Reviewerly/exHarmony huggingface dataset:
Degree Curriculum Contraction: A Vector Space Approach

This paper introduces a curriculum contraction technique in the context of university degree programs using a vector space embedding approach. We propose a way to model degrees and majors and define a contraction that takes the curriculum of a degree program and defines a smaller set of courses to approximate it. For example, a computer science degree curriculum could be generated that takes three years to complete instead of four (a 75% contraction). We use seven years of student enrollment data from a public university to train our embedding model. The most popular majors at the university, and their corresponding minors, are used to evaluate the validity of this contraction approach where minors are treated as major contractions.
Multiscale Fast Spectral Clustering based on k-d Tree

In recent years, spectral clustering has become one of the most popular clustering algorithms. However, it is difficult to be applied to large-scale data sets due to its high computational complexity of O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ), with n the number of data points. To solve this problem, we propose a novel spectral clustering algorithm named Multiscale Fast Spectral Clustering based on k-d tree. The computational complexity of Multiscale Fast Spectral Clustering is O(nd\log n) and its memory cost is O(m), where n, m and d are the number of samples, the super-rectangular regions, features. The super-rectangular regions are obtained by k-d tree. Extensive experiments on large data sets shows that this algorithm can get accurate clustering results and achieve significant speedups.
Methods to Form Both Anti-Interference and Hiding Radio Signals for Prospective Communication Systems

In order to more protect wireless prospective communication systems (PCS), methods are proposed for increasing the level of both interference immunity and hiding for synthesized radio signals. It was shown that in the presence of additive “white” Gaussian noise and signal-like interference the developed method of radio signal adaptation PCS provided the gain in noise immunity was up to 5 dB compared to the DSSS QPSK signal with spreading by the ensemble of supplemented MLS. For choice radio signal that is resistant to structure analysis, the set of radio signals with controlled coupling between in-phase and quadrature components synthesized using combined criterion “spectral efficiency - peak-to-average-power-ratio” was considered. Meanwhile, these radio signals are comparable with the known types of bandwidth-efficient radio signals in terms of the general spectral and energy characteristics. The training sequence to reduce symbol time and carrier frequency estimation variance in operation of the PCS in multipath is produced. It is shown that synthesized training sequence allows reducing the variance of the symbol time offset estimate by 1.5-1.8 times and carrier frequency offset is 1.2-1.4 times.
Synthesizing Machine-Learning Datasets from Parameterizable Agents Using Constrained Combinatorial Search

The tedious, often hand-modeled, activity of designing and implementing simulation scenarios can benefit from modern-day data-driven methods, i.e., machine-learning (ML). We envision a toolchain that exploits information obtained during live operations, such as the observed maneuvers, techniques, and procedures of all interacting players in live operational settings, that serves as input into an ML-based scenario authoring process. We present a mechanism, called the Parameter Diversifier (PD), that takes a base scenario structure and synthesizes the comprehensive datasets needed for the supervised machine-learning of a scenario authoring model. The design of the PD explores and exploits low-level agent state search space as it relates to it high-level implications at the scenario level. This work demonstrates an explicit sampling of the scenario parameter search space to build an implicit model for use in simulation scenario generation.
Faster compressed quadtrees

Real-world point sets tend to be clustered, so using a machine word for each point is wasteful. In this paper we first show how a compact representation of quadtrees using O(1) bits per node can break this bound on clustered point sets, while offering efficient range searches. We then describe a new compact quadtree representation based on heavy-path decompositions, which supports queries faster than previous compact structures. We present experimental evidence showing that our structure is competitive in practice.
And Quiet Flows the Don: the Sholokhov-Kryukov authorship debate

Abstract ‘And Quiet Flows the Don’ is an epic novel, considered one of the most significant works of Russian and world literature. The debate on the authorship of ‘And Quiet Flows the Don’ had been surrounding the novel since its first release in 1928 by Mikhail Sholokhov, who was repeatedly accused of plagiarism. The supporters of the plagiarism theory often indicate that the real author of the novel is the Cossack writer, Fyodor Kryukov, who died before ‘And Quiet Flows the Don’ was published. In the present study we applied the information-based similarity analysis (Yang et al., 2003a, Linguistic analysis of human heartbeats using frequency and rank order statistics. Physical Review Letters, 90: 108103; Yang et al., 2003b, Information categorization approach to literary authorship disputes. Physica A, 329, 473) and Burrows's Delta (Burrows, 2002, ‘Delta’: a measure of stylistic difference and a guide to likely authorship. Literary and Linguistic Computing, 17(3):267–87) to a corpus of Russian literature of XIX and XX centuries. We next used these two methods to compare ‘And Quiet Flows the Don’ to Sholokhov’s and Kryukov’s writings. It was found that Fyodor Kryukov writings are distinct from ‘And Quiet Flows the Don’, whilst Sholokhov’s writings being close to the Don novel. The results also highlight how both information similarity analysis and Delta analysis can be used Russian language.
Clustering Based Cybersecurity Model for Cloud Data

