Reviewerly/exHarmony huggingface dataset:
Mining patterns in graphs with multiple weights

Graph pattern mining aims at identifying structures that appear frequently in large graphs, under the assumption that frequency signifies importance. In real life, there are many graphs with weights on nodes and/or edges. For these graphs, it is fair that the importance (score) of a pattern is determined not only by the number of its appearances, but also by the weights on the nodes/edges of those appearances. Scoring functions based on the weights do not generally satisfy the apriori property, which guarantees that the number of appearances of a pattern cannot be larger than the frequency of any of its sub-patterns, and hence allows faster pruning. Therefore, existing approaches employ other, less efficient, pruning strategies. The problem becomes even more challenging in the case of multiple weighting functions that assign different weights to the same nodes/edges. In this work we propose a new family of scoring functions that respects the apriori property, and thus can rely on effective pruning strategies. We provide efficient and effective techniques for mining patterns in multi-weighted graphs, and we devise both an exact and an approximate solution. In addition, we propose a distributed version of our approach, which distributes the appearances of the patterns to examine among multiple workers. Extensive experiments on both real and synthetic datasets prove that the presence of edge weights and the choice of scoring function affect the patterns mined, and the quality of the results returned to the user. Moreover, we show that, even when the performance of the exact algorithm degrades because of an increasing number of weighting functions, the approximate algorithm performs well and with fairly good quality. Finally, the distributed algorithm proves to be the best choice for mining large and rich input graphs.
SLCV–a supervised learning—computer vision combined strategy for automated muscle fibre detection in cross-sectional images

Muscle fibre cross-sectional area (CSA) is an important biomedical measure used to determine the structural composition of skeletal muscle, and it is relevant for tackling research questions in many different fields of research. To date, time consuming and tedious manual delineation of muscle fibres is often used to determine the CSA. Few methods are able to automatically detect muscle fibres in muscle fibre cross-sections to quantify CSA due to challenges posed by variation of brightness and noise in the staining images. In this paper, we introduce the supervised learning-computer vision combined pipeline (SLCV), a robust semi-automatic pipeline for muscle fibre detection, which combines supervised learning (SL) with computer vision (CV). SLCV is adaptable to different staining methods and is quickly and intuitively tunable by the user. We are the first to perform an error analysis with respect to cell count and area, based on which we compare SLCV to the best purely CV-based pipeline in order to identify the contribution of SL and CV steps to muscle fibre detection. Our results obtained on 27 fluorescence-stained cross-sectional images of varying staining quality suggest that combining SL and CV performs significantly better than both SL-based and CV-based methods with regards to both the cell separation- and the area reconstruction error. Furthermore, applying SLCV to our test set images yielded fibre detection results of very high quality, with average sensitivity values of 0.93 or higher on different cluster sizes and an average Dice similarity coefficient of 0.9778.
Abnormal crowd density estimation in aerial images

The unpreceded growth of intelligent surveillance systems has resulted in an urgent need for automatic analysis of the captured scenes. Automatic detection of an abnormal crowd in aerial images can provide useful information to prevent disasters. In fact, aerial images have the advantage of covering a very large view of the people distributed over a scene. Accordingly, we propose a high density crowd detection method in aerial images. In this method, we adapted the bag of words technique using the multiblock local binary pattern as a texture descriptor to extract low-level features. In addition, we also used a three-level classification strategy to reduce confusion between crowd density classes. The experimental results reveal the performance of our method while estimating the crowd density in a challenging context.
The WPI patent test collection

To support the objectives of the journal, to publish new research and insights covering a broad spectrum of Intellectual Property information retrieval and patent analytics related practices and methods, the editors, together with the team at IFI CLAIMS® Patent Services, have put together a patent research collection, publicly available and for free, to foster scientific good practice: comparability, reproducibility, transparency and repeatability of experiments and results. The WPI collection is for this reason static. It will not be updated with new data. Otherwise, changes in experimental results would be impossible to trace with confidence to the new methods being proposed and not seen as a result of changes in the dataset. The WPI collection complements existing test collections, which are vertical (one domain or one authority over many years). Instead, the WPI collection is horizontal: it includes all technical domains from the major patenting authorities over the relatively short time span of two years.
OCE quantification of Poisson's ratio through 2D speckle tracking

We describe a real-time 2D motion tracking method based on speckle analysis. We implemented this method in real-time using graphic s processing unit (GPU). The capability to track both axial and lateral motions will enable more comprehensive characterization of tissue mechanical properties including Poisson's ratio.
Privacy-Preserving Data Outsourcing with Integrity Auditing for Lightweight Devices in Cloud Computing

