Reviewerly/exHarmony huggingface dataset:
Dynamic K-Means Clustering of Workload and Cloud Resource Configuration for Cloud Elastic Model

Cloud elasticity involves timely provisioning and de-provisioning of computing resources and adjusting resources size to meet the dynamic workload demand.This requires fast, and accurate resource scaling methods at minimum cost (e.g.pay as you go) that match with workload demands.Two dynamic changing parameters must be defined in an elastic model, the workload resource demand classes, and the data center resource reconfiguration classes.These parameters are not labeled for cloud management system while data center logs are being captured.Building an advance elastic model is a critical task, which defines multiple classes under these two categories i.e. for workload and for provisioning.A dynamic method is therefore required to define (during configuration time window) the workload classes and resource provisioning classes.Unsupervised learning model such as K-Means has many challenges such as time complexity, selection of optimum number of clusters (representing the classes), and determining centroid values of the clusters.All clustering methods depend on minimizing mean square error between center of population in same class member.These methods are often enhanced using guidelines to find out the centroids, but they suffer from K-Means limitations.For the application of clustering cloud log traces, most of the reported work use K-Means clustering to label workload types.However, there is no work reported that label data center scaling classes.In this work, a novel method is proposed to analyze the characteristics of both workloads and datacenter configurations using clustering method, and is based on random variable model transformation (kernel density estimator) guide.This method enhances K-Means clustering by automatically determining optimum number of classes and finding the mean centroids for the clusters.In addition, it improves the accuracy and the time complexity of standard K-Means clustering model, by best correlating between clustering attributes using statistical correlation methods.
A tool for proving Michelson Smart Contracts in WHY3

This paper introduces a deductive verification tool for smart contracts written in Michelson, which is the low-level language of the Tezos blockchain. Our tool accepts a formally specified Michelson contract and automatically translates it to an equivalent program written in WhyML, the programming and specification language of the Why3 framework. Smart contract instructions are mapped into a corresponding WhyML shallow-embedding of the their axiomatic semantics, which we also developed in the context of this work. One major advantage of this approach is that it allows an out-of-the-box integration with the Why3 framework, namely its VCGen and the backend support for several automated theorem provers. We also discuss the use of our tool to automatically prove the correctness of diverse annotated smart contracts.
A learning perspective on the emergence of abstractions: the curious case of phone(me)s

Abstract In this study, we propose an operationalization of the concept of emergence which plays a crucial role in usage-based theories of language. The abstractions linguists operate with are assumed to emerge through a process of generalization over the data language users are exposed to. Here, we use two types of computational learning algorithms that differ in how they formalize and execute generalization and, consequently, abstraction, to probe whether a type of language knowledge that resembles linguistic abstractions could emerge from exposure to raw data only. More specifically, we investigated whether a phone, undisputedly the simplest of all linguistic abstractions, could emerge from exposure to speech sounds using two computational learning processes: memory-based learning and error-correction learning (ECL). Both models were presented with a significant amount of pre-processed speech produced by one speaker. We assessed (1) the consistency or stability of what these simple models learn and (2) their ability to approximate abstract categories. Both types of models fare differently regarding these tests. We show that only ECL models can learn abstractions and that at least part of the phone inventory and its grouping into traditional types can be reliably identified from the input.
Building a practice-oriented model of pre-service teacher education

The article analyses modern trends in the university graduates’ training based on a practice-oriented approach, which is interpreted as training focused on the specifics of solving pedagogical problems that arise in real practice. The world experience of practice-oriented educational practice aimed at the formation of a creative, thinking teacher is analysed. The author’s three-segment practice-oriented model of teacher training has been developed. The structural components of the model and the links between them are described: principles of model building (practice-oriented orientation, principles of flexibility, validity, compliance and integrativity); system-forming subjects (professional community, university and school); blocks (target, factorial, theoretical-methodological, content-technological, effective). The peculiarity of the visual image of the constructed model (fractal) is that each separate segment of the system retains the properties of the entire system, which means that at each level, target guidelines are preserved and the basic requirements for the preparation process are consistent with each other.
Classroom education effect evaluation model based on MFO intelligent optimization algorithm

