Reviewerly/exHarmony huggingface dataset:
Elite Opposition-Based Cognitive Behavior Optimization Algorithm for Global Optimization

Abstract This paper presents an elite opposition-based cognitive behavior optimization algorithm (ECOA). The traditional COA is divided into three stages: rough search, information exchange and share, and intelligent adjustment process. In this paper, we introduce the elite opposition-based learning in the third stage of COA, with a view to avoid the latter congestion as well as to enhance the convergence speed. ECOA is validated by 23 benchmark functions and three engineering design problems, and the experimental results have proven the superior performance of ECOA compared to other algorithms in the literature.
Gabor Frames and Deep Scattering Networks in&#x0D; Audio Processing

This paper introduces Gabor scattering, a feature extractor based on Gabor frames and Mallat's scattering transform. By using a simple signal model for audio signals specific properties of Gabor scattering are studied. It is shown that for each layer, specific invariances to certain signal characteristics occur. Furthermore, deformation stability of the coefficient vector generated by the feature extractor is derived by using a decoupling technique which exploits the contractivity of general scattering networks. Deformations are introduced as changes in spectral shape and frequency modulation. The theoretical results are illustrated by numerical examples and experiments. Numerical evidence is given by evaluation on a synthetic and a "real" data set, that the invariances encoded by the Gabor scattering transform lead to higher performance in comparison with just using Gabor transform, especially when few training samples are available.
AdaptScale: An adaptive data scaling controller for improving the multiple performance requirements in Clouds

Data scaling issue has become a bottleneck in multi-tenancy cloud environment. Fluctuated workloads bring challenges to current automatic data scaling strategies on meeting variable user performance requirements in a shared storage system. To this end, this paper develops an adaptive data scaling controller to meet multiple performance requirements in Clouds. The controller consists of three components: (1) a performance model, which determines whether the nodes are over-loaded; (2) a workload monitor and a predictor, which are responsible for collecting workload information and estimating the fluctuating trends, respectively; (3) a data scaling strategy generator, which enables the data scaling solution for over-loaded or under-loaded nodes. The numerical results show that the developed controller achieves the goal of automatic data scaling, which not only satisfies diversified performance requirements, but also reduces the execution time of MOVE operations with regards to system performance.
Practical Specification of Belief Manipulation in Games

Actions that affect knowledge asymmetrically between agents occur in numerous domains, from card games such as poker to the secure transmission of information. Applications in such domains often depend on reflection over knowledge, including what an agent knows about what other agents know. We are interested in enabling formal specification of these systems which may be used for executable prototyping as well as verification and other formal reasoning. Dynamic Epistemic Logic provides a formal basis for such reasoning, but is often prohibitively cumbersome to use in practice. We present an implementation and macro system called Ostari, backed by a particular flavor of Dynamic Epistemic Logic, which allows us to scale the ideas to more realistic problems. We demonstrate how actions that manipulate agents' beliefs can be written concisely and how this capability can be applied to modeling a popular card game by utilizing our system's ability to execute action sequences, answer queries about knowledge states, and find action sequences to satisfy a particular goal.
Simulating Player Behavior for Data-Driven Interactive Narrative Personalization

Data-driven approaches to interactive narrative personalization show significant promise for applications in entertainment, training, and education. A common feature of data-driven interactive narrative planning methods is that an enormous amount of training data is required, which is rarely available and expensive to collect from observations of human players. An alternative approach to obtaining data is to generate synthetic data from simulated players. In this paper, we present a long short-term memory (LSTM) neural network framework for simulating players to train data-driven interactive narrative planners. By leveraging a small amount of previously collected human player interaction data, we devise a generative player simulation model. A multi-task neural network architecture is proposed to estimate player actions and experiential outcomes from a single model. Empirical results demonstrate that the bipartite LSTM network produces the better-performing player action prediction models than several baseline techniques, and the multi-task LSTM derives comparable player outcome prediction models within a shorter training time. We also find that synthetic data from the player simulation model contributes to training more effective interactive narrative planners than raw human player data alone.
Detection of Leukemia using Image Processing

In olden days, cancer detection at early stage and its prevention became a difficult task to predict and enhance the stage and efficient image techniques were not available. Due to wrong analysis of cancer presence, patients are treated wrongly. In our method, we detect acute myeloid leukemia effectively. After plotting data from image histogram plot and knowing the segmented region, area of cancer can be obtained by classification of cancer and non-cancer cells. Wrong diagnosis leads to patient’s death, so adequate steps are taken to make diagnosis accurate and to know the features. Effective usage of image tools and regions of infected part are extracted, and detection of cancer based on respective growth of region is observed which is 91 accurate.
A Psychophysical Evaluation of Texture Compression Masking Effects