Due to the inexorable notoriety of ubiquitous mobile devices and cloud processing, storing of data (for example photographs, recordings, messages, and texts) in the cloud has turned into a pattern among individual and hierarchical clients. Be that as it may, cloud service providers can’t be trusted entirely to guarantee the accessibility or honesty of client data re-appropriated/transferred to the cloud. Consequently, to enhance the cybersecurity level of cloud data, a new security model is introduced along with optimal key selection. In the proposed study, first cluster the secret information which we are taken using K-Mediod clustering algorithm based on a data distance measure. Then, the clustered data are encrypted using Blowfish Encryption (BE) and stored in the cloud. To improve the cybersecurity level, the optimal key is chosen based on the maximum key breaking time; for that, we presented a technique called Improved Dragonfly Algorithm (IDA). The result demonstrates that the optimal blowfish algorithm improves the accuracy of cybersecurity for all secret information compared to existing algorithms.
Application of Improved Ant Colony Algorithm in Path Planning

Aiming at the problem of path planning in topography, this paper studies the influence of relevant factors on speed and path planning, and proposes an intelligent algorithm for terrain path planning based on slope factor and ant colony algorithm. Firstly, the slope factors affecting walking speed are analyzed, then the terrain data are pretreated, and the ant colony algorithm is improved according to the walking requirements. Finally, the optimal walking path is obtained. The simulation results show that the algorithm can achieve better terrain path planning according to walking requirements.
Proficiency Based Planner for Safe Path Planning and Applications in Surgical Training

Instrument navigation is a fundamental task in laparoscopic surgical training. To assist a trainee in this task, suggested path needs to be collision free and should maintain sufficient distance, or clearance from the obstacle so that the possibility of collision is minimized. Clearance highly depends on the ability of a trainee to perform the task. In this paper, we propose a new algorithm for suggesting a path for a navigation task, that considers Proficiency Level of the trainee and provides a path according to how well a trainee can perform. The suggested approach is based on Probabilistic Roadmap Planners (PRM) and focuses on certain configuration spaces where most planners may fail to find a path while guaranteeing path's clearance. The new method provides a way to compensate for clearance constraints in regions wherever such compensation is necessary. Finally, the simulation in a surgical trainer demonstrates the effectiveness of the method.
Development and Application of an Integrated Curriculum Centered on Mathematics for Elderly generations

Purpose To develop an education program based on adult learning principles and actual experiences of elderly generations to develop an integrated curriculum centered on Mathematics. Methods Using a theme-based integrated curriculum design for a particular group of elderly learners (n=4x18), this study followed a sequence in program development: Assessing the needs, choosing a theme, studying a topic, writing a rationale, brainstorming, stating the learning objective, planning the project, establishing detailed activity plans, programming the learning time, and assessing the efficiency and the validity. Results Using our data analysis, core concepts were applied to everyday experiences of travel and cooking; the need to develop numeracy for elderly generations informed the development of unit objectives and learning outcomes within a Korean adult literacy curriculum. Implementation of the curriculum led to some improvement as evident from the pre- and post-results using the K-mmse; it was also possible for the elderly generations to engage in cooperative learning events and complement each other during the problem solving processes. Conclusion This study is significant in that it develops and applies the principles of Problem-based Learning (PBL) - an integrated curriculum design, use of real-life topics and consideration of the needs of the learner, in this case, elderly generations. As a learner-centered curriculum, it gave greater motivation to learners and made the mathematics subject relevant to their needs. It is necessary to consider the diversity across generations of learners and develop curricula in accordance with their needs. Keywords: Integration Curriculum; Elderly generations; Mathematics; Curriculum; Thematic
The Bat Algorithm with Dynamic Niche Radius for Multimodal Optimization

In this paper, we proposed Bat Algorithm extending with Dynamic Niche Radius (DNRBA) which enables solutions to locate multiple local and global optima for solving multimodal optimization problems. This proposed algorithm is designed Bat Algorithm (BA) dealing with the exploration and the exploitation search with Niche Radius which is calculated by the fitness landscape and the number of local and global optima to avoid converging solutions at the same optimum. Although the Niche Radius is an effective niching method for locating solutions at the peaks in the fitness landscape, it is not applicable for uneven multimodal functions and easily fails to keep multiple optima. To overcome this problem, we introduce a dynamic niche sharing scheme which is able to adjust the distance of the niche radius in the search process dynamically for the BA. In the experiment, we employ several multimodal functions and compare DNRBA with the conventional BA to evaluate the performance of DNRBA.
Due Diligence Planning as Technology for Business Risk Assessment

