Reviewerly/exHarmony huggingface dataset:
MESH: A Flexible Manifold-Embedded Semantic Hashing for Cross-Modal Retrieval

Hashing based methods for cross-modal retrieval has been widely explored in recent years. However, most of them mainly focus on the preservation of neighborhood relationship and label consistency, while ignore the proximity of neighbors and proximity of classes, which degrades the discrimination of hash codes. And most of them learn hash codes and hashing functions simultaneously, which limits the flexibility of algorithms. To address these issues, in this article, we propose a two-step cross-modal retrieval method named Manifold-Embedded Semantic Hashing (MESH). It exploits Local Linear Embedding to model the neighborhood proximity and uses class semantic embeddings to consider the proximity of classes. By so doing, MESH can not only extract the manifold structure in different modalities, but also can embed the class semantic information into hash codes to further improve the discrimination of learned hash codes. Moreover, the two-step scheme makes MESH flexible to various hashing functions. Extensive experimental results on three datasets show that MESH is superior to 10 state-of-the-art cross-modal hashing methods. Moreover, MESH also demonstrates superiority on deep features compared with the deep cross-modal hashing method.
A Study on the Educational Efficacy of a Maritime English Learning and Testing Platform

According to international regulations, it is mandatory for navigators or engineers to acquire suitable skillsets before their designation as a duty officer on board. One of the most important elements is Maritime English (ME), wherein students are taught a required set of basic skills that enable them to process various documents related to accidents, ship conditions, and inspections. Students have to be equipped not only with the use of general English skills but also with the coherent use of technical terms and phrases. However, due to the unique circumstances that exist in the maritime domain, the methods used for imparting maritime knowledge and the manner in which it is evaluated are restricted. Hence, this study aims to utilize an online Maritime English learning and testing platform that can be accessed on smart devices to analyze its impact on the students’ learning process. An experiment was conducted on two groups of cadets, one that used the platform and another group that did not. After six-week, the experiment results showed a significant difference between the ME test scores of the two groups. The test scores were further analyzed by incorporating the students’ personal elements to measure the efficacy of the ME test platform. Therefore, the learning and evaluation processes are expected to be implemented in ways that are appropriate and convenient to specific circumstances and be widely used in the field of maritime education in the future.
A Convolutional Neural Network with Word-level Attention for Text Classification

Text classification is a classic task in the NLP area which aims to predict the categories for given texts. Many neural network models are applied to this task with the development of the neural network technology. One of the typical neural network structures on the task is the convolutional neural network (CNN). However, the existing traditional CNN model used for classification is not sensitive to the key words in the texts, which causes it cannot capture the important information from the texts. Therefore, in this paper, we propose a new attention-based convolutional neural network (ACNN) which is trained to pay more attention to important words to assist in classifying texts. We evaluate our model and the classic CNN model on several public datasets, and the result of our experiment shows that the new proposed ACNN model outperforms the basic CNN model.
Adaptive State Fidelity Estimation for Higher Dimensional Bipartite Entanglement

An adaptive method for quantum state fidelity estimation in bipartite higher dimensional systems is established. This method employs state verifier operators which are constructed by local POVM operators and adapted to the measurement statistics in the computational basis. Employing this method, the state verifier operators that stabilize Bell-type entangled states are constructed explicitly. Together with an error operator in the computational basis, one can estimate the lower and upper bounds on the state fidelity for Bell-type entangled states in few measurement configurations. These bounds can be tighter than the fidelity bounds derived in [Bavaresco et al., Nature Physics (2018), 14, 1032-1037], if one constructs more than one local POVM measurements additional to the measurement in the computational basis.
An improved adaptive neuro-fuzzy inference system using probability trajectory-based clustering ensemble

Research findings established the usefulness of Adaptive neuro-fuzzy inference system (ANFIS) in predicting outcomes of events, processes or systems from their numerical representations (datasets). However, its effectiveness in terms of prediction accuracy and complexity depends on some factors specifically its set of rules that models data behavior, and tune-able parameters (antecedent, consequent and rule base). Although several promising non-heuristic and meta-heuristic optimization techniques like gradient descent, Ant Colony, Genetic Algorithm, Particle Swarm, and Invasive Weed have been proposed to improve on the prediction accuracy of ANFIS through proper optimization of its tune-able parameters, but absolutely no work has applied clustering ensemble to improving on ANFIS for better accuracy especially on rules improvement. Therefore, in this paper, we propose an improved ANFIS that uses an agglomerative-based clustering ensemble of fuzzy c-means to help extract rules from a given dataset that represent a process, event or system. The ensemble uses probability trajectories of random walk processes on base clustering partitions of the dataset so as to refine direct co-association relationships or links among data objects of the base partitions thereby improving on these relationships that later metamorphosed to fuzzy rules and finally, a better accuracy. Comparative analysis of our proposed improved ANFIS alongside with the conventional ANFIS using two standard petrophysical datasets for lithology prediction shows that the proposed ANFIS is better than the conventional ANFIS in terms of prediction model accuracy and stability although, both maintain the same architecture in terms of the number of rules and tune-able parameters as well as the training methods.
T–S Fuzzy Model Based Multi-Branch Deep Network Architecture

