Reviewerly/exHarmony huggingface dataset:
Description, Implementation, and Evaluation of a Generic Design for Tabled CLP

Abstract Logic programming with tabling and constraints (TCLP, tabled constraint logic programming ) has been shown to be more expressive and in some cases more efficient than LP, CLP, or LP + tabling. Previous designs of TCLP systems did not fully use entailment to determine call/answer subsumption and did not provide a simple and well-documented interface to facilitate the integration of constraint solvers in existing tabling systems. We study the role of projection and entailment in the termination, soundness, and completeness of TCLP systems and present the design and an experimental evaluation of Mod TCLP, a framework that eases the integration of additional constraint solvers. Mod TCLP views constraint solvers as clients of the tabling system, which is generic w.r.t. the solver and only requires a clear interface from the latter. We validate our design by integrating four constraint solvers: a previously existing constraint solver for difference constraints, written in C; the standard versions of Holzbaur’s and , written in Prolog; and a new constraint solver for equations over finite lattices. We evaluate the performance of our framework in several benchmarks using the aforementioned solvers. Mod TCLP is developed in Ciao Prolog, a robust, mature, next-generation Prolog system.
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A convolutional code C over Z p r ((D)) is a Z p r ((D))-submodule of Z n p r ((D)) that admits a polynomial set of generators, where Z p r ((D)) stands for the ring of (semi-infinity) Laurent series.In this paper we study several structural properties of its dual C ⊥ .We use these results to provide a constructive algorithm to build an explicit generator matrix of C ⊥ .Moreover, we show that the transpose of such a matrix is a parity-check matrix (also called syndrome former) of C.
Variational approximation error in non-negative matrix factorization

Non-negative matrix factorization (NMF) is a knowledge discovery method that is used in many fields. Variational inference and Gibbs sampling methods for it are also well-known. However, the variational approximation error has not been clarified yet, because NMF is not statistically regular and the prior distribution used in variational Bayesian NMF (VBNMF) has zero or divergence points. In this paper, using algebraic geometrical methods, we theoretically analyze the difference in negative log evidence (a.k.a. free energy) between VBNMF and Bayesian NMF, i.e., the Kullback–Leibler divergence between the variational posterior and the true posterior. We derive an upper bound for the learning coefficient (a.k.a. the real log canonical threshold) in Bayesian NMF. By using the upper bound, we find a lower bound for the approximation error, asymptotically. The result quantitatively shows how well the VBNMF algorithm can approximate Bayesian NMF; the lower bound depends on the hyperparameters and the true non-negative rank. A numerical experiment demonstrates the theoretical result.
Aspherical Relative Presentations all Over Again

The concept of asphericity for relative group presentations was introduced twenty-five years ago. Since then, the subject has advanced and detailed asphericity classifications have been obtained for various families of one-relator relative presentations. Through this work the definition of asphericity has evolved and new applications have emerged. In this article we bring together key results on relative asphericity, update them, and exhibit them under a single set of definitions and terminology. We describe consequences of asphericity and present techniques for proving asphericity and for proving non-asphericity. We give a detailed survey of results concerning one-relator relative presentations where the relator has free product length four.
A fast learning algorithm based on extreme learning machine for regular fuzzy neural network

The regular fuzzy neural network (RFNN) is a kind of fuzzy neural network by fuzzifying the feed-forward neural network. The RFNN can directly deal with the language information and it has the merits of fuzzy system and neural network. It is presented a fast learning algorithm based on the extreme learning machine (ELM) for the RFNN in this paper. The RFNN referred here is a three-layer feed-forward fuzzy neural network and the connected weights in the RFNN are all fuzzy numbers. A simulation example is given to approximately realize the fuzzy if-then rules by the RFNN. The results show that the RFNN trained by the proposed algorithm has good performance and approximation ability.
Information Flows at Inter-team Boundaries in Agile Information Systems Development

Agile software development methods are being used on larger projects thus the study of inter-team communication are becoming an important topic of interest for researchers. This research addresses inter-team communication by exploring the tools and three different boundaries, inter-team, team and customers, and geographically separated teams. In this research, we gathered data from semi-structured face-to-face interviews which were analyzed following the grounded theory approach. Our study reveals consensus from different teams on the importance of virtual Kanban boards. Also, some team members tend to adapt to other teams' preferred communication tool. We observed challenges around interdependent user stories among the different teams and highlighted the problems that rise at the different boundaries.
Motivating Students in Collaborative Activities With Game-Theoretic Group Recommendations

