nils-herrmann/clean_scirepeval huggingface dataset:
A study of maximum power point tracking algorithms for stand-alone Photovoltaic Systems

The Photovoltaic (PV) energy is one of the renewable energies that attracts attention of researchers in the recent decades. Since the conversion efficiency of PV arrays is very low, it requires maximum power point tracking (MPPT) control techniques to extract the maximum available power from PV arrays. In this paper, two categories of MPPT algorithms, namely indirect and direct methods are discussed. In addition to that, the advantages and disadvantages of each MPPT algorithm are reviewed. Simulations of PV modules were also performed using Perturb and Observe algorithm and Fuzzy Logic controller. The simulation results produced by the two algorithms are compared with the expected results generated by Solarex MSX60 PV modules. Besides that, the P-V characteristics of PV arrays under partial shaded conditions are discussed in the last section.
Effectivecollaborative movie recommender system using asymmetric user similarity and matrix factorization

Recommender systems are becoming ubiquitous these days to advise important products to users. Conventional collaborative filtering methods suffer from sparsity, scalability, and cold start problem. In this work, we have implemented a novel and improved method of recommending movies by combining the asymmetric method of calculating similarity with matrix factorization and Tyco (typicality-based collaborative filtering). The asymmetric method describes that similarity of user A with B is not the similar as the similarity of B with A. Matrix factorization shows items (movies) as well as users by vectors of factors derived from rating pattern of items (movies). In Tyco clusters of movies of the same genre are created, and typicality degree (a measure of how much a movie belongs to that genre) of each movie in that cluster was considered and subsequently of each user in a genre was calculated. The similarity between users was calculated by using their typicality in genres rather than co-rated items. We had combined these methods and employed Pearson correlation coefficient method to calculate similarity to optimize results when compared to cosine similarity, Linear Regression to make predictions that gave better results. In this research work stochastic gradient descent is also used for optimization and regularization to avoid the problem of over fitting. All these approaches together provide better prediction and handle problems of sparsity, cold start, and scalability well as compared to conventional methods. Experimental results confirm that our HYBRTyco gives improved results than Tyco regarding mean absolute error (MAE)and mean absolute percentage error (MAPE), especially on the sparse dataset.
Pictory - neural style transfer and editing with coreML

This work presents advances in the design and implementation of Pictory, an iOS app for artistic neural style transfer and interactive image editing using the CoreML and Metal APIs. Pictory combines the benefits of neural style transfer, e.g., high degree of abstraction on a global scale, with the interactivity of GPU-accelerated state-of-the-art image-based artistic rendering on a local scale. Thereby, the user is empowered to create high-resolution, abstracted renditions in a two-stage approach. First, a photo is transformed using a pre-trained convolutional neural network to obtain an intermediate stylized representation. Second, image-based artistic rendering techniques (e.g., watercolor, oil paint or toon filtering) are used to further stylize the image. Thereby, fine-scale texture noise---introduced by the style transfer---is filtered and interactive means are provided to individually adjust the stylization effects at run-time. Based on qualitative and quantitative user studies, Pictory has been redesigned and optimized to support casual users as well as mobile artists by providing effective, yet easy to understand, tools to facilitate image editing at multiple levels of control.
Characterizing and Countering Communal Microblogs During Disaster Events

The huge amount of tweets posted during a disaster event includes information about the present situation as well as the emotions/opinions of the masses. While looking through these tweets, we realized that a large amount of communal tweets, i.e., abusive posts targeting specific religious/racial groups are posted even during natural disasters—this paper focuses on such category of tweets, which is in sharp contrast to most of the prior research concentrating on extracting situational information. Considering the potentially adverse effects of communal tweets during disasters, in this paper, we develop a classifier to distinguish communal tweets from noncommunal ones, which performs significantly better than existing approaches. We also characterize the communal tweets posted during five recent disaster events, and the users who posted such tweets. Interestingly, we find that a large proportion of communal tweets are posted by popular users (having tens of thousands of followers), most of whom are related to media and politics. Further, users posting communal tweets form strong connected groups in the social network. As a result, the reach of communal tweets is much higher than noncommunal tweets. We also propose an event-independent classifier to automatically identify anticommunal tweets and also indicate a way to counter communal tweets, by utilizing such anticommunal tweets posted by some users during disaster events. Finally, we develop a real-time service to automatically collect tweets related to a disaster event and identify communal and anticommunal tweets from that set. We believe that such a system is really helpful for government and local monitoring agencies to take appropriate decisions like filtering or promoting some particular contents.
Computational Decomposition of Style for Controllable and Enhanced Style Transfer

