nils-herrmann/clean_scirepeval huggingface dataset:
Semantic web-mining and deep vision for lifelong object discovery

Autonomous robots that are to assist humans in their daily lives must recognize and understand the meaning of objects in their environment. However, the open nature of the world means robots must be able to learn and extend their knowledge about previously unknown objects on-line. In this work we investigate the problem of unknown object hypotheses generation, and employ a semantic web-mining framework along with deep-learning-based object detectors. This allows us to make use of both visual and semantic features in combined hypotheses generation. Experiments on data from mobile robots in real world application deployments show that this combination improves performance over the use of either method in isolation.
Optimum auto exposure based on high-dynamic-range histogram

This paper introduces a new approach for optimum auto exposure (AE) based on the high-dynamic-range histogram (HDH) of a scene. The exposure control is optimal in terms of recorded information of the scene. The advantages over AE based on mean values of images will be shown as well as a proof of concept implementation and results. Futhermore possible extensions to this approach are discussed. The consideration of the HDH for auto exposure enables new possibilities for auto exposure control for mutliple-slope cameras. An AE method for multiple-slope cameras is proposed which controls exposure as well as the transition curve parameters in order to record a maximum of information from the scene. It considers controllable dynamic range as well as corse quantization in bright image regions.  Optimum Auto Exposure, High Dynamic Range (HDR), multiple-slope camera January 26, 2007
Predict Stock Price with Financial News Based on Recurrent Convolutional Neural Networks

People have been interested in making profits from financial stock market prediction. However, stock market forecast has always been a challenging problem because of its uncertainty and volatility. We take a different approach by a model called recurrent convolutional neural networks (RCN) that combines the advantages of convolutions, sequence modeling, word embedding for stock price analysis and information extraction from financial news. We then combine RCN with technical analysis indicators to predict stock price. The results show that the technical analysis model combining with RCN performs better than the technical analysis alone. Besides, the prediction error of RCN is lower than that of Long-short term memory networks.
Phrase-based Machine Translation is State-of-the-Art for Automatic Grammatical Error Correction

In this work, we study parameter tuning towards the M2 metric, the standard metric for automatic grammar error correction (GEC) tasks. After implementing M2 as a scorer in the Moses tuning framework, we investigate interactions of dense and sparse features, different optimizers, and tuning strategies for the CoNLL-2014 shared task. We notice erratic behavior when optimizing sparse feature weights with M2 and offer partial solutions. To our surprise, we find that a bare-bones phrase-based SMT setup with task-specific parameter-tuning outperforms all previously published results for the CoNLL-2014 test set by a large margin (46.37% M2 over previously 40.56%, by a neural encoder-decoder model) while being trained on the same data. Our newly introduced dense and sparse features widen that gap, and we improve the state-ofthe-art to 49.49% M2.
Interactive Phone Call : Exploring Interactions during Phone Calls using Projector Phones

Despite all the benefits the mobile phone offers, during a phone call it largely remains unused. Access to its applications, services and vast amount of online information is prohibited since the user interface is engaged with an active call. In this paper we explore interactions during active phone calls to satisfy common scenarios and tasks and also support interpersonal distributed interactions between callers. Using a projector phone we project a virtual mobile phone interface from the bottom of the mobile phone whilst the user is holding the device to their ear. Combining this with touch technology we allow the user to directly interact with their mobile phone, applications and services during a call. By considering alternative forms of projection technology, for example an ear worn device which encompasses a pico projector and camera we can support two handed interaction between the projection and the physical mobile phone and provide multi-touch interactions in mobile settings, ideal for nomadic users. The paper presents several real life scenarios which benefit from the ability in being able to interact with a mobile phone during a call.
Social cognition in schizophrenia

Individuals with schizophrenia exhibit impaired social cognition, which manifests as difficulties in identifying emotions, feeing connected to others, inferring people's thoughts and reacting emotionally to others. These social cognitive impairments interfere with social connections and are strong determinants of the degree of impaired daily functioning in such individuals. Here, we review recent findings from the fields of social cognition and social neuroscience and identify the social processes that are impaired in schizophrenia. We also consider empathy as an example of a complex social cognitive function that integrates several social processes and is impaired in schizophrenia. This information may guide interventions to improve social cognition in patients with this disorder.
Deep Boltzmann Machines