In order to improve the evaluation effect of classroom education, this paper proposes the MFO intelligent optimization algorithm based on the idea of machine learning, and builds the classroom education effect evaluation model based on the MFO intelligent optimization algorithm. Moreover, this paper uses a logarithmic spiral to simulate the path of the moth to the flame and invert the pending parameters in the mathematical model, and adds vertical and horizontal algorithms and chaos operators on this basis. The crisscross algorithm allows different moth individuals and the same moth to perform cross calculations with different computing dimensions to increase the diversity of moth populations, so that moths in the search space can traverse the entire search space as much as possible to find a better solution. Moreover, in view of the problems of BP neural network such as low fitting accuracy, this paper applies the CCMFO algorithm to improve the BP neural network to form the CCMFO-BP algorithm, and improves the weight and threshold update process of the BP neural network to make the network operation more efficient and accurate. Finally, this paper designs experiments to analyze the performance of the model constructed in this paper. The research results show that the model constructed in this paper meets the expected requirements.
Secure Deployment of Decentralized Cloud in Blockchain Environment using Inter-Planetary File System

Abstract Nowadays, the entire globe with enormous resources is connected using web-based technologies. These integrations are implemented using an advanced network environment and need a higher degree of security and integrity. Most of the people are now using smart gadgets to access the corporate, social, and government services. The smart gadgets are having high performance operating systems including Android, iOS, Windows, Blackberry, Tizen, Lune OS, Kai OS, and many others. With the increase of such platforms, the need for security and integrity arises and because of this, the term “Blockchain” is getting huge prominence for multiple secured services. Blockchain was invented as the decentralised transaction chief of the Bitcoin crypto currency Satoshi Nakamoto was called in 2008 by a individual ( or group of people). Satoshi Nakamoto’s name appears to date uncertain. The blockchain inventor for Bitcoin made it the first digital currency to address the double dilemma without the need for a trustworthy regulator or a central server. This manuscript underlines the assorted features of the Inter-Planetary File System (IPFS) as the advanced implementation for a secured blockchain environment.
Voicefox: Leveraging Inbuilt Transcription to Enhance the Security of Machine-Human Speaker Verification against Voice Synthesis Attacks

In this paper, we propose Voicefox1, a defense against the threat of automated voice synthesis attacks in machine-based and human-based speaker verification applications. Voicefox is based on a hitherto undiscovered potential of speech-to-text transcription, already built into these applications. Voicefox relies on the premise that while the synthesized samples might be falsely accepted by the speaker verification systems and human listeners, they cannot be transcribed as accurately as a natural human voice by transcribers. Voicefox is not a speaker verification system, but rather an independent module that can be integrated with any speaker verification system to enhance its security against voice synthesis attacks.
A Decentralized PHD Filter for Multi-target Tracking in Asynchronous Multi-static Radar System

Asynchronism among the local radars is one of the most important challenges for multi-target tracking (MTT) in multi-static radar systems. In order to address this challenge in the framework of random finite set (RFS) based on Bayesian inference, we propose a decentralized probability hypothesis density (PHD) filter based on the asynchronous periodical sequential estimation (APSE). First, starting from a multi-target Bayesian filter, we derive the multi-target density update expressions for the APSE solution in the RFS framework. Next, we develop the PHD recursion expressions of the APSE solution, named as APSE-PHD, and describe the Gaussian mixture (GM) implementation of the APSE-PHD. Simulation results for a challenging tracking scenario confirm that the proposed APSE-PHD algorithm is effective for MTT in the asynchronous multistatic radar system and outperforms the existing PHD-based algorithm.
Examining the Causal Structures of Deep Neural Networks Using Information Theory

Deep Neural Networks (DNNs) are often examined at the level of their response to input, such as analyzing the mutual information between nodes and data sets. Yet DNNs can also be examined at the level of causation, exploring "what does what" within the layers of the network itself. Historically, analyzing the causal structure of DNNs has received less attention than understanding their responses to input. Yet definitionally, generalizability must be a function of a DNN's causal structure as it reflects how the DNN responds to unseen or even not-yet-defined future inputs. Here, we introduce a suite of metrics based on information theory to quantify and track changes in the causal structure of DNNs during training. Specifically, we introduce the effective information (EI) of a feedforward DNN, which is the mutual information between layer input and output following a maximum-entropy perturbation. The EI can be used to assess the degree of causal influence nodes and edges have over their downstream targets in each layer. We show that the EI can be further decomposed in order to examine the sensitivity of a layer (measured by how well edges transmit perturbations) and the degeneracy of a layer (measured by how edge overlap interferes with transmission), along with estimates of the amount of integrated information of a layer. Together, these properties define where each layer lies in the "causal plane", which can be used to visualize how layer connectivity becomes more sensitive or degenerate over time, and how integration changes during training, revealing how the layer-by-layer causal structure differentiates. These results may help in understanding the generalization capabilities of DNNs and provide foundational tools for making DNNs both more generalizable and more explainable.
Sound acts: towards a sonic pragmatism