Lossy texture compression is increasingly used to reduce GPU memory and bandwidth consumption. However, as raised by recent studies, evaluating the quality of compressed textures is a difficult problem. Indeed using Peak Signal-to-Noise Ratio (PSNR) on texture images, like done in most applications, may not be a correct way to proceed. In particular, there is evidence that masking effects apply when the texture image is mapped on a surface and combined with other textures (e.g., affecting geometry or normal). These masking effects have to be taken into account when compressing a set of texture maps, in order to have a real understanding of the visual impact of the compression artifacts on the rendered images. In this work, we present the first psychophysical experiment investigating the perceptual impact of texture compression on rendered images. We explore the influence of compression bit rate, light direction, and diffuse and normal map content on the visual impact of artifacts. The collected data reveal huge masking effects from normal map to diffuse map artifacts and vice versa, and reveal the weakness of PSNR applied on individual textures for evaluating compression quality. The results allow us to also analyze the performance and failures of image quality metrics for predicting the visibility of these artifacts. We finally provide some recommendations for evaluating the quality of texture compression and show a practical application to approximating the distortion measured on a rendered 3D shape.
The percentage cube

OLAP cubes provide exploratory query capabilities combining joins and aggregations at multiple granularity levels. However, cubes cannot intuitively or directly show the relationship between measures aggregated at different grouping levels. One prominent example is the percentage, which is widely used in most analytical applications. Considering this limitation, we introduce percentage cube as a generalized data cube that takes percentages as its basic measure. More precisely, a percentage cube shows the fractional relationship in every cuboid between each aggregated measure on several dimensions and its rolled-up measure aggregated by fewer dimensions. We propose the syntax and introduce query optimizations to materialize the percentage cube. We justify that percentage cubes are significantly harder to evaluate than standard data cubes because in addition to the exponential number of cuboids, there is an additional exponential number of grouping column pairs (grouping columns at the individual level and the total level) on which percentages are computed. We propose alternative methods to prune the cube to identify interesting percentages including a row count threshold, a percentage threshold, and selecting the top k percentages. We study percentage aggregations within the classification of distributive, algebraic, and holistic functions. Finally, we also consider the problem of incremental computation of percentage cube. Experiments compare our query optimizations with existing SQL functions, evaluate the impact and speed of lattice pruning methods and study the effectiveness of the incremental computation.
Integrating UML With Service Refinement for Requirements Modeling and Analysis

The Unified Modeling Language (UML) is the de facto standard for requirements modeling and analysis in the software industry.However, it lacks the ability of formal analysis and verification.In this paper, we propose a synthetic approach UML-SR that integrates UML with service refinement (SR) to support the formal requirements modeling and analysis as well as formal verification.The UML-SR requirements model contains a use case diagram, the system sequence diagrams of use cases, a conceptual class diagram, and the formal contracts of system interfaces.To make this integration viable, we extend service refinement with the concepts of visibility in UML.With the visibility extension, we are able to formally specify and verify both internal and external interactions of the system.To demonstrate the effectiveness of our proposed approach, we investigate a case study of an Online Shopping System.The results show that a consistent requirements model can be eventually derived through formal refinement and verification.The proposed approach is useful and can be further applied for the requirements modeling and formal verification in the software industry.
Implementing Interfaces to Transport Services

The Transport Services architecture [I-D.ietf-taps-arch] defines asystem that allows applications to use transport networking protocolsflexibly. This document serves as a guide to implementation on how tobuild such a system.
Biometric Face Recognition Based on Enhanced Histogram Approach

Biometric face recognition including digital processing and analyzing a subject's facial structure. This system has a certain number of points and measures, including the distances between the main features such as eyes, nose and mouth, angles of features such as the jaw and forehead with the lengths of the different parts of the face. With this information, the implemented algorithm creates a unique model with all the digital data. This model can then be compared with the huge databases of images of the face to identify the subject. The recognition features are retrieved here using histogram equalization technique. A high-resolution result is obtained applying this algorithm under the conditions of a specific image database.
Metric-Optimized Example Weights

