nils-herrmann/clean_scirepeval huggingface dataset:
Password sharing: implications for security design based on social practice

Current systems for banking authentication require that customers not reveal their access codes, even to members of the family. A study of banking and security in Australia shows that the practice of sharing passwords does not conform to this requirement. For married and de facto couples, password sharing is seen as a practical way of managing money and a demonstration of trust. Sharing Personal Identification Numbers (PINs) is a common practice among remote indigenous communities in Australia. In areas with poor banking access, this is the only way to access cash. People with certain disabilities have to share passwords with carers, and PIN numbers with retail clerks. In this paper we present the findings of a qualitative user study of banking and money management. We suggest design criteria for banking security systems, based on observed social and cultural practices of password and PIN number sharing.
DeepPatent: patent classification with convolutional neural networks and word embedding

Patent classification is an essential task in patent information management and patent knowledge mining. However, this task is still largely done manually due to the unsatisfactory performance of current algorithms. Recently, deep learning methods such as convolutional neural networks (CNN) have led to great progress in image processing, voice recognition, and speech recognition, which has yet to be applied to patent classification. We proposed DeepPatent, a deep learning algorithm for patent classification based on CNN and word vector embedding. We evaluated the algorithm on the standard patent classification benchmark dataset CLEF-IP and compared it with other algorithms in the CLEF-IP competition. Experiments showed that DeepPatent with automatic feature extraction achieved a classification precision of 83.98%, which outperformed all the existing algorithms that used the same information for training. Its performance is better than the state-of-art patent classifier with a precision of 83.50%, whose performance is, however, based on 4000 characters from the description section and a lot of feature engineering while DeepPatent only used the title and abstract information. DeepPatent is further tested on USPTO-2M, a patent classification benchmark data set that we contributed with 2,000,147 records after data cleaning of 2,679,443 USA raw utility patent documents in 637 categories at the subclass level. Our algorithms achieved a precision of 73.88%.
Foreign body granulomas after all injectable dermal fillers: part 2. Treatment options.

Foreign body granulomas occur at certain rates with all injectable dermal fillers. They have to be distinguished from early implant nodules, which usually appear 2 to 4 weeks after injection. In general, foreign body granulomas appear after a latent period of several months at all injected sites at the same time. If diagnosed early and treated correctly, they can be diminished within a few weeks. The treatment of choice of this hyperactive granulation tissue is the intralesional injection of corticosteroid crystals (triamcinolone, betamethasone, or prednisolone), which may be repeated in 4-week cycles until the right dose is found. To lower the risk of skin atrophy, corticosteroids can be combined with antimitotic drugs such as 5-fluorouracil and pulsed lasers. Because foreign body granulomas grow fingerlike into the surrounding tissue, surgical excision should be the last option. Surgery or drainage is indicated to treat normal lumps and cystic foreign body granulomas with little tissue ingrowth. In most patients, a foreign body granuloma is a single event during a lifetime, often triggered by a systemic bacterial infection.
Inference during reading.

Most current theories of text processing assume a constructionist view of inference processing. In this article, an alternative view is proposed, labeled the minimalist hypothesis. According to this hypothesis, the only inferences that are encoded automatically during reading are those that are based on easily available information, either from explicit statements in the text or from general knowledge, and those that are required to make statements in the text locally coherent. The minimalist hypothesis is shown to be supported by previous research and by the results of several new experiments. It is also argued that automatically encoded minimalist inferences provide the basic representation of textual information from which more goal-directed, purposeful inferences are constructed.
The Love of Large Numbers: A Popularity Bias in Consumer Choice.

Social learning-the ability to learn from observing the decisions of other people and the outcomes of those decisions-is fundamental to human evolutionary and cultural success. The Internet now provides social evidence on an unprecedented scale. However, properly utilizing this evidence requires a capacity for statistical inference. We examined how people's interpretation of online review scores is influenced by the numbers of reviews-a potential indicator both of an item's popularity and of the precision of the average review score. Our task was designed to pit statistical information against social information. We modeled the behavior of an "intuitive statistician" using empirical prior information from millions of reviews posted on Amazon.com and then compared the model's predictions with the behavior of experimental participants. Under certain conditions, people preferred a product with more reviews to one with fewer reviews even though the statistical model indicated that the latter was likely to be of higher quality than the former. Overall, participants' judgments suggested that they failed to make meaningful statistical inferences.
A review of recent advances in risk analysis for wildfire management

