Reviewerly/exHarmony huggingface dataset:
Leveraging Universal Sentence Encoder to Predict Movie Genre

Multi-label text classification (MLTC) refers to the problem of dealing with textual data classification based on multiple labels or tags. There are numerous real-world scenarios where the need for assigning labels to a particular object arises, such that the labels are descriptive of the properties of that object. However, in real life, it is not uncommon for one object to hold more than a singular property describing itself, hence it needs multiple labels to be associated with it. In the cases of textual data, one such scenario is assigning labels to a movie describing the genre, which needs more than one genre to specify the plot in a practical scenario. This makes movie genre prediction the desired choice for multi-label classification in many kinds of literature. This paper explores and presents an in-depth analysis of the approach of solving movie-genre prediction problems using the sequential model with universal sentence encoder (USE) for text encoding, alongside the use of label powerset (LP) as the problem transformation approach. Along with that, a comparative analysis of the performance of the model with different optimizers is performed. The best outcome achieved is f1-score 0.69 and accuracy of 0.89 with Adam optimizer, which, upon comparison with other literature of the similar domain, is either an equal or better in performance.
A Comprehensive Analysis of Keystroke Recognition System

Together with the widening of the Internet plays in the continuous outstretch of the online environment as a potential for business has also increased the fortunes of online malicious attacks and intrusions. They all have their way of stealing the user’s identity. Usernames and passwords which are very weak can be easily cracked by the attackers. User’s credentials thus can be discovered, phished, looted, and then hacked in several different ways. Keystroke recognition is a good technology to facilitate better authentication and protect data theft and has a very minimum amount of drawbacks. Other biometrics needs additional hardware costs whereas this is based on the user typing behavior which just requires a keyboard. Thus Keystroke recognition allows authenticating users through their way of typing on a keyboard of a computer. This paper presents a biometric access control measure: access of computers via keystroke recognition and talks about how identity thefts and user data theft can be prevented by using this keystroke dynamics.
Machine Learning with System/Software Engineering in Selection and Integration of Intelligent Algorithms

Machine learning has become an essential component in the design of intelligent systems across several disciplines. This widespread use of machine learning has led to the importance of evaluating how Systems/Software Engineering approaches go hand in hand with Machine Learning to reliably integrate intelligence in software systems. In this research effort, our motivation is to develop a systematic approach also termed as Machine Learning Engineering for the selection and integration of machine learning algorithms in systems. The proposed approach discusses combining a structured approach for designing and developing system/software with an experimental analysis that data scientists perform on machine learning algorithms. This experimental analysis is essential as some of the characteristics exhibited by intelligent algorithms cannot be predicted or guaranteed compared to systems without intelligent algorithms. In this paper, we elaborate on our system/software engineering guided disciplined approach by comparing two machine learning algorithms that focus on the recognition of handwritten digits. The algorithms we compare are the Logistic Regression and Neural Network algorithms. After the analysis, we identify the contracts that should be associated with intelligent components to better predict the behavior of the system as a result of the selection of one of the components to be a machine learning algorithm. Finally, we indicate how the results can be used by Systems/Software Engineers in integrating intelligent algorithms.
Research on College Mathematics Teaching Method Based on Modeling Thought

Abstract Nowadays, modeling ideas are more and more widely used in university mathematics teaching. Based on this, this paper mainly analyzes the necessity of infiltrating mathematical modeling ideas into college mathematics teaching and it also discussed college mathematics teaching methods based on modeling ideas.
Stakeholder Approach Applications Aimed at Monitoring the Sustainability of Higher Education Institutions

The article reveals the significant aspects of stakeholder groups when establishing control over activities of higher educational establishments in the Russian Federation. Identification of major stakeholders of higher educational institutions as agents of control over their activities will allow us both to select the objects of control and formulate the responsibility of higher educational institutions in front of the stakeholders. Areas requiring the most attention from the top management of the institutions of higher education are concentrated with regard to the competitiveness of the University itself, its programs and alumni, as they reflect the objects control for most of stakeholders.
A Deep Learning Framework to Preserve Privacy in Federated (Collaborative) Learning

