Reviewerly/exHarmony huggingface dataset:
Facilitating Secure and Efficient Spatial Query Processing on the Cloud

Database outsourcing is a common cloud computing paradigm that allows data owners to take advantage of its on-demand storage and computational resources. The main challenge is maintaining data confidentiality with respect to untrusted parties i.e., cloud service provider, as well as providing relevant query results in real-time to authenticated users. Existing approaches either compromise confidentiality of the data or suffer from high communication cost between the server and the user. To overcome this problem, we propose a dual transformation and encryption scheme for spatial data, where encrypted queries are executed entirely at the service provider on the encrypted database and encrypted results are returned to the user. The user issues encrypted spatial range queries to the service provider and then uses the encryption key to decrypt the query response returned. This allows a balance between the security of data and efficient query response as the queries are processed on encrypted data at the cloud server. Moreover, we compare with existing approaches on large datasets and show that this approach reduces the average query communication cost between the authorized user and service provider, as only a single round of communication is required by the proposed approach.
Pauline Rowson video on choosing locations for crime novels

Pauline Rowson talks to Tony Smith about how she chooses the locations for her thriller novels and the DI Horton marine mystery crime novels.
On Dummett’s Pragmatist Justification Procedure

I show that propositional intuitionistic logic is complete with respect to an adaptation of Dummett’s pragmatist justification procedure. In particular, given a pragmatist justification of an argument, I show how to obtain a natural deduction derivation of the conclusion of the argument from, at most, the same assumptions.
Measurement-driven analog of adiabatic quantum computation for frustration-free Hamiltonians

The adiabatic quantum algorithm has drawn intense interest as a potential approach to accelerating optimization tasks using quantum computation. The algorithm is most naturally realised in systems which support Hamiltonian evolution, rather than discrete gates. We explore an alternative approach in which slowly varying measurements are used to mimic adiabatic evolution. We show that for certain Hamiltonians, which remain frustration-free all along the adiabatic path, the necessary measurements can be implemented through the measurement of random terms from the Hamiltonian. This offers a new, and potentially more viable, method of realising adiabatic evolution in gate-based quantum computer architectures.
An introduction to the Scottish mineral Geological Conservation Review sites

Scotland for its small size possesses a more complex geology than anywhere else in the world, consequently the country has a rich mineral legacy. There are around 650 mineral species now known to occur in Scotland; most of these are silicates, but the assemblage also includes oxides, sulphides and sulphates. Most formed by igneous or metamorphic processes. The igneous minerals produced depends on the nature of the intrusion, be it granitoid or basic to ultrabasic, and also on when it formed in the intrusive cycle; i.e., in early magmatic stages or in late-stage veining. Metamorphic minerals are the product of temperature, pressure and the nature of the precursor rock. A few minerals, such as carbonates and iron oxides are of sedimentary origin, and some formed by the addition of material (mostly metal elements) to rocks, a process known as mineralisation. The choice of mineral GCR sites is based on the presence of rare species and/or unusually large concentrations of minerals and/or the provision of good evidence of geological processes. All sites have national significance and the Meall Tairneachan-Creag An Loch Ba–Zn–Pb deposit is of world-class importance. The coverage of the nation’s mineral species is demonstrated by providing a brief description of them which highlights the sites at which they occur. Thus it will be seen that some minerals are predominant at a number of sites: e.g., galena is the main ore mineral at Leadhills-Wanlockhead, Tyndrum and Strontian. Similarly silica, mostly in the form of quartz is present at many sites, but only in its more exotic form such as agate or cairngorm is it the raison d’etre for its choice as a GCR site.
A Privacy Mechanism for Access Controlled Graph Data

There has been significant interest in the development of anonymization schemes for publishing graph data. However, privacy is a major concern in dealing with graph data. In this paper, an integrated framework for ensuring privacy in the presence of an authorization mechanism is proposed. Access control mechanisms provide additional safeguard against data breaches and ensure that only authorized information is available to end-users based on their assigned roles. The integrated framework highlights a tradeoff between privacy and authorized privileges. To attain a pre-specified privacy level, access privileges might need to be relaxed. For the proposed framework, we formulate the k-anonymous Bi-objective Graph Partitioning (k-BGP) problem and provide its hardness results. Heuristics solutions are developed to solve the constraint problem. The framework provides an anonymous view based on the target class of role-based workloads for graph data. The proposed heuristics are empirically evaluated and a detailed security analysis of the framework in terms of risk associated with re-identification attack is conducted.
The Entropic Envelope

This article introduces a computational model of the temporal experience of moving image sequences. The heart of the model, the concept of an entropic envelope, is based on information theoretic ideas. The concept is first described informally and then explained in a more mathematically precise manner. The article finally concludes with tentative reflections on the methodology of computation model building in cinema studies.
Low-overhead code concatenation approaches for universal quantum computation

