Reviewerly/exHarmony huggingface dataset:
HCache: A Hash-based Hybrid Caching Model for Real-Time Streaming Data Analytics

Up-to-date results in data stream analytics are difficult to obtain because the data are in rapid sequence and can be accessed only once. Due to the exactly-once delivery nature of streaming data, online processing is quite unstable. To guarantee comprehensive and accurate results, aggregating historical data is essential when processing streaming data. In this paper, we propose a hash-based hybrid cache model, namely, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HCache</i> , for fast data analytics covering real-time streaming data and historical data. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HCache</i> model integrates the online cache and batch cache for hybrid online and batch processing and uses a hash structure to accelerate storage. When executing analytic tasks, the batch cache and online cache are accessed in parallel. Computed streaming data are stored in the online cache, which returns qualified results based on one-time visiting. The most recently visited historical data are stored in the batch cache, and they are also used to correct errors in the online cache. Efficient replacement strategies are used to keep the caches within a relatively stable size. To coordinate the online cache with the batch cache, an LRU-based selection strategy is designed to achieve comprehensive results. Experimental results show that the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HCache</i> model can quickly and efficiently execute analytic tasks with little additional overhead; moreover <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HCache</i> is more stable and effective at data storage, access and query with less memory utilization than other models.
DangerNeighbor attack: Information leakage via postMessage mechanism in HTML5

The postMessage mechanism in HTML5 enables webpages with different origins to communicate with each other on a hosting webpage. When the hosting webpage contains multiple receiver functions from different origins, all receiver functions can receive any messages sent to this webpage. However, if one receiver function is malicious and may deliberately eavesdrop on all messages sent to the hosting webpage, there exists a risk of information leakage. In this paper, we perform a systematic study on this new type of information leakage threat named DangerNeighbor attack, which can eavesdrop messages sent through postMessage by inserting a malicious receiver function into the hosting page. We implemented two proof of concept prototypes of DangerNeighbor attacks using malicious third party service provider and malicious browser extension, respectively. To evaluate the feasibility of DangerNeighbor attack, we study Alexa top 5000 websites and 1200 Chrome extensions, and our analysis results verify the wide existence of postMessage vulnerability in the wild. Particularly, we perform a case study of DangerNeighbor attack against the OAuth access token. We find that 39.61% of websites using Facebook OAuth and 23.38% of websites using Google OAuth may leak users’ private information. Even worse, an attacker can successfully login 11 vulnerable websites with the compromised OAuth access token. Finally, we propose two countermeasures to thwart DangerNeighbor attacks.
Nonlinear Curve Fitting Using Extreme Learning Machines and Radial Basis Function Networks

Fast neural network techniques-extreme learning machine and radial basis function networks-are used to solve the traditional curve fitting problems, where the localized effect of a network basis function is corrected by introducing a linear neuron. Numerical experiments demonstrate that the neural-based method is accurate and well-suited for fitting large volume of data.
Scene-dependent proposals for efficient person detection

In this paper, we present a new method that provides a substantial speed-up of person detection while showing high classification accuracy. Our method learns a Gaussian Mixture Model of locations and scales of the persons in the scene under observation. The model is learnt in an unsupervised way from a set of detections extracted from a small number of frames, so that each component of the mixture represents the expectation of finding a target in a region of the image at a specific scale. At runtime, the windows that most likely contain a person are sampled from the components and evaluated by the classifier. Experimental results show that replacing the classic sliding window approach with our scene-dependent proposals in state of the art person detectors allows us to drastically reduce the computational complexity while granting equal or higher performance in terms of accuracy.
Measurement of the impact of identical twin voices on automatic speaker recognition

Most speaker recognition systems operate on low-level speech features that are dependent on the physical anatomy of the speaker as well as, to a lesser extent, learned habits of speaking. It is reasonable to believe that identical twins could lessen the discriminatory power of these systems. However, accurate measurements of these individuals’ impact have not been possible due to experiments using limited, available databases of twins. In this paper, verification trials using read and conversational speech samples collected from 167 pairs of identical twins resulted in consistently lower verification accuracies based on comparisons of the equal error rate (EER) from a similarly collected non-twins database. An EER of 2.012% and twin misidentification rate of 2.4% resulted when test utterances consisted of read speech. Verification accuracy of identical twins is lower compared to previous works as a result of the inclusion of more individuals and more diverse speech data.
Multi-stage Reinforcement Learning for Object Detection

