Reviewerly/exHarmony huggingface dataset:
Battlefield Target Grouping by a Hybridization of an Improved Whale Optimization Algorithm and Affinity Propagation

Target grouping, which is essentially a data clustering problem, is a research hotspot in the field of battlefield situation assessment. To address an unknown battlefield environment, affinity propagation based on an improved whale optimization algorithm (APBWOA) is proposed from the perspective of clustering. First, we propose a whale optimization algorithm based on a chaotic map and nonlinear inertia weight improvement called CPIW-WOA, which uses an improved circle map to generate initial populations and introduces nonlinear inertia weights to improve its convergence efficiency. The test results on nine benchmark functions show that the CPIW-WOA algorithm has superior performance to existing methods. Second, based on the fact that it fully considers the weights of the attributes in a given sample, the weighted Mahalanobis distance is adopted to replace the Euclidean distance. In addition, the silhouette index is introduced to determine the optimal number of clusters. By iteratively updating through CPIW-WOA to search for the optimal settings, the limitation of manually entering specified parameters can be overcome. Test results on real-world datasets show that the new method is more accurate and effective than other methods; therefore, it can provide effective solutions with respect to battlefield target grouping.
Adaptive rewiring of random neural networks generates convergent–divergent​ units

Brain networks are adaptively rewired continually, adjusting their topology to bring about functionality and efficiency in sensory, motor and cognitive tasks. In model neural network architectures, adaptive rewiring generates complex, brain-like topologies. Present models, however, cannot account for the emergence of complex directed connectivity structures. We tested a biologically plausible model of adaptive rewiring in directed networks, based on two algorithms widely used in distributed computing: advection and consensus. When both are used in combination as rewiring criteria, adaptive rewiring shortens path length and enhances connectivity. When keeping a balance between advection and consensus, adaptive rewiring produces convergent-divergent units consisting of convergent hub nodes, which collect inputs from pools of sparsely connected, or local, nodes and project them via densely interconnected processing nodes onto divergent hubs that broadcast output back to the local pools. Convergent-divergent units operate within and between sensory, motor, and cognitive brain regions as their connective core, mediating context-sensitivity to local network units. By showing how these structures emerge spontaneously in directed networks models, adaptive rewiring offers self-organization as a principle for efficient information propagation and integration in the brain.
Blended Learning as a Means of Foreign Students’ Integration into a University Educational Process

This paper considers the problem of blended learning as a means of foreign students’ integration into higher school environment. This process is not that simple, it demands special methods to help foreign students to be integrated easily. The use of blended learning in education facilitates easy students’ integration and leads to positive learning results. However, the application of blended learning is connected with challenges both educators and foreign students can face. The aim of the article is to consider and assess the use of blended learning at the lessons of English implemented into a university educational process. For that purpose, we applied such methods as a needs analysis (among lecturers of English from South Ural State University), a questionnaire and an interview (among three groups of students from similar bachelor’s programs). The findings show that the majority of those surveyed do understand the importance of blended learning as a means of foreign students’ integration, but they are not familiar with the types and how to use them. Based on the results of this analysis, the authors applied blended learning into the process of English teaching, such as flipped classroom, station rotation, and a new online language development course, “Methodological support to the textbook by McCarthy, M. “Touchstone”. The drawn conclusions indicate that blended learning allows not only to boost foreign language skills, but to promote easy foreign students’ integration into the educational process of the university.
Development of a regional voice dataset and speaker classification based on machine learning

Abstract At present, voice biometrics are commonly used for identification and authentication of users through their voice. Voice based services such as mobile banking, access to personal devices, and logging into social networks are the common examples of authenticating users through voice biometrics. In Pakistan, voice-based services are very common in banking and mobile/cellular sector, however, these services do not use voice features to recognize customers. Therefore, the chance to use these services with false identity is always high. It is essential to design a voice-based recognition system to minimize the risk of false identity. In this paper, we developed regional voice datasets for voice biometrics, by collecting voice data in different local accents of Pakistan. Although, there is a global need for voice biometrics especially when voice-based services are common, however, this paper uses Pakistan as a use case to show how to build regional voice dataset for voice biometrics. To build voice dataset, voice samples were recorded from 180 male and female speakers with two languages English and Urdu in form of five regional accents. Mel Frequency Cepstral Coefficient (MFCC) features were extracted from the collected voice samples to train Support Vector Machine (SVM), Artificial Neural Network (ANN), Random Forest (RF) and K-nearest neighbor (KNN) classifiers. The results indicate that ANN outperformed SVM, RF and KNN by achieving 88.53% and 86.58% recognition accuracy on both datasets respectively.
Cybersecurity Awareness Level: The Case of Saudi Arabia University Students