The cloud can provide unlimited storage space to users via the Internet. Unlike locally data storing, users will lose the direct control of the data after outsourcing it to the cloud. Moreover, the cloud is an untrusted entity. It is possible that the cloud may try to extract, discard and destroy users’ data due to benefits. Hence, the data security in cloud computing needs to be well guaranteed. In this paper, we propose a privacy-preserving data outsourcing scheme with integrity auditing for lightweight devices in cloud computing. On the one hand, the blind signature is used in the proposed scheme to delegate the generation of users’ data signatures to the TPA. On the other hand, based on the property of the BLS signature, the blinded signatures received from the TPA can be verified by the user and the data integrity stored in the cloud can be audited by the TPA. In addition, the proposed scheme supports batch operation. Security analysis shows that the proposed scheme achieves the properties of correctness, privacy-preserving and non-forgeability. Performance analysis indicates that the proposed scheme can be performed with high efficiency.
Metaheuristic algorithm to train product and sigmoid neural network classifiers

Abstract This paper develops three frameworks based on a metaheuristic algorithm to train neural network classifiers. The architecture is a single‐hidden‐layer feedforward network. The first methodology spreads a base configuration over the nodes of a computing cluster; each of them executes the same algorithm to train the neural network with a different parameter setting. The second approach does a refined training via a biphase metaheuristic algorithm to maintain the diversity a period longer than the usual; it may be run in a sequential or distributed way. The third framework performs a data preparation phase by means of feature subset selection to reduce the number of inputs to the biphase metaheuristic algorithm. The two first methodologies have been tested using a complete test bed with product and unipolar sigmoid units in the hidden layer, and the statistical tests reveal that product nodes are significantly the most accurate. The third framework has included four feature subset selectors with different properties to reduce the number of inputs to the product unit artificial neural network, and the nonstatistical test shed light on that the results with a preprocessing phase are significantly more accurate than the results with the raw data.
Shaping Curricular Guidelines for Associate-Degree Cybersecurity Programs

As projections of the shortage of cybersecurity workers grow [4], the spotlight is on cybersecurity education. In December of 2017, the Joint Task Force on Cybersecurity Education published Cybersecurity Curricula 2017: Curriculum Guidelines for Post-Secondary Degree Programs in Cybersecurity [6] (CSEC2017), representing a new discipline in ACM's Computing Curricula Series [5]. Now the ACM CCECC (Committee for Computing Education in Community Colleges) is leading creation of a similar set of guidelines for two-year cybersecurity programs at the associate-degree level, called CSEC2Y. This special session, led by the ACM CCECC, will engage participants in the curriculum development process for associate-degree programs in the increasingly important field of cybersecurity. Participants will have the opportunity to review the current CSEC2Y draft and related materials, to recommend changes, plan further revisions, and otherwise assist in crafting the curricular guidance.
Aggregation and Mapping of Social Media Attribute Names Extracted from Chat Conversation for Personalized E-Learning

Nowadays learners are using social media applications to perform collaborative learning tasks. Learners from the same class can perform the task through discussions and conversations using different applications. These discussions generate valuable chat conversations contain information related to learners' characteristics. For educators, to understand the personal characteristics of those learners, collecting and analyzing these conversations is required. Due to the varied structure of these applications, an aggregation and mapping mechanism is required. This paper presents an aggregation model for the conversation data collected from different social media applications. This aggregation is built, based on the attributes required to enhance personalization services. The attributes identified have been used to construct a unified format for the chat data generated, using different social web applications. To perform the aggregation task, similarity matching technique using ontology-based model has been adopted. The promising results from the matching process indicate the usefulness of the model.
Research Data Literacy Perception and Practices in the Information Environment

The paper addresses conceptual contexts of research data, research data literacy and research data perception and practices. The concepts of research data and research data literacy are explained in the context of the information environment. A typology of research data is presented based on a qualitative study among Slovak researchers. Research data types are represented by a concept map. Results of a survey among Slovak scholars are presented as part of a multinational online questionnaire survey on Data Literacy and Research Data Management (257 subjects). Findings confirm positive perception of data management issues among researchers. Majority of them are willing to share their data, but express concerns regarding misuse and misinterpretation of data. Most researchers are interested in research data management trainings. In conclusion, we propose the implications for LIS education with regard to data management, including the courses on data analysis and visualization and for development and maintenance of research data infrastructures in the information environment.
Designing a Neural Network from Scratch for Big Data Powered by Multi-node GPUs

