Reviewerly/exHarmony huggingface dataset:
partial-FORCE: A fast and robust online training method for recurrent neural networks

Recurrent neural networks (RNNs) are helpful tools for modeling dynamical systems by neuronal populations, but efficiently training RNNs has been a challenging topic. In recent years, a recursive least squares (RLS) based method for modifying all the recurrent connections, called the full-Force method, has been gaining attention as a fast and robust online training rule. This method introduces a second network (called the teacher reservoir) during training to provide suitable target dynamics to all the hidden units of the task-performing network (called the student network). Thanks to the RLS-based approach, the full-FORCE method can be applied to training continuous-time networks and spiking neural networks. In this study, we propose a generalized version of the full-FORCE method: the partial-FORCE method. In the proposed method, only part of the student network neurons (called supervised neurons) is supervised by only part of the teacher reservoir neurons (called supervising neurons). As a result of this relaxation, the size of the student network and that of the teacher reservoir can be different, which is biologically plausible as a possible model of the memory transfer in the brain. Furthermore, we numerically show that the partial-FORCE method converges faster and is more robust against variations in parameter values and initial conditions than the full-FORCE method, even without the price of computational cost.
Computer Vision Based Eye Gaze Controlled Virtual Keyboard for People with Quadriplegia

Disabilities or physical impairments caused by genetic or traumatic injury hold people from proclaiming their basic needs, feelings, or ideas. This paper presents an eye gaze controlled virtual keyboard which is an assistive system for disable and able-bodied people. Disability such as quadriplegia restrain people from using their four limbs, virtual keyboards proposed in this paper can be used for expressing their thoughts. The proposed virtual keyboard has 40 keys including the delete key, English fonts, numerical digits, and several Latin symbols where each key lights up sequentially in the forward or backward direction. The user’s eye gaze navigates the activation of the key and eye blinking is used for typing the active key. The proposed keyboard has salient features such as sound is played when a key has been pressed and delete key eliminates unintentionally typed letters. The Character Per Minute (CPM), Word Per Minute (WPM), total error rate have been calculated from the tests conducted by several users which show an improved result than the existing models. The proposed virtual keyboard is a low-cost assistive system as it requires only a webcam for performing.
An efficient algorithm for high average utility itemset mining with buffered average utility-list

As a hot topic of association rule mining, high average utility itemset mining (HAUIM) provides a scientific measurement of an itemset’s utility, thus it can be adopted to find out the rules that are critically useful to the real applications. However, there is a prominent deficiencty in existing algorithms for HAUIM, a huge number of memory consumption are needed during the generation of high average utility itemset. Hence, an efficient algorithm called BAUL-Miner is proposed to solve the memory problem, in which a buffered average utility list with a more compact upper bound and two pruning strategies are introduced to reduce the searching space. Finally, detail experiments in synthetic and industrial datasets verify the effectiveness of BAUL-Miner, it outperforms traditional HAUIM algorithms in runtimes, memory consumption and scalability.
VPNet: Vision and Planning Network for Robotic Navigation

Robotic navigation like human drivers is a key capability for autonomous driving in real, dynamic urban environments. How can a delivery robot reliably navigate to the destination of a new office building with a minimum of priori information? Traditional autonomous driving methods are realized by multiple components such as perception, planning and control. Due to complex assumptions and interdependencies, these methods are difficult to be generalized to different scenarios. More recently, an end-to-end driven approach has emerged and is suitable for new environments by directly learning and exporting the data provided. However, many existing approaches neglect to examine the confidence level of driving behavior and the ability to recover from driving errors. This paper introduces a two-level hierarchical approach, called vision and planning network (VPNet), which integrates vision and path planning. The experimental results suggest that under various weather and lighting conditions, our network can reliably generate trajectories in different urban environments.
On Profiling Space Reduction Efficiency in Vector Space Modeling-Based Natural Language Processing

Space reduction is widely used in natural language processing tasks. In this paper, we use automatic text summarization as a use case to compare the quality of automatically generated summaries using two space reduction-based variants of the same summarization protocol. Obtained results show that non-linear space reduction-based summarization approaches outperform linear space-reduction-based ones. This research’s salient outcome is that it explains obtained results based on a rigorous study of the generated space sparsities
Earthquake Prediction in California Using Feature Selection Techniques

