Reviewerly/exHarmony huggingface dataset:
Secure MPC: Laziness Leads to GOD

Motivated by what we call "honest but lazy" parties in the context of secure multi party computation, we revisit the notion of multi-key FHE schemes (MFHE). In MFHE, any message encrypted using a public key $$pk_i$$ can be "expanded" so that the resulting ciphertext is encrypted with respect to a set of public keys $$(pk_1,..,pk_n)$$ . Such expanded ciphertexts can be homomorphically evaluated with respect to any circuit to generate a ciphertext $$ct$$ . Then, this ciphertext $$ct$$ can be partially decrypted using a secret key $$sk_i$$ (corresponding to the public key $$pk_i$$ ) to produce a partial decryption $$p_i$$ . Finally, these partial decryptions $$\{p_{i}\}_{i\in [n]}$$ can be combined to recover the output. However, this definition of MFHE works only for n-out-of-n access structures and, thus, each node in the system is a point of failure. In the context of "honest but lazy" parties, it is necessary to be able to decrypt even when only given a subset of partial decryptions (say t out of n). In order to solve this problem, we introduce a new notion of multi-key FHE designed to handle arbitrary access patterns that can reconstruct the output. We call it a threshold multi-key FHE scheme (TMFHE). Our main contributions are the following:
Exploiting multiplex data relationships in Support Vector Machines

In this paper, a novel method for introducing multiplex data relationships to the SVM optimization process is presented. Different properties about the training data are encoded in graph structures, in the form of pairwise data relationships. Then, they are incorporated to the SVM optimization problem, as modified graph-regularized basekernels, each highlighting a different property about the training data. The contribution of each graph-regularized kernel to the SVM classification problem, is estimated automatically. Thereby, the solution of the proposed modified SVM optimization problem lies in a regularized space, where data similarity is expressed by a linear combination of multiple single-graph regularized kernels. The proposed method exploits and extends the findings of Multiple Kernel Learning and graph-based SVM method families. It is shown that the available kernel options for the former can be broadened, and the exhaustive parameter tuning for the latter can be eliminated. Moreover, both method families can be considered as special cases of the proposed formulation, hereafter. Our experimental evaluation in visual data classification problems denote the superiority of the proposed method. The obtained classification performance gains can be explained by the exploitation of multiplex data relationships, during the classifier optimization process.
Automatic CV processing for scientific research using data mining algorithm

To manage and measure the performance of scientific research at the university, managers or policymakers need synthetic indicators that are cleverly grouped in several indicators which aim to offer to the leaders the necessary tools, so as to improve scientific research. The governance of information system has posed a serious challenge for the leadership of universities especially through the use of decision aid. In order to improve the information system, especially scientific research, a study of automatizing curriculum vitae for researchers of different disciplines belonging to various research laboratories is discussed in this paper. The use of natural language processing with data mining classifier Decision Tree is presented in order to predict the field of work of each researcher. The choice of Decision Tree classifier among One Rule classifier and Naive Bayes classifier is not arbitrary; it is chosen by comparing performance metrics- such as Precision, Recall, F-measure, Correctly Classify Instance, Incorrectly Classify Instance, Kappa Statistic, Root Mean Squad Error, Relative Absolute Error, and Root Relative Squad Error.
Heterogeneity Aware Workload Management in Distributed Sustainable Datacenters

The tremendous growth of cloud computing and large-scale data analytics highlight the importance of reducing datacenter power consumption and environmental impact of brown energy. While many Internet service operators have at least partially powered their datacenters by green energy, it is challenging to effectively utilize green energy due to the intermittency of renewable sources, such as solar or wind. We find that the geographical diversity of internet-scale services can be carefully scheduled to improve the efficiency of applying green energy in datacenters. In this paper, we propose a holistic heterogeneity-aware cloud workload management approach, sCloud, that aims to maximize the system goodput in distributed self-sustainable datacenters. sCloud adaptively places the transactional workload to distributed datacenters, allocates the available resource to heterogeneous workloads in each datacenter, and migrates batch jobs across datacenters, while taking into account the green power availability and QoS requirements. We formulate the transactional workload placement as a constrained optimization problem that can be solved by nonlinear programming. Then, we propose a batch job migration algorithm to further improve the system goodput when the green power supply varies widely at different locations. Finally, we extend sCloud by integrating a flexible batch job manager to dynamically control the job execution progress without violating the deadlines. We have implemented sCloud in a university cloud testbed with real-world weather conditions and workload traces. Experimental results demonstrate sCloud can achieve near-to-optimal system performance while being resilient to dynamic power availability. sCloud with the flexible batch job management approach outperforms a heterogeneity-oblivious approach by 37 percent in improving system goodput and 33 percent in reducing QoS violations.
Trustworthy Website Detection Based on Social Hyperlink Network Analysis

