Reviewerly/exHarmony huggingface dataset:
Adaptive Evaluation of Virtual Machine Placement and Migration Scheduling Algorithms Using Stochastic Petri Nets

More and more mobile applications rely on the combination of both mobile and cloud computing technology to bring out their full potential.The cloud is usually used for providing additional computing resources that cannot be handled efficiently by the mobile devices.Cloud usage, however, results in several challenges related to the management of virtualized resources.A large number of scheduling algorithms are proposed to balance between performance and cost of data center.Due to huge cost and time consuming of measure-based and simulation method, this paper proposes an adaptive method to evaluate scheduling algorithms.In this method, the virtual machine placement and migration process are modeled by using Stochastic Reward Nets.Different scheduling methods are described as reward functions to perform the adaptive evaluation.Two types of performance metrics are also discussed: one is about quality of service, such as system availability, mean waiting time, and mean service time, and the other is the cost of runtime, such as energy consumption and cost of migration.Compared to a simulation method, the analysis model in this paper only modifies the reward function for different scheduling algorithms and does not need to reconstruct the process.The numeric results suggest that it also has a good accuracy and can quantify the influence of scheduling algorithms on both quality of service and cost of runtime.
Evolutionary Optimisation of Fully Connected Artificial Neural Network Topology

This paper proposes an approach to selecting the amount of layers and neurons contained within Multilayer Perceptron hidden layers through a single-objective evolutionary approach with the goal of model accuracy. At each generation, a population of Neural Network architectures are created and ranked by their accuracy. The generated solutions are combined in a breeding process to create a larger population, and at each generation the weakest solutions are removed to retain the population size inspired by a Darwinian 'survival of the fittest'. Multiple datasets are tested, and results show that architectures can be successfully improved and derived through a hyper-heuristic evolutionary approach, in less than 10% of the exhaustive search time. The evolutionary approach was further optimised through population density increase as well as gradual solution max complexity increase throughout the simulation.
Multiple ellipse fitting of densely connected contours

Multiple ellipse fitting is challenging and at the same time essential as it has a variety of applications in biology, chemistry, and nanotechnology. Accurate, effective, and reliable approach for the fitting problem has been always desirable. In this paper, we address a category of multiple ellipse fitting problem which fits densely connected contours. We propose a framework rather than design an algorithm for the problem. The framework streamlines five processes which include: sorting the contour points, doing ellipse fitting in sliding windows, detecting the context anomaly, performing clustering, and obtaining multiple ellipses through second ellipse fitting. The framework is evaluated in a real-world application of handprint identification and various synthetic datasets. Experimental results show that the framework can extract multiple ellipses from contours with satisfactory accuracy and efficiency.
STD: An Automatic Evaluation Metric for Machine Translation Based on Word Embeddings

Lexical-based metrics such as BLEU, NIST, and WER have been widely used in machine translation (MT) evaluation. However, these metrics badly represent semantic relationships and impose strict identity matching, leading to moderate correlation with human judgments. In this paper, we propose a novel MT automatic evaluation metric Semantic Travel Distance (STD) based on word embeddings. STD incorporates both semantic and lexical features (word embeddings and n-gram and word order) into one metric. It measures the semantic distance between the hypothesis and reference by calculating the minimum cumulative cost that the embedded n-grams of the hypothesis need to “travel” to reach the embedded n-grams of the reference. Experiment results show that STD has a better and more robust performance than a range of state-of-the-art metrics for both the segment-level and system-level evaluation.
MixGeo

As the location-based applications are flourishing, we will witness soon a prodigious amount of spatial data will be stored in the public cloud with the geometric range query as one of the most fundamental search functions. The rising demand of outsourced data is moving larger-scale datasets and wider-scope query size. To protect the confidentiality of the geographic information of individuals, the outsourced data stored at the cloud server should be preserved especially when they are queried. While the problem of secure range query on outsourced encrypted data has been extensively studied, the current schemes are far from the practice in terms of efficiency and scalability. In this paper, we propose a novel solution based on Geohash and predicate symmetric searchable encryption for secure range queries named as MixGeo. We present a multi-level indexes structure tailored for efficient and large-scale spatial data lookup in the cloud server while preserving data privacy.
Improving Credit Card Fraud Detection by Profiling and Clustering Accounts