It is a well accepted fact that data powers deep learning models and perform well as the training datasets get larger and more diverse. But centralized collection of data raises serious privacy concerns as the data collected may contain sensitive private information. Moreover, in industries such as Medical Health Care, such collection and storage of data in the cloud is not permitted by law in many countries. As a result, due to less diverse data, the deep learning models may end up with less accuracy on test data. In this chapter, an approach for multiple parties jointly to learn a neural network model without disclosing their local training data to a central cloud server is discussed. The above approach is achieved by using Distributed Stochastic gradient descent combined with additively homomorphic encryption (HE). This process thereby bridges both Deep learning and cryptography. A privacy-preserving deep learning framework is discussed in which all clients encrypt using a different public key in contrast to that of in where each client encrypts using same key. Since the cryptosystem used is additively homomorphic the accuracy of the Neural Network model in our system (Distributed SGD combined with Homomorphic Encryption) remains same compared to that of Distributed SGD system where gradients are not encrypted. Encryption using additively homomorphic encryption adds tolerable communication overhead and helps in preserving privacy of client’s datasets. A neural network is trained for recognizing hand written digits and achieved an accuracy of \( 96.88\% \) which is same as Distributed SGD. The communication overhead in the above system is naturally high due to addition of HE. However, in the proposed method the communication overhead is reduced by using packing mechanism which is tolerable. Homomorphic Encryption makes privacy preserving outsourced data storage and computation feasible. Since the processing of data can be done in encrypted domain and the cloud server learns nothing at all, the privacy-preserving deep learning framework discussed in this chapter can be adopted in various practical scenarios where sharing of personal data is not feasible.
The Coordinated Development of Vocational College Talent Training Model and Student Financial Aid System

The scale of vocational education is developing rapidly and growing day by day, and it has become an important part of my country's education field. Vocational colleges undertake the great mission of cultivating senior professional and technical talents in our country and become the main body of our country's vocational education. There are many differences between vocational colleges’ families with financial difficulties and ordinary undergraduate colleges’ students in many aspects. In order to solve the practical problems of vocational colleges’ families with financial difficulties, a student funding system that develops in coordination with the vocational education talent training model is established , to effectively help students from vocational colleges with financial difficulties to achieve real poverty alleviation in material and ideology, which is of great significance for cultivating outstanding professional and technical talents, promoting the development of vocational education in China, and maintaining social stability.
Analysis and Management of Risk Related to Using Cloud Computing in Business: Employee’s Perception

With the recent advancement of the technology and its application to our daily business and social life; precisely the cloud computing system, however it has a risks that might lead to hesitation and lose of enthusiasm of using it. The main objective of this research is to find the risk factors, which may affect the business performance of the companies that use “Cloud Computing”, and understand the consequences and its managements. To the best of my knowledge, this is the first study in Iraq on risk analysis and managements in cloud computing. The objectives achieved by distributing a questionnaire to 30 private companies in Iraq which are using cloud computing to risk managements by interviewed the employees who were responsible on managing “Cloud Computing” in their companies. That leads to recognize the potentials risk, and how much impact we might make to take the right decision in order to manage the risk to help using “Cloud Computing” without fears.
Homework [EIC'S Message]

One can call it working at home or living at work; 2020 will be remembered for it. Here, the author offers a few data points associated with this recent lifestyle change based on recent survey results.
Parallel Variational Autoencoders for Multiple Responses Generation

Variational autoencoders (VAEs) have been widely adopted in the field of diverse responses generation. The existing VAE-based responses generation models can be roughly divided into two categories: the first is to map multiple responses to a joint variable distribution; the second is to construct an individual variable distribution for each response. Models based on the joint distribution can generate diverse responses to some extent, but the difference among responses is inapparent. On the contrary, models based on the individual distribution can ensure the distinction among responses, while they are not good at modeling the correlation among responses. To combine these two kinds of VAEs and make the best of their respective advantages, we propose a Parallel Variational Autoencoders (PVAE) model that innovatively contains two variable distributions: correlation and distinction. The joint correlation distribution captures the common feature of all responses. Therefore, it can better retain the relevance to the given context. The individual distinction distribution models the unique content of each response, which promotes responses' diversity. In the generation step, keywords are sampled from these two distributions via the improved sampling process, which also enhances the diversity of responses. The experimental results demonstrate that our model achieves state-of-the-art performance on a weibo dataset.
Summary and Analysis of Typical Technologies in 5G

As an emerging mobile communication technology, 5G is more comprehensive in the face of differentiated performance requirements after 2020. Its main goal can be summarized as ‘Enhancing broadband and interconnection of all things’. 5G will have more abundant service providing capabilities compared with previous communication technologies, so it has been widely concerned and studied by enterprises, research institutes and universities all over the world. This paper first introduces the history of 5G and the current situation at home and abroad, and introduces and analyzes some key technologies of 5G from three aspects of wireless transmission, wireless access and the application of AI in 5G system. Finally, the development direction and prospect of 5G in the future are prospected and analyzed.
Methods and Means Used in Forming the Preschoolers Musical Culture

