Reviewerly/exHarmony huggingface dataset:
virtFlow: Guest Independent Execution Flow Analysis Across Virtualized Environments

An agent-less technique to understand virtual machines (VMs) behavior and their changes during the VM life-cycle is essential for many performance analysis and debugging tasks in the cloud environment. Because of privacy and security issues, ease of deployment and execution overhead, the method preferably limits its data collection to the physical host level, without internal access to the VMs. We propose a host-based, precise method to recover execution flow of virtualized environments, regardless of the level of virtualization. Given a VM, the Any-Level VM Detection Algorithm (ADA) and Nested VM State Detection (NSD) Algorithm compute its execution path along with the state of virtual CPUs (vCPUs) from the host kernel trace. The state of vCPUs is displayed in an interactive trace viewer (TraceCompass) for further inspection. Then, a new approach for profiling threads and processes inside the VMs is proposed. Our proposed VM trace analysis algorithms have been open-sourced for further enhancements and to the benefit of other developers. Our new techniques are being evaluated with workloads generated by different benchmarking tools. These approaches are based on host hypervisor tracing, which brings a lower overhead (around 1 percent) as compared to other approaches.
Delta Rule and Backpropagation

Assuming that the reader is already familiar with the general concept of Artificial Neural Network and with the Perceptron learning rule, this paper introduces the Delta learning rule, as a basis for the Backpropagation learning rule. After discussing the necessity of using multi-layer Artificial Neural Networks for solving non-linearly separable problems, the paper describes all the mathematical steps that allow us to pass from the simple gradient descent formulation to the Backpropagation algorithm, still nowadays one of the most used methods to train feed-forward multi-layer Artificial Neural Networks. The paper is concluded by discussing issues related to overfitting in feed-forward multi-layer Artificial Neural Networks, and by presenting some heuristics and ideas for an appropriate parameter setting.
Interpretation-aware Cognitive Map construction for time series modeling

We raise a few issues regarding time series modeling using Cognitive Maps, which is an example of a qualitative rather than a purely quantitative approach. Methods that operate at the level of concepts instead of numerical values are a worthy alternative for time series processing thanks to certain desirable properties such as abstraction and ease of interpretation. Those features are unavailable in classical time series modeling algorithms, where the main concern is numerical accuracy of a prediction expressed as a series of numerical values. In this work, we argue that it is desirable for Cognitive Map-based models to be simplified, ideally to the point where their descriptiveness and numerical accuracy is not compromised. In particular, we show experimentally that both the size of the map and the size of the set of possible weight values can be minimized without increasing the error rate.
Popup-Plots: Warping Temporal Data Visualization

Temporal data visualization is used to analyze dependent variables that vary over time, with time being an independent variable. Visualizing temporal data is inherently difficult, due to the many aspects that need to be communicated to the users (e.g., time and variable changes). This is an important topic in visualization, and a wide range of visualization techniques dealing with different tasks have already been designed. In this paper we propose popup-plots, a novel concept where the common interaction of 3D rotation is used to navigate through the data. This allows the users to view the data from different perspectives without having to learn and adapt to new interaction concepts. Popup-plots are therefore a novel method for visualizing and interacting with dependent variables over time. We extend 2D plots with the temporal information by bending the space according to the time. The bending is calculated based on a spherical coordinates approach, which is continuously influenced by the viewing direction towards the plot. Hence, the plot can be viewed from various angles with seamless transitions in between, offering the possibility to analyze different aspects of the represented data. As the current viewing direction is inherently depicted by the shape of the data, the users are able to deduce which part of the data is currently viewed. The temporal information is encoded into the visualization itself, resembling annual rings of a tree. We demonstrate our method by applying it to data from two different domains, comprising measurements at spatial positions over time, and we also evaluated the usability of our solution.
CPU Elasticity to Mitigate Cross-VM Runtime Monitoring

In this paper, We present a new technique that offers lightweight, general, and elastic protection against Crum (Cross-VM runtime monitoring) attacks. Our protection, called Crease (CPU Resource Elasticity as a Service), enables a VM (called principal) to purchase a higher clock rate from the cloud, through lowering the frequency of a malicious VM (called peer), to support its security-critical operations within a short period. During that period, the weakened peer becomes unable to catch up with the pace of the strengthened principal, therefore losing the capability to effectively collect its sensitive information. In the meantime, our approach can also make up for the performance impact on the peer through refunding schedule credits or service credits, in line with the service level agreement of today's cloud. At the center of our design is the novel application of on-demand frequency scaling and schedule quantum randomization, together with a situation-awareness mechanism that dynamically assesses the security risk posed by the peer. We analyzed the security guarantee of our design, implemented a prototype and evaluated it on a well-known Crum attack (an LLC side-channel attack) and various workloads. Our study shows that Crease is effective at protecting the principal, with only a small impact on the peer's operations.
Semantic-based Compound Keyword Search over Encrypted Cloud Data