Lately, Machine Learning has taken a crucial role in the society in different vertical sectors. For complex problems with high-dimensionality, Deep Learning has become an efficient solution for learning in the context of supervisioned learning. Deep Learning [1] consists in using Artificial Neural Networks (ANN or NN) with several hidden layers, typically also with a large number of nodes in each layer.
Anonymous multi-receiver public key encryption based on third order linear sequences

Multi-receiver cryptosystem enables any sender to encrypt a message and broadcast the cipher-text to a group of authorized users, while no one out this group can decrypt the message. Multi-receiver encryption is of great importance in many sectors such as broadcast communication, cloud computing, wireless communications, networking applications, and medical applications. This paper proposes an efficient multi-receiver public key encryption scheme based on third order linear sequences. The results of the computational analysis show that, the proposed scheme is better against renown attacks and prevailing anonymous multi-receiver algorithms.
Introduction of Service-Oriented Architecture (SOA) Adoption

The purpose of this study is to discover the factors that affect Service-Oriented Architecture (SOA) adoption in an organization and estimate the impact of SOA adoption on the performance of enterprises. In order to narrow the scope of this study down and based on the objectives of this research, the researcher focuses on organizations which have already adopted SOA. Data will be collected from skilled experts in SOA all over the world using online questionnaire. Some technical and organizational concepts will be presented in this study, but its purpose is to present a general horizon of SOA adoption and its performance.
Rhombic Dodecahedron Grid—Coordinate System and 3D Digital Object Definitions

We propose a new non-orthogonal basis to express the 3D Euclidean space in terms of a regular grid. Every grid point, each represented by integer 3-coordinates, corresponds to rhombic dodecahedron centroid. Rhombic dodecahedron is a space filling polyhedron which represents the close packing of spheres in 3D space and the Voronoi structures of the face centered cubic (FCC) lattice. In order to illustrate the interest of the new coordinate system, we propose the characterization of 3D digital plane with its topological features, such as the interrelation between the thickness of the digital plane and the separability constraint we aim to obtain. A characterization of a 3D digital sphere with relevant topological features is proposed as well with the help of a 48 symmetry that comes with the new coordinate system.
Semi-Automatic Generation of Intelligent Curricula to Facilitate Learning Analytics

Several Learning Analytics applications are limited by the cost of generating a computer understandable description of the course domain, what is called an Intelligent Curriculum. The following work contributes a novel approach to (semi-)automatically generate Intelligent Curriculum through ontologies extracted from existing learning materials such as digital books or web content. Through a series of natural language processing steps, the semi-structured information present in existing content is transformed into a concept-graph. This work also evaluates the proposed methodology by applying it to learning content for two different courses and measuring the quality of the extracted ontologies against manually generated ones. The results obtained suggest that the technique can be readily used to provide domain information to other Learning Analytics tools.
An Autonomic Model-Driven Architecture to Support Runtime Adaptation in Swarm Behavior

The use of unmanned vehicles in swarms requires significant runtime adaptation within the managing software due to the unpredictability of the environment it operates. This is compounded by rapid context changes occurring within the software elevating its operational complexity to a magnitude that renders them infeasible for humans to effectively manage. Our approach to addressing this challenge is model-driven self-adaptation using autonomic methods. This work extends and refines ongoing work on an unmanned vehicle swarm platform based on probabilistic finite state machines as behavioral runtime models and the formation of subswarms in the context of communication constrained search. We present the architecture of our work in progress as a reflection mechanism controlling short and long-term adaptive behavior. We realize short-term behavior change by the continuous transformation of structural models at runtime. To validate the architecture’s autonomic properties, we provide a walkthrough of an indicative scenario pertaining to swarm resilience as proof of principle of the architecture’s ability to dynamically replan under element failure.
Communication Technologies for Social Integration of Inmates in Nigeria

In recent years, political instability has resulted in the rising wave of unemployment, poverty rate, and insecurity, all which have attendant effect on the crime rate across Nigeria. Presently, the prison population is 68,259: a number which has soared from 44,450 in year 2000. Several reports on the activities of the Nigerian prisons services widely indicate that there is a disturbing crime rate. Going by these statistics, the focus of this chapter is on the use of communication technologies skills for rehabilitation and reintegration of inmates into society. Since communication technologies provide a unified learning process for knowledge sharing, prisoners need to have access to these new forms of information exchange. This clearly implies that inmates need info-communication skills to prevent recidivism. Using the backdrop of the Nigeria's prison terrain, the chapter recommends an overhaul of the prisons with a reliable information and communication framework.
Gradient descent optimization for visual tracking with geometrics transformation adaptation