Trustworthy website detection plays an important role in providing users with meaningful web pages, from a search engine. Current solutions to this problem, however, mainly focus on detecting spam websites, instead of promoting more trustworthy ones. In this paper, we propose the enhanced OpinionWalk (EOW) algorithm to compute the trustworthiness of all websites and identify trustworthy websites with higher trust values. The proposed EOW algorithm treats the hyperlink structure of websites as a social network and applies social trust analysis to calculate the trustworthiness of individual websites. To mingle social trust analysis and trustworthy website detection, we model the trustworthiness of a website based on the quantity and quality of websites it points to. We further design a mechanism in EOW to record which websites' trustworthiness need to be updated while the algorithm “walks” through the network. As a result, the execution of EOW is reduced by 27.1 percent, compared to the OpinionWalk algorithm. Using the public dataset, WEBSPAM-UK2006, we validate the EOW algorithm and analyze the impacts of seed selection, size of seed set, maximum searching depth and starting nodes, on the algorithm. Experimental results indicate that EOW algorithm identifies 5.35 to 16.5 percent more trustworthy websites, compared to TrustRank.
A novel security media cloud framework

The challenges facing media cloud mainly come from two aspects, which are data security issues (copyright management and privacy protection) and security risks caused by cloud computing. This paper analyzes the security threats that media cloud faces in providing services, building a “cloud-edge-end” media cloud service hierarchy. For the first time, this paper constructs a security media cloud framework with focus on protecting multimedia data and services. The framework contains three security boundaries, two levels of data storage protection and a security access control protocol adapted to video services. Analysis shows that the proposed framework has good security and is compatible with a variety of existing media cloud frameworks.
Efficient Multi-Class Probabilistic SVMs on GPUs

Recently, many researchers have been working on improving other traditional machine learning algorithms (besides deep learning) using high-performance hardware such as Graphics Processing Units (GPUs). The recent success of machine learning is not only due to more effective algorithms, but also more efficient systems and implementations. In this paper, we propose a novel and efficient solution to multi-class SVMs with probabilistic output (MP-SVMs) accelerated by GPUs. MP-SVMs are an important technique for many pattern recognition applications. However, MP-SVMs are very time-consuming to use, because using an MP-SVM classifier requires training many binary SVMs and performing probability estimation by combining results of all the binary SVMs. GPUs have much higher computation capability than CPUs and are potentially excellent hardware to accelerate MP-SVMs. Still, two key challenges for efficient GPU accelerations for MP-SVM are: (i) many kernel values are repeatedly computed as a binary SVM classifier is trained iteratively, resulting in repeated accesses to the high latency GPU memory; (ii) performing training or estimating probability in a highly parallel way requires a much larger memory footprint than the GPU memory. To overcome the challenges, we propose a solution called GMP-SVM which exploits two-level (i.e., binary SVM level and MP-SVM level) optimization for training MP-SVMs and high parallelism for estimating probability. GMP-SVM reduces high latency memory accesses and memory consumption through batch processing, kernel value reusing and sharing, and support vector sharing. Experimental results show that GMP-SVM outperforms the GPU baseline by two to five times, and LibSVM with OpenMP by an order of magnitude. Also, GMP-SVM produces the same SVM classifier as LibSVM.
Improving lexical coverage of text simplification systems for Spanish

The current bottleneck of all data-driven lexical simplification (LS) systems is scarcity and small size of parallel corpora (original sentences and their manually simplified versions) used for training. This is especially pronounced for languages other than English. We address this problem, taking Spanish as an example of such a language, by building new simplification-specific datasets of synonyms and paraphrases using freely available resources. We test their usefulness in the LS task by adding them, in various combinations, to the existing text simplification (TS) training dataset in a phrase-based statistical machine translation (PBSMT) approach. Our best systems significantly outperform the state-of-the-art LS systems for Spanish, by the number of transformations performed and the grammaticality, simplicity and meaning preservation of the output sentences. The results of a detailed manual analysis show that some of the newly built TS resources, although they have a good lexical coverage and lead to a high number of transformations, often change the original meaning and do not generate simpler output when used in this PBSMT setup. The good combinations of these additional resources with the TS training dataset and a good choice of language model, in contrast, improve the lexical coverage and produce sentences which are grammatical, simpler than the original, and preserve the original meaning well.
Triple storage for random-access versioned querying of RDF archives