Real-world machine learning applications often have complex test metrics, and may have training and test data that are not identically distributed. Motivated by known connections between complex test metrics and cost-weighted learning, we propose addressing these issues by using a weighted loss function with a standard loss, where the weights on the training examples are learned to optimize the test metric on a validation set. These metric-optimized example weights can be learned for any test metric, including black box and customized ones for specific applications. We illustrate the performance of the proposed method on diverse public benchmark datasets and real-world applications. We also provide a generalization bound for the method.
Reducing the upfront cost of private clouds with clairvoyant virtual machine placement

Although public clouds still occupy the largest portion of the total cloud infrastructure, private clouds are attracting increasing interest from both industry and academia because of their better security and privacy control. According to the existing studies, the high upfront cost is among the most critical challenges associated with private clouds. To reduce cost and improve performance, virtual machine placement (VMP) methods have been extensively investigated, however, few of these methods have focused on private clouds. This paper proposes a heterogeneous and multidimensional clairvoyant dynamic bin packing (CDBP) model, in which the scheduler can conduct more efficient VMP processes using additional information on the arrival time and duration of virtual machines to reduce the datacenter scale and thereby decrease the upfront cost of private clouds. In addition, a novel branch-and-bound algorithm with a divide-and-conquer strategy (DCBB) is proposed to effectively and efficiently handle the derived problem. One state-of-the-art and several classic VMP methods are also modified to adapt to the proposed model to observe their performance and compare with our proposed algorithm. Extensive experiments are conducted on both real-world and synthetic workloads to evaluate the accuracy and efficiency of the algorithms. The experimental results demonstrate that DCBB delivers near-optimal solutions with a convergence rate that is much faster than those of the other search-based algorithms evaluated. In particular, DCBB yields the optimal solution for a real-world workload with an execution time that is an order of magnitude shorter than that required by the original branch-and-bound (BB) algorithm.
THE ANALYSIS OF SKIMMING AND SCANNING TECHNIQUE TO IMPROVE STUDENTS IN TEACHING READING COMPREHENSION

This research deals with The Analysis of Skimming and Scanning Technique to Improve Students Teaching Reading Comprehension. This research explores the implementation of Skimming and Scanning in reading comprehension and to find out the advantages and disadvantages of skimming and scanning used in learning process. The objective of this research is to find out the effectiveness of skimming and scanning strategy in improving students reading comprehension. Data collection technique focus on participant observation, interviews, and documentation. Skimming and Scanning is the appropriate technique to find the main idea, key word and content of the text without read the whole of the text.
A Scalable Sparse Matrix-Based Join for SPARQL Query Processing

In this paper, we present gSMat, a SPARQL query engine for RDF datasets. It employs join optimization and data sparsity. We bifurcate gSMat into three submodules e.g. Firstly, SM Storage (Sparse Matrix-based Storage) which lifts the storage efficiency, by storing valid edges, introduces a predicate-based hash index on the storage and generate a statistic file for optimization. Secondly, Query Planner which holds Query Parser and Query Optimizer. The Query Parser module parses a SPARQL query and transformed it into a query graph and the latter generates the optimal query plan based on statistical input from SM Storage. Thirdly, Query Executor module executes query in an efficient manner. Lastly, gSMat evaluated by comparing with some well-known approaches like gStore and RDF3X on very large datasets (over 500 million triples). gSMat is proved as significantly efficient and scalable.
Multi-perspective neural architecture for recommendation system

Currently, there starts a research trend to leverage neural architecture for recommendation systems. Though several deep recommender models are proposed, most methods are too simple to characterize users' complex preference. In this paper, for a fine-grained analysis, users' ratings are explained from multiple perspectives, based on which, we propose our neural architectures. Specifically, our model employs several sequential stages to encode the user and item into hidden representations. In one stage, the user and item are represented from multiple perspectives and in each perspective, the representation of user and that of item put attentions to each other. Last, we metric the output representations from the final stage to approach the users' ratings. Extensive experiments demonstrate that our method achieves substantial improvements against baselines.
Sub-Optimal SAT-Based Approach to Multi-Agent Path-Finding Problem

In multi-agent path finding (MAPF) the task is to find nonconflicting paths for multiple agents. In this paper we focus on finding suboptimal solutions for MAPF for the sum-of-costs variant. Recently, a SAT-based approached was developed to solve this problem and proved beneficial in many cases when compared to other search-based solvers. In this paper, we present SAT-based unbounded- and bounded-suboptimal algorithms and compare them to relevant algorithms. Experimental results show that in many case the SAT-based solver significantly outperforms the search-based solvers.
Reranking optimization for person re-identification under temporal-spatial information and common network consistency constraints

