nils-herrmann/clean_scirepeval huggingface dataset:
Deep Learning Face Representation from Predicting 10,000 Classes

This paper proposes to learn a set of high-level feature representations through deep learning, referred to as Deep hidden IDentity features (DeepID), for face verification. We argue that DeepID can be effectively learned through challenging multi-class face identification tasks, whilst they can be generalized to other tasks (such as verification) and new identities unseen in the training set. Moreover, the generalization capability of DeepID increases as more face classes are to be predicted at training. DeepID features are taken from the last hidden layer neuron activations of deep convolutional networks (ConvNets). When learned as classifiers to recognize about 10, 000 face identities in the training set and configured to keep reducing the neuron numbers along the feature extraction hierarchy, these deep ConvNets gradually form compact identity-related features in the top layers with only a small number of hidden neurons. The proposed features are extracted from various face regions to form complementary and over-complete representations. Any state-of-the-art classifiers can be learned based on these high-level representations for face verification. 97:45% verification accuracy on LFW is achieved with only weakly aligned faces.
Optimizations and Analysis of BSP Graph Processing Models on Public Clouds

Large-scale graph analytics is a central tool in many fields, and exemplifies the size and complexity of Big Data applications. Recent distributed graph processing frameworks utilize the venerable Bulk Synchronous Parallel (BSP) model and promise scalability for large graph analytics. This has been made popular by Google's Pregel, which provides an architecture design for BSP graph processing. Public clouds offer democratized access to medium-sized compute infrastructure with the promise of rapid provisioning with no capital investment. Evaluating BSP graph frameworks on cloud platforms with their unique constraints is less explored. Here, we present optimizations and analyses for computationally complex graph analysis algorithms such as betweenness-centrality and all-pairs shortest paths on a native BSP framework we have developed for the Microsoft Azure Cloud, modeled on the Pregel graph processing model. We propose novel heuristics for scheduling graph vertex processing in swaths to maximize resource utilization on cloud VMs that lead to a 3.5x performance improvement. We explore the effects of graph partitioning in the context of BSP, and show that even a well partitioned graph may not lead to performance improvements due to BSP's barrier synchronization. We end with a discussion on leveraging cloud elasticity for dynamically scaling the number of BSP workers to achieve a better performance than a static deployment, and at a significantly lower cost.
Application of support vector machines in ! nancial time series forecasting

This paper deals with the application of a novel neural network technique, support vector machine (SVM), in !nancial time series forecasting. The objective of this paper is to examine the feasibility of SVM in !nancial time series forecasting by comparing it with a multi-layer back-propagation (BP) neural network. Five real futures contracts that are collated from the Chicago Mercantile Market are used as the data sets. The experiment shows that SVM outperforms the BP neural network based on the criteria of normalized mean square error (NMSE), mean absolute error (MAE), directional symmetry (DS) and weighted directional symmetry (WDS). Since there is no structured way to choose the free parameters of SVMs, the variability in performance with respect to the free parameters is investigated in this study. Analysis of the experimental results proved that it is advantageous to apply SVMs to forecast !nancial time series. ? 2001 Elsevier Science Ltd. All rights reserved.
Push the limit of WiFi based localization for smartphones

Highly accurate indoor localization of smartphones is critical to enable novel location based features for users and businesses. In this paper, we first conduct an empirical investigation of the suitability of WiFi localization for this purpose. We find that although reasonable accuracy can be achieved, significant errors (e.g., $6\sim8m$) always exist. The root cause is the existence of distinct locations with similar signatures, which is a fundamental limit of pure WiFi-based methods. Inspired by high densities of smartphones in public spaces, we propose a peer assisted localization approach to eliminate such large errors. It obtains accurate acoustic ranging estimates among peer phones, then maps their locations jointly against WiFi signature map subjecting to ranging constraints. We devise techniques for fast acoustic ranging among multiple phones and build a prototype. Experiments show that it can reduce the maximum and 80-percentile errors to as small as $2m$ and $1m$, in time no longer than the original WiFi scanning, with negligible impact on battery lifetime.
Flat-tree: A Convertible Data Center Network Architecture from Clos to Random Graph

Clos networks are easy to implement, whereas random graphs have good performance. We propose flat-tree, a convertible data center network architecture, to combine the best of both worlds. Flat-tree can change the network topology dynamically, so the data center can be implemented as a Clos network and be converted to approximate random graphs of different sizes. To serve the heterogeneous workloads in data centers, flat-tree can organize the network as functionally separate zones each having a different topology. Workloads are placed into suitable zones that best optimize the performance. Simulation results demonstrate that flat-tree has similar performance to random graphs.
Incremental Natural Actor-Critic Algorithms