Predicting the magnitude of earthquakes is of vital importance and, at the same time, of extreme complexity, where each attribute contributes differently in the process, even introducing noise. Preprocessing using attribute selection techniques helps to alleviate this drawback. In this work, this is demonstrated through an extensive comparison of 47 years of data from the Northern California Earthquake Data Center, where a wide range of feature selection algorithms are applied composed by different search, like population, local and ranking search based; and evaluators, like Correlations, consistency and distance metrics. After that, prediction algorithms will allow to compare the result with and without the application of feature selection, showing that the number of existing attributes can be reduced by 80%, improving metrics of the original, ensuring that the use of attribute selection in this type of problem is quite promising.
The balanced quaternary sequence of odd period with low autocorrelation

Abstract In this paper, we construct some balanced quaternary sequences of odd period p with low autocorrelation from two types of Legendre sequences and its cyclic shift or complement and inverse Gray mapping, where p is odd prime. Firstly, we choose a Legendre sequence pair of period p, which include p ≡ 1(mod 4) and p ≡ 3(mod 4), then we get a new Legendre sequence pair of period p by cyclic shift or complement, finally, we can get a quaternary sequence of period p by utilizing inverse Gray mapping into the new Legendre sequence pair.
The Communication Value of a Quantum Channel

There are various ways to quantify the communication capabilities of a quantum channel. In this work we study the communication value (cv) of channel, which describes the optimal success probability of transmitting a randomly selected classical message over the channel. The cv also offers a dual interpretation as the classical communication cost for zero-error channel simulation using non-signaling resources. We first provide an entropic characterization of the cv as a generalized conditional min-entropy over the cone of separable operators. We evaluate the cv exactly for all qubit channels and the Werner-Holevo family of channels. The latter is shown to have non-multiplicative cv when d > 2. On the other hand, we prove that any pair of qubit channels have multiplicative cv when used in parallel. Even stronger, all entanglement-breaking channels and the partially depolarizing channel are shown to have multiplicative cv when used in parallel with any channel. We then turn to the entanglement-assisted cv and prove that it is equivalent to the conditional min-entropy of the Choi matrix of the channel. Combining with previous work on zero-error channel simulation, this implies that the entanglement-assisted cv is the classical communication cost for perfectly simulating a channel using quantum non-signaling resources. A final component of this work investigates relaxations of the channel cv to other cones such as the set of operators having a positive partial transpose (PPT).
Association Rules Analysis Using Algorithm Apriori And Fuzzy Normalization

Apriori algorithm is an algorithm to find a combination of items in a dataset that has a relationship value between items by paying attention to the minimum support and minimum confidence values. A common problem in the a priori algorithm is the formation of an item set when getting a large number of items from a dataset. The more items in a dataset, the more itemset will be formed. This will lead to increased storage space and poor algorithm performance. Some techniques can be applied to help correct these deficiencies, namely fuzzy logic. Fuzzy logic can normalize the items in the dataset by grouping the data according to the given rules. The formation of association rules using an a priori algorithm with fuzzy normalization obtained a total of 63 rules, a support value of 6.80%, and a confidence value of 88.4%. While the conventional a priori algorithm obtained a total of 13 rules, a support value of 6.80%, and a confidence value of 85%. Based on the results obtained, it can be concluded that the fuzzy normalization process on the a priori algorithm has a fairly good impact. The results obtained are not much different, but the difference can be seen from the number of rules and itemset formed.
Stacking Ensemble Learning in Deep Domain Adaptation for Ophthalmic Image Classification

Domain adaptation is an attractive approach given the availability of a large amount of labeled data with similar properties but different domains. It is effective in image classification tasks where obtaining sufficient label data is challenging. We propose a novel method, named SELDA, for stacking ensemble learning via extending three domain adaptation methods for effectively solving real-world problems. The major assumption is that when base domain adaptation models are combined, we can obtain a more accurate and robust model by exploiting the ability of each of the base models. We extend Maximum Mean Discrepancy (MMD), Low-rank coding, and Correlation Alignment (CORAL) to compute the adaptation loss in three base models. Also, we utilize a two-fully connected layer network as a meta-model to stack the output predictions of these three well-performing domain adaptation models to obtain high accuracy in ophthalmic image classification tasks. The experimental results using Age-Related Eye Disease Study (AREDS) benchmark ophthalmic dataset demonstrate the effectiveness of the proposed model.
Joint Graph Contextualized Network for Sequential Recommendation