Neural style transfer has been demonstrated to be powerful in creating artistic image with help of Convolutional Neural Networks (CNN). However, there is still lack of computational analysis of perceptual components of the artistic style. Different from some early attempts which studied the style by some pre-processing or post-processing techniques, we investigate the characteristics of the style systematically based on feature map produced by CNN. First, we computationally decompose the style into basic elements using not only spectrum based methods including Fast Fourier Transform (FFT), Discrete Cosine Transform (DCT) but also latent variable models such Principal Component Analysis (PCA), Independent Component Analysis (ICA). Then, the decomposition of style induces various ways of controlling the style elements which could be embedded as modules in state-of-the-art style transfer algorithms. Such decomposition of style brings several advantages. It enables the computational coding of different artistic styles by our style basis with similar styles clustering together, and thus it facilitates the mixing or intervention of styles based on the style basis from more than one styles so that compound style or new style could be generated to produce styled images. Experiments demonstrate the effectiveness of our method on not only painting style transfer but also sketch style transfer which indicates possible applications on picture-to-sketch problems.
SmartEdge : A Smart Contract for Edge Computing

Edge computing has emerged as an effective offloading strategy for constrained devices. It enables low-capability devices to leverage nearby resources for assistance with computationally-intensive tasks. We envision a future where Internet of Things (IoT) devices may autonomously transact with other more powerful devices to request such offloading services. We believe blockchain-based technologies can help facilitate this process by tracking usage and managing payments. In this work we introduce SmartEdge, an Ethereumbased smart contract for edge computing and show that it is a low-cost, low-overhead tool for compute-resource management.
LDAExplore: Visualizing Topic Models Generated Using Latent Dirichlet Allocation

We present LDAExplore, a tool to visualize topic distributions in a given document corpus that are generated using Topic Modeling methods. Latent Dirichlet Allocation (LDA) is one of the basic methods that is predominantly used to generate topics. One of the problems with methods like LDA is that users who apply them may not understand the topics that are generated. Also, users may find it difficult to search correlated topics and correlated documents. LDAExplore, tries to alleviate these problems by visualizing topic and word distributions generated from the document corpus and allowing the user to interact with them. The system is designed for users, who have minimal knowledge of LDA or Topic Modelling methods. To evaluate our design, we run a pilot study which uses the abstract’s of 322 Information Visualization papers, where every abstract is considered a document. The topics generated are then explored by users. The results show that users are able to find correlated documents and group them based on topics that are similar.
Towards Real-time Simulation of Hyperelastic Materials

TOWARDS REAL-TIME SIMULATION OF HYPERELASTIC MATERIALS Tiantian Liu Ladislav Kavan We propose a new method for physics-based simulation supporting many different types of hyperelastic materials from mass-spring systems to three-dimensional finite element models, pushing the performance of the simulation towards real-time. Fast simulation methods such as Position Based Dynamics exist, but support only limited selection of materials; even classical materials such as corotated linear elasticity and Neo-Hookean elasticity are not supported. Simulation of these types of materials currently relies on Newton’s method, which is slow, even with only one iteration per timestep. In this work, we start from simple material models such as mass-spring systems or as-rigid-as-possible materials. We express the widely used implicit Euler time integration as an energy minimization problem and introduce auxiliary projection variables as extra unknowns. After our reformulation, the minimization problem becomes linear in the node positions, while all the non-linear terms are isolated in individual elements. We then extend this idea to efficiently simulate a more general spatial discretization using finite element method. We show that our reformulation can be interpreted as a quasi-Newton method. This insight enables very efficient simulation of a large class of hyperelastic materials. The quasi-Newton interpretation also allows us to leverage ideas from numerical optimization. In particular, we show that our solver can be further accelerated using L-BFGS updates (Limited-memory Broyden-FletcherGoldfarb-Shanno algorithm). Our final method is typically more than ten times faster than one iteration of Newton’s method without compromising quality. In fact, our result is often more accurate than the result obtained with one iteration of Newton’s method. Our method is also easier to implement, implying reduced software development costs.
Language, music, syntax and the brain

The comparative study of music and language is drawing an increasing amount of research interest. Like language, music is a human universal involving perceptually discrete elements organized into hierarchically structured sequences. Music and language can thus serve as foils for each other in the study of brain mechanisms underlying complex sound processing, and comparative research can provide novel insights into the functional and neural architecture of both domains. This review focuses on syntax, using recent neuroimaging data and cognitive theory to propose a specific point of convergence between syntactic processing in language and music. This leads to testable predictions, including the prediction that that syntactic comprehension problems in Broca's aphasia are not selective to language but influence music perception as well.
Diff/TS: A Tool for Fine-Grained Structural Change Analysis