When publishing Linked Open Datasets on the Web, most attention is typically directed to their latest version. Nevertheless, useful information is present in or between previous versions. In order to exploit this historical information in dataset analysis, we can maintain history in RDF archives. Existing approaches either require much storage space, or they expose an insufficiently expressive or efficient interface with respect to querying demands. In this article, we introduce an RDF archive indexing technique that is able to store datasets with a low storage overhead, by compressing consecutive versions and adding metadata for reducing lookup times. We introduce algorithmsbased on this technique for efficiently evaluating queries at a certain version, between any two versions, and for versions. Using the BEAR RDF archiving benchmark, we evaluate our implementation, called OSTRICH. Results show that OSTRICH introduces a new tradeoff regarding storage space, ingestion time, and querying efficiency. By processing and storing more metadata during ingestion time, it significantly lowers the average lookup time for versioning queries. OSTRICH performs better for many smaller dataset versions than for few larger dataset versions. Furthermore, it enables efficient offsets in query result streams, which facilitates random access in results. Our storage technique reduces query evaluation time for versioned queries through a preprocessing step during ingestion, which only in some cases increases storage space when compared to other approaches. This allows data owners to store and query multiple versions of their dataset efficiently, lowering the barrier to historical dataset publication and analysis.
Improving Object Retrieval Quality by Integration of Similarity Propagation and Query Expansion

Re-ranking is an essential step for accurate image retrieval, due to its well-known power in performance improvement. Although numerous works have been proposed for re-ranking, many of them are only customized for a certain image representation model. In contrast to most existing techniques, we develop generalized re-ranking algorithms that are applicable to different kinds of image encodings in this paper. We first employ a quite successful theory of similarity propagation to reconstruct vectors of a query and its top ranked images and, subsequently, get a re-ranked list by comparing the new image vectors. Furthermore, considering that the just mentioned strategy is directly compatible with query expansion and, thus, in order to leverage advantages of this milestone, we then propose integrating them into a unified framework for maximizing re-ranking benefits. Our re-ranking algorithms are memory and computation efficient, and experimental results on benchmark datasets demonstrate that they compare favorably with the state of the art. Our code is available at https://github.com/MaJinWakeUp/rerank.
A hierarchical structure for optimal resource allocation in geographically distributed clouds

Due to the growth of users’ requests for various resources in cloud computing, the optimal resource allocation is one of the most important challenges in cloud environments. The optimal resource allocation is achieved by considering user requirements stated in Service Level Agreements (SLAs) and the Quality of Services (QoSs) provided by resources. Since some user requirements (objectives) conflict with some others, a optimal trade-off between them is required in the selection of resources. Obtaining such a trade-off is a complicated and NP-hard problem because we may come up with a lot of permutations (choices) of the resources with the desired QoS. If a cloud environment is geographically distributed, the problem becomes more complicated because in the geographically distributed cloud there are a lot of candidate datacenters with qualified resources. The user requirements considered in this paper are availability and reliability of resources should be maximized and resource cost and response time should be minimized as well as the minimization of the network traffic. The maximization of and the minimization of the requirements conflict with each other; therefore a trade-off between them is required. In this paper, a hierarchical structure with two resource selections methods called Simplex Linear Programming (SLP) Method and GrEA-based method are used where the hierarchical structure is used to present connections between distributed datacenters and the methods are used to select optimal resources among the datacenters. The most important feature of the hierarchical structure is to prevent the occurrence of an accumulation of requests in a datacenter leading to increase the rate of finding optimal VMs. Moreover, in most studies on geographically distributed clouds, one user requirement is considered for the optimal selection of resources; however, in this study, datacenters are selected based on 4 user requirements as well as the network traffic. Moreover, while in most studies, the requests for one user is considered at one time, this work does the requests for several users sent to the cloud simultaneously. Our results showed an optimal trade-off between user conflicting requirements and the resource utilization by 92%. Moreover, the performance indexes coverage ratio and the maximum spread indexes showed more performance compared to related studies.
Next Generation Indexing for Genomic Intervals

One-dimensional intervals incremental inverted index (Di4) is a multi-resolution, single-dimension indexing framework for efficient, scalable, and extensible computation of genomic interval expressions. The framework has a tri-layer architecture: the semantic layer provides orthogonal and generic means (including the support of user-defined function) of sense-making and higher-lever reasoning from region-based datasets; the logical layer provides building blocks for region calculus and topological relations between intervals; the physical layer abstracts from persistence technology and makes the model adaptable to variety of persistence technologies, spanning from small-scale (e.g., B+tree) to large-scale (e.g., LevelDB). The extensibility of Di4 to application scenarios is shown with an example of comparative evaluation of ChIP-seq and DNase-Seq replicates. Performance of Di4 is benchmarked for small and large scale scenarios under common bioinformatics application scenarios. Di4 is freely available from https://genometric.github.io/Di4.
Online Mutual Foreground Segmentation for Multispectral Stereo Videos