Sequential recommendation aims to suggest items to users based on sequential dependencies. Graph neural networks (GNNs) are recently proposed to capture transitions of items by treating session sequences as graph-structured data. However, existing graph construction approaches mainly focus on the directional dependency of items and ignore benefits of feature aggregation from undirectional relationship. In this paper, we innovatively propose a joint graph contextualized network (JGCN) for sequential recommendation, which constructs both the directed graphs and undirected graphs to jointly capture current interests and global preferences. Specifically, we introduce gate graph neural networks and model the combined embedding of weighted position and node information from directed graphs for capturing current interests. Besides, to learn global preferences, we propose a graph collaborative attention network with correlation-based similarity of items from undirected graphs. Finally, a feed-forward layer with the residual connection is applied to synthetically obtain accurate transitions of items. Extensive experiments conducted on three datasets show that JGCN outperforms state-of-the-art methods.
DEVELOPING THE COMPETENCE OF UNIVERSITY STAFF’S PROFESSIONAL COMMUNICATION WITH INTERNATIONAL STUDENTS IN MULTICULTURAL EDUCATIONAL ENVIRONMENT

A modern educational environment of higher professional education is becoming more multicultural. However, not all universities are able to provide a comfortable multicultural educational environment: university staff experience different issues in communication with international students. The aim of the article is to specify the term “professional communication competence of the university staff with international students” and define the forms and methods for developing this competence. The term “professional communication competence of the university staff with international students” is defined in the basis of the analysis of research literature on the topic of intercultural communication in higher education. Taking into account the principles of andragogy and the concept of humane oriented education, the forms and methods of adult learning and teaching were defined (discussions, case study, gamification). The research results can be used to design and implement the training programs to advance university staff proficiency. The novelty of the research is in the description of the forms and methods of developing the university staff’s professional communication competence in view of age-specific features in a multicultural educational environment.
Cost-sensitive meta-learning framework

Purpose This paper aims to describe the use of a meta-learning framework for recommending cost-sensitive classification methods with the aim of answering an important question that arises in machine learning, namely, “Among all the available classification algorithms, and in considering a specific type of data and cost, which is the best algorithm for my problem?” Design/methodology/approach This paper describes the use of a meta-learning framework for recommending cost-sensitive classification methods for the aim of answering an important question that arises in machine learning, namely, “Among all the available classification algorithms, and in considering a specific type of data and cost, which is the best algorithm for my problem?” The framework is based on the idea of applying machine learning techniques to discover knowledge about the performance of different machine learning algorithms. It includes components that repeatedly apply different classification methods on data sets and measures their performance. The characteristics of the data sets, combined with the algorithms and the performance provide the training examples. A decision tree algorithm is applied to the training examples to induce the knowledge, which can then be used to recommend algorithms for new data sets. The paper makes a contribution to both meta-learning and cost-sensitive machine learning approaches. Those both fields are not new, however, building a recommender that recommends the optimal case-sensitive approach for a given data problem is the contribution. The proposed solution is implemented in WEKA and evaluated by applying it on different data sets and comparing the results with existing studies available in the literature. The results show that a developed meta-learning solution produces better results than METAL, a well-known meta-learning system. The developed solution takes the misclassification cost into consideration during the learning process, which is not available in the compared project. Findings The proposed solution is implemented in WEKA and evaluated by applying it to different data sets and comparing the results with existing studies available in the literature. The results show that a developed meta-learning solution produces better results than METAL, a well-known meta-learning system. Originality/value The paper presents a major piece of new information in writing for the first time. Meta-learning work has been done before but this paper presents a new meta-learning framework that is costs sensitive.
Measure as a Philosophical Category in the Discourse of Ontology

The purpose of the study is to identify the role of measure in ontology. The paper considers the semantic content of the category “measure” in the evolutionary and gnoseological aspects, defines the notion of “measure” on the basis of the natural-scientific interpretation, provides the most illustrative method for determining the scope of measure, examines the issue of the influence of measure on being as a process of interaction between systems. Scientific novelty lies in the interdisciplinary and comprehensive methods of considering the issue, which made it possible to specify the influence of measure on being as a process, special attention is paid to mechanisms of hermeneutic and systemic approaches. As a result of the study, the author has determined the relative subjectivity of the phenomenon of measure, the importance and necessity of understanding and applying measure in the process of being as an interaction between systems.
A comparative study to recognize fake ratings in recommendation system using classification techniques