Nowadays, audit companies are introducing new professional services. There is due diligence among such audit services. Due diligence is a modern business technology, that helps to identify business-risks and make decision on how to improve business processes. The study of the problem is of interest because it helps to identify new problems and trends in the market of audit services. The study objective is to develop the risk-oriented approach methodology for planning due diligence. Based on the results of the study, special matrix for performing due diligence is presented. All stages of due diligence are described. The algorithm of risk-oriented planning of due diligence is disclosed. Due to the absence of special standards for planning agreed-upon procedures, including due diligence, the scheme and the due diligence algorithm are proposed that provide an understanding of the process of conducting an expertise of due diligence object, as well as the features of the pre-due diligence planning.
Video Classification Using Deep Autoencoder Network

We present a deep learning framework for video classification applicable to face recognition and dynamic texture recognition. A Deep Autoencoder Network Template (DANT) is designed whose weights are initialized by conducting unsupervised pre-training in a layer-wise fashion using Gaussian Restricted Boltzmann Machines. In order to obtain a class specific network and fine tune the weights for each class, the pre-initialized DANT is trained for each class of video sequences, separately. A majority voting technique based on the reconstruction error is employed for the classification task. The extensive evaluation and comparisons with state-of-the-art approaches on Honda/UCSD, DynTex, and YUPPEN databases demonstrate that the proposed method significantly improves the performance of dynamic texture classification.
Spin weighted spherical wavelets derived from approximate identities

Spin weighted functions, introduced in 1966 by Newman and Penrose, are used primarily for description of the cosmic microwave background radiation. In the present paper, an analysis tool for spin weighted functions is proposed, namely spin weighted spherical wavelets based on approximate identities. In the spin 0 case there exist two different definitions of continuous wavelet transforms: the group-theoretical one introduced by Antoine et al. and that based on approximate identities, pioneered by Freeden et al. The first one has been recently extended to the case of spin weighted functions by McEwen et al. and the present research is the approximate-identities-counterpart of their work. Wavelets presented here can be constructed for and the wavelet transform can be applied to functions of each integer spin weight. Further, the set of parameters – scale and position – is continuous. A big advantage of the construction is that the wavelet transform is invertible directly by an integral. Last but not least, wavelets can reveal directional features such that the directional intensity of a signal can be probed.
SAKURA a Model Based Root Cause Analysis Framework for vIMS (poster)

Model based machine learning (MBML) techniques solve novel diagnosis problems and provide explanations for their decisions. However, current MBMLs suffer some limitations, since virtualization of network brings new challenges such as the dynamic topology and elasticity. Those limitations include the high dependency on previous knowledge and the difficulty to represent the model. To face those limitations, we propose SAKURA: a root cause analysis framework for the virtual Ip Multimedia Subsystem (vIMS). SAKURA is composed of a self-modeling and a constraints solver.
Using neural network models in the quality management system for the software defect prediction

Abstract Reasonable distribution of resources for regression testing execution of software is considered to be the most important task. Finding the best solution for it may significantly reduce expenses on the whole system development. Neural network model may be used for testing management, as it has fault-prediction ability in each program module. Code parameters are independent variables and presence of errors is a dependent value in such model. Neural network can learn on real data – real testing product. Testing results received from different environment may be integrated easily in the knowledge base. This allows neural network to learn during each testing iteration. The module that potentially contains an error is tested at the first place and more thoroughly. Presented method may predict testing results and distribute resources accordingly.
Towards Cloud Distributed Image Indexing by Sparse Hashing

Distributing multimedia indexes to multiple nodes enables search over very large datasets (i.e., over one billion images and videos), but comes with a set of challenges: \textithow to distribute documents and queries effectively across nodes to support concurrent querying? andhow to deal with the increased potential for lack of response from nodes (e.g., node fail-stops or dropping of network packages)? An index where partitions are based on the distribution of feature vectors in the original space can improve redundancy and increase efficiency: nearest neighbors are only present on a small, set number of partitions, reducing the number of nodes to inspect for each query. This paper describes how sparse hashes can help find this balance and create better distribution policies for high-dimensional feature vectors. Inspired by existing literature on distributed text and media indexes, our proposal distributes and balances documents and queries to a subset of the nodes, according to their orthogonal similarities. We performed exhaustive benchmarks of our approach on a commercial cloud service. Experiments on a one billion vector dataset show that our approach has a low partitioning overhead (3 to 5 ms per query), achieves balanced document and query distribution (the variation in document and query distribution across nodes is smaller than 1% and 10%, respectively), handles concurrent queries effectively and degrades gracefully with node failures (less than 2% of precision loss per node down).
A UML Profile for the Service Discovery in the Enterprise Cloud Bus (ECB) Framework