Recommending educational resources to groups of students is a common task in collaborative learning contexts. However, differences in within-group motivational factors might lead to conflicts in students' intention to use the resources. Previous methods fail to achieve high goodness of recommendation for the majority of students in heterogeneous groups. This study demonstrates a game-theoretic solution for recommending educational resources to homogeneous and heterogeneous groups. The group members are the players, the resources comprise the set of possible actions, and selecting those items that will maximize all students' motivation in the collaborative activity is a problem of finding the Nash Equilibrium (NE). In case the NE is Pareto efficient, none of the players can get more payoff (motivation) without decreasing the payoff of any other player, indicating an optimal benefit for the group as a whole. The suggested approach was empirically evaluated in a controlled experiment with a real dataset. The relevance of each delivered item to its corresponding students was explored both from the perspective of the group and its the individual students. The accuracy of the predicted group/individual motivation, the goodness of the ranked list of recommendations, and the problem-solving performance for the treatment group were significantly higher compared to the control groups. Limitations of the approach, as well as future work plans conclude the paper.
Global Image Dehazing via Frequency Perception Filtering

In outdoor scenes, haze limits the visibility of images, and degrades people’s judgement of the objects. In this paper, based on an assumption of human visual perception in frequency domain, a novel image haze removal filtering is proposed. Combining this assumption with the theory of frequency domain filtering, we first estimate the cut-off frequency to divide the frequency domain of the hazy image into three components — low-frequency domain, intermediate-frequency domain and high-frequency domain. Then, by introducing the weighting factors, the three components are recombined together. After the theoretical deduction of frequency domain, the establishment of the actual model and adjusting the cut-off frequency and weighting factors, we finally acquire a global and adaptive filtering. This filtering can restore the details and the contours of the images, which have less noise, and improve the visibility of the objects in hazy images. Moreover, our method is simple in structure and strongly applicable, and rarely affected by parameters. Our algorithm is stable and performs well in heavy fog and the scene changes.
Singular spectral analysis‐based denoising without computing singular values via augmented Lagrange multiplier algorithm

This study proposes an augmented Lagrange multiplier-based method to perform the singular spectral analysis-based denoising without computing the singular values. In particular, the one-dimensional (1D) signal is first mapped to a trajectory matrix using the window length L. Second, the trajectory matrix is represented as the sum of the signal dominant matrix and the noise-dominant matrix. The determination of these two matrices is formulated as an optimisation problem with the objective function being the sum of the rank of the signal dominant matrix and the norm of the noise-dominant matrix. This study employs the Schatten q-norm operator with and the double nuclear-norm penalty for approximating the rank operator as well as the minimum concave penalty (MCP)-norm operator for approximating the -norm operator. Third, some auxiliary variables are introduced and the augmented Lagrange multiplier algorithm is applied to find the optimal solution. Finally, the 1D denoised signal is obtained by applying the diagonal averaging method to the obtained signal dominant matrix. Computer numerical simulation results show that the authors' proposed method outperforms the existing methods.
Pattern detection and design rationale traceability: an integrated approach to software design quality

Design rationale is the synthesis of the structure that is consistent with the model of an artefact, decisions, alternative approaches, and the reasoning behind it. Hence, bypassing it will result in poor quality systems engineering. Ambiguous representation of design rationale goals is just one of the many limitations that contributes to the intricacy of design patterns, thereby this research aims to introduce an approach to support the structuring, evaluation, and analysis of design patterns. The authors have proposed a method and implemented an associated tool with a mechanism that validates the class relationships and properties of design patterns comprehensively, resulting in a reliable pattern detection result. The preliminary results of the proposed research show that although detection and evaluation of a design pattern require some overhead using the approach, it does contribute to the confidence of the developers and gives affirmation that the design pattern implemented in the source code achieves its defined rationale goals based on the gang of four definition of design patterns through the reliable pattern detection output and straightforward evaluation result. The conclusion could be drawn that through the proposed approach, the preliminary result resolves the intricacy of design rationale traceability while achieving software quality is permissible.
Sequential pattern mining for ICT risk assessment and management

ICT risk assessment and management relies on the analysis of data on the joint behavior of a target system and its attackers. The tools in the Haruspex suite model intelligent, goal-oriented attackers that reach their goals through sequences of attacks. The tools synthetically generate these sequences through a Monte Carlo method that runs multiple simulations of the attacker behavior. This paper presents a sequential pattern mining analysis of the attack sequence database to extract a high-level and succinct understanding of the attacker strategies against the system to assess. Such an understanding is expressed as a set of sequential patterns that cover, and possibly partition, the attack sequences. This set can be extracted in isolation, or in contrast with the behavior of other attackers. In the latter case, the patterns represent a signature of the behavior of an attacker. The dynamic tools of the suite use this signature to deploy dynamic counter-measures that reduce the security risk. We formally motivate the need for using the class of maximal sequential patterns in covering attack sequences, instead of frequent or closed sequential patterns. When contrasting the behavior of different attackers, we resort to distinguishing sequential patterns. We report an extensive experimentation on a system with 36 nodes, 6 attackers, and 600K attack sequences.
Quantum encoded quantum evolutionary algorithm for the design of quantum circuits