The recommendation system is affected with attacks when the users are given liberty to rate the items based on their impression about the product or service. Some malicious user or other competitors’ try to inject fake rating to degrade the item’s graces that are mostly adored by several users. Attacks in the rating matrix are not executed just by a single profile. A group of users profile is injected into rating matrix to decrease the performance. It is highly complex to extract the fake ratings from the mixture of genuine profile as it resides the same pattern. Identifying the attacked profile and the target item of the fake rating is a challenging task in the big data environment. This paper proposes a unique method to identify the attacks in collaborating filtering method. The process of extracting fake rating is carried out in two phases. During the initial phase, doubtful user profile is identified from the rating matrix. In the following phase, the target item is analysed using push attack count to reduce the false positive rates from the doubtful user profile. The proposed model is evaluated with detection rate and false positive rates by considering the filler size and attacks size. The experiment was conducted with 6%, 8% and 10% filler sizes and with different attack sizes that ranges from 0%–100%. Various classification techniques such as decision tree, logistic regression, SVM and random forest methods are used to classify the fake ratings. From the results, it is witnessed that SVM model works better with random and bandwagon attack models at an average of 4% higher accuracy. Similarly the decision tree method performance better at an average of 3% on average attack model.
Modeling and Verification of Contactless Mobile Banking System in E-Banking Using SPIN

During this prevailing generation of the digital world, mobile users are multiplying globally by leaps and bounds. A mobile banking system is an electronic channel for Electronic Banking (E-Banking) all over the world. The utility of mobile banking systems has become one of the innovations to transform financial institutions from the traditional to the digital world with all the banking services. However, financial institutions do not provide enhanced banking services and electronic cheques using the mobile banking system globally. This paper proposes a new contactless mobile banking system (C-MBS) that integrates enhanced banking services with novel functions like electronic cheques, registration of the user, and cancellation of the user account included in the model. This paper develops an extended finite state machine model with parameters, variables, and constraints for C-MBS. This paper also develops a verification model of C-MBS with system properties specified utilizing process meta language (PROMELA) and security properties applying linear temporal logic (LTL). A simple promela interpreter (SPIN) is employed to verify the verification model of C-MBS. SPIN verification results confirm that the proposed C-MBS model is free from deadlocks and errors. Hence, the financial institutions can implement this model as a secure enhanced mobile banking system in E-banking. Banking users can use the enhanced banking services remotely using C-MBS on mobile and will play a significant role towards a cashless society in the digital world.
Hybrid Restricted Boltzmann Algorithm for Audio Genre Classification

The purpose of this paper is to use restricted Boltzmann algorithm for creating a neural network with deep belief. In the results obtained, 2 and 3 classification of classes tend to be similar for deep neural network along with small datasets of conventional versions. The expectation was that deep learning would surpass in terms of performance. Improvement has been seen in terms of accuracy and performance, when more dataset is generated from the limited stock of primary audio track abstract.
Scene Generated with Text Guidance (VAAB System)

Concepts and ethics at a younger age are better understood and inculcated respectively when they are visualized. To build a better society and better tomorrow the future generation has to be ethically and morally strong. This can be inculcated efficiently through the means of visualization. This can be achieved by the proposed system which uses Refined Novel Generative Adversarial Network. The proposed system is one of the applications of the Refined Novel Generative Adversarial Network, which could bring a revolutionary change in the teaching-learning process.
Temporal-Level Operators Over Intuitionistic Fuzzy Sets

The Intuitionistic Fuzzy Set (IFS) was introduced in 1983 as one of the first extensions of Zadeh’s fuzzy set. In the following years, on the one hand, it was extended to intuitionistic L-fuzzy set (1984), interval valued IFS (1989), IFS of second type (1989) (although some authors have incorrectly called it Pythagorean fuzzy set) and more generally of n-th type, Temporal IFS (TIFS) and others. On the other hand, different relations, operations and operators have been introduced over IFSs. The operators over IFS are of modal, topological, level and other types. In the present paper, as a continuation and fusion of the ideas of TIFS and of level operations over IFSs, temporal-level operators are introduced and some of their basic properties are studied.
The Quality Evaluation of Online Book Reviews Based on Graph Neural Network

Abstract With the development of the Internet, the way people read has changed, the readers are more and more willing to share their reading opinions via the reading platforms. However, as the continuous increase in the number of users and reviews on the book platforms, the review data is growing exponentially. It is difficult for people to find useful information quickly in the massive reviews. Therefore, using automated methods to identify high-quality reviews among a large number of book reviews has become a hot topic in current research. This paper studies a quality evaluation method of book reviews based on graph convolution neural network, which introduces the LDA document topic generation model to add topic nodes on the basis of review and word nodes, builds the relationship between the three nodes as edges to generate heterogeneous graph. The graph is sent to a two-layer GCN model for training. In the last, this article did experimental evaluation and analysis based on the book review data of non-commercial platforms. The results show that the proposed method is superior to the machine learning method and the traditional neural network model.