Enterprise Cloud Bus (ECB) framework is the cloud computing based service model that references to model of web services, which provide services on request over the network. Agent based cloud computing has increased significantly today due to rapid growth and quick increase in number of clouds and their services. With the increasing number of cloud services to specify the performance issues, ECB framework is introduced. There is semi-formal representation of service discovery in ECB for agent based technique. Consequently, the design and implementation complexity of service discovery has significantly increased. Therefore, there is dire need to simplify the service discovery process in ECB framework for large and complex system by exploiting the concepts of Model Driven Architecture (MDA). In this article, a UML profile is proposed to model service discovery requirements in ECB. Particularly, provider agent, client, meta service registry, cloud agent and scheduler agent stereotypes are proposed to simplify the specification of publishing services to meta registry, providing services to clients and receiving requests from cloud agent. This delivers the bases or foundation to convert source models into required target models. The applicability of proposed profile is validated through flight reservation system case study. The results prove that the proposed profile significantly reduces the design and implementation complexity of service discovery in ECB.
TAR Based Hotspot Prediction in Cloud Data Centres

In this article, the authors studied hotspots in cloud data centers, which are caused due to a lack of resources to satisfy the peak immediate requests from clients. The nature of resource utilization in cloud data centers are totally dynamic in context and may lead to hotspots. Hotspots are unfavorable situations which cause SLA violations in some scenarios. Here they use trend aware regression (TAR) methods as a load prediction model and perform linear regression analysis to detect the formation of hotspots in physical servers of cloud data centers. This prediction model provides an alarm period for the cloud administrators either to provide enough resources to avoid hotspot situations or perform interference aware virtual machine migration to balance the load on servers. Here they analyzed the physical server resource utilization model in terms of CPU utilization, memory utilization and network bandwidth utilization. In the TAR model, the authors consider the degree of variation between the current points in the prediction window to forecast the future points. The TAR model provides accurate results in its predictions.
An approach for initializing the random adaptive grouping algorithm for solving large-scale global optimization problems

Abstract Many real-world optimization problems deal with high dimensionality and are known as large-scale global optimization (LSGO) problems. LSGO problems, which have many optima and are not separable, can be very challenging for many heuristic search algorithms. In this study, we have proposed a novel two-stage hybrid heuristic algorithm, which incorporates the coordinate descent algorithm with the golden-section search (CDGSS) and the random adaptive grouping for cooperative coevolution of the Self-adaptive Differential Evolution with Neighborhood Search (DECC-RAG) algorithm. At the first stage, the proposed algorithm roughly scans the search space for a better initial population for the DECC-RAG algorithm. At the second stage, the algorithm uses the DECC-RAG framework for solving the given LSGO problem. We have evaluated the proposed approach (DECC-RAG1.1) with 15 most difficult LSGO problems from the IEEE CEC’2013 benchmark set. The experimental results show that DECC-RAG1.1 outperforms the standard DECC-RAG and some the state-of-the-art LSGO algorithms.
Research on Fault Diagnosis Based on Artificial Neural Network

According to classification framework of classical neural network, contemporary neural network, and soft computing, the basic concepts of the feedforward neural network (MLP, BP, and RBF), the feedback neural network (Hopfield, Boltzmann, Elman), the self-organizing neural network (SOM, ART, CPN), deep and extreme neural network (Deep learning, extreme learning), the novel neural network (SVM, PNN), and soft computing neural networks combined with various methods are introduced, and the research progress and typical applications of the neural network in fault diagnosis are given. The existing problems and future development directions of fault diagnosis are also discussed.
Novel Encryption Method of GPS Information in Image File Using Format-Preserving Encryption

Global Positioning System (GPS) information is stored in EXchangeable Image File Format (EXIF) in the image file. It is considered personal information; therefore, sharing it without the owner’s consent could lead to personal information disclosure. GPS information is embedded on the images uploaded by many people on social network services or stored in the Cloud. Images must be encrypted to prevent GPS information from being disclosed. However, there are no existing methods to encrypt them in EXIF. Using block cipher for encryption can exceed the tag value range of EXIF. In this paper, we propose a method to encrypt the GPS information of an image file using Format-Preserving Encryption, which preserves the length and format of the plaintext. We confirmed that GPS information can be secured by encrypting it in image files.