Cybersecurity plays an important role in reliance on digital equipment and programs to manage daily lives chores including the transmission and storage of personal information.Therefore, it is a global issue in our growing society, and it becomes increasingly important to measure and analyze the awareness of it.In this paper, a questionnaire has been designed to measure the current level of cybersecurity awareness (CSA) among Saudi university students.Cybersecurity students' awareness level questionnaire has been adapted from few other previous cybersecurity awareness campaigns.In this questionnaire, a total of 136 students have participated in the survey.The questionnaire was collected to measure the cybersecurity students' awareness level through their knowledge, culture, and surrounding environment or through students' behavior by thee affected factors.These are: gender, location, and study department of the students.The study findings reveal that the students' awareness is in an average has no significant difference in cybersecurity awareness level between male and female students, but females show a bit more concern about cybersecurity.However, there is a clear and high awareness of students of computer and information technology departments compared to others.Moreover, urban students outperformed students in remote areas in awareness of cybersecurity.The survey results indicate that the study model has been effective in measuring students' awareness.
Project-Based Activity as a Mechanism of Reflection Development in Students of Psychology and Education Studies

Purpose of the study: to theoretically substantiate and experimentally test the system of organizational and pedagogical conditions of project designing, which is regarded as a mechanism for creating the reflecting thinking in the students of psychology and education. The research uses the methods of theoretical review and analysis of psychological and pedagogical literature related to the subject, expert view on the development of students’ reflective abilities following a theoretical and systemic model of reflection and statistical data processing. We have theoretically stated and experimentally confirmed that project activity is a mechanism for the development of such reflection components as creativity, pattern construction and objectification, provided that the project’s concept includes formulation and solution of divergent tasks by the project participants; implementation involves work on a strategy and tactics of cooperation between all the project participants; and the results are realized by all participants in the process of joint and objective assessment. A formative experiment has been carried out and resulted in identification of the requirements for the project activities’ organization among the students majoring in psychology and education, there have been revealed conditions contributing to their reflective activity; questionnaires for expert assessment and monitoring the development of reflection during the project activities have been created, theoretically supported and tested. Novelty is determined by insufficient study of the development of reflection in the process of project activities designed by the students of psychology and education field
Exploiting Contextual Word Embedding of Authorship and Title of Articles for Discovering Citation Intent Classification

The number of scientific publications is growing exponentially. Research articles cite other work for various reasons and, therefore, have been studied extensively to associate documents. It is argued that not all references carry the same level of importance. It is essential to understand the reason for citation, called citation intent or function. Text information can contribute well if new natural language processing techniques are applied to capture the context of text data. In this paper, we have used contextualized word embedding to find the numerical representation of text features. We further investigated the performance of various machine-learning techniques on the numerical representation of text. The performance of each of the classifiers was evaluated on two state-of-the-art datasets containing the text features. In the case of the unbalanced dataset, we observed that the linear Support Vector Machine (SVM) achieved 86% accuracy for the “background” class, where the training was extensive. For the rest of the classes, including “motivation,” “extension,” and “future,” the machine was trained on less than 100 records; therefore, the accuracy was only 57 to 64%. In the case of a balanced dataset, each of the classes has the same accuracy as trained on the same size of training data. Overall, SVM performed best on both of the datasets, followed by the stochastic gradient descent classifier; therefore, SVM can produce good results as text classification on top of contextual word embedding.
Development of majority vote ensemble feature selection algorithm augmented with rank allocation to enhance Turkish text categorization

The increase in the number of texts as digital documents from numerous sources such as customer reviews,news, and social media has made text categorization crucial in order to be able to manage the enormous amount ofdata. The high dimensional nature of these texts requires a preliminary feature selection task to reduce the featurespace with a potential increase in the prediction accuracy. In this study, we developed an ensemble feature selectionmethod, namely majority vote rank allocation, was developed for Turkish text categorization purposes. The methoduses a majority voting ensemble strategy in combination with a rank allocation approach to combine weak filters suchas information gain, symmetric uncertainty, relief, and correlation-based feature selection. Thus, the proposed methodmeasures the quality of the features among all features with the majority votes of the filters and ranking allocation. The feature selection efficacy of the method was tested on two datasets, one from the literature and a newly collected dataset.The effect of the obtained features on the classification prediction performance was evaluated on top of the naive bayes,support vector machine J48, and random forests algorithms. It was empirically observed that the developed methodimproved the prediction accuracies of the classifiers compared to the mentioned filters. The statistical significance of theexperimental results were also validated with the use of a two-way analysis of variance test.
Uncertain Knowledge Reasoning Based on the Fuzzy Multi Entity Bayesian Networks