Keyword search over encrypted data is essential for accessing outsourced sensitive data in cloud computing. In some circumstances, the keywords that the user searches on are only semantically related to the data rather than via an exact or fuzzy match. Hence, semantic-based keyword search over encrypted cloud data becomes of paramount importance. However, existing schemes usually depend upon a global dictionary, which not only affects the accuracy of search results but also causes inefficiency in data updating. Additionally, although compound keyword search is common in practice, the existing approaches only process them as single words, which split the original semantics and achieve low accuracy. To address these limitations, we initially propose a compound concept semantic similarity (CCSS) calculation method to measure the semantic similarity between compound concepts. Next, by integrating CCSS with Locality-Sensitive Hashing function and the secure kk<; inline-graphic xlink:href="wang-ieq1-2847318.gif"/>-Nearest Neighbor scheme, a semantic-based compound keyword search (SCKS) scheme is proposed. SCKS achieves not only semantic-based search but also multi-keyword search and ranked keyword search. Additionally, SCKS also eliminates the predefined global library and can efficiently support data update. The experimental results on real-world dataset indicate that SCKS introduces low overhead on computation and the search accuracy outperforms the existing schemes.
Feature-space SVM adaptation for speaker adapted word prominence detection

Prosodic cues such as the word prominence play a fundamental role in human communication, e.g., to express important information. Since different speakers use a wide variety of features to express prominence, there is a large difference in performance between speaker dependently and speaker independently trained models. To cope with these variations without training a new speaker dependent model, in speech recognition speaker adaptation techniques such as feature-space Maximum Likelihood Linear Regression (fMLLR) turned out to be very useful. These methods are developed for GMM-HMM based classifiers under the assumption that the data can be well modeled via the mixture of a few Gaussian distributions. However, in many cases these assumptions are too restrictive. In particular a discriminative classifier such as an SVM often yields far superior results to a GMM. Therefore, we propose a new adaptation method, which adapts the data to the radial basis function kernel of the SVM. To avoid overfitting we apply two regularization terms. The first is based on fMLLR and the second is an L1 regularization to enforce a sparse transformation matrix. We analyze the method in the context of speaker adaptation for word prominence detection, with varying amounts of adaptation data and different weights of the regularization terms. We show that our novel method clearly outperforms fMLLR-GMM and fMLLR-SVM based adaptation.
A prediction-based approach for features aggregation in Visual Sensor Networks

Visual Sensor Networks (VSNs) constitute a key technology for the implementation of several visual analysis tasks. Recent studies have demonstrated that such tasks can be efficiently performed following an operative paradigm where cameras transmit to a central controller local image features, rather than pixel-domain images. Furthermore, features from multiple camera views may be efficiently aggregated exploiting the spatial redundancy between overlapping views. In this paper we propose a routing protocol designed for supporting aggregation of image features in a VSN. First, we identify a predictor able to estimate the efficiency of local features aggregation between different cameras in a VSN. The proposed predictor is chosen so as to minimize the prediction error while keeping the network overhead cost low. Then, we harmonically integrate the proposed predictor in the Routing Protocol for Low-Power and Lossy Networks (RPL) in order to support the task of in-network feature aggregation. We propose a RPL objective function that takes into account the predicted aggregation efficiency and build the routes from the camera nodes to a central controller so that either energy consumption or used network bandwidth is minimized. Extensive experimental results confirm that the proposed approach can be used to increase the efficiency of VSNs.
Parallel fractal decomposition based algorithm for big continuous optimization problems

Fractal Decomposition Algorithm (FDA) is a metaheuristic that was recently proposed to solve high dimensional continuous optimization problems. This approach is based on a geometric fractal decomposition which divides the search space while looking for the optimal solution. While FDA and its fractal decomposition has shown to be an effective optimization algorithm, its running time grows significantly as the problems dimension increases. To overcome this expensive computational time, a parallelized version of FDA, called Parallel Fractal Decomposition Algorithm (PFDA) is proposed. The focus was on parallelizing the exploration and exploitation phases of the original algorithm on a multi-threaded environment. The performances of PFDA were evaluated on the same Benchmark used to illustrate FDA efficiency, the SOCO 2011. It is composed of 19 functions with dimensions going from 50 to 5000. Results show that PFDA reaches similar performances as the original version with a significantly reduced computational time.
A FAIR archive based on the CERIF model

