Reviewerly/exHarmony huggingface dataset:
Software Tool for FAM-based Modeling and Simulation of Fuzzy Systems

This paper discusses methods of using fuzzy associative memories (FAM) for modeling and simulation of fuzzy systems. Presented software tool allows to generate the FAM matrices from the classic description of a fuzzy system consisting of fuzzy sets and fuzzy rules. Generated FAMs are used for fast simulation of a fuzzy system.
A Brief Review on Methodology of Cryptanalysis

Cryptanalysis comes into deferent forms in order to support that rigorous analysis of the structure cryptographic primitive to evaluate and verify its claimed security margins. This analysis will follow the attack models represented previously in order to exploit possible weakness in the primitive. Thus, achieving the associated attack goals which will vary from a distinguishing attack to a total break that is deﬁned based on the security margins or claims of the primitive under study. For example, for a hash function, total break constitutes ﬁnding a collision or obtaining the message from the hash value. While in block ciphers it revolves around recovering the secret key. When it comes to the claimed security margins, the design approaches will follow certain security models as in provable security or practical security or a mixture of both. The role of cryptanalyst is to subject these primitives to diﬀerent existing categories of cryptanalysis approaches and tailor new ones that will push the design’s security margins if possible to new limits where these attacks are not applicable any more This chapter will introduce the prominent methods of cryptanalysis that utilize certain behavior in the cipher structure. Such behavior disturbs the assumed randomness of the output or the cipher text. This Paper will explore the basic deﬁnitions of prominent cryptanalysis methods that targets the speciﬁc structure of a cipher namely diﬀerential and linear cryptanalysis and their diﬀerent variants. It will also discuss other potential crytpanalytic methods that are usually used in symmetric-key ciphers analysis especially block ciphers.
On Eisenstein polynomials and zeta polynomials II

Eisenstein polynomials, which were defined by the second author, are analogues to the concept of an Eisenstein series. The second author conjectured that there exist some analogous properties between Eisenstein series and Eisenstein polynomials. In the previous paper, the first author provided new analogous properties of Eisenstein polynomials and zeta polynomials for the Type II case. In this paper, the analogous properties of Eisenstein polynomials and zeta polynomials are shown to also hold for the Type I, Type III, and Type IV cases. These properties are finite analogies of certain properties of Eisenstein series.
Nominal Syntax with Atom Substitutions: Matching, Unification, Rewriting

Unification and matching algorithms are essential components of logic and functional programming languages and theorem provers. Nominal extensions have been developed to deal with syntax involving binding operators: nominal unification takes into account $$\alpha $$-equivalence; however, it does not take into account non-capturing substitutions, which are not primitive in nominal syntax. We consider an extension of nominal syntax with non-capturing substitutions and show that matching is decidable and finitary but unification is undecidable. We provide a matching algorithm and characterise problems for which matching is unitary, giving rise to expressive and efficient rewriting systems.
Automatic Analysis of Calibration Board Image Orientation for Online Hand-Eye Calibration

Visual robot hand-eye calibration is the most basic step in robots researching, and the accuracy of hand-eye calibration has a great impact on the working process. In order to perform hand-eye calibration accurately, it is necessary to collect a large amount of robot position data and calibration plate image data effectively. Different data will bring different calibration results. Collecting reasonable data will improve the accuracy of calibration results. This paper proposes an analytical method for the orientation of the calibration board, which can achieve the automatic adjustment of the shooting position of the robot arm. The method combines the image ratio analysis with the block image histogram to complete the calibration board orientation analysis. The image ratio analysis first blocks the image, then binarizes the sub-image, and obtains the proportional parameter of the desired pixel in the sub-image. The block image histogram uses the second-order Gaussian function to fit the histogram to obtain the peak, peak position and half-width information, and obtains the orientation information of the calibration board by solving the peak position and the area of the peak. Experiments show, this method can accurately determine the orientation of the calibration board, then improve the data acquisition efficiency of the calibration board and hand-eye calibration accuracy.
Feature Representation and Feature Matching for Heterogeneous Defect Prediction

Software Defect Prediction (SDP) is one of the highly influential software engineering research topics. Thae Hsu Hsu Mon Early within-project defect prediction (WPDP) used intra-project data. Hnin Min Oo However, it has limitations in prediction efficiency for new projects and projects without adequate training data. Studies of prediction have been carried out on cross-project defect prediction models (CPDP), i.e. models that are trained using other projects historical data. Heterogeneous defect prediction (HDP) is the special case of CPDP with different metric sets of source and target project. Despite the effectiveness of existing HDP methods, they can be affected by the issue of class imbalance that may decrease prediction performance. The proposed framework aims to exploit cost-sensitive principal component analysis (PCA) and the feature matching to build highly effective prediction model.
Relationship Analysis between the GRIT Scale and Key Competencies for Engineering Students