This paper reports on a tool for fine-grained analysis of structural changes made between revisions of programs. The tool, called Diff/TS, calculates, visualizes and classifies edit operations including "moves'' that will change one revision into another by means of detailed tree structural analysis on source code. Such analysis tends to be time consuming and inflexible. We have extended a general tree comparison algorithm with heuristics driven control configurable for multiple programming languages and have achieved both processing speed and analysis precision needed for investigating large-scale software projects. The tool is capable of processing Python, Java, C and C++ projects. We present several applications including software "archaeology'' on a widely known open source software project and automated "phylogenetic'' malware classification based on control flows. These examples suggest that tree differencing is useful for measuring distance or dissimilarity between tree structured artifacts, and offer good precision tests of the method.
Optimized three-dimensional stencil computation on Fermi and Kepler GPUs

Stencil based algorithms are used intensively in scientific computations. Graphics Processing Units (GPU) based implementations of stencil computations speed-up the execution significantly compared to conventional CPU only systems. In this paper we focus on double precision stencil computations, which are required for meeting the high accuracy requirements, inherent for scientific computations. Starting from two baseline implementations (using two dimensional and three dimensional thread block structures respectively), we employ different optimization techniques which lead to seven kernel versions. Both Fermi and Kepler GPUs are used, to evaluate the impact of different optimization techniques for the two architectures. Overall, the GTX680 GPU card performs best for a kernel with 2D thread block structure and optimized register and shared memory usage. We show that, whereas shared memory is not essential for Fermi GPUs, it is a highly efficient optimization technique for Kepler GPUs (mainly due to the different L1 cache usage). Furthermore, we evaluate the performance of Kepler GPU cards designed for desktop PCs and notebook PCs. The results indicate that the ratio of execution time is roughly equal to the inverse of the ratio of power consumption.
A Digital Watermark Algorithm for QR Code

Technology that combines a 2D BarCode with a digital watermark is a topic of great interest in current research related to the security field. This paper presents a new digital watermark method for the QR Code(Quick Response Code). An invisible watermark is embedded in the QR Code image using watermark technology, and while this is being done, the DCT intermediate frequency coefficients are compared. To prevent the overflow of the QR Code in the DCT domain of the image, QR image need fuzzy processing and be added noise to. In order to resist image distortion after print and scan operations, the watermark is embedded repeatedly. The watermark is extracted by using the two maximum membership degree of fuzzy pattern recognition, without the original image. Experimental results have demonstrated the feasibility of the algorithm.
DeepLife: An Entity-aware Search, Analytics and Exploration Platform for Health and Life Sciences

Despite the abundance of biomedical literature and health discussions in online communities, it is often tedious to retrieve informative contents for health-centric information needs. Users can query scholarly work in PubMed by keywords and MeSH terms, and resort to Google for everything else. This demo paper presents the DeepLife system, to overcome the limitations of existing search engines for life science and health topics. DeepLife integrates large knowledge bases and harnesses entity linking methods, to support search and exploration of scientific literature, newspaper feeds, and social media, in terms of keywords and phrases, biomedical entities, and taxonomic categories. It also provides functionality for entityaware text analytics over health-centric contents.
Analyzing spatial user behavior in computer games using geographic information systems

An important aspect of the production of digital games is user-oriented testing. A central problem facing practitioners is however the increasing complexity of user-game interaction in modern games, which places challenges on the evaluation of interaction using traditional user-oriented approaches. Gameplay metrics are instrumentation data which detail user behavior within the virtual environment of digital games, forming accurate and detailed datasets about user behavior that supplement existing user-testing methods such as playtesting and usability testing. In this paper existing work on gameplay metrics is reviewed, and spatial analysis of gameplay metrics introduced as a new approach in the toolbox of user-experience testing and research. Furthermore, Geographic Information Systems (GIS) are introduced as a tool for performing spatial analysis. A case study is presented with Tomb Raider: Underworld, showcasing the merger of GIS with gameplay metrics analysis and its application to game testing and design.
Face Recognition Machine Vision System Using Eigenfaces

Face Recognition is a common problem in Machine Learning. This technology has already been widely used in our lives. For example, Facebook can automatically tag people’s faces in images, and also some mobile devices use face recognition to protect private security. Face images comes with different background, variant illumination, different facial expression and occlusion. There are a large number of approaches for the face recognition. Different approaches for face recognition have been experimented with specific databases which consist of single type, format and composition of image. Doing so, these approaches don’t suit with different face databases. One of the basic face recognition techniques is eigenface which is quite simple, efficient, and yields generally good results in controlled circumstances. So, this paper presents an experimental performance comparison of face recognition using Principal Component Analysis (PCA) and Normalized Principal Component Analysis (NPCA). The experiments are carried out on the ORL (ATT) and Indian face database (IFD) which contain variability in expression, pose, and facial details. The results obtained for the two methods have been compared by varying the number of training images. MATLAB is used for implementing algorithms also.
SWITCHED-CAPACITOR DC-DC CONVERTERS FOR LOW-POWER ON-CHIP APPLICATIONS