Visual tracking is a fundamental task in many computer vision applications and has been well studied in the last decades. In the field of visual tracking, there are many issues to consider which make the development of a robust tracking method very difficult, among these complications; the appearance change of the target, the fast motion, the background clutter, the camera motion, and scale variation. To override these problems, we develop an effective general framework for object tracking that addresses most of these issues. First the tracking problem is formulated in the form of a robust cost function which is a composition of the appearance and dynamic model, this formulation ensures the integration of the appearance of the object and also the majority of basic geometric transformations. Second the minimization is accomplished by the gradient descent optimization. We present experimental results made on different challenging sequences, the experimentations results demonstrate the efficiency and effectiveness of our methods.
Maximizing Nonlocal Self-Similarity Prior for Single Image Super-Resolution

Prior knowledge plays an important role in the process of image super-resolution reconstruction, which can constrain the solution space efficiently. In this paper, we utilized the fact that clear image exhibits stronger self-similarity property than other degradated version to present a new prior called maximizing nonlocal self-similarity for single image super-resolution. For describing the prior with mathematical language, a joint Gaussian mixture model was trained with LR and HR patch pairs extracted from the input LR image and its lower scale, and the prior can be described as a specific Gaussian distribution by derivation. In our algorithm, a large scale of sophisticated training and time-consuming nearest neighbor searching is not necessary, and the cost function of this algorithm shows closed form solution. The experiments conducted on BSD500 and other popular images demonstrate that the proposed method outperforms traditional methods and is competitive with the current state-of-the-art algorithms in terms of both quantitative metrics and visual quality.
Entanglement Purification on Separate Atoms in an Error-Detected Pattern

We propose an entanglement purification protocol (EPP) for purifying separate atoms embedded in single-sided optical cavities. The protocol works in an error-detected pattern, the reflection coefficients only appear as a global coefficient, the success or failure cases are heralded by the detection of photons, these merits make the protocol more robust and practical. We discuss the performance of the protocol with current experimental parameters, the results show the success probability of the present EPP is relatively high. The EPP could provide a promising building block for quantum repeaters and quantum networks in the future.
METRICS FOR FORMAL STRUCTURES, WITH AN APPLICATION TO KRIPKE MODELS AND THEIR DYNAMICS

Abstract The paper introduces a broad family of metrics applicable to finite and countably infinite strings, or, by extension, to formal structures serving as semantics for countable languages. The main focus is on applications to sets of pointed Kripke models, a semantics for modal logics. For the resulting metric spaces, the paper classifies topological properties including which metrics are topologically equivalent, providing sufficient conditions for compactness, characterizing clopen sets and isolated points, and characterizing the metrical topologies by a concept of logical convergence. We then apply the approach to maps from dynamic epistemic logic, showing that product updates with action models yield continuous maps, hence allowing for an interpretation of the iterated updates as discrete time dynamical systems.
A FPGA Implementation of Farneback Optical Flow by High-Level Synthesis

Optical flow algorithm, which estimates the motion detection of consequent video frames, is widely used in surveillance system, Advanced Driver Assistance Systems (ADAS) and object movement estimation in scene analysis. Among different optical flow algorithms, Farneback version provides a better accuracy and brightness-change-resistant displacements by estimating the flow from polynomial domain rather than intensive maps. However, high computation complexity and inconsistent data access patterns make it difficult to be implemented on a hardware platform. In this work, we present a micro-architecture design of Farneback optical flow, which is flexible for optimization with high level Synthesis (HLS) tools. The original software-based implementation was decomposed into functional blocks to balance latency of different stages and flows of data were rearranged to accommodate better memory access patterns. The data flow arrangement is based on a proposed backtrace mechanism, where DRAM accesses of polynomial coefficients in current frame makes consistent traffic patterns, and therefore make it possible to integrate more functional blocks into a deeper pipeline. For several micro-architecture design versions, we demonstrate options of fixed and floating points, optimization techniques such as multiple DMAs and different levels of pipeline integration. We implemented our design on Zedboard Mini-ITX 7045. The results show a 17x end-to-end speedup against a naive HLS version with an image size of 160x120. Considering only the hardware-accelerated part, our FPGA implementation is 40x faster than the naive HLS version with only 50% of the FPGA hardware resources.