There are two well-known evaluation methods of key competencies for engineering students. One is the GRIT test, which is a subjective evaluation, the other is the PROG competency test, which is an objective evaluation. In order to evaluate the relationship between a score on the GRIT scale and a score in each category of the PROG competency test, this paper analyzes the relationship between the two methods, and examines whether the GRIT scale is useful to evaluate key competencies. We conclude that the correlation coefficient between “tenacity” in the GRIT scores and the total score of the PROG competency test tends to be high.
A Risk-Based Multisensor Optimization Scheduling Method for Target Threat Assessment

The reasonable scheduling of multisensor systems to maximize combat benefits has become a research hotspot in the field of sensor management. To minimize the uncertainty in the threat level of targets and improve the survivability of sensors, a risk-based multisensor scheduling method is proposed in this paper. In this scheduling problem, the best sensors are systematically selected to observe targets for the trade-off between the threat assessment risk and the emission risk. First, the scheduling problem is modelled as a partially observable Markov decision process (POMDP) for target threat assessment. Second, the calculation methods of the threat assessment risk and the emission risk are proposed to quantify the potential loss caused by the uncertainty in the threat level of targets and the emission of sensors. Then, a nonmyopic sensor scheduling objective function is built to minimize the total risk which is the weighted sum of the threat assessment risk and the emission risk. Furthermore, to solve the high complexity computational problem in optimization, a decision tree search algorithm based on branch pruning is designed. Finally, simulations are conducted, and the results show that the proposed algorithm can significantly reduce the searching time and memory consumption in optimization compared with those of traditional algorithms, and the proposed method has a better risk control effect than the existing sensor scheduling methods.
Decentralized infrastructure for knowledge discovery in the Semantic Web

An enormous volume of well-structured data with explicit semantics, in accordance with W3C’s standards, becomes a reality in the Web of Linked Data. However, the Semantic Web promise to turn it into a machine-processable global graph of knowledge still encounters numerous impediments. Efficient acc ess and discovery along with the semantic heterogeneity have been identified as major stumbling blocks. Following the design principles for Semantic Web and Linked Data, we present ActiveDiscovery, a decentralized infrastructure for distributed SPARQL query evaluation based on its terminological entities, namely the ontologies used in a query. ActiveDiscovery’s main goal is to facilitate distributed and transparent semantic search based on structural rather than keyword-based querying in the Semantic Web. Key architectural extensions regarding metadata, indexing and ontology alignment are proposed to achieve transparency for federated query execution in a decentralized manner. The rewriting procedure for extensional SPARQL query is considered regarding the proposed components and SERVICE clause as a standard recommendation for query federation. We investigate the feasibility of our approach and present preliminary results of initial evaluation. We conclude by indicating questions which need to be addressed in future work.
Green, Yellow, Yield: End-Host Traffic Scheduling for Distributed Deep Learning with TensorLights

The recent success of Deep Learning (DL) in a board range of AI services has led to a surging amount of DL workloads in production clusters. To support DL jobs at scale, the parameter server (PS) architecture is the most popular approach for distributing the computation in a compute cluster. Concurrent DL jobs consisting of PS tasks and worker tasks are typically launched on available compute nodes by a cluster resource manager to ensure high cluster resource utilization. As a PS needs to distribute model updates to every remote worker, its communication has very large fan-out. We observe that network contention among colocated PSes would cause stragglers among workers, resulting in application performance degradation and resource under-utilization. To mitigate the straggler effect, we propose TensorLights, which introduces traffic prioritization at host NICs to manage traffic contention among PSes. We evaluate TensorLights experimentally and show that it effectively mitigates stragglers, improves the average completion time of DL applications by up to 31%, and increases resource utilization. TensorLights is highly practical as it provides benefits without needing changes to the DL software stack.
Dislocation Theory Based Level Set Image Segmentation

Dislocation theory of material science is introduced into the level set method. The curve evolution of level set method is viewed as the slipping of edge dislocation, and the curve evolution is driven by the dislocation configuration force which is derived based on the dislocation dynamics mechanism. In the image segmentation, the proposed algorithm can effectively avoid the phenomenon that level set function stop evolution because of the abnormal image gradient, and the phenomenon of boundary leakage because of the smaller image gradient. Experimental results show that the proposed algorithm has better segmentation performance for images with weak boundaries.
Voice Conversion from Tibetan Amdo Dialect to Tibetan U-tsang Dialect Based on Generative Adversarial Networks