We present a new learning algorithm for Boltzmann machines that contain many layers of hidden variables. Data-dependent expectations are estimated using a variational approximation that tends to focus on a single mode, and dataindependent expectations are approximated using persistent Markov chains. The use of two quite different techniques for estimating the two types of expectation that enter into the gradient of the log-likelihood makes it practical to learn Boltzmann machines with multiple hidden layers and millions of parameters. The learning can be made more efficient by using a layer-by-layer “pre-training” phase that allows variational inference to be initialized with a single bottomup pass. We present results on the MNIST and NORB datasets showing that deep Boltzmann machines learn good generative models and perform well on handwritten digit and visual object recognition tasks.
Dictionary Learning for Sparse Coding: Algorithms and Convergence Analysis

In recent years, sparse coding has been widely used in many applications ranging from image processing to pattern recognition. Most existing sparse coding based applications require solving a class of challenging non-smooth and non-convex optimization problems. Despite the fact that many numerical methods have been developed for solving these problems, it remains an open problem to find a numerical method which is not only empirically fast, but also has mathematically guaranteed strong convergence. In this paper, we propose an alternating iteration scheme for solving such problems. A rigorous convergence analysis shows that the proposed method satisfies the global convergence property: the whole sequence of iterates is convergent and converges to a critical point. Besides the theoretical soundness, the practical benefit of the proposed method is validated in applications including image restoration and recognition. Experiments show that the proposed method achieves similar results with less computation when compared to widely used methods such as K-SVD.
SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition

We consider visual category recognition in the framework of measuring similarities, or equivalently perceptual distances, to prototype examples of categories. This approach is quite flexible, and permits recognition based on color, texture, and particularly shape, in a homogeneous framework. While nearest neighbor classifiers are natural in this setting, they suffer from the problem of high variance (in bias-variance decomposition) in the case of limited sampling. Alternatively, one could use support vector machines but they involve time-consuming optimization and computation of pairwise distances. We propose a hybrid of these two methods which deals naturally with the multiclass setting, has reasonable computational complexity both in training and at run time, and yields excellent results in practice. The basic idea is to find close neighbors to a query sample and train a local support vector machine that preserves the distance function on the collection of neighbors. Our method can be applied to large, multiclass data sets for which it outperforms nearest neighbor and support vector machines, and remains efficient when the problem becomes intractable for support vector machines. A wide variety of distance functions can be used and our experiments show state-of-the-art performance on a number of benchmark data sets for shape and texture classification (MNIST, USPS, CUReT) and object recognition (Caltech- 101). On Caltech-101 we achieved a correct classification rate of 59.05%(±0.56%) at 15 training images per class, and 66.23%(±0.48%) at 30 training images.
A prioritized grid long short-term memory RNN for speech recognition

Recurrent neural networks (RNNs) are naturally suitable for speech recognition because of their ability of utilizing dynamically changing temporal information. Deep RNNs have been argued to be able to model temporal relationships at different time granularities, but suffer vanishing gradient problems. In this paper, we extend stacked long short-term memory (LSTM) RNNs by using grid LSTM blocks that formulate computation along not only the temporal dimension, but also the depth dimension, in order to alleviate this issue. Moreover, we prioritize the depth dimension over the temporal one to provide the depth dimension more updated information, since the output from it will be used for classification. We call this model the prioritized Grid LSTM (pGLSTM). Extensive experiments on four large datasets (AMI, HKUST, GALE, and MGB) indicate that the pGLSTM outperforms alternative deep LSTM models, beating stacked LSTMs with 4% to 7% relative improvement, and achieve new benchmarks among uni-directional models on all datasets.
Translational Recommender Networks

Representing relationships as translations in vector space lives at the heart of many neural embedding models such as word embeddings and knowledge graph embeddings. In this work, we study the connections of this translational principle with collaborative ltering algorithms. We propose Translational Recommender Networks (TransRec), a new aentive neural architecture that utilizes the translational principle to model the relationships between user and item pairs. Our model employs a neural aention mechanism over a Latent Relational Aentive Memory (LRAM) module to learn the latent relations between user-item pairs that best explains the interaction. By exploiting adaptive user-item specic translations in vector space, our model also alleviates the geometric inexibility problem of other metric learning algorithms while enabling greater modeling capability and ne-grained ing of users and items in vector space. e proposed architecture not only demonstrates the state-of-the-art performance across multiple recommendation benchmarks but also boasts of improved interpretability. alitative studies over the LRAM module shows evidence that our proposed model is able to infer and encode explicit sentiment, temporal and aribute information despite being only trained on implicit feedback. As such, this ascertains the ability of TransRec to uncover hidden relational structure within implicit datasets.
A static power model for architects