Credit card fraud is a problem that can cost banks billions of dollars annually, leading to increased incentives among financial institutions for the development of fast, effective and dynamic fraud detection systems. This research paper addresses credit card fraud detection through a semi-supervised approach, in which clusters of account profiles are created and used for modeling classifiers. Accounts are profiled based on their behavioral trends and clustered into similar groups. Groups are further identified as distinct customer segments based on purchase characteristics such as amount, frequency or distance. Random forest and XGBoost classifiers are trained on an entire sample and compared against classifiers trained at the transaction level across each cluster. This research concludes that the overall weighted performance of classifiers trained at the cluster level does not significantly outperform classifiers trained on the full sample. However, this research finds that clustering can be used to find meaningful groups of account holders that also have varying fraud rates across each cluster. Additionally, some classifiers trained on specific clusters yield significant improvements in performance over the baseline, whereas classifiers for other clusters do not perform as well as the baseline. This research also concludes that the optimal classifier for a given cluster varies by cluster, highlighting the potential for further development of new classifiers which may perform well on clusters that currently exhibit underperforming models.
ERUDITE

Adaptive video streaming systems are expected to provide the best user experience to improve service engagement. To this purpose, the video player implements a controller to dynamically choose the most suitable video representation to be downloaded. It is well-known that finding one tuning of the controller's parameters which performs satisfactorily in a wide range of scenarios is very challenging. This paper studies the problem of providing users with (near) optimal Quality of Experience (QoE) for Dynamic Adaptive Streaming over HTTP (DASH) systems. We present ERUDITE, a closed-loop system to optimally tune - at run-time - the adaptive streaming controller's parameters to adapt to changing scenario's parameters. The proposed system is based on a Deep Neural Network (DNN) which continuously provides the streaming controller with estimates of optimal parameters based on measured metrics such as bandwidth samples and overall obtained QoE. The DNN is trained using a dataset that we have built by finding, for thousands of scenarios, the optimal adaptive streaming controller's parameters using a Bayesian optimization algorithm. Results, gathered considering a large number of diverse scenarios, show that ERUDITE is able to provide near optimal performances by reducing impairments due to rebuffering and video level switching.
Bitcoin Security Under Temporary Dishonest Majority

We prove Bitcoin is secure under temporary dishonest majority. We assume the adversary can corrupt a specific fraction of parties and also introduce crash failures, i.e., some honest participants are offline during the execution of the protocol. We demand a majority of honest online participants on expectation. We explore three different models and present the requirements for proving Bitcoin's security in all of them: we first examine a synchronous model, then extend to a bounded delay model and last we consider a synchronous model that allows message losses.
A Simplified and Efficient Version of Inclined Planes system Optimization Algorithm

The efficiency of heuristic optimization methods has led researchers to considerably use these methods to solve complex engineering problems. Inclined planes system optimization (IPO) is a relatively new heuristic algorithm that is inspired by the dynamic movement of objects on inclined planes without friction. This paper presents a simplified and effective version of IPO (called SIPO) with the aim of simplifying the main IPO equations, creating a powerful trade-off between the concepts of exploitation and exploration, and modifying the complexity of their structural parameters. The performance of the proposed algorithm is measured on 10 benchmark functions. The results clearly demonstrate the improved performance and superiority of SIPO compared to the standard version of the IPO and its improved versions.
Making Up Instruments

The design of a new technology entails the materialisation of values emerging from the specific community, culture and context in which that technology is created. Within the domain of musical interaction, HCI research often examines new digital tools and technologies which can carry unstated cultural assumptions. This paper takes a step back to present a value discovery exercise exploring the breadth of perspectives different communities might have in relation to the values inscribed in fictional technologies for musical interaction. We conducted a hands-on activity in which musicians active in different contexts were invited to envision not-yet-existent musical instruments. The activity revealed several sources of influence on participants' artefacts, including cultural background, instrumental training, and prior experience with music technology. Our discussion highlights the importance of cultural awareness and value rationality for the design of interactive systems within and beyond the musical domain.
Generalized Zero-Shot Cross-Modal Retrieval

Cross-modal retrieval is an important research area due to its wide range of applications, and several algorithms have been proposed to address this task. We feel that it is the right time to take a step back and analyze the current status of research in this area. As new object classes are continuously being discovered over time, it is necessary to design algorithms that can generalize to data from previously unseen classes. Towards that goal, our first contribution is to establish protocols for generalized zero-shot cross-modal retrieval and analyze the generalization ability of the standard cross-modal algorithms. Second, we propose a semantic-aware ranking algorithm that can be used as an add-on to any existing cross-modal approach to improve its performance on both seen and unseen classes. Finally, we propose a modification of the standard evaluation metric (MAP for single-label data and NDCG for multi-label data), which we feel is a more intuitive measure of the cross-modal retrieval performance. Extensive experiments on two single-label and three multi-label cross-modal datasets show the effectiveness of the proposed approach.
Investigating the Common Authorship of Signatures by Off-Line Automatic Signature Verification Without the Use of Reference Signatures