This paper proposes a Voice Conversion (VC) method from Tibetan Amdo dialect to Tibetan U-tsang dialect based on Generative Adversarial Networks (GANs). An inevitable problem with the traditional VC framework is that the acoustic feature vector output from the conversion model is over-smoothing, which leads to a drop in the quality of the converted speech. This is because in the training phase of acoustic model, a specific probability model is used to model the distribution of data, so that the output of a relatively average parameter of the model is considered to be optimal. Acoustic parameter over-smoothing occurs as long as the analytical form of the model distribution is artificially designed. In order to overcome this problem, the VC framework proposed in this paper uses GANs as the modeling network of the acoustic model, directly uses a generator model to learn the distribution of data, and guides the generator through a discriminator model. The training of the model makes the sample distribution of the model close to the distribution of the target speaker data samples, thus alleviating the problem of over-smoothing of the converted speech spectrum. The experimental results show that the proposed method is superior to VC based on Deep Neural Networks (DNNs) in the sound quality and similarity of the converted speech.
Design and research of somatosensory wristband based on people with language barriers

Abstract Because of their physical defects, people with language barriers are difficult to communicate smoothly with ordinary people who are not familiar with sign language. Therefore, there is an urgent need for a tool that can realize the conversion between the sign language and normal language, so as to meet their needs of exchanging information with the outside world and freely expressing their emotions. Considering the plight faced by people with language barriers, this paper, based on the gesture recognition technology, proposes a set of solutions, which is the comprehensive application of the somatosensory wristband and mobile terminal APP.
Research on Quanzhou Sports Brand Communication Based on Neural Network Recommendation Algorithm

How to enhance the brand value in the fierce market competition is the main factor for the sports brand to stand on the market. In the actual marketing process, it is necessary to combine the actual needs of customers and accurately recommend the sports brand to the user interface through the personalized recommendation algorithm. Based on the neural network recommendation algorithm, this study combined the personalized recommendation requirements to construct a network brand promotion model suitable for sports brand communication and used Quanzhou sports brand communication as a verification model case. The research shows that the proposed algorithm has certain effects and can provide theoretical reference for subsequent related research.
Design and Implementation a Smart E-Voting Model : Decentralization Using Blockchain

The transparency of the block-chain allows more auditing and considerate of elections. These attributes are particular of the necessities of a voting system. These features derive from decentralized network, and can bring additional democratic processes to elections, particularly to direct election systems. For e-voting to develop further open, transparent, and independently auditable, a possible resolution would be base it on blockchain technology. In this research work to proposed technique for voting system using blockchain. The blockchain will be publicly provable and distributed in a method that no one will be intelligent to corrupt it. In this research work proposed a blockchain-based model with Consensus Protocol and SHA256 hash algorithm related with the priorities of the ballot-privacy, veri?ability, suitability, extensiveness, uniqueness, sturdiness, and coercion- resistance.
Community Education Course Recommendation Based on Intelligent Recommendation Algorithm

The user interest model based on the intelligent recommendation algorithm designed in this study realizes the recommendation of community education courses. Considering the tag information in the educational resource library in the research, based on the LDA topic model, this paper establishes a topic model for the resources in the educational resource library. At the same time, combined with the distribution of the project on the theme and the user project scoring matrix, the user interest model based on the topic model is further established to reflect the user’s interest more comprehensively. In addition, this paper uses the advantages of the topic model itself to reduce the dimension of the user project scoring matrix, thereby reducing the computational complexity of the recommendation algorithm. Research shows that the proposed algorithm has certain effects.
A scheme of bipartite microwave polarization entanglement based on Josephson mixer

The basic principles for preparation and detection of continuous variable polarization entanglement in quantum optics, as well as the generalized criterion of bipartite polarization entanglement, are briefly introduced. Based on theories and experiments in quantum optics, a scheme of bipartite quantum microwave polarization entanglement is proposed and simulated. Inseparability of Stokes vector as I(Ŝ1,Ŝ2) and I(Ŝ2,Ŝ3) are obtained in order to witness the polarization entanglement in 100 MHz operation bandwidth of Josephson mixer. Furthermore, relations between inseparability I and squeezing parameter r as well as amplitude ratio Q are discussed respectively. The results show that I(Ŝ1,Ŝ2) is sensitive of the variance of Q, while I(Ŝ2,Ŝ3) seems to be sensitive of the variance of r. Meanwhile, I(Ŝ1,Ŝ2) remains above 1 in the paper, while I(Ŝ2,Ŝ3) is on the contrary well below 1. It can be concluded that Ŝ2 and Ŝ3 of Stokes vectors are inseparable, which means that output signals Êa and Êb of the scheme exhibit bipartite entanglement in polarization. The best entanglement appears at 70 MHz or so with the minimum I(Ŝ2,Ŝ3) value of 0.25. Apart from these, precision of witness for polarization entanglement and quadrature entanglement is compared. Polarization entanglement appears to be easier and more precise to witness according to our research.
Machine Perception MU—Shape Classes

