Reviewerly/exHarmony huggingface dataset:
Optimized multimodal biometric system based fusion technique for human identification

This paper presents three novelty aspects in developing biometric system-based face recognition software for human identification applications. First, the computations cost is greatly reduced by eliminating the feature extraction phase and considering only the detected face features from the phase congruency. Secondly, a motivation towards applying a new technique, named mean-based training (MBT) is applied urgently to overcome the matching delay caused by the long feature vector. The last novelty aspect is utilizing the one-to-one mapping relationship for fusing the edge-to-angle unimodal classification results into a multimodal system using the logical-OR rule. Despite some dataset difficulties like Unconstrained Facial Images(UFI) which includes varying illuminations, expressions, occlusions, and poses, the multimodal system has highly improved the accuracy rate and achieved a promising recognition result, where the decision fusion is classified correctly (84, 92, and 72%) with only one training vector per MBT in contrast to (80, 62, and 68%) with five training vectors for Normal matching. These results are measured by Eucledian, Manhattan, and Cosine distance measure respectively.
Survey on computational intelligence based image encryption techniques

Image encryption is an important area in visual cryptography that helps in protecting images when shared through internet. There is lot of cryptography algorithms applied for many years in encrypting images. In the recent years, artificial intelligence techniques are combined with cryptography algorithms to support image encryption. Some of the benefits that artificial intelligence techniques can provide are prediction of possible attacks on cryptosystem using machine learning algorithms, generation of cryptographic keys using optimization algorithms, etc. Computational intelligence algorithms are popular in enhancing security for image encryption. The main computational intelligence algorithms used in image encryption are neural network, fuzzy logic and genetic algorithm. In this paper, a review is done on computational intelligence-based image encryption methods that have been proposed in the recent years and the comparison is made on those methods based on their performance on image encryption.
School-based Cybersecurity Education Programme for Schoolchildren in South Africa! A Timely Call from Bloemfontein

The interconnection of many countries of the world today is hugely tied to the overwhelming improvements in the Internet.However, the improved Internet connectivity is not without diverse security risks such as bullying, scamming, hate speech, and identity theft especially for minors.It is unfortunate that the security risks attached to the use of the Internet are often ignored in the rush to log online by schoolchildren in South Africa.The cyber insecurities in South Africa became more precarious as schools do not teach cybersecurity as a subject in South Africa at present.Consequently, it behooves the researcher to unveil how the agents of education in South Africa learning ecologies can use the school-based cyber security education programme to protect schoolchildren against cyber scammers, and propose a blue-print on developing a school-based cybersecurity education programme for South African schoolchildren.In this paper, the space transition theory is used to explain the causation of insecurities confronting cyber savvy schoolchildren in South Africa.This paper methodically explicated related scientific papers that were published from 2005 to 2019 on cybercrimes, cybersecurity, and school-based participatory research.The community-driven model developed by Lavery et al. (2005) was used to design the school-based cybersecurity educational programme.This paper exhaustively explained the operations of the school-based cybersecurity educational programme and illustrated how teachers can use the informal cybersecurity educational programme to teach cybersecurity in South African schools.
Training Encrypted Models with Privacy-preserved Data on Blockchain

Currently, training neural networks often requires a large corpus of data from multiple parties. However, data owners are reluctant to share their sensitive data to third parties for modelling in many cases. Therefore, Federated Learning (FL) has arisen as an alternative to enable collaborative training of models without sharing raw data, by distributing modelling tasks to multiple data owners. Based on FL, we premodel sent a novel and decentralized approach to training encrypted models with privacy-preserved data on Blockchain. In our approach, Blockchain is adopted as the machine learning environment where different actors (i.e., the model provider, the data provider) collaborate on the training task. During the training process, an encryption algorithm is used to protect the privacy of data and the trained model. Our experiments demonstrate that our approach is practical in real-world applications.
Genetic Algorithm-Based Anisotropic Diffusion Filter and Clustering Algorithms for Thyroid Tumor Detection

Medical imaging is a technique that has been used for diagnosis and treatment of a large number of diseases. Therefore it has become necessary to conduct a good image processing to extract the finest desired result and information. In this study, genetic algorithm (GA)-based clustering technique (K-means and Fuzzy C Means (FCM)) were used to segment thyroid Computed Tomography (CT) images to an extraction thyroid tumor. Traditional GA, K-means and FCM algorithms were applied separately on the original images and on the enhanced image with Anisotropic Diffusion Filter (ADF). The resulting cluster centers from K-means and FCM were used as the initial population in GA for the implementation of GAK-Mean and GAFCM. Jaccard index was used to study resemblance, dissimilarity, distance between two sets of images, and effect of ADF on enhancing the CT images. The results showed that ADF increases the segmentation accuracy, where the value of Jaccard index of similarity between the ground truth image and segmented image was increased for all segmentation algorithms, in particular for FCM and GAFCM where similarity percent was up to 88%.
Improving new wavelet threshold by artificial fish swarm and its application for denoising