The segmentation of video sequences into foreground and background regions is a low-level process commonly used in video content analysis and smart surveillance applications. Using a multispectral camera setup can improve this process by providing more diverse data to help identify objects despite adverse imaging conditions. The registration of several data sources is however not trivial if the appearance of objects produced by each sensor differs substantially. This problem is further complicated when parallax effects cannot be ignored when using close-range stereo pairs. In this work, we present a new method to simultaneously tackle multispectral segmentation and stereo registration. Using an iterative procedure, we estimate the labeling result for one problem using the provisional result of the other. Our approach is based on the alternating minimization of two energy functions that are linked through the use of dynamic priors. We rely on the integration of shape and appearance cues to find proper multispectral correspondences, and to properly segment objects in low contrast regions. We also formulate our model as a frame processing pipeline using higher order terms to improve the temporal coherence of our results. Our method is evaluated under different configurations on multiple multispectral datasets, and our implementation is available online.
Dynamics of simulated quantum annealing in random Ising chains

Simulated quantum annealing (SQA) is a classical computational strategy that emulates a quantum annealing (QA) dynamics through a path-integral Monte Carlo whose parameters are changed during the simulation. Here we apply SQA to the one-dimensional transverse field Ising chain, where previous works have shown that, in the presence of disorder, a coherent QA provides a quadratic speedup with respect to classical simulated annealing, with a density of Kibble-Zurek defects decaying as ${\ensuremath{\rho}}_{\mathrm{KZ}}^{\mathrm{QA}}\ensuremath{\sim}{({log}_{10}\ensuremath{\tau})}^{\ensuremath{-}2}$ as opposed to ${\ensuremath{\rho}}_{\mathrm{KZ}}^{\mathrm{SA}}\ensuremath{\sim}{({log}_{10}\ensuremath{\tau})}^{\ensuremath{-}1}, \ensuremath{\tau}$ being the total annealing time, while for the ordered case both give the same power law ${\ensuremath{\rho}}_{\mathrm{KZ}}^{\mathrm{QA}}\ensuremath{\approx}{\ensuremath{\rho}}_{\mathrm{KZ}}^{\mathrm{SA}}\ensuremath{\sim}{\ensuremath{\tau}}^{\ensuremath{-}1/2}$. We show that the dynamics of SQA, while correctly capturing the Kibble-Zurek scaling ${\ensuremath{\tau}}^{\ensuremath{-}1/2}$ for the ordered case, is unable to reproduce the QA dynamics in the disordered case at intermediate $\ensuremath{\tau}$. We analyze and discuss several issues related to the choice of the Monte Carlo moves (local or global in space), the time-continuum limit needed to eliminate the Trotter-discretization error, and the long autocorrelation times shown by a local-in-space Monte Carlo dynamics for large disordered samples.
A hybrid universal blind quantum computation

In blind quantum computation (BQC), a client delegates her quantum computation to a server with universal quantum computers who learns nothing about the client's private information. In measurement-based BQC model, entangled states are generally used to realize quantum computing. However, to generate a large-scale entangled state in experiment becomes a challenge issue. In circuit-based BQC model, single-qubit gates can be realized precisely, but entangled gates are probabilistically successful. This remains a challenge to realize entangled gates with a deterministic method in some systems. To solve above two problems, we propose the first hybrid universal BQC protocol based on measurements and circuits, where the client prepares single-qubit states and the server performs universal quantum computing. We analyze and prove the correctness, blindness and verifiability of the proposed protocol.
Improving comprehension of knowledge representation languages: A case study with Description Logics

Knowledge representation languages are frequently difficult to understand, particularly for those not trained in formal logic. This is the case for Description Logics, which have been adopted for knowledge representation on the Web and in a number of application areas. This work looks at the difficulties experienced with Description Logics; and in particular with the widely-used Manchester OWL Syntax, which employs natural language keywords. The work comprises three studies. The first two identify a number of difficulties which users experience, e.g. with negated intersection, functional properties, the use of subproperties and restrictions. Insights from cognitive psychology and the study of language are applied to understand these difficulties. Whilst these difficulties are in part inherent in reasoning about logic, and Description Logics in particular, they are made worse by the syntax. In the third study, alternative syntactic constructs are proposed which demonstrate some improvement in accuracy and efficiency of comprehension. In addition to proposing alternative syntactic constructs, the work makes some suggestions regarding training and support systems for Description Logics.
QPCF: Higher-Order Languages and Quantum Circuits

qPCF is a paradigmatic quantum programming language that extends PCF with quantum circuits and a quantum co-processor. Quantum circuits are treated as classical data that can be duplicated and manipulated in flexible ways by means of a dependent type system. The co-processor is essentially a standard QRAM device, albeit we avoid to store permanently quantum states in between two co-processor’s calls. Despite its quantum features, qPCF retains the classic programming approach of PCF. We introduce qPCF syntax, typing rules, and its operational semantics. We prove fundamental syntactic properties of the system. Moreover, we provide some higher-order examples of circuit encoding.