nils-herrmann/clean_scirepeval huggingface dataset:
Solution of non-convex economic load dispatch problem using Grey Wolf Optimizer

Grey Wolf Optimizer (GWO) is a recently developed meta-heuristic search algorithm inspired by grey wolves (Canis lupus), which simulate the social stratum and hunting mechanism of grey wolves in nature and based on three main steps of hunting: searching for prey, encircling prey and attacking prey. This paper presents the application of GWO algorithm for the solution of non-convex and dynamic economic load dispatch problem (ELDP) of electric power system. The performance of GWO is tested for ELDP of small-, medium- and large-scale power systems, and the results are verified by a comparative study with lambda iteration method, Particle Swarm Optimization algorithm, Genetic Algorithm, Biogeography-Based Optimization, Differential Evolution algorithm, pattern search algorithm, NN-EPSO, FEP, CEP, IFEP and MFEP. Comparative results show that the GWO algorithm is able to provide very competitive results compared to other well-known conventional, heuristics and meta-heuristics search algorithms.
PANORAMA: A 3D Shape Descriptor Based on Panoramic Views for Unsupervised 3D Object Retrieval

We present a novel 3D shape descriptor that uses a set of panoramic views of a 3D object which describe the position and orientation of the object’s surface in 3D space. We obtain a panoramic view of a 3D object by projecting it to the lateral surface of a cylinder parallel to one of its three principal axes and centered at the centroid of the object. The object is projected to three perpendicular cylinders, each one aligned with one of its principal axes in order to capture the global shape of the object. For each projection we compute the corresponding 2D Discrete Fourier Transform as well as 2D Discrete Wavelet Transform. We further increase the retrieval performance by employing a local (unsupervised) relevance feedback technique that shifts the descriptor of an object closer to its cluster centroid in feature space. The effectiveness of the proposed 3D object retrieval methodology is demonstrated via an extensive consistent evaluation in standard benchmarks that clearly shows better performance against state-of-the-art 3D object retrieval methods.
Maximum likelihood linear transformations for HMM-based speech recognition

This paper examines the application of linear transformations for speaker and environmental adaptation in an HMM-based speech recognition system. In particular, transformations that are trained in a maximum likelihood sense on adaptation data are investigated. Other than in the form of a simple bias, strict linear feature-space transformations are inappropriate in this case. Hence, only model-based linear transforms are considered. The paper compares the two possible forms of model-based transforms: (i) unconstrained, where any combination of mean and variance transform may be used, and (ii) constrained, which requires the variance transform to have the same form as the mean transform (sometimes referred to as feature-space transforms). Re-estimation formulae for all appropriate cases of transform are given. This includes a new and e cient \full" variance transform and the extension of the constrained model-space transform from the simple diagonal case to the full or block-diagonal case. The constrained and unconstrained transforms are evaluated in terms of computational cost, recognition time e ciency, and use for speaker adaptive training. The recognition performance of the two model-space transforms on a large vocabulary speech recognition task using incremental adaptation is investigated. In addition, initial experiments using the constrained model-space transform for speaker adaptive training are detailed.
Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex

We discuss relations between Residual Networks (ResNet), Recurrent Neural Networks (RNNs) and the primate visual cortex. We begin with the observation that a shallow RNN is exactly equivalent to a very deep ResNet with weight sharing among the layers. A direct implementation of such a RNN, although having orders of magnitude fewer parameters, leads to a performance similar to the corresponding ResNet. We propose 1) a generalization of both RNN and ResNet architectures and 2) the conjecture that a class of moderately deep RNNs is a biologically-plausible model of the ventral stream in visual cortex. We demonstrate the effectiveness of the architectures by testing them on the CIFAR-10 dataset. This work was supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF 1231216. 1 ar X iv :1 60 4. 03 64 0v 1 [ cs .L G ] 1 3 A pr 2 01 6
Approximation Algorithms

There are many variations of local search: Hill-climbing: The name for the basic method described above.  Pick a random y ∈ N(x). If f(y) < f(x), set x = y. If f(y) > f(x), set x = y with some probability. Simulated annealing: Like Metropolis, but where the probability of moving to a higher-cost neighbor decreases over time. Tabu search: Like the previous two, but with memory in order to avoid getting stuck in suboptimal regions or in plateaus (“taboo” areas). Parallel search: Do more than one search, and occasionally replace versions that are doing poorly with copies of versions that are doing well. Genetic algorithm: Keep a population of searches that changes over time via “breeding” of the best-performing searches.
A search engine for 3D models