We present a reinforcement learning approach for detecting objects within an image. Our approach performs a step-wise deformation of a bounding box with the goal of tightly framing the object. It uses a hierarchical tree-like representation of predefined region candidates, which the agent can zoom in on. This reduces the number of region candidates that must be evaluated so that the agent can afford to compute new feature maps before each step to enhance detection quality. We compare an approach that is based purely on zoom actions with one that is extended by a second refinement stage to fine-tune the bounding box after each zoom step. We also improve the fitting ability by allowing for different aspect ratios of the bounding box. Finally, we propose different reward functions to lead to a better guidance of the agent while following its search trajectories. Experiments indicate that each of these extensions leads to more correct detections. The best performing approach comprises a zoom stage and a refinement stage, uses aspect-ratio modifying actions and is trained using a combination of three different reward metrics.
Unifying Sum and Weighted Aggregations for Efficient Yet Effective Image Representation Computation

Embedding and aggregating a set of local descriptors (e.g. SIFT) into a single vector is normally used to represent images in image search. Standard aggregation operations include sum and weighted aggregations. While showing high efficiency, sum aggregation lacks discriminative power. In contrast, weighted aggregation shows promising retrieval performance but suffers extremely high time cost. In this work, we present a general mixed aggregation method that unifies sum and weighted aggregation methods. Owing to its general formulation, our method is able to balance the trade-off between retrieval quality and image representation efficiency. Additionally, to improve query performance, we propose computing multiple weighting coefficients rather than one for each to be aggregated vector by partitioning them into several components with negligible computational cost. Extensive experimental results on standard public image retrieval benchmarks demonstrate that our aggregation method achieves state-of-the-art performance while showing over ten times speedup over baselines.
Algebraic List-Decoding in Projective Space: Decoding With Multiplicities and Rank-Metric Codes

The problem of list decoding algebraic subspace codes and rank-metric codes is considered. We develop two separate methods, via two different approaches, for list decoding subspace codes and rank-metric codes. These methods provide, for certain code parameters, improved tradeoffs between rate and error-correction capability than that of the Koetter-Kschischang codes, in the domain of subspace codes, and than that of the Gabidulin codes, in the domain of rank-metric codes, and several other extensions thereof. In the first approach, we introduce the notion of root multiplicities for a certain sub-ring of the ring of linearized polynomials. In the list-decoding algorithm, multiple roots are enforced for the interpolation polynomial in order to achieve an improved error-correction radius for an extended family of Koetter-Kschischang subspace codes. The normalized error-correction radius for this approach is τ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</sub> =2(L+1)/(r+1)-1-L(L+1)(L+n)/r(r+1)R, where L is the maximum list size, n is the subspace code dimension, R is the rate of the code, and r is the multiplicity parameter. In the second approach, we construct a folded version of Koetter-Kschischang codes. A linear-algebraic list-decoding algorithm is proposed for these codes that achieves the error-correction radius τ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</sub> =s(1-sR), where s is the folding parameter. As opposed to the first approach, the size of output list in the second approach depends on the underlying field size and is at most q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m(s-1)</sup> , where q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> is the size of the field that message symbols are chosen from. It is also shown that the output list size is 1, with high probability, in a probabilistic setting. We utilize the techniques of the second approach in the domain of rank-metric codes to construct folded Gabidulin codes to enable a linear-algebraic list-decoding algorithm for such codes. Our algorithm makes it possible to recover.
Large families of pseudorandom binary lattices by using the multiplicative inverse modulo P

Hubert, Mauduit and Sárközy introduced pseudorandom measures for finite pseudorandom binary lattices. Gyarmati, Mauduit, Sárközy and Stewart presented some natural and flexible constructions, which are the two-dimensional extensions and modifications of a few one-dimensional constructions. The upper estimates for the pseudorandom measures of their binary lattices are based on the principle that character sums or exponential sums in two variables can be estimated by fixing one of the variables. In this paper, we constructed two large families of [Formula: see text] dimensional pseudorandom binary lattices by using the multiplicative inverse modulo [Formula: see text], and study the properties: pseudorandom measure, collision and avalanche effect.
Hybrid GI-DIC measurement procedure for hierarchical assessment of strain fields

Engineering design supported by numerical modeling allows to build devices optimized in terms of price, weight and parameters. Optimized designs includes complex components and new materials that in many cases have to be experimentally investigated. In the paper we present a hybrid measurement methodology, that combines the advantages of Grating Interferometry and Digital Image Correlation techniques and allows strain measurements with different accuracy levels at the same time. Both methods are used simultaneously in a single setup in order to reduce time and cost of the required experimental mechanics measurements and in consequence - speed up the design process of engineering objects.
Ethics-Based Cooperation in Multi-agent Systems