Risk analysis evolved out of the need tomake decisions concerning highly stochastic events, and is well suited to analyse the timing, location and potential effects of wildfires. Over the past 10 years, the application of risk analysis to wildland firemanagement has seen steady growthwith new risk-based analytical tools that support a wide range of fire and fuels management planning scales from individual incidents to national, strategic interagency programs. After a brief review of the three components of fire risk – likelihood, intensity and effects – this paper reviews recent advances in quantifying and integrating these individual components of fire risk. We also review recent advances in addressing temporal dynamics of fire risk and spatial optimisation of fuels management activities. Risk analysis approaches have become increasingly quantitative and sophisticated but remain quite disparate. We suggest several necessary and fruitful directions for future research and development in wildfire risk analysis.  burn probability, fire likelihood, hazard, risk assessment, risk science.  August 2011, accepted 29 June 2012, published online 14 September 2012
A Model for Attribute-Based User-Role Assignment

The Role-Based Access Control (RBAC) model is traditionally used to manually assign users to appropriate roles, based on a specific enterprise policy, thereby authorizing them to use the roles' permissions. In environments where the service-providing enterprise has a huge customer base this task becomes formidable. An appealing solution is to automatically assign users to roles. The central contribution of this paper is to describe a model to dynamically assign users to roles based on a finite set of rules defined by the enterprise. These rules take into consideration the attributes of users and any constraints set forth by the enterprise’s security policy. The model also allows dynamic revocation of assigned roles based on conditions specified in the security policy. The model provides a language to express these rules and defines a mechanism to determine seniority among different rules. The paper also shows how to use the model to express Mandatory Access Controls (MAC).
Space-Time block codes from orthogonal designs

We introduce space–time block coding , a new paradigm for communication over Rayleigh fading channels using multiple transmit antennas. Data is encoded using a space–time block code and the encoded data is split inton streams which are simultaneously transmitted usingn transmit antennas. The received signal at each receive antenna is a linear superposition of the n transmitted signals perturbed by noise. Maximumlikelihood decoding is achieved in a simple way through decoupling of the signals transmitted from different antennas rather than joint detection. This uses the orthogonal structure of the space–time block code and gives a maximum-likelihood decoding algorithm which is based only on linear processing at the receiver. Space–time block codes are designed to achieve the maximum diversity order for a given number of transmit and receive antennas subject to the constraint of having a simple decoding algorithm. The classical mathematical framework of orthogonal designs is applied to construct space–time block codes. It is shown that space–time block codes constructed in this way only exist for few sporadic values ofn. Subsequently, a generalization of orthogonal designs is shown to provide space–time block codes for both real and complex constellations for any number of transmit antennas. These codes achieve the maximum possible transmission rate for any number of transmit antennas using any arbitrary real constellation such as PAM. For an arbitrary complex constellation such as PSK and QAM, space–time block codes are designed that achieve1=2 of the maximum possible transmission rate for any number of transmit antennas. For the specific cases of two, three, and four transmit antennas, space–time block codes are designed that achieve, respectively, all, 3=4, and 3=4 of maximum possible transmission rate using arbitrary complex constellations. The best tradeoff between the decoding delay and the number of transmit antennas is also computed and it is shown that many of the codes presented here are optimal in this sense as well.
From high-level deep neural models to FPGAs

Deep Neural Networks (DNNs) are compute-intensive learning models with growing applicability in a wide range of domains. FPGAs are an attractive choice for DNNs since they offer a programmable substrate for acceleration and are becoming available across different market segments. However, obtaining both performance and energy efficiency with FPGAs is a laborious task even for expert hardware designers. Furthermore, the large memory footprint of DNNs, coupled with the FPGAs' limited on-chip storage makes DNN acceleration using FPGAs more challenging. This work tackles these challenges by devising DnnWeaver, a framework that automatically generates a synthesizable accelerator for a given (DNN, FPGA) pair from a high-level specification in Caffe . To achieve large benefits while preserving automation, DNNWEAVER generates accelerators using hand-optimized design templates. First, DnnWeaver translates a given high-level DNN specification to its novel ISA that represents a macro dataflow graph of the DNN. The DnnWeaver compiler is equipped with our optimization algorithm that tiles, schedules, and batches DNN operations to maximize data reuse and best utilize target FPGA's memory and other resources. The final result is a custom synthesizable accelerator that best matches the needs of the DNN while providing high performance and efficiency gains for the target FPGA. We use DnnWeaver to generate accelerators for a set of eight different DNN models and three different FPGAs, Xilinx Zynq, Altera Stratix V, and Altera Arria 10. We use hardware measurements to compare the generated accelerators to both multicore CPUs (ARM Cortex A15 and Xeon E3) and many-core GPUs (Tegra K1, GTX 650Ti, and Tesla K40). In comparison, the generated accelerators deliver superior performance and efficiency without requiring the programmers to participate in the arduous task of hardware design.
Suitability of existing Software development Life Cycle (SDLC) in context of Mobile Application Development Life Cycle (MADLC)