As the number of 3D models available on the Web grows, there is an increasing need for a search engine to help people find them. Unfortunately, traditional text-based search techniques are not always effective for 3D data. In this article, we investigate new shape-based search methods. The key challenges are to develop query methods simple enough for novice users and matching algorithms robust enough to work for arbitrary polygonal models. We present a Web-based search engine system that supports queries based on 3D sketches, 2D sketches, 3D models, and/or text keywords. For the shape-based queries, we have developed a new matching algorithm that uses spherical harmonics to compute discriminating similarity measures without requiring repair of model degeneracies or alignment of orientations. It provides 46 to 245% better performance than related shape-matching methods during precision--recall experiments, and it is fast enough to return query results from a repository of 20,000 models in under a second. The net result is a growing interactive index of 3D models available on the Web (i.e., a Google for 3D models).
Few-Shot Adversarial Domain Adaptation

This work provides a framework for addressing the problem of supervised domain adaptation with deep models. The main idea is to exploit adversarial learning to learn an embedded subspace that simultaneously maximizes the confusion between two domains while semantically aligning their embedding. The supervised setting becomes attractive especially when there are only a few target data samples that need to be labeled. In this few-shot learning scenario, alignment and separation of semantic probability distributions is difficult because of the lack of data. We found that by carefully designing a training scheme whereby the typical binary adversarial discriminator is augmented to distinguish between four different classes, it is possible to effectively address the supervised adaptation problem. In addition, the approach has a high “speed” of adaptation, i.e. it requires an extremely low number of labeled target training samples, even one per category can be effective. We then extensively compare this approach to the state of the art in domain adaptation in two experiments: one using datasets for handwritten digit recognition, and one using datasets for visual object recognition.
Improving Adversarial Robustness by Data-Specific Discretization

A recent line of research proposed (either implicitly or explicitly) gradient-masking preprocessing techniques to improve adversarial robustness. However, as shown by Athaley-Carlini-Wagner, essentially all these defenses can be circumvented if an attacker leverages approximate gradient information with respect to the preprocessing. This thus raises a natural question of whether there is a useful preprocessing technique in the context of white-box attacks, even just for only mildly complex datasets such as MNIST. In this paper we provide an affirmative answer to this question. Our key observation is that for several popular datasets, one can approximately encode entire dataset using a small set of separable codewords derived from the training set, while retaining high accuracy on natural images. The separability of the codewords in turn prevents small perturbations as in `∞ attacks from changing feature encoding, leading to adversarial robustness. For example, for MNIST our code consists of only two codewords, 0 and 1, and the encoding of any pixel is simply 1[x > 0.5] (i.e., whether a pixel x is at least 0.5). Applying this code to a naturally trained model already gives high adversarial robustness even under strong white-box attacks based on Backward Pass Differentiable Approximation (BPDA) method of Athaley-Carlini-Wagner that takes the codes into account. We give density-estimation based algorithms to construct such codes, and provide theoretical analysis and certificates of when our method can be effective. Systematic evaluation demonstrates that our method is effective in improving adversarial robustness on MNIST, CIFAR-10, and ImageNet, for either naturally or adversarially trained models.
A High-Performance Network Intrusion Detection System

In this paper we present a new approach for network intrusion detection based on concise specifications that characterize normal and abnormal network packet sequences. Our specification language is geared for a robust network intrusion detection by enforcing a strict type discipline via a combination of static and dynamic type checking. Unlike most previous approaches in network intrusion detection, our approach can easily support new network protocols as information relating to the protocols are not hard-coded into the system. Instead, we simply add suitable type definitions in the specifications and define intrusion patterns on these types. We compile these specifications into a high-performance network intrusion detection system. Important components of our approach include efficient algorithms for pattern-matching and information aggregation on sequences of network packets. In particular, our techniques ensure that the matching time is insensitive to the number of patterns characterizing different network intrusions, and that the aggregation operations typically take constant time per packet. Our system participated in an intrusion detection evaluation organized by MIT Lincoln Labs, where our system demonstrated its effectiveness (96% detection rate on low-level network attacks) and performance (real-time detection at 500Mbps), while producing very few false positives (0.05 to 0.1 per attack).
Unsupervised refinement of color and stroke features for text binarization