Recent research of person Re-identification (ReID) most focuses on exploring person appearance feature and distance measure between specific camera pair, and seldom considers complex camera network consistence and physical information as supplemental factors for improving the re-identification performance. In this paper, a re-ranking optimization framework under temporal-spatial information and common network consistency constraints is proposed to compensate the accuracy deterioration caused by appearance only based ReID methods in all pairwise cameras. Firstly, a correction function is introduced for describing the influence factor of temporal-spatial information on similarity scores. Then the amended similarity score strategy is provided to tradeoff between the person appearance and temporal-spatial information. Finally the global optimization problem of the jointing temporal-spatial and common consistence constraints is solved by integer programming algorithm. Furthermore, we try to solve the generalized situation where cameras and persons are more random by introducing topology information according to geometry around the cameras, taking the place of network consistency. The experiment results validate that the proposed framework significantly improves performance compared to the other person re-identification methods on the multi-cameras RAiD dataset and TMin dataset both in simple and generalized camera network.
Local control theory for superconducting qubits

In this work, we develop a method to design control pulses for fixed-frequency superconducting qubits coupled via tunable couplers based on local control theory, an approach commonly employed to steer chemical reactions. Local control theory provides an algorithm that only requires a single forward time propagation of the system wave function to shape an external pulse that monotonically increases the population of a desired final state of a quantum system given an initial state. The method can serve as a starting point for additional refinements that lead to new pulses with improved properties. Among others, we propose an algorithm to design pulses that transfer population in a reversible manner between given initial and final states of coupled fixed-frequency superconducting qubits.
When to use and how to report the results of PLS-SEM

Purpose The purpose of this paper is to provide a comprehensive, yet concise, overview of the considerations and metrics required for partial least squares structural equation modeling (PLS-SEM) analysis and result reporting. Preliminary considerations are summarized first, including reasons for choosing PLS-SEM, recommended sample size in selected contexts, distributional assumptions, use of secondary data, statistical power and the need for goodness-of-fit testing. Next, the metrics as well as the rules of thumb that should be applied to assess the PLS-SEM results are covered. Besides presenting established PLS-SEM evaluation criteria, the overview includes the following new guidelines: PLSpredict (i.e., a novel approach for assessing a model’s out-of-sample prediction), metrics for model comparisons, and several complementary methods for checking the results’ robustness. Design/methodology/approach This paper provides an overview of previously and recently proposed metrics as well as rules of thumb for evaluating the research results based on the application of PLS-SEM. Findings Most of the previously applied metrics for evaluating PLS-SEM results are still relevant. Nevertheless, scholars need to be knowledgeable about recently proposed metrics (e.g. model comparison criteria) and methods (e.g. endogeneity assessment, latent class analysis and PLSpredict), and when and how to apply them to extend their analyses. Research limitations/implications Methodological developments associated with PLS-SEM are rapidly emerging. The metrics reported in this paper are useful for current applications, but must always be up to date with the latest developments in the PLS-SEM method. Originality/value In light of more recent research and methodological developments in the PLS-SEM domain, guidelines for the method’s use need to be continuously extended and updated. This paper is the most current and comprehensive summary of the PLS-SEM method and the metrics applied to assess its solutions.
Searching for MobileNetV3

We present the next generation of MobileNets based on a combination of complementary search techniques as well as a novel architecture design. MobileNetV3 is tuned to mobile phone CPUs through a combination of hardware-aware network architecture search (NAS) complemented by the NetAdapt algorithm and then subsequently improved through novel architecture advances. This paper starts the exploration of how automated search algorithms and network design can work together to harness complementary approaches improving the overall state of the art. Through this process we create two new MobileNet models for release: MobileNetV3-Large and MobileNetV3-Small which are targeted for high and low resource use cases. These models are then adapted and applied to the tasks of object detection and semantic segmentation. For the task of semantic segmentation (or any dense pixel prediction), we propose a new efficient segmentation decoder Lite Reduced Atrous Spatial Pyramid Pooling (LR-ASPP). We achieve new state of the art results for mobile classification, detection and segmentation. MobileNetV3-Large is 3.2% more accurate on ImageNet classification while reducing latency by 20% compared to MobileNetV2. MobileNetV3-Small is 6.6% more accurate compared to a MobileNetV2 model with comparable latency. MobileNetV3-Large detection is over 25% faster at roughly the same accuracy as MobileNetV2 on COCO detection. MobileNetV3-Large LRASPP is 34% faster than MobileNetV2 R-ASPP at similar accuracy for Cityscapes segmentation.
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Nils Reimers, Iryna Gurevych. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.