The paper describes switched-capacitor dc-dc converters (charge pumps) suitable for on-chip, low-power applications. The proposed configurations are based on connecting two identical but opposite-phase SC converters in parallel, thus eliminating the need for separate bootstrap gate drivers. We focus on emerging very low-power VLSI applications such as batterypowered or self-powered signal processors where high power conversion efficiency is important and where power levels are in the milliwatt range. Conduction and switching losses are considered to allow design optimization in terms of switching frequency and component sizes. Open-loop and closed-loop operation of an experimental, fully integrated, 10MHz voltage doubler is described. The doubler has 2V or 3V input and generates 3.3V or 5V output at up to 5mW load. The converter circuit fabricated in a standard 1.2μ CMOS technology takes 0.7mm of the chip area.
A Stroke Order Verification Method for On-Line Handwritten Chinese Characters Based on Tempo-spatial Consistency Analysis

This paper proposes a method to recognize stroke orders of on-line handwritten Chinese characters based on analyzing both spatial and temporal information. A novel control-point-based analysis method is presented for spatial information analysis to match strokes with various shapes and styles. Its computation complexity is much lower than image correlation method and is suitable for applications on mobile devices. For temporal information analysis, Hidden Markov Model is adopted and a proposed rectification method is integrated to find the optimal pair-wise matching result of stroke sequences. Experimental results proved the effectiveness of the proposed method. The verification rate is 99.6% on the test set.
Automatic identification of bird species based on sinusoidal modeling of syllables

Syllables are elementary building blocks of bird song. In sounds of many songbirds a large class of syllables can be approximated as amplitude and frequency varying brief sinusoidal pulses. In this article we test how well bird species can be recognized by comparing simple sinusoidal representations of isolated syllables. Results are encouraging and show that with limited sets of bird species a recognizer based on this signal model may already be sufficient.
A concise introduction to autonomic computing

School of Computing and Mathematics, Faculty of Engineering, University of Ulster at Jordanstown, Shore Road, Newtownabbey, County Antrim BT37 0QB, Northern Ireland, UK The Applied Software Systems Laboratory (TASSL), Electrical and Computer Engineering, Rutgers, The State University of New Jersey, 94 Brett Road, Piscataway, NJ 08854-8058, USA School of Computing and Mathematical Sciences, The SRIF/SHEFC Centre for Virtual Organization Technology Enabling Research (VOTER), Glasgow Caledonian University, 70 Cowcaddens Road, Glasgow G4 0BA, UK Institute for Computer Science and Business Information Systems (ICB), University of Duisburg-Essen, Schützenbahn 70, 45117 Essen, Germany
Upvoting hurricane Sandy: event-based news production processes on a social news site

This paper uses the case of Hurricane Sandy and reddit's topical community (subreddit) /r/sandy to examine the production and curation of news content around events on a social news site. Through qualitative analysis, we provide a coded topology of produced content and describe how types of networked gatekeeping impact the framing of a crisis situation. This study also examines, through quantitative modeling, what kind of information becomes negotiated and voted as relevant. We suggest that highly scored content shared in a social news setting focused more on human-interest media and perspective-based citizen journalism than professional news reports. We conclude by discussing how the mechanisms of social news sites conflict with the social norms and culture of reddit to produce differing expectations around news.
CryptVMI: Encrypted Virtual Machine Introspection in the Cloud

Virtualization techniques are the key in both public and private cloud computing environments. In such environments, multiple virtual instances are running on the same physical machine. The logical isolation between systems makes security assurance weaker than physically isolated systems. Thus, Virtual Machine Introspection techniques become essential to prevent the virtual system from being vulnerable to attacks. However, this technique breaks down the borders of the segregation between multiple tenants, which should be avoided in a public cloud computing environment. In this paper, we focus on building an encrypted Virtual Machine Introspection system, CryptVMI, to address the above concern, especially in a public cloud system. Our approach maintains a query handler on the management node to handle encrypted queries from user clients. We pass the query to the corresponding compute node that holds the virtual instance queried. The introspection application deployed on the compute node processes the query and acquires the encrypted results from the virtual instance for the user. This work shows our design and preliminary implementation of this system.