Color and strokes are the salient features of text regions in an image. In this work, we use both these features as cues, and introduce a novel energy function to formulate the text binarization problem. The minimum of this energy function corresponds to the optimal binarization. We minimize the energy function with an iterative graph cut-based algorithm. Our model is robust to variations in foreground and background as we learn Gaussian mixture models for color and strokes in each iteration of the graph cut. We show results on word images from the challenging ICDAR 2003/2011, born-digital image and street view text datasets, as well as full scene images containing text from ICDAR 2013 datasets, and compare our performance with state-of-the-art methods. Our approach shows significant improvements in performance under a variety of performance measures commonly used to assess text binarization schemes. In addition, our method adapts to diverse document images, like text in videos, handwritten text images.
CLOS: Integrating Object-Oriented and Functional Programming

Lisp has a long history as a functional language,* where action is invoked by calling a procedure, and where procedural abstraction and encapsulation provide convenient modularity boundaries. A number of attempts have been made to graft object-oriented programming into this framework without losing the essential character of Lisp—to include the benefits of data abstraction, extensible type classification, incremental operator definition, and code reuse through an inheritance hierarchy.
The Common Lisp Object System (CLOS) , a result of the ANSI standardization process for Common Lisp, represents a marriage of these two traditions. This article explores the landscape in which the major object-oriented facilities exist, showing how the CLOS solution is effective within the two contexts.
Rule extraction from autoencoder-based connectionist computational models

1College of Computer Science, Sichuan Normal University, Chengdu, Sichuan, China 2Developmental Neurocognition Lab, Department of Psychological Sciences, Birkbeck, University of London, London, UK 3College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China Correspondence Juan Yang, College of Computer Science, Sichuan Normal University, Chengdu, Sichuan, China.  jkxy_yjuan@sicnu.edu.cn Funding information National Natural Science Foundation of China, Grant/Award Number: (61402309); UK Economic and Social Research Council, Grant/Award Number: RES-062-23-2721  Mapping problems are typical research topics related to natural language learning, and they include not only classification mappings but also nonclassification mappings, such as verbs and their past tenses. Connectionist computational models are one of the most popular approaches for simulating those mapping problems; however, their lack of explanatory ability has prevented them from being further used to understand the language learning process. Therefore, the work of extracting rational rules from a connectionist model is as important as simulating the mapping behaviors. Unfortunately, there is no available technique that can be directly applied in those computational models to simulate nonclassification problems. In this paper, an autoencoder-based connectionist computational model is proposed to derive a rule extraction method that can construct “If-Then” rational relations with high fidelity for nonclassification mapping problems. To demonstrate its generalizability, this computational model is extended to a modified version to address a multi-label classification mapping problem related to cognitive style prediction. Experiments prove this computational model's simulation ability and its explanatory ability on nonclassification problems by comparing its fidelity performances with those of the classical connectionist computational model (multilayer perceptron artificial neural network), and its similar ability on a multi-label classification problem (Felder-Silverman learning style classification) by comparing its prediction accuracy with those of other rule induction techniques.
Non-viral vectors for gene-based therapy

Gene-based therapy is the intentional modulation of gene expression in specific cells to treat pathological conditions. This modulation is accomplished by introducing exogenous nucleic acids such as DNA, mRNA, small interfering RNA (siRNA), microRNA (miRNA) or antisense oligonucleotides. Given the large size and the negative charge of these macromolecules, their delivery is typically mediated by carriers or vectors. In this Review, we introduce the biological barriers to gene delivery in vivo and discuss recent advances in material sciences, nanotechnology and nucleic acid chemistry that have yielded promising non-viral delivery systems, some of which are currently undergoing testing in clinical trials. The diversity of these systems highlights the recent progress of gene-based therapy using non-viral approaches.
Control Knowledge to Improve Plan Quality

Generating production-quality plans is an essential element in transforming planners from research tools into real-world applications. However most of the work to date on learning planning control knowledge has been aimed at improving the efficiency of planning; this work has been termed “speed-up learning”. This paper focuses on learning control knowledge to guide a planner towards better solutions, i.e. to improve the quality of the plans produced by the planner, as its problem solving experience increases. We motivate the use of quality-enhancing search control knowledge and its automated acquisition from problem solving experience. We introduce an implemented mechanism for learning such control knowledge and some of our preliminary results in a process planning domain.
Can Twitter be used to predict county excessive alcohol consumption rates?

The current study analyzes a large set of Twitter data from 1,384 US counties to determine whether excessive alcohol consumption rates can be predicted by the words being posted from each county.



Data from over 138 million county-level tweets were analyzed using predictive modeling, differential language analysis, and mediating language analysis.



Twitter language data captures cross-sectional patterns of excessive alcohol consumption beyond that of sociodemographic factors (e.g. age, gender, race, income, education), and can be used to accurately predict rates of excessive alcohol consumption. Additionally, mediation analysis found that Twitter topics (e.g. 'ready gettin leave') can explain much of the variance associated between socioeconomics and excessive alcohol consumption.