Static power dissipation due to transistor leakage constitutes an increasing fraction of the total power in modern semiconductor technologies. Current technology trends indicate that the contribution will increase rapidly, reaching one half of total power dissipation within three process generations. Developing power efficient products will require consideration of static power in the earliest phases of design, including architecture and microarchitecture definition. We propose a simple equation for estimating static power consumption at the architectural level: , where VCC is the supply voltage, N is the number of transistors, kdesign is a design dependent parameter, and is a technology dependent parameter. This model enables high-level reasoning about the likely static power demands of alternative microarchitectures. Reasonably accurate values for the factors within the equation may be obtained directly from the high-level designs or by straightforward scaling arguments. The factors within the equation also suggest opportunities for static power optimization, including reducing the total number of devices, partitioning the design to allow for lower supply voltages or slower, less leaky transistors, turning off unused devices, favoring certain design styles, and favoring high bandwidth over low latency. Speculation is also examined as a means to employ slower transistors without a significant performance penalty.
Semi-Supervised Ensemble Ranking

Ranking plays a central role in many Web search and information retrieval applications. Ensemble ranking, sometimes called meta-search, aims to improve the retrieval performance by combining the outputs from multiple ranking algorithms. Many ensemble ranking approaches employ supervised learning techniques to learn appropriate weights for combining multiple rankers. The main shortcoming with these approaches is that the learned weights for ranking algorithms are query independent. This is suboptimal since a ranking algorithm could perform well for certain queries but poorly for others. In this paper, we propose a novel semi-supervised ensemble ranking (SSER) algorithm that learns query-dependent weights when combining multiple rankers in document retrieval. The proposed SSER algorithm is formulated as an SVM-like quadratic program (QP), and therefore can be solved efficiently by taking advantage of optimization techniques that were widely used in existing SVM solvers. We evaluated the proposed technique on a standard document retrieval testbed and observed encouraging results by comparing to a number of state-of-the-art techniques.
Critical infrastructures: a perspective from systems of systems

This paper introduces a system of systems (SoS)-based perspective for critical infrastructure (CI) and derives implications for future development of the CI field. At a most fundamental level, CIs provide goods and services essential to public wellbeing. The importance of the CI field has never been more pronounced as the 21st century continues to unfold, beset by unprecedented levels of complexity, emergence, interdependence, and uncertainty. There are legions of threats to CIs and so too are the emerging and fragmented array of methods and tools to address different concerns within the field. This paper suggests that since SoS is concerned with integration and coordination of multiple systems to act in unison to achieve higher levels of performance, it offers a unique perspective on how we might address different concerns at systems level in the CI field. The paper examines that state of knowledge for the CI field and introduces an organizing construct within which the CI field might be framed.
SOCIAL IMPLICATIONS OF THE INTERNET

Paul DiMaggio1, Eszter Hargittai1, W. Russell Neuman2, and John P. Robinson3 1Department of Sociology, Princeton University, Princeton, New Jersey 08540;  dimaggio@princeton.edu, eszter@princeton.edu 2Annenberg School for Communication, University of Pennsylvania, Philadelphia, Pennsylvania 19104;  rneuman@asc.upenn.edu 3Department of Sociology, University of Maryland, College Park, Maryland;
SIFT Flow: Dense Correspondence across Scenes and Its Applications

While image alignment has been studied in different areas of computer vision for decades, aligning images depicting different scenes remains a challenging problem. Analogous to optical flow, where an image is aligned to its temporally adjacent frame, we propose SIFT flow, a method to align an image to its nearest neighbors in a large image corpus containing a variety of scenes. The SIFT flow algorithm consists of matching densely sampled, pixelwise SIFT features between two images while preserving spatial discontinuities. The SIFT features allow robust matching across different scene/object appearances, whereas the discontinuity-preserving spatial model allows matching of objects located at different parts of the scene. Experiments show that the proposed approach robustly aligns complex scene pairs containing significant spatial differences. Based on SIFT flow, we propose an alignment-based large database framework for image analysis and synthesis, where image information is transferred from the nearest neighbors to a query image according to the dense scene correspondence. This framework is demonstrated through concrete applications such as motion field prediction from a single image, motion synthesis via object transfer, satellite image registration, and face recognition.
Malformations in a cohort of 284 women with Mayer-Rokitansky-Küster-Hauser syndrome (MRKH)

The aim of this retrospective study was to describe the spectrum of genital and associated malformations in women with Mayer-Rokitansky-Küster-Hauser syndrome using evaluated diagnostic procedures and the Vagina Cervix Uterus Adnex - associated Malformation classification system (VCUAM).