In Automatic Signature Verification, questioned specimens are usually compared with reference signatures.In writer-dependent schemes, a number of reference signatures are required to build up the individual signer model whilst a writerindependent system requires a set of reference signatures from several signers to develop the model of the system.This paper addresses the problem of automatic signature verification when no reference signatures are available.The scenario we explore consists of a set of signatures, which could be signed by the same author or by multiple signers.As such, we discuss three methods which estimate automatically the common authorship of a set of off-line signatures.The first method develops a score similarity matrix, worked out with the assistance of duplicated signatures; the second uses a feature-distance matrix for each pair of signatures, and the last method introduces pre-classification based on the complexity of each signature.Publicly available signatures were used in the experiments, which gave encouraging results.As a baseline for the performance obtained by our approaches, we carried out a visual Turing Test where forensic and non-forensic human volunteers, carrying out the same task, performed less well than the automatic schemes.
Detection of speech tampering using sparse representations and spectral manipulations based information hiding

Speech tampering has brought serious problems to speech security. Information hiding method can be used for tampering detection if it can satisfy several competitive requirements, e.g., inaudibility, robustness, blindness, and fragility. According to preliminary analysis, spectral envelope and formants are important indicators of tampering, since tampering the speech will unavoidably modify the shape of the spectral envelope and the locations/magnitudes of the formants. By taking advantage of this, this paper proposes a spectral manipulations based information hiding method for tampering detection in sparse domain. To robustly extract the embedded information, the Robust Principal Component Analysis (RPCA) is employed to decompose the original speech into sparse component and low-rank component. The sparse component which contains the main spectral envelope and formant structure is selected for information hiding/embedding via spectral manipulations, by controlling the shape and power of formants with line spectral frequencies (LSFs). Evaluation results suggest that the proposed method can satisfy inaudibility, robustness, and fragility. Furthermore, it is able to detect both the temporal tampering and acoustic feature based tampering with reasonable accuracy.
Efficient profile maximum likelihood for universal symmetric property estimation

Estimating symmetric properties of a distribution, e.g. support size, coverage, entropy, distance to uniformity, are among the most fundamental problems in algorithmic statistics. While these properties have been studied extensively and separate optimal estimators have been produced, in striking recent work Acharya et al. provided a single estimator that is competitive for each. They showed that the value of the property on the distribution that approximately maximizes profile likelihood (PML), i.e. the probability of observed frequency of frequencies, is sample competitive with respect to a broad class of estimators. Unfortunately, prior to this work, there was no known polynomial time algorithm to compute such an approximation or use PML to obtain a universal plug-in estimator.
Research on Digital Copyright Management System Based on Blockchain Technology

The thesis proposes a decentralized digital rights management and transaction system architecture based on blockchain technology. Using Tendermint as a consensus mechanism for block chain, a blockchain system is built in a way that consumes less resources and throughput. In the system design, a transaction filtering strategy and a state data recovery engine are introduced to solve the regulatory problems in the block chain system. Design account management, copyright registration, and copyright trading strategies to manage digital copyright information in an efficient and controlled manner. Through experiments and analysis, the designed model can meet the needs of copyright management.
Contrast-enhanced repacked soil cores as a proxy for soil organic matter spatial arrangement

Soil organic matter (SOM) plays a key role in soil structure formation, although the bidirectional relationship between SOM and the soil pore network is complex and needs further investigation. Despite great advances provided by X-ray computed microtomography (µCT), it has only been used in a few studies to investigate the organic matter 3D-arrangement within the soil matrix. Results are based on the X-ray linear attenuation coefficient (α), and mixtures of organic and mineral soil fractions could imply overlapping of information that makes any segmentation procedure difficult. The aim of this study was to visualise, segment, and quantify the particulate organic matter fraction (POM) within the soil matrix through X-ray µCT in combination with contrast agents (phosphomolybdic acid and silver nitrate). Two series of repacked soil cores, ‘dry’ and ‘wet’, were scanned through X-ray µCT at a 7-µm resolution. Different segmentation approaches were tested to separate POM from other soil phases: manual, global, and local thresholding methods. Reported algorithms were also compared with a supervised grey value-based (GV) approach followed by morphological operations. Results showed contrast agents increased α of POM, simplifying its identification and the following segmentation on dry cores. The POM was discriminated from the mineral fraction and its content correctly estimated. This was particularly accurate when applying manual thresholding or GV approach with respect to indicator kriging, suggesting that operator-based ability to set threshold level is still the best solution for accurate POM segmentation. Beyond single-phase accounting, different thresholding algorithms and morphological operations also affected POM morphological characteristics. In particular, the simpler was an object shape, the easier was its segmentation. Improvements are thus required to increase the efficiency of automated thresholding algorithms. Moreover, wet cores were exposed to washing-out phenomena that compromised any digital image processing and further POM quantification, implying that more effort should be made to find other suitable staining agents.
Verifying the spatial consistency of the CAMS Radiation Service and HelioClim-3 satellite-derived databases of solar radiation using a dense network of measuring stations: the case of The Netherlands