Mobile technology has seen a steep rise in its demand and usage, which has further increased the need for specialized software for mobile devices. For developing mobile applications, existing software development process models are incorporated. But looking at the remarkable growth of mobile devices, the complexity involved in development of mobile applications needs a dedicated life cycle process model, instead of using traditional life cycle process models. Although many existing life cycle models are adapted for mobile apps development but there are certain constraints that needs to be addressed specifically, such as Performance Factor, Power Factor, Band Factor, Connectivity Factor, Context Factor, Graphic Interface Factor, Input Interface Factor, etc. It is a stimulating task to develop mobile application due to these technical constraints. In this paper, a comparative analysis of traditional software life cycle models that are widely used in software development and also discussed the strengths and weakness of using these models for mobile application development. It also emphasize on need for dedicated mobile application development life cycle process in order to provide high quality mobile apps and minimize mobile app development problems and errors that are faced at later stage.
A Survey of Incentive Mechanisms for Participatory Sensing

Participatory sensing is now becoming more popular and has shown its great potential in various applications. It was originally proposed to recruit ordinary citizens to collect and share massive amounts of sensory data using their portable smart devices. By attracting participants and paying rewards as a return, incentive mechanisms play an important role to guarantee a stable scale of participants and to improve the accuracy/coverage/timeliness of the sensing results. Along this direction, a considerable amount of research activities have been conducted recently, ranging from experimental studies to theoretical solutions and practical applications, aiming at providing more comprehensive incentive procedures and/or protecting benefits of different system stakeholders. To this end, this paper surveys the literature over the period of 2004-2014 from the state of the art of theoretical frameworks, applications and system implementations, and experimental studies of the incentive strategies used in participatory sensing by providing up-to-date research in the literature. We also point out future directions of incentive strategies used in participatory sensing.
TopicRNN: A Recurrent Neural Network with Long-Range Semantic Dependency

In this paper, we propose TopicRNN, a recurrent neural network (RNN)-based language model designed to directly capture the global semantic meaning relating words in a document via latent topics. Because of their sequential nature, RNNs are good at capturing the local structure of a word sequence – both semantic and syntactic – but might face difficulty remembering long-range dependencies. Intuitively, these long-range dependencies are of semantic nature. In contrast, latent topic models are able to capture the global semantic structure of a document but do not account for word ordering. The proposed TopicRNN model integrates the merits of RNNs and latent topic models: it captures local (syntactic) dependencies using an RNN and global (semantic) dependencies using latent topics. Unlike previous work on contextual RNN language modeling, our model is learned endto-end. Empirical results on word prediction show that TopicRNN outperforms existing contextual RNN baselines. In addition, TopicRNN can be used as an unsupervised feature extractor for documents. We do this for sentiment analysis on the IMDB movie review dataset and report an error rate of 6.28%. This is comparable to the state-of-the-art 5.91% resulting from a semi-supervised approach. Finally, TopicRNN also yields sensible topics, making it a useful alternative to document models such as latent Dirichlet allocation.
Off-Line Arabic Character Recognition – A Review

Off-line recognition requires transferring the text under consideration into an image file. This represents the only available solution to bring the printed materials to the electronic media. However, the transferring process causes the system to lose the temporal information of that text. Other complexities that an off-line recognition system has to deal with are the lower resolution of the document and the poor binarisation, which can contribute to readability when essential features of the characters are deleted or obscured. Recognising Arabic script presents two additional challenges: orthography is cursive and letter shape is context sensitive. Certain character combinations form new ligature shapes, which are often font-dependent. Some ligatures involve vertical stacking of characters. Since not all letters connect, word boundary location becomes an interesting problem, as spacing may separate not only words, but also certain characters within a word. Various techniques have been implemented to achieve high recognition rates. These techniques have tackled different aspects of the recognition system. This review is organised into five major sections, covering a general overview, Arabic writing characteristics, Arabic text recognition system, Arabic OCR software and conclusions.
Semi-Supervised Learning with Deep Generative Models