With the rapid development of the semantic web and the ever-growing size of uncertain data, representing and reasoning uncertain information has become a great challenge for the semantic web application developers. In this pa... | Find, read and cite all the research you need on Tech Science Press
An Enhanced Unsupervised Extreme Learning Machine Based Method for the Nonlinear Fault Detection

Although the unsupervised extreme learning machine (UELM) based methods have been widely used to diagnosis the nonlinear process faults recently, the UELM algorithm is only designed to preserve the local adjacency similarity of the input dataset instead of mining the intra-class variations. Besides, the determination of the optimal UELM hidden nodes number is a tough issue. In order to deal with these two problems, a novel enhanced UELM (EUELM) based scheme is developed to effectively detect the nonlinear process faults in our work. In the proposed EUELM approach, the UELM algorithm is first improved by naturally incorporating the diversity analysis technique into the original UELM objective function to preserve both the intra-class variation and the local adjacency similarity of the input dataset. Then, to settle the difficult issue of selecting the optimal number of hidden nodes, kernel trick is further employed in the EUELM approach to mine the data strong nonlinearity. Based on the extracted diversity and local similarity low dimensional feature information, the k-nearest neighbor (KNN) principle is applied to derive a monitoring statistic for fault detection. At last, the experiments and comparisons on the monitoring effectiveness of the suggested EUELM based approach are made on a numerical nonlinear system and the benchmark Tennessee Eastman (TE) process. The obtained monitoring results illustrate that the significant improvements can be achieved by the proposed EUELM based fault detection approach compared with other popular and related approaches.
The Emoji Linguistic Functions on Facebook Interactions among Undergraduate Students at Jadara University in Jordan

This study has made use of a mixed mode design where two instruments are employed namely a corpus based tool and a questionnaire. For three months, the researchers have observed Raseef Jadara Facebook website in order to explore the use of emoji in posts and comments. The results showed that five functions were emerged from the data collected, emotive, conative, phatic, poetic, referential and metalingual. Descriptive statistics were used in conducting a questionnaire in order to show the differences between male and female students in using emoji. The results showed that females prefer using emoji in their chatting or messaging. The researchers suggest further studies to be carried out on the language functions of emoji in other social media platforms Furthermore. It is recommended for other studies to be directed into investigating all language functions.
All-optical image identification with programmable matrix transformation

An optical neural network is proposed and demonstrated with programmable matrix transformation and nonlinear activation function of photodetection (square-law detection). Based on discrete phase-coherent spatial modes, the dimensionality of programmable optical matrix operations is 30~37, which is implemented by spatial light modulators. With this architecture, all-optical classification tasks of handwritten digits, objects and depth images are performed on the same platform with high accuracy. Due to the parallel nature of matrix multiplication, the processing speed of our proposed architecture is potentially as high as7.4T~74T FLOPs per second (with 10~100GHz detector)
Application of Credit Card Fraud Detection Based on CS-SVM

With the development of e-commerce, credit card fraud is also increasing. At the same time, the way of credit card fraud is also constantly innovating. Support Vector Machine, Logical Regression, Random Forest, Naive Bayes and other algorithms are often used in credit card fraud identification. However, the current fraud detection technology is not accurate, and may cause significant economic losses to cardholders and banks. This paper will introduce an innovative method to optimize the support vector machine by cuckoo search algorithm to improve its ability of identifying credit card fraud. Cuckoo search algorithm improves classification performance by optimizing the parameters of support vector machine kernel function (C, g). The results demonstrate that CS-SVM is superior to SVM in Accuracy, Precision, Recall, F1-score, AUC, and superior to Logistic. Regression, Random Forest, Decision Tree, Naive Bayes, whose accuracy is 98%.
From (horizontal and sectoral) data access solutions towards data governance systems

Data Access, Consumer Interests and Public Welfare , Seite 439 - 476
Low-Cost and Device-Free Human Activity Recognition Based on Hierarchical Learning Model