Abstract. The present work deals with two well-known databases of hourly mean of solar irradiance that are derived from satellite imagery: the CAMS Radiation Service version 3.2, abbreviated as CAMS-Rad and part of the Copernicus Atmosphere Monitoring Service version 3.2 (CAMS), and the HelioClim-3 version 5, abbreviated as HC3v5. It adds up to the continuous documentation of these two databases that demonstrates that both databases capture the temporal and spatial variability of the solar radiation and are reliable sources of data. The spatial consistency of the uncertainties of these databases is verified against measurements performed within a dense network of ground stations in the Netherlands from the Royal Meteorological Institute KNMI for the period 2014–2017. For the CAMS-Rad database, the correlation coefficients between ground measurements and estimates are around 0.94–0.97 for irradiance E and 0.85–0.89 for clearness index KT. The bias ranges between −12 and 27 W m−2 (−4 % and 10 %). The standard deviation for E is almost constant at all stations and around 67 W m−2 (24 %) except at Valkenburg (57 W m−2, 20 %) and De Bilt (73 W m−2, 27 %). For this database, the statistical indicators are constant at inland stations showing the spatial consistency of the performances of CAMS-Rad dataset. At seashore stations, statistical indicators are more variable. The sea proximity influences CAMS-Rad performances and a weak spatial consistency is observed near seashore. For the HC3v5 database, the correlation coefficients are around 0.96–0.98 for E and 0.86–0.91 for KT. The bias ranges between −3 and 15 W m−2 (−1 % and 5 %). The standard deviation ranges between 48 (17 %) and 60 W m−2 (22 %). It is constant at inland stations and around 54 W m−2 (20 %) with peaks at Ell (59 W m−2, 21 %) and De Bilt (60 W m−2, 22 %). For this database, statistical indicators are constant at all stations as a whole, showing a strong spatial consistency of uncertainties. An exception is the standard deviation which tends to decrease at the seashore stations and is close to 50 W m−2 (17 %); this could be related to the general trend of decreasing standard deviation with increasing KT already reported in the scientific literature. As a whole, it is found that both databases are reliable sources on solar radiation in the Netherlands.
Identifying Complements and Substitutes of Products

Complements and substitutes are two typical product relationships that deserve consideration in online product recommendation. One of the key objectives of recommender systems is to promote cross-selling, which heavily relies on recommending the appropriate type of products in specific scenarios. Research on consumer behavior has shown that consumers usually prefer substitutes in the browsing stage whereas complements in the purchasing stage. Thus, it is of great importance to identify the complementary and substitutable relationships between products. In this article, we design a neural network based framework that integrates the textual content and non-textual information of online reviews to mine product relationships. For the textual content, we utilize methods such as LDA topic modeling to represent products in a succinct form called “embedding.” To capture the semantics of complementary and substitutable relationships, we design a modeling process that transfers the product embeddings into semantic features and incorporates additional non-textual factors of product reviews. Extensive experiments are conducted to verify the effectiveness of the proposed product relationship mining model. The advantages and robustness of our model are discussed from various perspectives.
DBSVEC: Density-Based Clustering Using Support Vector Expansion

DBSCAN is a popular clustering algorithm that can discover clusters of arbitrary shapes with broad applications. However, DBSCAN is computationally expensive, as it performs range queries for all the points to determine their neighbors and grow the clusters. To address this problem, we propose a novel approximate density-based clustering algorithm named DBSVEC. DBSVEC introduces support vectors into density-based clustering, which allows performing range queries only on a small subset of points called the core support vectors. This technique significantly improves the efficiency while retaining high-quality cluster results. We evaluate the performance of DBSVEC via extensive experiments on real and synthetic datasets. The results show that DBSVEC is up to three orders of magnitude faster than DBSCAN. Compared with the state-of-the-art approximate density-based clustering methods, DBSVEC is up to two orders of magnitude faster, and the clustering results of DBSVEC are more similar to those of DBSCAN.