In the traditional CNN design, the hyperparameters, such as the size of the convolutional kernel and stride, are difficult to determine. In this paper, a new convolutional network architecture, named multi-branch fuzzy architecture network (MBFAN), was proposed for this problem. In MBFAN, some branches with a certain convolutional neural network architecture are connected in parallel. In each branch, a different-sized convolutional kernel is applied. By data training and normalization, a weight is given to each branch. By these weights, the important features in the final output are strengthened. By normalization, the branches were interconnected together, making the training process more efficient. Due to overfitting, with the increase of branches, the MBFAN accuracy increases, and then decreases. The number of branches is optimized when the MBFAN accuracy is highest. On the other hand, the location of the convolutional kernel center in an image has a great influence on the convolutional results. This is also discussed in MBFAN. For the experiments, the proposed MBFAN was adopted and tested in a simple convolutional network and a VGG16 network.
A Study on Current Operation and Perception of General Education in Colleges

The purpose of this study was to investigate the current status and awareness of general education operation in colleges in oder to suggest the differentiated general education model that fits both of the essence of basic discipline curriculums and objectives of college education. Forty-two colleges responded the survey on three categories (I. General education system, II. Actual education status in colleges, III. Perception of innovation for general education). As a main result, first, it was found that only a few colleges have established separate school regulations as well as the managing department for general education. Second, in Colleges, there have been many courses for the subjects in the literacy area and liberal arts, and recently development of a variety of new subjects in general education curriculum is being required. Third, as the members in colleges agreed on the need for innovation in general education, it is necessary to realize the financial and institutional support for general education at the college level. Based on the understanding of the current status of general education in colleges, this study proposed the college model for general education which includes support system and operational processes.
Design of Intelligent Search Engine Service Performance Evaluation System

As the prototype or primary state of future brain computer interface, intelligent search engine is an important direction of artificial intelligence technology, which has become a field of high attention and continuous research in academia. In this paper, the performance evaluation index of intelligent search engine service is proposed in view of the unclear evaluation method and imperfect evaluation method. The calculation method of service performance evaluation of intelligent search engine is determined, and the service performance evaluation system of intelligent search engine is constructed, and the Chinese search engine A and B as an example of the experimental analysis, which provides theoretical support and technical support for the development of service performance evaluation of intelligent search engine.
Analysis of Development Tool Usage in Software Engineering Classes

In this paper, the survey, dedicated to the usage of software systems in a software development process, is analysed. The survey was conducted among the students of Innopolis University. Based on the result of the survey, the following conclusions were made: (1) Windows, macOS and Linux-based operating systems have almost equal share of usage among future software developers (2) the most popular IDE is IntelliJ IDEA, however, to the end of the studying process students the diversity of IDEs usage increases (3) the mostly used code management system by far is Github, with almost 100% share (4) Trello and Jira are the most popular project management software for lightweight and complex industrial projects respectively. The obtained results will be used for the prioritization of the development of integration agents for InnoMetrics project, as well as for the adaptation of a studying process in academic institutions to make it more relevant to the given trends and for the market analysis of software engineering environment.
Entropy in Image Analysis II

Image analysis is a fundamental task for any application where extracting information from images is required [...].
PENGAMANAN POLYALPHABETIC DENGAN AFFINE CIPHER BERDASARKAN BARISAN FIBONACCI

Affine cipher is a classic cryptographic algorithm substitution technique. Substitution technique is the encryption process for every character in the plaintext will be subtituted by another character. Affine cipher uses two types of keys. Each character of plaintext to be encrypted substituted by the same key. This research discusses about modify one of the key affine cipher, to produce a different key that will be substituted with each plaintext character. Key modifications are made by the Fibonacci sequence rules. This study also compares affine cipher and key modification affine cipher by finding corelation coeffiecient values. The results obtained from the comparison of the two algorithms, encryption that uses affine cipher key modification is better than affine cipher.&#x0D; Keywords: Cryptography, Affine Cipher, Fibonacci, Correlation Value
Efficient Role Assignment with Priority in Robocup3D