The Common European Research Information Format (CERIF) has been developed as a flexible model for all kinds of research data. The focus has so-far been primarily on the construction of CRIS systems and inter-system data transfer. In the article we describe how the CERIF model can be used in the construction of an electronic archive. The advantage of basing the archive on CERIF is that an archive compliant to the FAIR guiding principles is easier to archive, in particular in areas concerning issues regarding findability, re-usability, and accessibility. In our example we represent the archive tree by cfProjects and the archived objects by cfResult* entities with their descriptive metadata given in attached CERIF entities. Archival preservation metadata is stored in the Premis format inside attached cfMeasurement entities. An example in which EPrints repository items are transferred to the archive is also presented.
Oracle Separation of BQP and PH

We present a distribution 𝓓 over inputs in {± 1} 2 N , such that: (1) There exists a quantum algorithm that makes one (quantum) query to the input, and runs in time O (log N ), that distinguishes between 𝓓 and the uniform distribution with advantage Ω (1/log N ). (2) No Boolean circuit of quasi-polynomial size and constant depth distinguishes between 𝓓 and the uniform distribution with advantage better than polylog(N)/√ N . By well-known reductions, this gives a separation of the classes Promise- BQP and Promise- PH in the black-box model and implies an oracle relative to which BQP is not contained in PH .
Latent-Smoothness Nonrigid Structure From Motion by Revisiting Multilinear Factorization

How to implement an effective factorization for nonrigid structure from motion (NRSFM) has attracted much attention in recent years. A straightforward factorization scheme is to multilinearly solve NRSFM in an alternating manner, where each of the unknown variables in NRSFM is updated by fixing the others at each iteration. However, recent works show that most existing multilinear factorization (MLF) methods achieve poorer performances than some state-of-the-art sequential factorization methods. In this paper, we reinvestigate the MLF scheme for improving factorization accuracy, and first propose an MLF method with the only low-rank prior for NRSFM in the presence of missing data. Then, for further improving the performances of such MLF methods, a latent "smoothness" characteristic on unknown 3-D deformable shapes is investigated, which is independent of temporal relations among deformable shapes. Accordingly, a latent-smoothness prior for solving NRSFM is derived from the latent smoothness characteristic, and it is able to effectively recover 3-D deformable shapes from unordered data, which is hard for the traditional temporal-smoothness prior to handle. Finally, a regularized factorization method is proposed by integrating MLF with the explored latent-smoothness prior for further pursuing better performances. Extensive experimental results show the effectiveness of our methods in comparison to eight existing multilinear/sequential methods.
Customized Structural Elicitation

Expert elicitation is a powerful tool when modelling complex problems, especially in the common scenario when current probabilities are unknown and data is unavailable for certain regions of the probability space. Such methods are now widely developed, well understood, and have been used to model systems in a variety of domains including climate change, food insecurity, and nuclear risk assessment Barons et al. (), Rougier and Crucifix (), Hanea et al. (). However, eliciting expert probabilities faithfully has proved to be a sensitive task, particularly in multivariate settings. We argue that first eliciting structure is critical to the accuracy of the model, particularly as conducting a probability elicitation is time and resource-intensive.
Optimal Binning for Genomics

Genome sequencing is expected to be the most prolific source of big data in the next decade; millions of whole genome datasets will open new opportunities for biological research and personalized medicine. Genome sequences are abstracted in the form of interesting regions, describing abnormalities of the genome. The parallel execution on the cloud of complex operations for joining and mapping billions of genomic regions is increasingly important. Genome binning, i.e., partitioning of the genome into small-size segments, adapts classic data partitioning methods to genomics; region distributions to bins must reflect operation-specific correctness rules. As a consequence, determining the optimal bin size for such operations is a complex mathematical problem, whose solution requires careful modeling. The main result of this paper is the mathematical formulation and solution of the optimal binning problem for join and map operations in the context of GMQL, a query language over genomic regions; the model is validated by experiments showing its accuracy and sensitivity to the variation of operations' parameters. We also optimize sequences of operations by inheriting the binning between two consecutive operations and we show the deployment of GMQL and the tuning of the proposed model on different cloud computing systems.
On strengthening the logic of iterated belief revision: Proper ordinal interval operators