The article makes a case for a pragmatist approach to sound studies. My starting point is the ostensible lack of language for analysing the agentic, creative and expressive aspects of sound outside of domains of speech and music. At the same time, recent aesthetic and technological developments have brought about a plethora of sound practices that defy the apparatuses of musicology and linguistic as much as they do the listening-centred perspectives of sound studies. Discussing six examples of such practices taken from interactive sound art and participatory online cultures, I sketch out possible directions for a sonic pragmatism. In the first part, I put forward three possible premises for such a theory: John Dewey’s aesthetics, G.H. Mead’s ontology of acts, and actor-network theory. In the second part, I discuss three potential categories for the analysis of sound acts – affordance, perspective, and gesture – emphasising, respectively, their material, social, and pragmatic aspects. I argue that a pragmatist epistemology can offer substantial insights into both philosophical and cultural aspects of contemporary soundmaking, suspending the familiar dichotomies of perception and production, subject and object, human and nonhuman.
A Domain Ontology for Task Instructions

Knowledge graphs and ontologies represent information in a variety of different applications. One use case, the Intelligence, Surveillance, & Reconnaissance: Mutli-Attribute Task Battery (ISR-MATB), comes from Cognitive Science, where researchers use interdisciplinary methods to understand the mind and cognition. The ISR-MATB is a set of tasks that a cognitive or human agent perform which test visual, auditory, and memory capabilities. An ontology can represent a cognitive agent’s background knowledge of the task it was instructed to perform and act as an interchange format between different Cognitive Agent tasks similar to ISR-MATB. We present several modular patterns for representing ISR-MATB task instructions, as well as a unified diagram that links them together.
Cascaded hourglass feature fusing network for saliency detection

Convolutional neural networks have been widely applied in saliency detection task because of its powerful feature extraction capability. Most of existing saliency detection models have achieved great progress by aggregating the strong multi-level features. However, it is still a challenging task to design the feature fusing strategy because of the various differences between multi-level features. In this paper, we explore the effect of cascaded pooling operations for saliency detection and propose a novel network to decode saliency cues from multi-level features progressively. We refer to the architecture as “cascaded hourglass” feature fusing network. The proposed network equips with three cascaded sub-modules to capture the multi-scale context and integrate multi-level features progressively. Specifically, we first propose a multi-scale context-aware feature extraction block with different dilated convolutional branches to obtain multi-scale context-aware saliency cues. Then, a hourglass feature fusing block with successive steps of pooling operations is applied to convert the features to multiple feature spaces. Furthermore, we stack a serial of the hourglass feature fusing blocks to purify the multi-level coarse features progressively. Finally, we combine the selective features with cascaded feature decoder to produce final saliency map. Extensive experiments demonstrate the proposed network compares favorably against state-of-the-art methods. Additionally, our model is efficient with the real-time speed of 28 FPS when processing a 400×300 image.
Robust Detection of Copy-Move Forgery Based on Wavelet Decomposition and Firefly Algorithm

Abstract Nowadays, the whole world is covered with the revolution of digital information so that digital data are easy to transfer, access and process. Modern image processing tools are used to create forged images; by using these tools, forgers introduced forged region in the original image to spread the rumor in the public. Now there is a challenge for the forensic department to prove the authenticity of the original image. So there is required a new efficient copy-move tamper detection method. To fulfill this requirement, a new computational efficient method for copy-move forgery detection using firefly algorithm is proposed.
Pros and Cons of Tangible and Digital Wireframes

This innovative practice is a work in progress that analyzes how a tangible wireframe can be more effective for team communication than a digital wireframe at specific development stages through design activities and processes. It has been found that tangible wireframes are easy to work with and can provide a clearer view of a product's design. To validate this finding, the research focused on the effectiveness of communication through three main aspects of the wireframe: Design, Features, and Layout. These aspects indicate functionality and aesthetic of the website design. The additional outcome implies pros and cons of both tangible and digital wireframes. Research information has been analyzed from data collection of classes activity recorded at an IT faculty. The results showed team communication through wireframe creation depends upon the user experience of team members, understanding of goals, and ability to select tools for digital or tangible wireframe suitable for the design stage. Therefore, this research can guide an instructor of web development by choosing the right tools and techniques appropriate for learners-those without and with experience. It is counted as contributions to the computing educational field.
An unsupervised approach for traffic motion patterns extraction