Fault-tolerant quantum circuit design can be done by using a set of transversal gates. However, as there is no quantum error correction code with a universal set of transversal gates, several approaches have been proposed which, in combination of transversal gates, make universal fault-tolerant quantum computation possible. Magic state distillation, gauge fixing, code switching, code concatenation and pieceable fault tolerance are well-known examples of such approaches. However, the overhead of these approaches is one of the main bottlenecks for large-scale quantum computation. In this paper, two approaches for universal fault-tolerant quantum computation, mainly based on code concatenation, are proposed. The first approach combines the code concatenation approach with code switching, pieceable fault tolerance or magic state distillation and the second approach extends the nonuniformity of the concatenated codes by allowing to apply CNOT gates between different codes. The proposed approaches outperform the code concatenation approach in terms of both number of qubits and code distance and have also significantly less resource overhead than magic state distillation. This is achieved at the cost of reducing the effective distance of the concatenated code for implementing non-transversal gates.
Generate Believable Causal Plots with User Preferences Using Constrained Monte Carlo Tree Search

We construct a large scale of causal knowledge in term of Fabula elements by extracting causal links from existing common sense ontology ConceptNet5. We design a Constrained Monte Carlo Tree Search (cMCTS) algorithm that allows users to specify positive and negative concepts to appear in the generated stories. cMCTS can find a believable causal story plot. We show the merits by experiments and discuss the remedy strategies in cMCTS that may generate incoherent causal plots.
Constructing a paraphrase database for agglutinative languages

Paraphrase databases (PPDBs) are valuable resources for applications that use natural language processing (NLP) technology. In order to construct a high-quality PPDB for agglutinative languages, we propose a phrasal paraphrase extraction method; namely, affix modification-based bilingual pivoting method (AMBPM). AMBPM is suitable for agglutinative languages because it addresses the problems of lexical data sparsity and of not considering morphological word structure. In addition, we propose “improved AMBPM,” which is an improvement on AMBPM by addressing the problem of extracting incorrect stem paraphrase pairs caused by low semantic content stems (LSCSs) by using a rule-based filtering approach. In our experiments on AMBPM, we evaluate AMBPM and compare two state-of-the-art paraphrase extraction methods: the syntactic constraints-based bilingual pivoting method (SCBPM) and word embedding method. In the experiments on improved AMPBM, we evaluate our method and compare the resulting PPDB with four types of databases; PPDB constructed by using the original AMBPM, two PPDBs constructed by using two types of word-embedding-based methods (stem embedding and phrase embedding), and an existing thesaurus. The comparison is performed by using two NLP applications: sentential paraphrase generation and a question answering (QA) system. The experimental results demonstrate that, AMBPM outperforms the state-of-the-art paraphrase extraction methods. In addition, the improved AMBPM, which uses a rule-based filtering method, significantly improves AMBPM. Moreover, although a small amount of training data was used with no aid from linguistic resources, the PPDB constructed with the improved AMBPM is more useful than the four databases for the agglutinative language used in our study. We also publicized the Korean PPDB that was constructed using the improved AMBPM.
LNTP-MDBN: Big Data Integrated Learning Framework for Heterogeneous Image Set Classification

With the explosive growth of global data, the term Big Data describes the enormous size of dataset through the detailed analysis. The big data analytics revealed the hidden patterns and secret correlations among the values. The major challenges in Big data analysis are due to increase of volume, variety, and velocity. The capturing of images with multi-directional views initiates the image set classification which is an attractive research study in the volumetricbased medical image processing.This paper proposes the Local N-ary Ternary Patterns (LNTP) and Modified Deep Belief Network (MDBN) to alleviate the dimensionality and robustness issues. Initially, the proposed LNTP-MDBN utilizes the filtering technique to identify and remove the dependent and independent noise from the images. Then, the application of smoothening and the normalization techniques on the filtered image improves the intensity of the images.The LNTP-based feature extraction categorizes the heterogeneous images into different categories and extracts the features from each category. Based on the extracted features, the modified DBN classifies the normal and abnormal categories in the image set finally.The comparative analysis of proposed LNTP-MDBN with the existing pattern extraction and DBN learning models regarding classification accuracy and runtime confirms the effectiveness in mining applications.
Energy-Efficient Purpose Ordering Scheduler

Distributed applications are composed of multiple objects. An object is an unit of computation resource. Conflicting transactions have to be serialized to keep objects mutually consistent. In this paper, the energy-efficient purpose ordering (EEPO) scheduler is proposed to not only serialize multiple conflicting transactions in the significant order of purposes assigned to the transactions but also reduce the total electric energy consumption of servers by omitting meaningless methods.
Completeness Integrity Protection for Outsourced Databases Using Semantic Fake Data