We present four new reinforcement learning algorithms based on actor-critic and natural-gradient ideas, and provide their convergence proofs. Actor-critic reinforcement learning methods are online approximations to policy iteration in which the value-function parameters are estimated using temporal difference learning and the policy parameters are updated by stochastic gradient descent. Methods based on policy gradients in this way are of special interest because of their compatibility with function approximation methods, which are needed to handle large or infinite state spaces. The use of temporal difference learning in this way is of interest because in many applications it dramatically reduces the variance of the gradient estimates. The use of the natural gradient is of interest because it can produce better conditioned parameterizations and has been shown to further reduce variance in some cases. Our results extend prior two-timescale convergence results for actor-critic methods by Konda and Tsitsiklis by using temporal difference learning in the actor and by incorporating natural gradients, and they extend prior empirical studies of natural actor-critic methods by Peters, Vijayakumar and Schaal by providing the first convergence proofs and the first fully incremental algorithms.
The fifth discipline: The art & practice of the learning organization [Book Review]

Built to Last's answer is to consciously build a compmy with even more care than the hotels, airplanes, or computers from which the company earns revenue. Building a company requires much more than hiring smart employees and aggressive salespeople. Visionary companies consider the personality of their potential employees and how they will fare in the company culture. They treasure employees dedicated to the company's mission, while those that don't are " ejected like a virus. " They carefully choose goals and develop cultures that encourage innovation and experimentation. Visionary companies plan for the future, measure their current production, and revise plans when conditions change. Much like the TV show Biography, Built to Last gives fascinating historical insight into the birth and growth of The most radical of the three books I reviewed, The Fifth Discipline, can fundamentally change the way you view the world. The Flremise is that businesses, schools, gopernments, and other organizations can best succeed if they are learning organizations. The Fifth Discipline is Peter Senge's vehicle for explaining how five complementary components-systems thinking, personal mastery, mental models, shared vision, and team learning-can support continuous learning and therefore sustainable iniprovement. Senge, a professor a t MIT's Sloan School of Government and a director of the Society for Organizational Learning, looks beyont: simple cause-and-effect explanation:j and instead advocates " systems thinking " to discover a more complete understanding of how and why events occur. Systems thinkers go beyond the data readily available, question assumptions, and try to identify the many types of activities that can occur simultaneously. The need for such a worldview is made clear early in the book with the role-playing " beer game. " In this game, three participants play the roles of store manager, beverage distributor, and beer brewer. Each has information that would typically he available: the store manager knows how many cases of beer are in inventory , how many are on order, and how many were sold in the last week. The distributor tracks the orders placed with the brewery, inventory, orders received this week from each store, and so on. As the customers' demands vary, the manager, distributor, and brewer make what seem to be reasonable decisions to change the amount they order or brew. Thousands of people have played this and, unfortunately, the results are extremely consistent. As each player tries to maximize profits, each fails to consider how his …
The security of machine learning

Machine learning’s ability to rapidly evolve to changing and complex situations has helped it become a fundamental tool for computer security. That adaptability is also a vulnerability: attackers can exploit machine learning systems. We present a taxonomy identifying and analyzing attacks against machine learning systems. We show how these classes influence the costs for the attacker and defender, and we give a formal structure defining their interaction. We use our framework to survey and analyze the literature of attacks against machine learning systems. We also illustrate our taxonomy by showing how it can guide attacks against SpamBayes, a popular statistical spam filter. Finally, we discuss how our taxonomy suggests new lines of defenses.
Fraternal Dropout

Recurrent neural networks (RNNs) form an important class of architectures among neural networks useful for language modeling and sequential prediction. However, optimizing RNNs is known to be harder compared to feed-forward neural networks. A number of techniques have been proposed in literature to address this problem. In this paper we propose a simple technique called fraternal dropout that takes advantage of dropout to achieve this goal. Specifically, we propose to train two identical copies of an RNN (that share parameters) with different dropout masks while minimizing the difference between their (pre-softmax) predictions. In this way our regularization encourages the representations of RNNs to be invariant to dropout mask, thus being robust. We show that our regularization term is upper bounded by the expectation-linear dropout objective which has been shown to address the gap due to the difference between the train and inference phases of dropout. We evaluate our model and achieve state-of-the-art results in sequence modeling tasks on two benchmark datasets – Penn Treebank and Wikitext-2. We also show that our approach leads to performance improvement by a significant margin in image captioning (Microsoft COCO) and semi-supervised (CIFAR-10) tasks.
CUNY-BLENDER TAC-KBP2010 Entity Linking and Slot Filling System Description