In the recent literature in Artificial Intelligence, ethical issues are increasingly discussed. Many proposals of ethical agents are made. However, those approaches consider mainly an agent-centered perspective, letting aside the collective dimension of multi-agent systems. For instance, when considering cooperation among such agents, ethics could be a key issue to drive the interactions among the agents. This paper presents a model for ethics-based cooperation. Each agent uses an ethical judgment process to compute images of the other agents’ ethical behavior. Based on a rationalist and explicit approach, the judgment process distinguishes a theory of good, namely, how values and moral rules are defined, and a theory of right, namely, how a behavior is judged with respect to ethical principles. From these images of the other agents’ ethics, the judging agent computes trust used to cooperate with the judged agents. We illustrate these functionalities in an asset management scenario with a proof of concept implemented in the JaCaMo multi-agent platform.
GPML: an XML-based standard for the interchange of genetic programming trees

Abstract We propose a genetic programming markup language (GPML), an XML-based standard for the interchange of genetic programming trees, and outline the benefits such a format would bring in allowing the deployment of trained genetic programming (GP) models in applications as well as the subsidiary benefit of allowing GP researchers to directly share trained trees. We present a formal definition of this standard and describe details of an implementation. In addition, we present a case study where GPML is used to implement a model predictive controller for the control of a building heating plant.
Performance Analysis of Structured, Un-Structured, and Cloud Storage Systems

Big Data management is an interesting research challenge for all storage vendors. Since data can be structured or unstructured, hence variety of storage systems has been designed to meet storage requirement as per organization's demands. The article focuses on different kinds of storage systems, their architecture and implementations. The first portion of the article describes different examples of structured (PostgreSQL) and unstructured databases (MongoDB, OrientDB and Neo4j) along with data models and comparative performance analysis between them. The second portion of the paper focuses on cloud storage systems. As an example of cloud storage, Google Cloud Storage and mainly its implementation details have been discussed. The aim of the article is not to eulogize any particular storage system, but to clearly point out that every storage has a role to play in the industry. It depends on the enterprise to identify the requirements and deploy the storage systems.
Digital Two-Dimensional Bijective Reflection and Associated Rotation

In this paper, a new bijective reflection algorithm in two dimensions is proposed along with an associated rotation. The reflection line is defined by an arbitrary Euclidean point and a straight line passing through this point. The reflection line is digitized and the 2D space is paved by digital perpendicular (to the reflection line) straight lines. For each perpendicular line, integer points are reflected by central symmetry with respect to the reflection line. Two consecutive digital reflections are combined to define a digital bijective rotation about arbitrary center, i.e. bijective digital rigid motion.
Are you thinking what i’m thinking? A conceptual modeling approach to understand stakeholders’ assessments of the fate of chesapeake oysters

The history of oyster management in the Chesapeake Bay is replete with examples of conflict, including an era commonly referred to as the Oyster Wars. Yet, the community of people who work with and depend on oysters has some shared challenges and some stories of success. Using conceptual modeling methods (fuzzy cognitive mapping in particular), we explore whether some stakeholders support and others oppose management proposals because they have fundamentally different predictions for what the outcome of the management actions or other perturbations to the system will be. Stakeholders across the oyster community completed a conceptual mapping exercise as part of the Chesapeake Oyster Summit to describe their perception of how the ecosystem (including humans) functions. This analysis takes those conceptual maps, aggregated by stakeholder group, to model their predictions under currently proposed or frequently discussed management scenarios. Results show more unity than one might expect in how the ecosystem is expected to respond to management initiatives and predicted environmental perturbations. Feedback loops also emerge in some scenarios to either buffer or exacerbate the effects of the management on the ecosystem.
The Unbounded Gatherer: Possibilities for posthuman writing-reading

This article develops a posthuman approach to authorship to challenge implied distinctions and superiorities between the social and material worlds, which can detach academics and their writing from societies and ecosystems. By reimagining academic texts that are open for richer interpretation and accessible to diverse audiences, this article offers two main contributions. Firstly, I develop a conceptualisation of the posthuman author as an 'unbounded gatherer', adding to others' attempts to destabilise predominant humanistic ways of writing about managing and organizing that view authors as autonomous agents. Secondly, by developing the idea of 'mediators' as a means to explore how the sociomaterial is implicated in writing, debates about materiality in writing are extended. Through an illustration of posthuman writing, five emergent categories of mediators are analysed, and three textual practices are performed and examined.
Impact of designer knowledge in the interactive evolutionary optimisation of analogue CMOS ICs by using iMTGSPICE