The article discusses information about effective methods and tools used in the formation of signs of musical culture of preschoolers. This should be carried out in a continuous process of education and upbringing. Therefore, it is widely stated that the methods and means set forth in the content of the article can be guided by the organization of each organized educational activity, the conduct of each educational event. As signs of the moral culture of preschoolers were taken: modesty, thrift, restraint, contentment, appropriate use, the tasks of moral education of preschoolers was determined.
Use of portfolio technology to evaluate teachers and student ratings

The article describes the methods and technology of using "portfolio" technology. Evaluation of the results of scientific activity of university teachers, students and masters, monitoring data allow to support the level of understanding of the goals and opportunities of the portfolio of teachers and students and its implementation. According to the research of teachers, the introduction of a portfolio has a positive impact on the organization of the learning process.
Adherent Nuclei Edge Detection Based on Caps-Unet

Accurate nuclei edge detection is a fundamental task of Computer Aided Diagnosis (CAD) systems. Automated detection of nuclei edges, especially the adherent ones, in histopathological images is crucial for splitting adherent nuclei, which is the key of early diagnosis of diseases. For this end, we propose a model named Caps-Unet aiming at accurately and efficiently detecting adherent nuclei edges in H&E stained histopathological images. The Caps-Unet model replaces the ordinary convolutional layers with proposed Capsule layers (Caps-layers for short) in both contracting pathway and expanding pathway of the U-net model. Each Caps-layer consists of four different-scale convolution units and a concatenated unit incorporating the four resulting feature maps. Evaluated experimental results on four different kinds of diseases in MICCAI2017 nuclei segmentation competition dataset show our model has a better performance than the original U-net model and the state-of-the-art TernausNet model.
A logical framework to study concept-learning biases in the presence of multiple explanations

When people seek to understand concepts from an incomplete set of examples and counterexamples, there is usually an exponentially large number of classification rules that can correctly classify the observed data, depending on which features of the examples are used to construct these rules. A mechanistic approximation of human concept-learning should help to explain how humans prefer some rules over others when there are many that can be used to correctly classify the observed data. Here, we exploit the tools of propositional logic to develop an experimental framework that controls the minimal rules that are simultaneously consistent with the presented examples. For example, our framework allows us to present participants with concepts consistent with a disjunction and also with a conjunction, depending on which features are used to build the rule. Similarly, it allows us to present concepts that are simultaneously consistent with two or more rules of different complexity and using different features. Importantly, our framework fully controls which minimal rules compete to explain the examples and is able to recover the features used by the participant to build the classification rule, without relying on supplementary attention-tracking mechanisms (e.g. eye-tracking). We exploit our framework in an experiment with a sequence of such competitive trials, illustrating the emergence of various transfer effects that bias participants' prior attention to specific sets of features during learning.
Contextual Safeguarding in Education: Bayesian Network Risk Analysis for Decision Support

A Multi Academy Trust in the UK operates thirty-five academies educating 17,000 students across seven local authority areas. Significant societal problems are increasing risk to young people, including exploitation and violent crime associated with gang culture and drugs. A predictive analytics system is being implemented to support the delivery of contextual safeguarding, where the interplay of the school, peer, family and community environments determine the safeguarding risk. Due to the intense level of human activity required by safeguarding teams to identify and intervene with those at risk, Bayesian network risk modelling is being integrated with traditional analytics to extend and augment human capacity. The participants are keenly aware of the potential for harm from this data; in its collation, appropriateness of methods, accuracy and validity of output, and the human interpretation and resulting actions and impact on young people.
Performance of MyNET Model on Handwriting Biometrics

This paper is in the continuation of Writer Identification using Neural Network[1]. Offline signature identification is the challenging task till date. Unlike to verification problem of one-to-onemapping, identification maps single with rest them to identify the signer. For this process we model a model using convolution neural network. This paper explains the performance of MyNET model on MyNET offline signature dataset which consists of 434 writers 20 signatures each.
Privacy Preserving with Surfed Social Media Content