Low-level skills such as walking and kicking are always the focus of work in recent years, which are the key factor in winning the game. However, as the RoboCup3D soccer league develops rapidly, the high-level strategy is taking its role and becoming a nonnegligible point. With the emergence and widespread use of the long pass technology, any space which are not watched for on the field can become a potential threat to be scored. As to best prevent opponents from scoring, this paper proposes a role assignment with priority using Kuhn-Munker algorithm, which can effienciently solve this problem and provide great flexibility and mobility for the team's tactical switch at the same time.
Davigo: Epic VR vs. PC Battles

DAVIGO is an asymmetrical VR and PC multiplayer game, where VR and PC players interact within the same virtual space, one as a giant and the other as a little warrior. This form of gameplay created novel challenges for our design process to effectively balance the game. These challenges included balancing player speeds across the two scales and ensuring mechanics such as throwing felt natural. Respectively, we solved these challenges by reducing the giant's speed in relation to the VR player's movement and implementing novel throwing mechanics such as physically stimulating joints and adding aim assist features.
The Role of Formal, Informal, and Family Credit in the Business Performance of Young Entrepreneurs in Benin

Young entrepreneurship is an important lever for economic growth and employment creation in developing countries. Credit uptake, however, continues to pose significant limitations to the sustainability of small-scale enterprises. We estimated the impact of credit uptake (formal, informal, and family) on young entrepreneurship performance in Benin, using 2014-2016 panel data from a World Bank survey on enterprise formalization. To address potential endogeneity and ensure robustness of results, we employed multiple models and estimation techniques (fixed-effects and Lewbel approach). Our results showed that, while formal credit was most important for larger firms, smaller firms benefited mainly from flexible (informal or family) credit. The impact of credit uptake was generally higher for women-owned firms. There were also variations in uptake according to firm ownerâs age: the impact of formal credit was relatively higher for older firm owners while younger owners benefited more from flexible credit. The findings highlight the importance of Informal and family credit sources, especially for start-ups and small firms.
An Approach of Feature Subset Selection Using Simulated Quantum Annealing

Feature selection is one of the important preprocessing steps in machine learning and data mining domain. However, finding the best feature subsets for large datasets is computationally expensive task. Meanwhile, quantum computing has emerged as a new computational model that is able to speed up many classical computationally expensive problems. Annealing-based quantum model, for example, finds the lowest energy state of an Ising model Hamiltonian, which is the formalism for Quadratic Unconstrained Binary Optimization (QUBO). Due to its capabilities in producing quality solution to the hard combinatorial optimization problems with less computational effort, quantum annealing has the potentiality in feature subset selection. Although several hard optimization problems are solved, using quantum annealing, not sufficient work has been done on quantum annealing based feature subset selection. Though the reported approaches have good theoretical foundation, they usually lack required empirical rigor. In this paper, we attempt to reduce classical benchmark feature evaluation functions like mRMR, JMI, and FCBF to QUBO formulation, enabling the use of quantum annealing based optimization to feature selection. We then apply QUBO of ten datasets using both Simulated Annealing (SA) and Simulated Quantum Annealing (SQA) and compared the result. Our empirical results confirm that, for seven in ten datasets, SQA is able to produce at most equal or less number of features in all selected subset compared to SA does. SQA also results in stable feature subsets for all datasets.
Research on the Preprocessing of Tai Le Character Recognition

Abstract Tai Le character is a kind of alphabetic writing with a long history, and its character recognition research plays an important role in inheriting and protecting Tai Le. Preprocessing is an key part of the Tai Le character recognition system, the quality of the preprocessing directly affects the recognition rate in the recognition system. Image preprocessing is the premise of feature extraction and recognition, since the directly input image usually contains noise, it affects the recognition effect. Therefore, it is of great application value and far-reaching historical significance to study the preprocessing technology of Tai Le character recognition. This paper proposes the image preprocessing method of Tai Le character, including graying, binarization, de-noise, and edge detection, and achieves good preprocessing results, which lays a foundation for feature extraction and recognition of Tai Le character.
Evaluating Explanation Methods for Deep Learning in Computer Security

Deep learning is increasingly used as a building block of security systems. Unfortunately, neural networks are hard to interpret and typically opaque to the practitioner. The machine learning community has started to address this problem by developing methods for explaining the predictions of neural networks. While several of these approaches have been successfully applied in the area of computer vision, their application in security has received little attention so far. It is an open question which explanation methods are appropriate for computer security and what requirements they need to satisfy.In this paper, we introduce criteria for comparing and evaluating explanation methods in the context of computer security. These cover general properties, such as the accuracy of explanations, as well as security-focused aspects, such as the completeness, efficiency, and robustness. Based on our criteria, we investigate six popular explanation methods and assess their utility in security systems for malware detection and vulnerability discovery. We observe significant differences between the methods and build on these to derive general recommendations for selecting and applying explanation methods in computer security.
A New Unsupervised Neural Approach to Stationary and Non-stationary Data