290 women with MRKH syndrome were clinically evaluated with using clinical examinations, abdominal and perineal/rectal ultrasound, MRI, and laparoscopy.



Classification of female genital malformation according to the Vagina Cervix Uterus Adnex - associated Malformation classification system was possible in 284 women (97.9%). Complete atresia of Vagina (V5b) and bilateral atresia of Cervix (C2b) were found in 284 patients (100%). Uterus: bilateral rudimentary or a plastic uterine horns were found in 239 women (84.2%). Adnexa: normal Adnexa were found in 248 women (87.3%). Malformations: associated malformations were found in 126 of 282 evaluable women (44.7%), 84 women (29.6%) had malformations of the renal system. Of 284 women with Mayer-Rokitansky-Küster-Hauser syndrome 212 women (74.7%) could be classified as V5bC2bU4bA0. The most frequent classification was V5bC2bU4bA0M0 (46.8%) diagnosed in 133 of 284 women.



Complete atresia of vagina and cervix were found in all patients, variable malformations were found with uterus and adnexa. A variety of associated malformations were present, predominantly of the renal system. It is therefore recommended that all patients with genital malformations should be evaluated for renal abnormalities.
Digital Forensic Model Based On Malaysian Investigation Process

Faculty Of Science & Technology Islamic Science University Of Malaysia  With the proliferation of the digital crime around the world, numerous digital forensic investigation models already being develop .In fact many of the digital forensic investigation model focus on technical implementation of the investigation process as most of it develop by traditional forensic expert and technologist. As an outcome of this problem most of the digital forensic practitioners focus on technical aspect and forget the core concept of digital forensic investigation model .In this paper we are introducing a new digital forensic model which will be capture a full scope of an investigation process based on Malaysia Cyber Law .The proposed model is also compared with the existing model which currently available and being apply in the investigation process.
Thwarting Leakage Abuse Attacks against Searchable Encryption - A Formal Approach and Applications to Database Padding

After the development of practical searchable encryption constructions, allowing for secure searches over an encrypted dataset outsourced to an untrusted server, at the expense of leaking some information to the server, many new attacks have recently been developed, targeting this leakage in order to break the confidentiality of the dataset or of the queries, through leakage abuse attacks. These works helped to understand the importance of considering leakage when analyzing the security of searchable encryption schemes, but did not explain why these attacks were so powerful despite the existence of rigorous security definitions and proofs, or how they could be efficiently and provably mitigated. This work addresses these questions by first proposing an analysis of existing leakage abuse attacks and a way to capture them in new security definitions. These new definitions also help us to devise a way to thwart these attacks and we apply it to the padding of datasets, in order to hide the number of queries’ results, and to provide provable security of some schemes with specific leakage profile against some common classes of leakage abuse attacks. Finally, we give experimental evidence that our countermeasures can be implemented efficiently, and easily applied to existing searchable encryption schemes.
A rating approach based on sentiment analysis

With the advent to social media the number of reviews for any particular product is in millions, as there exist thousand of websites where that particular product exists. As the numbers of reviews are very high the user ends up spending a lot of time for searching best product based on the experiences shared by review writers. This paper presents a sentiment based rating approach for food recipes which sorts food recipes present on various website on the basis of sentiments of review writers. The results are shown with the help of a mobile application: Foodoholic. The output of the application is an ordered list of recipes with user input as their core ingredient.
Privacy-Preserving Public Auditing For Secure Cloud Storage

By using Cloud storage, users can access applications, services, software whenever they requires over the internet. Users can put their data remotely to cloud storage and get benefit of on-demand services and application from the resources. The cloud must have to ensure data integrity and security of data of user. The issue about cloud storage is integrity and privacy of data of user can arise. To maintain to overkill this issue here, we are giving public auditing process for cloud storage that users can make use of a third-party auditor (TPA) to check the integrity of data. Not only verification of data integrity, the proposed system also supports data dynamics. The work that has been done in this line lacks data dynamics and true public auditability. The auditing task monitors data modifications, insertions and deletions. The proposed system is capable of supporting public auditability, data dynamics and Multiple TPA are used for the auditing process. We also extend our concept to ring signatures in which HARS scheme is used. Merkle Hash Tree is used to improve block level authentication. Further we extend our result to enable the TPA to perform audits for multiple users simultaneously through Batch auditing.