Darwiche and Pearl's seminal 1997 article outlined a number of baseline principles for a logic of iterated belief revision. These principles, the DP postulates, have been supplemented in a number of alternative ways. However, most of the suggestions for doing so have been radical enough to result in a dubious 'reductionist' principle that identifies belief states with orderings of worlds. The present paper offers a more modest strengthening of Darwiche and Pearl's proposal. While the DP postulates constrain the relation between a prior and a posterior conditional belief set, our new principles govern the relation between two posterior conditional belief sets obtained from a common prior by different revisions. We show that operators from the family that these principles characterise, which subsumes both lexicographic and restrained revision, can be represented as relating belief states that are associated with a 'proper ordinal interval' assignment, a structure more fine-grained than a simple ordering of worlds. We close the paper by noting that these operators satisfy iterated versions of a large number of AGM era postulates.
Agile software process improvement: a collaborative game toolbox

Software development organisations are interested in adopting new agile methods and techniques. However, they often do not have the time and resources to explore available approaches or to customise them to the current situation at the project or organisational levels. As software companies are usually dealing with significant pressure and have to demonstrate productivity, time for software process improvement (SPI) is very limited. Using serious collaborative games can be helpful for the deployment of certain best practices recommended by agile methods. With the objective of facilitating SPI in organisations, the authors propose a ready-to-use collaborative game toolbox that is oriented for practical use in project team meetings. The set of collaborative games included in the toolbox have been designed for agile teams, regardless of the maturity level, to innovate and improve communication and cohesion, and to bring business value out of agile ceremonies.
On Certain Adjacency Effects in Ellipsis Contexts

In this article, I show that crosslinguistically, there is a recurring pattern in various ellipsis constructions (e.g., fragment answers, right-dislocation, right-node raising, VP-ellipsis), to the effect that parts of a remnant can be additionally deleted under adjacency to a deletion site, often ignoring constituency. I argue that the phenomenon in question follows from the fact that PF deletion, being an operation in the component determining linear order, targets linearized strings, similarly to the fact that movement, being an operation in the component determining hierarchical relations, targets constituents.
A system for transformation of sentences from the enriched formalized Node of Knowledge record into relational database

Verbalized text contains knowledge necessary and sufficient for transfer of numerous human cognitions. The question is how this knowledge was saved into text. Authors believe that they found an important idea how to assemble knowledge into nodes of knowledge. Similar methods in the field of knowledge networks exist, but none of them does it in the same way as the Node of Knowledge (NOK) method. Basic terms and concepts in a language are represented in words whose meaning is final and cannot be divided in subterms. More complex meaning can be achieved by combining words in sentences. According to authors’ opinion, each sentence includes connective medium for words in the sentence (related to semantic reasons), which is inwrought in the Node of Knowledge method. Using the Node of Knowledge method each sentence can be presented as a network of connected words. This network is enriched with links between words so a computer can interpret meaning and knowledge of the sentence in the same way an intelligent person does. A formalized and semantically enriched record of sentences (called Formalized Node of Knowledge – FNOK) is developed. Authors find that in this way, even without statistical text analysis, an algorithm can give correct answer to a question set based on written text. This paper presents the system for transformation of textual knowledge expressed in natural language sentences into a relational database. The system is a part of a larger knowledge-based system based on the Node of Knowledge (NOK) conceptual framework for knowledge-based system development. This paper starts with sentences written in the formalized and enriched form for which a logical transformation into the structure of a relational database is proposed. The system and algorithms for the transformation of formalized sentences into n-tuples of the relational database are distributed and represented in several steps. The research has shown that it is possible to store semantically enriched sentences in relational databases. The solution presented in this paper is important for further development of the system for receiving questions from users and providing answers (i.e. question-answering system), with the ability to use well-developed relational SQL languages. Relational database of texts enables numerous applications in the field of expert and intelligent systems.
Adaptive elitist-ant system for medical clustering problem

In general, population based algorithms are superior to local search based algorithms in term of exploration the search space. In any case, the primary downside in different population based algorithms is in exploiting the search space. Recently, a Hybrid Elitist ant system approach is considered as a decent population based algorithm over various optimization problems. Along these lines, the objective of this work is to evaluate the hybrid elitist ant system approach performance to exploit the search space in clustering problem. Six benchmark datasets for medical clustering problem are utilized as a test domain (UCI Machine Learning Repository). Keeping in mind the end goal is to explore the performance of hybrid elitist ant system approach, a comparison performed between the hybrid elitist-ant system and different methodologies in the literature. The result of elitist ant system approach contrasted with different methodologies outcomes exhibits its viability. In any case, the impediment of hybrid elitist ant system approach is in tuning the importance of constraints parameter, where in each dataset needs to tune its own importance for every problem. Subsequently, its props motivate to enhance the hybrid elitist ant system approach by adaptively tuning the importance parameter. Computational outcomes demonstrate that the proposed adaptive elitist ant system approach is competent in delivering higher quality solutions (outcomes) than hybrid elitist ant system approach and different methodologies (outcomes) in the literature in all datasets.