As cloud storage and computing gains popularity, data entrusted to the cloud has the potential to be exposed to more people and thus more vulnerable to attacks. It is important to develop mechanisms to protect data privacy and integrity so that clients can safely outsource their data to the cloud. We present a method for ensuring data completeness which is one facet of the data integrity problem. Our approach converts a standard database to a Completeness Protected Database (CPDB) by inserting some semantic fake data before outsourcing it to the cloud. These fake data are initially produced using our generating function which uses Order Preserving Encryption, which allows the user to be able to regenerate these fake data and match them to fake data returned from a range query to check for completeness. The CPDB is innovative in the following ways: (1) fake data is deterministically generated but is semantically indistinguishable from other existing data; (2) since fake data is generated by deterministic functions, data owners do not need to locally store the fake data that have been inserted, instead they can re-generate fake data using the functions; (3) no costly data encryption/signature is used in our scheme compared to previous work which encrypt/sign the entire database.
A Fast Segmentation and Efficient Slice Reconstruction Technique for Head CT Images

Abstract The three-dimensional (3D) reconstruction of medical images usually requires hundreds of two-dimensional (2D) scan images. Segmentation, an obligatory part in reconstruction, needs to be performed for all the slices consuming enormous storage space and time. To reduce storage space and time, this paper proposes a three-stage procedure, namely, slice selection, segmentation and interpolation. The methodology will have the potential to 3D reconstruct the human head from minimum selected slices. The first stage of slice selection is based on structural similarity measurement, discarding the most similar slices with none or minimal impact on details. The second stage of segmentation of the selected slices is performed using our proposed phase-field segmentation method. Validation of our segmentation results is done via comparison with other deformable models, and results show that the proposed method provides fast and accurate segmentation. The third stage of interpolation is based on modified curvature registration-based interpolation, and it is applied to re-create the discarded slices. This method is compared to both standard linear interpolation and registration-based interpolation in 100 tomographic data sets. Results show that the modified curvature registration-based interpolation reconstructs missing slices with 96% accuracy and shows an improvement in sensitivity (95.802%) on par with specificity (95.901%).
Probability distributions of continuous measurement results for two noncommuting variables and postselected quantum evolution

We address the statistics of a simultaneous CWLM of two non-commuting variables on a few-state quantum system subject to a conditioned evolution. Both conditioned quantum measurement and that of two non-commuting variables differ drastically for either classical or quantum projective measurement, and we explore the peculiarities brought by the combination of the two. We put forward a proper formalism for the evaluation of the distributions of measurement outcomes. We compute and discuss the statistics in idealized and experimentally relevant setups. We demonstrate the visibility and manifestations of the interference between initial and final states in the statistics of measurement outcomes for both variables in various regimes. We analytically predict the peculiarities at the circle ${\cal O}^2_1+{\cal O}^2_2=1$ in the distribution of measurement outcomes in the limit of short measurement times and confirm this by numerical calculation at longer measurement times. We demonstrate analytically anomalously large values of the time-integrated output cumulants in the limit of short measurement times(sudden jump) and zero overlap between initial and final states, and give the detailed distributions. We present the numerical evaluation of the probability distributions for experimentally relevant parameters in several regimes and demonstrate that interference effects in the conditioned measurement can be accurately predicted even if they are small.
Parametric Synthesis of Electrical Devices Circuit Diagrams on the Basis of the Inverse Problem Solving

The method of the parametric synthesis of electrical devices circuit diagrams implemented on the basis of the strict inverse statement of the analysis problem is described in the paper. In this case, the input and output parameters of the device circuit diagram must be specified in accordance with the technical design assignment or can be defined from the equations of state on the basis of solving the direct problem. With the purpose of solving the inverse problem, that is, finding the diagram components parameters on the basis of the available information on the state variables vector and the input actions vector, the equation of state is used. The problem solving by the example of the series resonant inverter is presented, from which it follows that the parametric synthesis problem solution obtained on the basis of the suggested method is accurate. The conclusion is drawn that the unified methodology of the electrical devices parametric synthesis on the basis of the equations of state based on the strict inverse statement of the analysis problem is possible.
Techniques to Reduce Switching and Leakage Energy in Unrolled Block Ciphers