In this paper we present Quanrum Encoded Quantum Evolutionary Algorithm (QEQEA) and compare its performance against a a classical GPU accelerated Genetic Algorithm (GPUGA). The proposed QEQEA differs from existing quantum evolutionary algorithms in several points: representation of candidates circuits is using qubits and qutrits and the proposed evolutionary operators can theoretically be implemented on quantum computer provided a classical control exists. The synthesized circuits are obtained by a set of measurements performed on the encoding units of quantum representation. Both algorithms are accelerated using (general purpose graphic processing unit) GPGPU. The main target of this paper is not to propose a completely novel quantum genetic algorithm but to rather experimentally estimate the advantages of certain components of genetic algorithm being encoded and implemented in a quantum compatible manner. The algorithms are compared and evaluated on several reversible and quantum circuits. The results demonstrate that on one hand the quantum encoding and quantum implementation compatible implementation provides certain disadvantages with respect to the classical evolutionary computation. On the other hand, encoding certain components in a quantum compatible manner could in theory allow to accelerate the search by providing small overhead when built in quantum computer. Therefore acceleration would in turn counter weight the implementation limitations.
Tectonometamorphic Controls on Archaean Gold Mineralization in the Barberton Greenstone Belt, South Africa

The New Consort gold mine in the Paleo- to Mesoarchaean Barberton greenstone belt, South Africa, forms one of the oldest shear-zone hosted gold deposits on Earth. In contrast to the mainly greenschist facies gold deposits in the west-central parts of the Barberton greenstone belt, the mine is situated in upper greenschist to amphibolite facies wall rocks in the structural hanging wall of an exhumed gneiss dome along the northern margin of the belt. The structural controls and tectonic setting, but also the conditions and timing of the mineralisation and the fluid sources are controversial and far from being resolved. A detailed evaluation of the currently available structural, petrological, and geochronological data indicates a complex tectono-metamorphic evolution and polyphase mineralization over a protracted period of time, with the main phase of gold mineralization occurring very late in the overall tectonic evolution of the belt. Gold deposition occurred during a series of hydrothermal events related to the late-stage and, on a regional scale, rather cryptic evolution of the belt. The large time gap of almost 200 Ma between the main phase of accretionary tectonics at ca. 3230 Ma and the main episodes of gold mineralization remains somewhat enigmatic and distinguishes the Barberton-type mineralization from most other shear zone hosted deposits.
Using discussion logic in analyzing online group discussions: A text mining approach

We propose a Discussion Logic-based Text Analytics (DiLTA) framework, which combines theories developed in social science and text mining fields. The framework extracts features that uncover discussion logic and uses these features in analyzing online discussions. A series of models are proposed including conversation disentanglement, coherence analysis, and visualization. Validation experiments showed that DiLTA achieved significantly superior performance over existing text analytics methods in reconstructing internal structure. A case study using DiLTA-enabled visualization on a healthcare forum illustrates the great potential of DiLTA in assisting comprehension of the internal linkage, structure, and logic of online group discussions.
Affine Invariants of Vector Fields

Vector fields are a special kind of multidimensional data, which are in a certain sense similar to digital color images, but are distinct from them in several aspects. In each pixel, the field is assigned to a vector that shows the direction and the magnitude of the quantity, which has been measured. To detect the patterns of interest in the field, special matching methods must be developed. In this paper, we propose a method for the description and matching of vector field patterns under an unknown affine transformation of the field. Unlike digital images, transformations of vector fields act not only on the spatial coordinates but also on the field values, which makes the detection different from the image case. To measure the similarity between the template and the field patch, we propose original invariants with respect to total affine transformation. They are designed from the vector field moments. It is demonstrated by experiments on real data from fluid mechanics that they perform significantly better than potential competitors.
Design and implementation of reconfigurable acceleration for in-memory distributed big data computing