The CUNY-BLENDER team participated in the following tasks in TAC-KBP2010: Regular Entity Linking, Regular Slot Filling and Surprise Slot Filling task (per:disease slot). In the TAC-KBP program, the entity linking task is considered as independent from or a pre-processing step of the slot filling task. Previous efforts on this task mainly focus on utilizing the entity surface information and the sentence/document-level contextual information of the entity. Very little work has attempted using the slot filling results as feedback features to enhance entity linking. In the KBP2010 evaluation, the CUNY-BLENDER entity linking system explored the slot filling attributes that may potentially help disambiguate entity mentions. Evaluation results show that this feedback approach can achieve 9.1% absolute improvement on micro-average accuracy over the baseline using vector space model. For Regular Slot Filling we describe two bottom-up Information Extraction style pipelines and a top-down Question Answering style pipeline. Experiment results have shown that these pipelines are complementary and can be combined in a statistical re-ranking model. In addition, we present several novel approaches to enhance these pipelines, including query expansion, Markov Logic Networks based cross-slot/cross-system reasoning. Finally, as a diagnostic test, we also measured the impact of using external knowledge base and Wikipedia text mining on Slot Filling.
Clifford Support Vector Machines for Classification, Regression, and Recurrence

This paper introduces the Clifford support vector machines (CSVM) as a generalization of the real and complex-valued support vector machines using the Clifford geometric algebra. In this framework, we handle the design of kernels involving the Clifford or geometric product. In this approach, one redefines the optimization variables as multivectors. This allows us to have a multivector as output. Therefore, we can represent multiple classes according to the dimension of the geometric algebra in which we work. We show that one can apply CSVM for classification and regression and also to build a recurrent CSVM. The CSVM is an attractive approach for the multiple input multiple output processing of high-dimensional geometric entities. We carried out comparisons between CSVM and the current approaches to solve multiclass classification and regression. We also study the performance of the recurrent CSVM with experiments involving time series. The authors believe that this paper can be of great use for researchers and practitioners interested in multiclass hypercomplex computing, particularly for applications in complex and quaternion signal and image processing, satellite control, neurocomputation, pattern recognition, computer vision, augmented virtual reality, robotics, and humanoids.
Learning with Whom to Share in Multi-task Feature Learning

In multi-task learning (MTL), multiple tasks are learnt jointly. A major assumption for this paradigm is that all those tasks are indeed related so that the joint training is appropriate and beneficial. In this paper, we study the problem of multi-task learning of shared feature representations among tasks, while simultaneously determining “with whom” each task should share. We formulate the problem as a mixed integer programming and provide an alternating minimization technique to solve the optimization problem of jointly identifying grouping structures and parameters. The algorithm monotonically decreases the objective function and converges to a local optimum. Compared to the standard MTL paradigm where all tasks are in a single group, our algorithm improves its performance with statistical significance for three out of the four datasets we have studied. We also demonstrate its advantage over other task grouping techniques investigated in literature.
Novel Modeling and Damping Technique for Hybrid Stepper Motor

It is well known that commercial hybrid stepper system has one or more low-speed resonant points. However, this characteristic cannot be accurately modeled without high-order equations or complicated measurement of motor parameters. In this paper, a novel approach is proposed to model the behavior of a commercial 1.8deg hybrid stepper motor accurately and efficiently. Also, model-based damping algorithms for both open-loop control and servo control are proposed. Simulation and experimental results show that the proposed algorithms can effectively eliminate low-speed resonance and vibration of the stepper system. The algorithms are efficient enough to be implemented on commercial DSP-based controllers without sacrificing motion control performance.
AUTOMATIC DETECTION AND SEVERITY ANALYSIS OF BRAIN TUMORS USING GUI IN MATLAB

Medical image processing is the most challenging and emerging field now a day’s processing of MRI images is one of the parts of this field. The quantitative analysis of MRI brain tumor allows obtaining useful key indicators of disease progression. This is a computer aided diagnosis systems for detecting malignant texture in biological study. This paper presents an approach in computer-aided diagnosis for early prediction of brain cancer using Texture features and neuro classification logic. This paper describes the proposed strategy for detection; extraction and classification of brain tumour from MRI scan images of brain; which incorporates segmentation and morphological functions which are the basic functions of image processing. Here we detect the tumour, segment the tumour and we calculate the area of the tumour. Severity of the disease can be known, through classes of brain tumour which is done through neuro fuzzy classifier and creating a user friendly environment using GUI in MATLAB. In this paper cases of 10 patients is taken and severity of disease is shown and different features of images are calculated.
Omnivore: An Optimizer for Multi-device Deep Learning on CPUs and GPUs