This Letter describes an innovative interactive evolutionary computational tool to optimise robust analogue complementary metal-oxide-semiconductor (CMOS) integrated circuits (ICs), by using genetic algorithm, entitled iMTGSPICE. The main results demonstrate that the iMTGSPICE is able to reduce the optimisation cycle times of designs of robust single-ended single-stage and Miller operational transconductance amplifiers (OTAs) in up to 93.9% in comparison to the non-interactive optimisation process. Moreover, the iMTGSPICE is capable of reducing the influence of the knowledge levels of the analogue CMOS ICs designers (experts and non-experts) to obtain robust potential solutions (maximum error of 0.5% for the Miller OTA).
Improved Adaptive Wavelet Thresholding for Effective Speckle Noise Reduction in Low Contrast Medical Images

Informative images endure from poor contrast and noise during image acquisition. Significant information retrieval necessitates image contrast enhancement and removal of noise as a prerequisite before any further processing can be done. Dominant applications with low contrast images affected by speckle noise are medical ultrasound images. The objective of this work is to improve the effectiveness of the preprocessing stage in medical ultrasound images by enhancing the image while retaining its structural characteristics. For image enhancement, this work proposes to develop an automatic contrast enhancement technique using cumulative histogram equalization and gamma correction based on the image. For noise removal, this work proposes an algorithm Gamma Correction with Exponentially Adaptive Threshold (GCEAT) which suggests the use of GC for contrast enhancement along with a new wavelet-based adaptive soft thresholding technique for noise removal. The proposed GCEAT-based image de-noising is validated with other enhancement and noise removal techniques. Experimental results with low contrast synthetic and actual ultrasound images show that the suggested proposed system performs better than existing contrast enhancement techniques. Encouraging results were obtained with medical ultrasound images in terms of Peak-Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Structural Similarity Index Measure (SSIM) and Average Intensity (AI).
The New AI

For many decades, mainstream artificial intelligence (AI) refused to believe that deep learning with neural networks and backpropagation offer true brain-like general intelligence, despite numerous successes on tough engineering problems and mathematical advances. The tide changed in 2009 due to a $2 million NSF grant to Ng and LeCun which I signed off on, despite fierce objections which in today's US government environment would have prevented the action. Empirical success on well-known challenge problems led to follow-ons by DARPA, then by Google, and then a flood of interest by competitors trying to keep up. There are huge investments pursuing new applications of methods known and proven before 1990, but also a huge reservoir of more powerful methods proven out in engineering which have yet to be exploited in computer science, including mathematical principles which (along with related physics) begin to give us a real functional understanding of how the human brain and mind achieve their intelligence. The "Terminator" risk with AI is very serious, but as the Internet of Things (IOT) takes over the world, the risks of artificial stupidity (AS) are just as serious. There is an urgent need for us to develop new interfaces and integrated platforms, using more powerful AI in a human-friendly way along with more advanced unbreakable operating systems, quantum technology, and systems to fully support the deepest human potential. If we do not move quickly and effectively enough to develop more benign possibilities, the survival of humanity is at stake (see www.werbos.com/IT_big_picture.pdf).
Graph-based methods for simultaneous smoothing and sharpening of color images

In this work we introduce an image characterization of pixels based on local graphs that allows to distinguish different local regions around a pixel. This separation also permits us to develop a method for determining the role of each pixel in a neighborhood of any other, either for smoothing or for sharpening. Two methods for simultaneously conducting both processes are provided. Our solution overcomes the drawbacks of the classic two steps sequential smoothing and sharpening process: enhancing details while reducing noise and not losing critical information. The parameters of the methods are adjusted in two different ways: through observers visual quality optimization and with an objective optimization criterion. The results show that our methods outperform other recent state-of-the-art ones.
The AXIOM approach for probabilistic and causal modeling with expert elicited inputs

Expert informants can be used as the principal information source in the modeling of socio-techno-economic systems or problems to support planning, foresight and decision-making. Such modeling is theory-driven, grounded in expert judgment and understanding, and can be contrasted with data-driven modeling approaches. Several families of approaches exist to enable expert elicited systems modeling with varying input information requirements and analytical ambitions. This paper proposes a novel modeling language and computational process, which combines aspects from various other approaches in an attempt to create a flexible and practical systems modeling approach based on expert elicitation. It is intended to have high fitness in modeling of systems that lack statistical data and exhibit low quantifiability of important system characteristics. AXIOM is positioned against Bayesian networks, cross-impact analysis, structural analysis, and morphological analysis. The modeling language and computational process are illustrated with a small example model. A software implementation is also presented.