The ever-increasing size of modern data sets combined with the difficulty of obtaining label information has made semi-supervised learning one of the problems of significant practical importance in modern data analysis. We revisit the approach to semi-supervised learning with generative models and develop new models that allow for effective generalisation from small labelled data sets to large unlabelled ones. Generative approaches have thus far been either inflexible, inefficient or non-scalable. We show that deep generative models and approximate Bayesian inference exploiting recent advances in variational methods can be used to provide significant improvements, making generative approaches highly competitive for semi-supervised learning.
A Scalable Recipe Recommendation System for Mobile Application

There are more and more users to use mobile application to get the cooking recipe information, but to recommend right recipe for each user is challenging for the complex food and diet information. In this paper, a hybrid recommendation system is proposed for personalized recipe mobile application. We describe a scalable recommendation system to process the massive data from mobile applications based on the spark clusters, and design a hybrid recommendation algorithm, which combines content-based filtering and collaborative filtering to improve the recipe recommended effectiveness. Our experiments reveal that the recommendation system has scalable computational capability.
Word of Mouth Propagation in Online Social Networks

Online social networks (OSNs) are becoming an important propagation platform for Word of mouth (WOM). Therefore, it is of great significance to study the propagation of WOM in OSNs. A WOM propagation model named N-P-N is proposed in this paper, and some simulation experiments are carried out to investigate the mechanism of WOM propagation. From the sensitivity analysis of degree of initial information source node, it can be seen that the degree of initial information source node determines the scope and speed of the propagation of WOM in OSNs in some extent. Then the sensitivity analysis of number of initial information source nodes shows that the initial source nodes are crucial for controlling the propagation of negative information in OSNs. Moreover, from the user behavior respect, it is found that different user behavior in OSNs causes different propagation results, the more users who are willing to diffuse WOM, the more scope WOM can propagate and the faster the information diffuses. Findings in this paper are helpful for enterprises to form an effective WOM.
Analytic displacement mapping using hardware tessellation

Displacement mapping is ideal for modern GPUs since it enables high-frequency geometric surface detail on models with low memory I/O. However, problems such as texture seams, normal recomputation, and undersampling artifacts have limited its adoption. We provide a comprehensive solution to these problems by introducing a smooth analytic displacement function. Coefficients are stored in a GPU-friendly tile-based texture format, and a multiresolution mip hierarchy of this function is formed. We propose a novel level-of-detail scheme by computing per-vertex adaptive tessellation factors and select the appropriate prefiltered mip levels of the displacement function. Our method obviates the need for a precomputed normal map since normals are directly derived from the displacements. Thus, we are able to perform authoring and rendering simultaneously without typical displacement map extraction from a dense triangle mesh. This not only is more flexible than the traditional combination of discrete displacements and normal maps, but also provides faster runtime due to reduced memory I/O.
Spatiotemporal Sequential Influence Modeling for Location Recommendations: A Gravity-based Approach

Recommending to users personalized locations is an important feature of Location-Based Social Networks (LBSNs), which benefits users who wish to explore new places and businesses to discover potential customers. In LBSNs, social and geographical influences have been intensively used in location recommendations. However, human movement also exhibits spatiotemporal sequential patterns, but only a few current studies consider the spatiotemporal sequential influence of locations on users’ check-in behaviors. In this article, we propose a new gravity model for location recommendations, called LORE, to exploit the spatiotemporal sequential influence on location recommendations. First, LORE extracts sequential patterns from historical check-in location sequences of all users as a Location-Location Transition Graph (L2TG), and utilizes the L2TG to predict the probability of a user visiting a new location through the developed additive Markov chain that considers the effect of all visited locations in the check-in history of the user on the new location. Furthermore, LORE applies our contrived gravity model to weigh the effect of each visited location on the new location derived from the personalized attractive force (i.e., the weight) between the visited location and the new location. The gravity model effectively integrates the spatiotemporal, social, and popularity influences by estimating a power-law distribution based on (i) the spatial distance and temporal difference between two consecutive check-in locations of the same user, (ii) the check-in frequency of social friends, and (iii) the popularity of locations from all users. Finally, we conduct a comprehensive performance evaluation for LORE using three large-scale real-world datasets collected from Foursquare, Gowalla, and Brightkite. Experimental results show that LORE achieves significantly superior location recommendations compared to other state-of-the-art location recommendation techniques.