Twitter data can be used to predict public health concerns such as excessive drinking. Using mediation analysis in conjunction with predictive modeling allows for a high portion of the variance associated with socioeconomic status to be explained.
Smith-Petersen osteotomy in thoracolumbar deformity surgery.

To describe the technique and indications of a Smith-Petersen osteotomy in spinal deformity surgery.



Pertinent literature was reviewed to describe the indications and reported complications of this corrective technique.



The operative nuances of the technique are described.



A Smith-Petersen osteotomy is a safe and effective surgical technique to obtain correction of spinal deformity in both the sagittal and coronal planes.
Semantics in Visual Information Retrieval

V isual information retrieval systems have entered a new era. First-generation systems allowed access to images and videos through textual data. 1,2 Typical searches for these systems include, for example, " all images of paintings of the Florentine school of the 15th century " or " all images by Cezanne with landscapes. " Such systems expressed information through alphanumeric keywords or scripts. They employed representation schemes like relational models, frame models, and object-oriented models. On the other hand, current-generation retrieval systems support full retrieval by visual content. 3,4 Access to visual information is not only performed at a conceptual level, using keywords as in the textu-al domain, but also at a perceptual level, using objective measurements of visual content. In these systems, image processing, pattern recognition , and computer vision constitute an integral part of the system's architecture and operation. They objectively analyze pixel distribution and extract the content descriptors automatically from raw sensory data. Image content descriptors are commonly represented as feature vectors, whose elements correspond to significant parameters that model image attributes. Therefore, visual attributes are regarded as points in a multidimen-sional feature space, where point closeness reflects feature similarity. These advances (for comprehensive reviews of the field, see the " Further Reading " sidebar) have paved the way for third-generation systems, featuring full multimedia data management and networking support. Forthcoming standards such as MPEG-4 and MPEG-7 (see the Nack and Lindsay article in this issue) provide the framework for efficient representation, processing, and retrieval of visual information. Yet many problems must still be addressed and solved before these technologies can emerge. An important issue is the design of indexing structures for efficient retrieval from large, possibly distributed , multimedia data repositories. To achieve this goal, image and video content descriptors can be internally organized and accessed through multidimensional index structures. 5 A second key problem is to bridge the semantic gap between the system and users. That is, devise representations capturing visual content at high semantic levels especially relevant for retrieval tasks. Specifically, automatically obtaining a representation of high-level visual content remains an open issue. Virtually all the systems based on automatic storage and retrieval of visual information proposed so far use low-level perceptual representations of pictorial data, which have limited semantics. Building up a representation proves tantamount to defining a model of the world, possibly through a formal description language, whose semantics capture only a few …
Neural Attention Reader for Video Comprehension

Despite the increasing availability of informative video content, question answering on videos remains an under-researched and challenging topic. Owing to the free-flowing nature of the verbal content, long duration of videos and lack of clear demarcations on where the context is changing, answering questions from video transcripts remains challenging. We consider the problem of extracting answers for a given question from a pool of videos and propose a novel gated neural attention architecture with content bifurcation module (GABiNet) to infer answers from video content using transcript data. The proposed GABiNet model is efficient enough to consider a large number of candidate videos and jointly learns the question and content representation by incorporating question information into content representation. To deal with the lack of demarcation issue, we propose a number of content bifurcation techniques which enable the neural model to divide the transcript text into different meaningful chunks to enable tractable inference of answers. Based on experiments on a large dataset of educational videos, we investigate the benefits offered by the gating, attention and bifurcation mechanisms and demonstrate significant performance gains over a number of established baselines and stateof-the-art QA(Question Answering) techniques. We contend that our work is among the first to tackle open-domain question answering on video content, and our findings have implications for the design of video-based QA systems.
Representing Sentences as Low-Rank Subspaces

Sentences are important semantic units of natural language. A generic, distributional representation of sentences that can capture the latent semantics is beneficial to multiple downstream applications. We observe a simple geometry of sentences – the word representations of a given sentence (on average 10.23 words in all SemEval datasets with a standard deviation 4.84) roughly lie in a low-rank subspace (roughly, rank 4). Motivated by this observation, we represent a sentence by the low-rank subspace spanned by its word vectors. Such an unsupervised representation is empirically validated via semantic textual similarity tasks on 19 different datasets, where it outperforms the sophisticated neural network models, including skip-thought vectors, by 15% on average.