposed antenna radiates unidirectionally with low cross polarization and low back radiation. Furthermore, the time-domain characteristic of the proposed antenna is evaluated. In addition, a 2 × 2 element array using the proposed element is also investigated in this communication.
Unsupervised Construction of an Indoor Floor Plan Using a Smartphone

Indoor pedestrian tracking extends location-based services to indoor environments. Typical indoor positioning systems employ a training/positioning model using Wi-Fi fingerprints. While these approaches have practical results in terms of accuracy and coverage, they require an indoor map, which is typically not available to the average user and involves significant training costs. A practical indoor pedestrian tracking approach should consider the indoor environment without a pretrained database or floor plan. In this paper, we present an indoor pedestrian tracking system, called SmartSLAM, which automatically constructs an indoor floor plan and radio fingerprint map for anonymous buildings using a smartphone. The scheme employs odometry tracing using inertial sensors, an observation model using Wi-Fi signals, and a Bayesian estimation for floor-plan construction. SmartSLAM is a true simultaneous localization and mapping implementation that does not necessitate additional devices, such as laser rangefinders or wheel encoders. We implemented the scheme on off-the-shelf smartphones and evaluated the performance in our university buildings. Despite inherent tracking errors from noisy sensors, SmartSLAM successfully constructed indoor floor plans.
SDHM: A hybrid model for spammer detection in Weibo

As the microblogging service (such as Weibo) is becoming popular, spam becomes a serious problem of affecting the credibility and readability of Online Social Networks. Most existing studies took use of a set of features to identify spam, but without the consideration of the overlap and dependency among different features. In this study, we investigate the problem of spam detection by analyzing real spam dataset collections of Weibo and propose a novel hybrid model of spammer detection, called SDHM, which utilizing significant features, i.e. user behavior information, online social network attributes and text content characteristics, in an organic way. Experiments on real Weibo dataset demonstrate the power of the proposed hybrid model and the promising performance.
Improving Authorship Attribution: Optimizing Burrows' Delta Method

Burrows’ Delta Method (Burrows, 2002) is a leading method of authorship attribution. It can be used to shortlist potential authors from a list or to even identify potential authors. The technique has been extended by Hoover (2004a, 2006). In this investigation, we look at the choice of words for the word vector used, the size of the word vector, the similarity measure and the impact of corpus choice on the accuracy of text classification. Our results show a word frequency vector of between 200 and 300 words give the most accurate results (Aldridge, 2007). We also demonstrate a dramatic improvement in accuracy by adapting Burrows’ Delta to the cosine similarity measure. Additionally, our results indicate areas where the word vector can be optimized still further for more accurate results.
Joint Bi-layer Optimization for Single-Image Rain Streak Removal

We present a novel method for removing rain streaks from a single input image by decomposing it into a rain-free background layer B and a rain-streak layer R. A joint optimization process is used that alternates between removing rain-streak details from B and removing non-streak details from R. The process is assisted by three novel image priors. Observing that rain streaks typically span a narrow range of directions, we first analyze the local gradient statistics in the rain image to identify image regions that are dominated by rain streaks. From these regions, we estimate the dominant rain streak direction and extract a collection of rain-dominated patches. Next, we define two priors on the background layer B, one based on a centralized sparse representation and another based on the estimated rain direction. A third prior is defined on the rain-streak layer R, based on similarity of patches to the extracted rain patches. Both visual and quantitative comparisons demonstrate that our method outperforms the state-of-the-art.
Offline handwritten Arabic cursive text recognition using Hidden Markov Models and re-ranking

a Faculty of Science and Information Technology, Al-Zaytoona University of Jordan, Amman, Jordan b School of Informatics, University of Bradford, Bradford BD7 1DP, United Kingdom c Information & Computer Science Department, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia d Centre for excellence in Signal and Image Processing, Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, G1 1XW, United Kingdom
Outlier Detection with Autoencoder Ensembles

In this paper, we introduce autoencoder ensembles for unsupervised outlier detection. One problem with neural networks is that they are sensitive to noise and often require large data sets to work robustly, while increasing data size makes them slow. As a result, there are only a few existing works in the literature on the use of neural networks in outlier detection. This paper shows that neural networks can be a very competitive technique to other existing methods. The basic idea is to randomly vary on the connectivity architecture of the autoencoder to obtain significantly better performance. Furthermore, we combine this technique with an adaptive sampling method to make our approach more efficient and effective. Experimental results comparing the proposed approach with state-of-theart detectors are presented on several benchmark data sets showing the accuracy of our approach.
The attributional Style Questionnaire

Of current interest are the causal attributions offered by depressives for the good and bad events in their lives. One important attributional account of depression is the reformulated learned helplessness model, which proposes that depressive symptoms are associated with an attributional style in which uncontrollable bad events are attributed to internal (versus external), stable (versus unstable), and global (versus specific) causes. We describe the Attributional Style Questionnaire, which measures individual differences in the use of these attributional dimensions. We report means, reliabilities, intercorrelations, and test-retest stabilities for a sample of 130 undergraduates. Evidence for the questionnaire's validity is discussed. The Attributional Style Questionnaire promises to be a reliable and valid instrument.
Using clustering analysis to improve semi-supervised classification

Semi-supervised classification has become an active topic recently and a number of algorithms, such as Self-training, have been proposed to improve the performance of supervised classification using unlabeled data. In this paper, we propose a semi-supervised learning framework which combines clustering and classification. Our motivation is that clustering analysis is a powerful knowledge-discovery tool and it may clustering is integrated into Self-training classification to help train a better classifier. In particular, the semi-supervised fuzzy c-means algorithm and support vector machines are used for clustering and classification, respectively. Experimental results on artificial and real datasets demonstrate the advantages of the proposed framework. & 2012 Elsevier B.V. All rights reserved.
Optimization Landscape and Expressivity of Deep CNNs

We analyze the loss landscape and expressiveness of practical deep convolutional neural networks (CNNs) with shared weights and max pooling layers. We show that such CNNs produce linearly independent features at a “wide” layer which has more neurons than the number of training samples. This condition holds e.g. for the VGG network. Furthermore, we provide for such wide CNNs necessary and sufficient conditions for global minima with zero training error. For the case where the wide layer is followed by a fully connected layer we show that almost every critical point of the empirical loss is a global minimum with zero training error. Our analysis suggests that both depth and width are very important in deep learning. While depth brings more representational power and allows the network to learn high level features, width smoothes the optimization landscape of the loss function in the sense that a sufficiently wide network has a well-behaved loss surface with almost no bad local minima.