Human activity recognition (HAR) has been a vital human–computer interaction service in smart homes. It is still a challenging task due to the diversity and similarity of human actions. In this paper, a novel hierarchical deep learning-based methodology equipped with low-cost sensors is proposed for high-accuracy device-free human activity recognition. ESP8266, as the sensing hardware, was utilized to deploy the WiFi sensor network and collect multi-dimensional received signal strength indicator (RSSI) records. The proposed learning model presents a coarse-to-fine hierarchical classification framework with two-level perception modules. In the coarse-level stage, twelve statistical features of time–frequency domains were extracted from the RSSI measurements filtered by a butterworth low-pass filter, and a support vector machine (SVM) model was employed to quickly recognize the basic human activities by classifying the signal statistical features. In the fine-level stage, the gated recurrent unit (GRU), a representative type of recurrent neural network (RNN), was applied to address issues of the confused recognition of similar activities. The GRU model can realize automatic multi-level feature extraction from the RSSI measurements and accurately discriminate the similar activities. The experimental results show that the proposed approach achieved recognition accuracies of 96.45% and 94.59% for six types of activities in two different environments and performed better compared the traditional pattern-based methods. The proposed hierarchical learning method provides a low-cost sensor-based HAR framework to enhance the recognition accuracy and modeling efficiency.
English Teachers’ Perception toward the Switch from Offline to Online Teaching during lockdown in the Midst of Covid-19 Outbreak

Education is one of the sectors that is badly influenced by covid-19 pandemic where the learning process is carried out from home. This sudden pandemic requires teachers to switch from face-to-face learning into learning domain with technological network. The objective of this study was to describe English teachers’ perception toward the switch from offline to online teaching during lockdown in the midst of covid19 outbreak. The subject of study was 50 English teachers of Senior High School in Banyuwangi. The research method was case study with qualitative research approach. The data needed collected by distributing Google form questionnaire and interview. The result of study indicated that English teachers’ perception about online teaching have not been performed properly, they stated that only for about 35% students who were active and really serious in carrying out online learning and collecting the assignments given on time because of lacking flexibility and access from teachers in controlling students’ learning activities. 68% teachers felt that their workload was heavier if compared to face-to-face learning because they need additional time to operate appropriate online learning media and package interesting learning in order to be able to increase students’ motivation and desire in English lesson.
Identification of Hand Gestures Using the Inertial Measurement Unit of a Smartphone: A Proof-of-Concept Study

Assessing the hand postures is crucial to implement gesture-based user-computer interfaces for controlling robots and assistive devices. Apart from data gloves and optical tracking, techniques such as surface electromyography and force myography provide a straightforward, non-invasive way to estimate poses and intentions through the forearm muscles assessment. However, most of the myography systems rely on bulky, dedicated hardware with arrays of electrodes or force probes. Therefore, this work introduces the smartphone as an alternative for identifying gestures: with the mobile device attached to the forearm, the embedded inertial measurement unit detects muscular contractions produced during the transitions between postures, yielding signatures in acquired waveforms. After computing the correlation of measured and template patterns, a competitive layer votes the class with greater probability and identifies the gesture. Prior characterization studies evaluated the effect of smartphone placement and forearm orientation in the sensor response, revealing that the IMU signatures are repeatable and robust to positioning deviations. Next, using 10-fold cross-validation, the system discerned four gestures (fist, open hand, wave in, and wave out) performed by six volunteers in ten repetitions, providing 96.6% and 94.1% average accuracies for self-calibration and inter-participant analyses, respectively. The smartphone figures as a ubiquitous and straightforward alternative for assessing gestures, with further applications in human-robot interaction and assistive technologies.
Removing Word-Level Spurious Alignment between Images and Pseudo-Captions in Unsupervised Image Captioning

Unsupervised image captioning is a challenging task that aims at generating captions without the supervision of image-sentence pairs, but only with images and sentences drawn from different sources and object labels detected from the images.In previous work, pseudo-captions, i.e., sentences that contain the detected object labels, were assigned to a given image.The focus of the previous work was on the alignment of input images and pseudo-captions at the sentence level.However, pseudo-captions contain many words that are irrelevant to a given image.In this work, we investigate the effect of removing mismatched words from image-sentence alignment to determine how they make this task difficult.We propose a simple gating mechanism that is trained to align image features with only the most reliable words in pseudo-captions: the detected object labels.The experimental results show that our proposed method outperforms the previous methods without introducing complex sentence-level learning objectives.Combined with the sentence-level alignment method of previous work, our method further improves its performance.These results confirm the importance of careful alignment in word-level details. 1