Dealing with time-varying high dimensional data is a big problem for real time pattern recognition. Non-stationary topological representation can be addressed in two ways, according to the application: life-long modeling or by forgetting the past. The G-EXIN neural network addresses this problem by using life-long learning. It uses an anisotropic convex polytope, which, models the shape of the neuron neighborhood, and employs a novel kind of edge, called bridge, which carries information on the extent of the distribution time change. In order to take into account the high dimensionality of data, a novel neural network, named GCCA, which embeds G-EXIN as the basic quantization tool, allows a real-time non-linear dimensionality reduction based on the Curvilinear Component Analysis. If, instead, a hierarchical tree is requested for the interpretation of data clustering, the new network GH-EXIN can be used. It uses G-EXIN for the clustering of each tree node dataset. This chapter illustrates the basic ideas of this family of neural networks and shows their performance by means of synthetic and real experiments.
ANNs for the Daily Prediction of Horizontal Global Solar Irradiation in an Agrometeorological Station with Mediterranean Warm Summer Climate

The next day's global horizontal solar irradiation is predicted using artificial neural networks (ANNs) for its application in agricultural science and technology.The time series of eight-years data is measured in an agrometeorological station, which belongs to the SIAR irrigation system (Agroclimatic Information System for Irrigation, in Spanish), located in www.videleaf.comMansilla Mayor (León, Castile and León region, Spain).The zone has a Csb climate classification (i.e., Mediterranean Warm Summer Climate), according to Koppen-Geiger.The data for the years (2004)(2005)(2006)(2007)(2008)(2009)(2010) are used for ANNs training and the 2011 as the validation year.ANN models were designed and evaluated with different numbers of inputs and neurons in the hidden layer.A neuron was used in the output layer, for all models, where the simulation of global solar irradiation for the next day on the horizontal surface results.Evaluated values of the input data were the horizontal daily global irradiation of the current day [H(t)] and two days before [H(t-1), H(t-2)], the day of the year [J(t)], and the daily clearness index [Kt(t)].Validated results showed that best adjustment models are the ANN 7 model (RMSE = 3.76 MJ/(m 2 •d), with two inputs [H(t), Kt(t)] and four neurons in the hidden layer) and the ANN 4 model (RMSE = 3.75 MJ/(m 2 •d), with two inputs [H(t), J(t)] and two neurons in the hidden layer).Thus, the studied ANN models had better results compared to classic methods (CENSOLAR typical year, weighted moving mean, linear regression, Fourier and Markov analysis) and are practically easier as they need less input variables.
Multirobot Collaborative Navigation Algorithms Based on Odometer/Vision Information Fusion

Collaborative navigation is the key technology for multimobile robot system. In order to improve the performance of collaborative navigation system, the collaborative navigation algorithms based on odometer/vision multisource information fusion are presented in this paper. Firstly, the multisource information fusion collaborative navigation system model is established, including mobile robot model, odometry measurement model, lidar relative measurement model, UWB relative measurement model, and the SLAM model based on lidar measurement. Secondly, the frameworks of centralized and decentralized collaborative navigation based on odometer/vision fusion are given, and the SLAM algorithms based on vision are presented. Then, the centralized and decentralized odometer/vision collaborative navigation algorithms are derived, including the time update, single node measurement update, relative measurement update between nodes, and covariance cross filtering algorithm. Finally, different simulation experiments are designed to verify the effectiveness of the algorithms. Two kinds of multirobot collaborative navigation experimental scenes, which are relative measurement aided odometer and odometer/SLAM fusion, are designed, respectively. The advantages and disadvantages of centralized versus decentralized collaborative navigation algorithms in different experimental scenes are analyzed.
Stay-at-home tech's star turn: Even technophobes rely on consumer tech now

Remember March, when stay-at-home orders and advisories first went into effect in many U.S. states and other places around the world? That month is generally not a big time for consumer electronics sales; gadgets may fly off shelves during the December holiday season, but then there's a bit of a lull.
Systematic Literature Review of Security Solution Using Blockchain in Internet of Things (IoT)

The European Union Digital Library (EUDL) is the central repository for all content published by the different branches of the European Alliance for Innovation (EAI). EUDL also hosts or indexes content from selected partner or member organizations and affiliates.