Apache Spark is an efficient distributed computing framework for big data processing. It supports in-memory computation of RDDs (Resilient Distributed Datasets) and provides a provision of reusability, fault tolerance, and real-time stream processing. However, the tasks in Spark framework are only performed on CPU. The low degree of parallelism and power inefficiency of CPU may restrict the performance and scalability of the cluster. In order to improve the performance and power dissipation of the data center, heterogeneous accelerators such as FPGA, GPU, MIC (Many Integrated Core) exhibit more efficient performance than general-purpose processors in big data processing. In this work, we propose a framework to integrate FPGA accelerators into a Spark cluster, which achieves performance improvement and power dissipation reduction for distributed applications. We propose a method for connecting Spark with OpenCL application which is a standard for cross-platform, parallel programming of diverse processors and widely used in heterogeneous computing, and use FPGA to accelerate the Spark tasks developed with Python. We illustrate the performance and the energy efficiency of FPGA based Spark framework with a case study of K-means algorithm acceleration. The results show that FPGA based Spark implementation achieves 3.5x speedup and 4.06x energy efficiency over original Spark framework.
A Generalized and Robust Anti-Predatory Nature-Inspired Algorithm for Complex Problems

This article describes how nature-inspired algorithms (NIAs) have evolved as efficient approaches for addressing the complexities inherent in the optimization of real-world applications. These algorithms are designed to imitate processes in nature that provide some ways of problem solving. Although various nature-inspired algorithms have been proposed by various researchers in the past, a robust and computationally simple NIA is still missing. A novel nature-inspired algorithm that adapts to the anti-predatory behavior of the frog is proposed. The algorithm mimics the self defense mechanism of a frog. Frogs use their reflexes as a means of protecting themselves from the predators. A mathematical formulation of these reflexes forms the core of the proposed approach. The robustness of the proposed algorithm is verified through performance evaluation on sixteen different unconstrained mathematical benchmark functions based on best and worst values as well as mean and standard deviation of the computed results. These functions are representative of different properties and characteristics of the problem domain. The strength and robustness of the proposed algorithm is established through a comparative result analysis with six well-known optimization algorithms, namely: genetic, particle swarm, differential evolution, artificial bee colony, teacher learning and Jaya. The Friedman rank test and the Holm-Sidak test have been used for statistical analysis of obtained results. The proposed algorithm ranks first in the case of mean result and scores second rank in the case of “standard deviation”. This proves the significance of the proposed algorithm.
Isomorphism testing of groups of cube-free order

A group $G$ has cube-free order if no prime to the third power divides $|G|$. We describe an algorithm that given two cube-free groups $G$ and $H$ of known order, decides whether $G\cong H$, and, if so, constructs an isomorphism $G\to H$. If the groups are input as permutation groups, then our algorithm runs in time polynomial in the input size, improving on the previous super-polynomial bound. An implementation of our algorithm is provided for the computer algebra system {\sf GAP}.
Enhanced Clients for Data Stores and Cloud Services

Data stores and cloud services are typically accessed using a client-server paradigm wherein the client runs as part of an application process which is trying to access the data store or cloud service. This paper presents the design and implementation of enhanced clients for improving both the functionality and performance of applications accessing data stores or cloud services. Our enhanced clients can improve performance via multiple types of caches, encrypt data for providing confidentiality before sending information to a server, and compress data for reducing the size of data transfers. Our clients can perform data analysis to allow applications to more effectively use cloud services. They also provide both synchronous and asynchronous interfaces. An asynchronous interface allows an application program to access a data store or cloud service and continue execution before receiving a response which can significantly improve performance. We present a Universal Data Store Manager (UDSM) which allows an application to access multiple different data stores and provides a common interface to each data store. The UDSM also can monitor the performance of different data stores. A workload generator allows users to easily determine and compare the performance of different data stores. We also present NLU-SA, an application for performing natural language understanding and sentiment analysis on text documents. NLU-SA is implemented on top of our enhanced clients and integrates text analysis with Web searching. We present results from NLU-SA on sentiment on the Web towards major companies and countries. We also present a performance analysis of our enhanced clients.
Inspecting geothermal prospects in an integrated approach within the West Azarbaijan Province of Iran

West Azarbaijan Province is the richest geothermal region in Iran and its potentials are evaluated through geological, geochemical and geophysical data. Eleven areas hosting 30 warm springs are analyzed here. Scenes colored by Fe-oxides, alteration minerals and travertine confirm existence of the geothermal reservoirs. Analyzing the geochemistry of samples revealed all hot fluids fall into bicarbonate group (high HCO3 content) and suffered severe mixing (high Mg content). Silica and cation geothermometers proposed a rage of 50–150 °C as the reservoir temperature. Major and minor faults are also deciphered using geophysical data in 6 regions.