Energy consumption of block ciphers is critical in resource constrained devices. Unrolling has been explored in literature as a technique to increase efficiency by eliminating energy spent in loop control elements such as registers and multiplexers. However these savings are minimal and are offset by the increase in glitching power that comes with unrolling. We propose an efficient latch-based glitch filter for unrolled designs that reduces energy per encryption by an order of magnitude over a straightforward implementation, and by 28-45 percent over the best existing glitch filtering schemes. We explore the optimal number of glitch filters that should be used in order to minimize total energy, and provide estimates of the area cost. Partially unrolled designs also benefit from using our scheme with energies competitive to fully serialized implementations. Power gating to reduce leakage power and reuse of computed key enable unrolled designs to be more efficient than serialized ones without compromising latency advantages. We demonstrate our approach on the SIMON-128 and AES-128 block ciphers.
FPGA-Based Acceleration of Expectation Maximization Algorithm Using High-Level Synthesis

Expectation Maximization (EM) is a soft clustering algorithm which partitions data iteratively into M clusters. It is one of the most popular data mining algorithms that uses Gaussian Mixture Models (GMM) for probability density modeling and is widely used in applications such as signal processing and Machine Learning (ML). EM requires high computation time when dealing with large data sets. This paper presents an optimized implementation of EM algorithm on Stratix V and Arria 10 FPGAs using Intel FPGA Software Development Kit (SDK) for Open Computing Language (OpenCL). Comparison of performance and power consumption between Central Processing Unit (CPU), Graphics Processing Unit (GPU) and FPGA is presented for various dimension and cluster sizes. Compared to Intel <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> Xeon <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> CPU E5-2637, our fully optimized OpenCL model for EM targeting Arria 10 FPGA achieved up to 1000x speedup in terms of throughput (T) and 5395x speedup in terms of throughput per unit of power consumed (T/P). Compared to previous research on EM-GMM implementation on GPUs, Arria 10 FPGA obtained up to 64.74× speedup (T) and 486.78× speedup (T/P).
Periodic points and tail lengths of split polynomial maps modulo primes

Explicit formulas are obtained for the number of periodic points and maximum tail length of split polynomial maps over finite fields for affine and projective space. This work includes a detailed analysis of the structure of the directed graph for Chebyshev polynomials of non-prime degree in dimension 1 and the powering map in any dimension. The results are applied to an algorithm for determining the type of a given map through analysis of its cycle statistics modulo primes.
Techniques for Designing Bioinformatics Algorithms

This article deals with design techniques for algorithms, a fundamental topic for computational sciences, including bioinformatics. Owing to space limits, we can not hope to provide an in-depth discussion and thorough treatment of each of the design techniques that shall be presented. Rather, we aim at providing a modern introduction that, without sacrificing formal rigour when needed, emphasizes the pro and cons of each design technique, putting it in context.
Which is better? A modularized evaluation for topic popularity prediction

The purpose of topic popularity prediction is to predict whether a topic on the Internet will become popular. Various elegant models have been proposed for this problem. However, different datasets and evaluation metrics they use lead to low comparability. In this paper, we conduct a comprehensive survey, propose a modularized evaluation scheme for evaluating the models and apply it to existing methods. Our scheme has four modules: categorization; qualitative evaluation on several metrics; quantitative experiment on real world data; and final ranking with risk matrix and MinDis to reflect performances under different scenarios. Furthermore, we analyze the efficiency and contribution of features used in feature-oriented methods. Our work helps users compare models and select appropriate ones for different requirements.
An I-frame Methodology for Approximating Nonlinear Least Squares

The classic approach for estimating parameters of a model using historical data is to solve a nonlinear least squares optimization problem using numerical methods. We develop an I-frame methodology to solve the nonlinear least squares problem quickly which can be applied to both offline and online (where data is streamed in real time) parameter estimation. Using the concept of I-frames from imaging and animation, we approximate a solution to the nonlinear least squares problem via a two-step process, an I-frame optimization, and an incremental optimization. The I-frame optimization solves for the parameters using a subset of data points and the incremental optimization adjusts the parameters in between the I-frames. We show that the criterion of generating I-frames can affect the average squared error of the final solution. Our methodology benefits from being scalable as the number of parameters and amount of data increases with an appropriate I-frame generation criterion.
Multilevel Sentiment Analysis In Arabic

In this study, we aimed to improve the performance results of Arabic sentiment analysis. This can be achieved by investigating the most successful machine learning method and the most useful feature vector to classify sentiments in both term and document levels into two (positive or negative) categories. Moreover, specification of one polarity degree for the term that has more than one is investigated. Also to handle the negations and intensifications, some rules are developed. According to the obtained results, Artificial Neural Network classifier is nominated as the best classifier in both term and document level sentiment analysis (SA) for Arabic Language. Furthermore, the average F-score achieved in the term level SA for both positive and negative testing classes is 0.92. In the document level SA, the average F-score for positive testing classes is 0.94, while for negative classes is 0.93.