Automatic analysis, understanding typical activities, and identifying vehicle behaviour in crowded traffic scenes are fundamental and challenging tasks for traffic video surveillance. Some recent researches have been using machine learning approaches to extract meaningful patterns occurring in a traffic scene, for example, intersection. In this regard, we convert visual patterns and features to visual words using dense and sparse optical flow and learning traffic motion patterns with group sparse topical coding (GSTC) algorithm. In the first step of the proposed algorithm, the input traffic video is divided into non-overlapping clips. After that, motion vectors are extracted using dual TV-L1 as a dense optical flow and Lucas–Kanade as a sparse optical flow and converted to flow words. For learning traffic motion patterns, the GSTC algorithm, that is, a non-probabilistic topic model (TM) has been applied. These patterns represent priors on observable motion, which can be utilised to describe a scene and answer behaviour questions such as what are the motion patterns in a traffic scene and what is going on. The experimental results which have been obtained using a real dataset, QUML, show that the combination of the GSTC + dual TV-L1 extracts more traffic motion patterns in comparison with the GSTC + Lucas–Kanade and previous studies.
Self-Attention Underwater Image Enhancement by Data Augmentation

Underwater optical image play a significant role in the exploration ocean. However there are diverse causes of distorted underwater optical scenes, such as light refraction, absorption, scattering and so on. Images enhancement is indispensable in low-level and high-level underwater vision task. Therefore, a novel method based on Generative Adversarial Networks is presented in this paper, which is able to recover lost information for underwater distorted images. No matter from color, detail or texture, the underwater images reconstructed by our approach been greatly improved. In addition, a novel solution is provided for lacking paired dataset which is needed for network train. At last, many qualitative and quantitative experiments are carried out, which can demonstrate that our approach proposed in this paper is robust and effective.
Ensemble Kalman filter based sequential Monte Carlo sampler for sequential Bayesian inference

Abstract Many real-world problems require one to estimate parameters of interest, in a Bayesian framework, from data that are collected sequentially in time. Conventional methods for sampling from posterior distributions, such as Markov chain Monte Carlo cannot efficiently address such problems as they do not take advantage of the data’s sequential structure. To this end, sequential methods which seek to update the posterior distribution whenever a new collection of data become available are often used to solve these types of problems. Two popular choices of sequential method are the ensemble Kalman filter (EnKF) and the sequential Monte Carlo sampler (SMCS). While EnKF only computes a Gaussian approximation of the posterior distribution, SMCS can draw samples directly from the posterior. Its performance, however, depends critically upon the kernels that are used. In this work, we present a method that constructs the kernels of SMCS using an EnKF formulation, and we demonstrate the performance of the method with numerical examples.
Nonclassicality of photon-added <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e581" altimg="si231.svg"><mml:mi>q</mml:mi></mml:math>-squeezed first excited states

A new kind of the optical q-squeezed first excited states as eigenstates of an appropriate two-photon annihilation operator corresponding to the Biedenharn–Macfarlane q-oscillator are presented. It is found that deformation parameter q plays an important role on the nonclassical behaviors including quadrature squeezing and sub-Poissonian statistics as well as the photon antibunching effect. Also, the q-photon-added squeezed first excited states are constructed and squeezing properties of the Hillery-type N-powered amplitude in these states are examined.
Visual programming environment based on data visualization grammar specification

This paper presents a web-based platform for construction and execution of data visualization workflow. The environment enables construction of data visualization workflow with the visual programming interface and provides the ability of previewing the visualisation workflow result in real-time. The use of visual programming makes the workflow composition faster and it’s also lowers entry threshold for the process of data visualization. In this paper we discuss the architecture and main building blocks of the data visualization workflow.
Towards Fast and Robust Adversarial Training for Image Classification

The adversarial training, which augments the training data with adversarial examples, is one of the most effective methods to defend adversarial attacks. However, its robustness degrades for complex models, and the producing of strong adversarial examples is a time-consuming task. In this paper, we proposed methods to improve the robustness and efficiency of the adversarial training. First, we utilized a re-constructor to enforce the classifier to learn the important features under perturbations. Second, we employed the enhanced FGSM to generate adversarial examples effectively. It can detect overfitting and stop training earlier without extra cost. Experiments are conducted on MNIST and CIFAR10 to validate the effectiveness of our methods. We also compared our algorithm with the state-of-the-art defense methods. The results show that our algorithm is 4–5 times faster than the previously fastest training method. For CIFAR-10, our method can achieve above 46% robust accuracy, which is better than most of other methods.