We perform a study of the factors affecting training time in multi-device deep learning systems. Given a specification of a convolutional neural network, we study how to minimize the time to train this model on a cluster of commodity CPUs and GPUs. Our first contribution focuses on the single-node setting, in which we show that by using standard batching and data-parallel techniques throughput can be improved by at least 5.5× over state-of-the-art systems when training on CPUs. This ensures an end-to-end training time directly proportional to the throughput of a device regardless of its underlying hardware, allowing each node in the cluster to be treated as a black box. Our second contribution is a theoretical and empirical study of the tradeoffs affecting end-to-end training time in a multiple-device setting. We identify the degree of asynchronous parallelization as a key feature affecting both hardware and statistical efficiency. We show that asynchrony can be viewed as introducing a momentum parameter, which we use to limit our search space; in turn, this leads to a simpler optimizer, which is our third contribution. Our optimizer involves a predictive model for the total time to convergence and selects an allocation of resources to minimize that time. We demonstrate that the most popular distributed deep learning systems fall within our tradeoff space but do not optimize within the space. By doing such optimization, our prototype runs 1.9× to 12× faster than the fastest state-of-the-art systems.
Light-Field Database Creation and Depth Estimation

Light-field imaging research has been gaining a lot of traction in recent times, especially after the availability of consumer-level light-field cameras like the Lytro Illum. However, there is a dearth of light-field databases publicly available for research. The project is mainly divided into two parts creation of an extensive light-field database and evaluating a state-of-the-art depth estimation algorithm. The database has over 250 light-fields made publicly available for research. These light-fields have been captured using the Lytro Illum camera. The depth-maps, output images, and metadata obtained using the Lytro command line tool have also been included. As the second part of our project, we have implemented the depth estimation algorithm proposed by Tao et al.  This algorithm estimates depth by combining correspondence and defocus cues. The algorithm uses the complete 4D epipolar image to create a depth-map which proved to be qualitatively much better than the depth-map generated by the Lytro command line tool. 1. Light-field Database Light-field [4] cameras have the ability to capture the intensity as well as direction of the light hitting the sensor. This makes it possible to refocus the image  as well as shift one’s viewpoint of the scene after it has been captured. This also facilitates estimation of depth using light-fields. Light-fields have a wide range of potential applications in fields such as computer vision, and virtual reality. The main motivation for creating a light-field database is the fact that there are few publicly available light-field databases to conduct research in this emerging field. Our light-field database consist of over 250 lightfields of natural scenes captured using the Lytro Illum camera. The scenes and objects are captured taking into consideration different kinds of applications researchers in this field may potentially work on and consequently, these lightfields are divided into different categories. The 9 different categories included in the database are (1) Flowers and plants, (2) Bikes, (3) Fruits and vegetables, (4) Cars, (5) Occlusions, (6) Buildings, (7) People, (8) Miscellaneous, (9) Refractive and Reflective surfaces. These external standardized light fields extracted using the Lytro command line tool help in recreating the 14× 14 shifted views, which can then be used for various applications. Along with the light-fields, the depth-maps, processed images and metadata obtained using the command line tool have also been included. The light-field database can be accessed at http://lightfields.stanford.edu/.
Sensitivity and attachment: a meta-analysis on parental antecedents of infant attachment.

This meta-analysis included 66 studies (N = 4,176) on parental antecedents of attachment security. The question addressed was whether maternal sensitivity is associated with infant attachment security, and what the strength of this relation is. It was hypothesized that studies more similar to Ainsworth's Baltimore study (Ainsworth, Blehar, Waters, & Wall, 1978) would show stronger associations than studies diverging from this pioneering study. To create conceptually homogeneous sets of studies, experts divided the studies into 9 groups with similar constructs and measures of parenting. For each domain, a meta-analysis was performed to describe the central tendency, variability, and relevant moderators. After correction for attenuation, the 21 studies (N = 1,099) in which the Strange Situation procedure in nonclinical samples was used, as well as preceding or concurrent observational sensitivity measures, showed a combined effect size of r(1,097) = .24. According to Cohen's (1988) conventional criteria, the association is moderately strong. It is concluded that in normal settings sensitivity is an important but not exclusive condition of attachment security. Several other dimensions of parenting are identified as playing an equally important role. In attachment theory, a move to the contextual level is required to interpret the complex transactions between context and sensitivity in less stable and more stressful settings, and to pay more attention to nonshared environmental influences.