Abstract The world is going to become more digital day by day. A lot of data has been collected from Web Site users on how they think about society and what they believe in. we are targeting generalized part(content based retrieval) in Youtube surf material, in the proposed stage the data collection is more important and making of uncertain data to certain term, like find the sentimental relation between the contents and other equivalent structure of data to be identified, kind of group formulation and removing the out layers of uncertain data with privacy preserving, in such aspect we can able to achieve the correct content over area of people interest which can be used to improvise the surfing contents for any sectors. In order to obtain priority, information must be classified according to the approximate value of three variables. First, the frequency in which a subject appears in the mainstream media over time is a significant factor that can be referred to as the topic’s media focus (MF). Second, the topic’s temporal prominence in social media means that it has a high level of consumer interest (UA). Finally, the social media interaction between users who mention the topic reflect the intensity of the group debating it, and could be considered as user interaction (UI) with the subject. SociRank increases the consistency and variety of topics that are automatically listed, according to our tests.
System Performance and Empathetic Design Enhance User Experience for Fault Diagnosis Expert System

The fault diagnosis task in factories is technically demanding and time-consuming, especially for the green hands. In order to assist mechanicians to detect the fault parts, the fault diagnosis expert system is introduced to support decision-making and problem-solving processes. In this vision, how to enhance mechanicians’ willingness to use such a system becomes a key issue to the industry. The present study aims to find out the elements that affect the user experience of the fault diagnosis expert system. We carried out an experiment to investigate how system performance and empathetic design promotes user experience, and we also explored how those factors apply to different users with high and low expertise. Fourteen participants who majored in automatic control with a basic understanding of industrial automation devices took part in the study. During the experiment, the participants were instructed to repair a UV control cabinet with the help of the fault diagnosis expert system. The system has four levels of system performance (high, medium, low, and all-errors) * two levels of empathetic feature (standard design vs. empathetic design). After each task, the participants filled out a questionnaire to evaluate their experience. The results showed a beneficial effect of both system performance and empathetic design. Also, the interactive effect indicates that for the system with qualified performance, the empathetic design enhanced user experience; while for the system with all-error performance, the empathetic design no longer helped. In addition, the empathetic design played a better role among inexperienced users, while experienced users tended to be more sensitive to changes in system performance.
Application of Virtual Reality and Augmented Reality Technology for Teaching Biology at High School in Vietnam

Augmented Reality, or AR for short, is a technology that helps users interact with virtual content in real environments. Virtual Reality (VR), abbreviated as VR, is a virtualized (digitized) virtual environment and performed on computers or smart mobile devices. Thanks to the outstanding feature of simulating images in 3D, VR/ AR is one of the effective support tools in teaching in general and Biology in particular. In Vietnam, VR technology/AR technology (VRT/ART) has been initially applied in teaching, however, there are still many shortcomings for many reasons. In this article, the author proposes a process and assessment of the status of VRT/ART application in biology teaching at high schools in Vietnam and initially tests the effectiveness of applying VRT/ART to teaching specific content. This research plays a fundamental role, opening more in-depth studies in applying VRT/ART to teaching in Vietnam.
Efficient Verifiable Image Redacting based on zk-SNARKs

Image is a visual representation of a certain fact and can be used as proof of events. As the utilization of the image increases, it is required to prove its authenticity with the protection of its sensitive personal information. In this paper, we propose a new efficient verifiable image redacting scheme based on zk-SNARKs, a commitment, and a digital signature scheme. We adopt a commit-and-prove SNARK scheme which takes commitments as inputs, in which the authenticity can be quickly verified outside the circuit. We also specify relations between the original and redacted images to guarantee the redacting correctness. Our experimental results show that the proposed scheme is superior to the existing works in terms of the key size and proving time without sacrificing the other parameters. The security of the proposed scheme is proven formally.
Research on the calculation method of Apriori algorithm

When mining frequent itemsets, the traditional Apriori algorithm uses iterative calculation support level by layer. It needs to scan the database for many times and perform I/O operations frequently. Moreover, it is sensitive to the original data. When the data structure is complex, the number of data items is large, and the density of data elements is high, the problem of low efficiency of Apriori algorithm is obvious. In order to further improve the performance of Apriori algorithm, this paper analyzes the operation principle of the algorithm, and proposes MI_Apriori(Multiset intersection Apriori) algorithm. Based on the equivalent Boolean matrix of transaction records, the intersection operation of multiple sets is carried out, and the maximum frequent itemset is mined through a certain number of iterations. Compared with the traditional Apriori algorithm, the correctness and reliability of the mining results are verified. It shows that the MI_Apriori algorithm has better computational performance, and the improvement effect of the traditional Apriori algorithm is obvious.