Primary objective of machine perception MU is to construct the symbolic description of the visual content of an image and using this symbolic representation to solve the perceptual problems such as interpretation of perceived images. Symbolically represented visual knowledge provides a level of abstraction at which two otherwise dissimilar domains may look more alike. For example, the concepts of a planet and a ball are quite different, but if both are represented as a circle, it may facilitate analogical retrieval, mapping and transfer. The problem of perception and interpretation of images by application of the IN-perceptual transformation (described in Chap. 6 ), in order to find the solution to a perceptual problem, is solved within the framework of machine understanding. The machine understanding framework is referring to the human visual system that has a highly developed capability for interpretation of the visual data and detecting many classes of patterns based on statistically significant arrangements of image elements. These classes of patterns and statistically significant arrangements of image elements are called shapes.
The effect of information seeking behavior on online database usage among the undergraduate students

Every year the library of IIUM subscribes to quite a number of online databases with huge expenses but the utilization of these databases is countable. 10 out of 53 subscriptions have been terminated by 2015 to save the cost go in vain. The purpose of this paper is to develop a model based on the ISB elements to identify the reason behind this low usage and to increase the usage of online databases in the future. It has undertaken a quantitative approach to identify the elements of ISB among undergraduate (UG) students. The primary data has been collected through questionnaires based on the variables identified in the objective. There are 118 samples participating in this research and the proposed model has been adapted from Savolainen and Wilson. Psychological perception, searching strategies and the information resources have been considered as the independent variable and measured the usage of the online database among UG students as the dependent variable. The results have shown that there are seven elements which have an influence on the usage of the online database among IIUM UG students. Despite the limited number of samples and a specific group of students, the proposed model can enhance the usage of online database system subscribed to by IIUM library and a search engine can be developed to get the usage increased. In that sense, it is recommended to involve more participants from the different levels of students and users to get diverse elements of ISB. However, the paper contributes to bridging the gap between the online database usage and the users, believed to have an effective outcome for online database subscription. It is hoped that the proposed model would significantly fill this gap and help the library to increase the number of users for their expensive databases.
Dockerizing indri for OSIRRC 2019

The Lemur Project was set up in 2000 by the Center for Intelligent Information Retrieval at UMass Amherst. It is one of the longest lasting open-source projects in the information retrieval (IR) research community. Among the released tools is Indri, a popular search engine that was designed for language-modeling based approaches to IR. For OSIRRC 2019 we dockerized Indri and added support for the Robust04, Core18 and GOV2 test collections.
Design of Discriminative Dictionaries for Image Classification

In recent years, sparse representation theory has been widely used in the field of image classification. Based on kernel trick and the improved Fisher discrimination dictionary learning, this paper designs a dictionary learning algorithm that can effectively improve image classification performance. Kernel space transformation can learn non-linear structural information, which is very useful for image classification. The traditional kernel dictionary learning algorithm has high computational complexity, which is not conducive to practical application. We address this problem by proposing a sample preprocessing method based on Nystrom algorithm. By introducing the incoherent promoting terms into the Fisher discrimination dictionary learning model, we can obtain more discriminative coding coefficients while learning a structured dictionary. The effectiveness of the proposed kernel incoherent Fisher discrimination dictionary learning (KIFDDL) method is verified by the results of the classification experiments on several publicly image databases.
A New Manifold-Based Feature Extraction Method

Many traditional feature extraction methods takes the global or the local characteristics of training samples into consideration during the process of feature extraction. How to fully utilize the global or the local characteristics to improve the feature extraction efficiencies is worthy of research. In view of this, a new Manifold-based Feature Extraction Method (MFEM) is proposed. MFEM takes both the advantage of Linear Discriminant Analysis (LDA) in keeping the global characteristics and the advantage of Locality Preserving Projections (LPP) in keeping the local characteristics into consideration. In MFEM, Within-Class Scatter based on Manifold (WCSM) and Between-Class Scatter based on Manifold (BCSM) are introduced and the optimal projection can be obtained based on the Fisher criterion. Compared with LDA and LPP, MFEM considers the global information and local structure and improves the feature extraction efficiency.