Vibration signal is the main measurement signal of mechanical component fault diagnosis, and the presence of noise affects the feature extraction of the signal and the final fault diagnosis, so the test signal in practice needs to be denoised. The use of wavelet transform does not filter out the noise in the signal very well. This is because the hard threshold function is not continuous and some useful information is filtered out. There is a deviation in the soft threshold function and the noise in the signal cannot be completely filtered out. And the traditional threshold is fixed. In order to solve the problem of threshold function and threshold, this paper proposes a new threshold function, and uses artificial fish swarm algorithm to get the optimal threshold. Finally, the superiority and practicability of the method are verified by the unsteady test signal and the bearing dataset of Case Western Reserve University. From the final noise reduction result, the method can achieve better performance in noise reduction than other existing methods. The signal-to-noise ratio obtained by this method is 13%~16% higher than other methods. The root mean square error is 10%~41% lower than other methods.
The use of EduBlog in initial teachers training: an experience of a teaching innovation project

Digital technologies are central elements of today's society. In this context to achieve an adequate degree of development of Digital Competence for professional performance is essential, as well as for life in society and the exercise of citizenship. Higher Education must actively contribute to develop Digital Competence, training students to effectively use digital technologies. That need is exacerbated in the case of initial teacher training, not only because of the progress towards a digitalized school, but also because of the fundamental role in the training of future generations. With the aim of promoting the development of Digital Teaching Competence, this paper presents an innovation project implemented in the Degree of Teacher of Nursery and Primary Education of the University of Valencia that is based on the didactic use of EduBlog. The selection of this digital tool responds to its multiple benefits on the work of curricular content, the development of digital skills and the creation of a positive climate in the classroom. The project developed with a total of 131 second-year students demonstrates that the use of EduBlog has a positive impact on the learning process and on the increase in student engagement and motivation.
Campus Economic Analysis Based on K-Means Clustering and Hotspot Mining

With the advent of the era of big data and the development and construction of smart campuses, the campus is gradually moving towards digitalization, networking and informationization. The campus card is an important part of the construction of a smart campus, and the massive data it generates can indirectly reflect the living conditions of students at school. In the face of the campus card, how to quickly and accurately obtain the information required by users from the massive data sets has become an urgent problem that needs to be solved. This paper proposes a data mining algorithm based on K-Means clustering and time series. It analyzes the consumption data of a college student’s card to deeply mine and analyze the daily life consumer behavior habits of students, and to make an accurate judgment on the specific life consumer behavior. The algorithm proposed in this paper provides a practical reference for the construction of smart campuses in universities, and has important theoretical and application values.
Development of “soft skills” for students in the process of foreign language study as a necessary component of competitiveness of future specialists

Such concepts as “soft skills” and “hard skills” are studied in the article and it is grounded the necessity of development of “soft skills”&nbsp; that help to increase studens' competitiveness at the market of labour. The aim of this article is research of possibilities of development of “soft skills’’ of students in the process of study of foreign language. Foreign language is a means of&nbsp; professional competence increasing and personality development of students and it is&nbsp; necessary condition&nbsp; of successful employment and career. The author of the article examines possibilities of “soft skills” development for the students&nbsp; in the process of study of foreign language, using 4 groups of technologies of interactive studies after classification of L.&nbsp;Pyrozhenko and О. Pometun and comes to the conclusion that encouragement of students to active conversation, use of debatable form of work, cooperative realization of projects, participating in creative events, development of skills of self-presentation is the prime example of the use of interactive methods of studies and development of such “soft skills” as communicability, argumentation, adaptivity, self-control, creativity, self discipline, command work, loyalty and motivation.
Mitigating bias in deep nets with knowledge bases : The case of natural language understanding for robots

In this paper, we tackle the problem of lack of understandability of deep learning systems by integrating heterogeneous knowledge sources, and in the specific we present how we used FrameNet to guarantee the correct learning for an LSTM-based semantic parser in the task of Spoken Language Understanding for robots. The problem of the explainability of Artificial Intelligence (AI) systems, i.e. their ability to explain decisions to both experts and end users, has attracted growing attention in the latest years, affecting their credibility and trustworthiness. Trusting these systems is fundamental in the context of AI-based robotic companions interacting in natural language, as the users’ acceptance of the robot also relies on the ability to explain the reasons behind its actions. Following similar approaches, we first use the values of the neural attention layers employed in the semantic parser as a clue to analyze and interpret the model’s behavior and reveal the intrinsic bias induced by the training data. We then show how the integration of knowledge from external resources such as FrameNet can help minimizing, or mitigating, such bias, and consequently guarantee the model to provide the correct interpretations. Our preliminary, but promising results suggest that (i) attention layers can improve the model understandability; (ii) the integration of different knowledge bases can help overcoming the limitations of machine learning models; and (iii) an approach combining the strengths of both knowledge engineering and machine learning can foster the development of more transparent, understandable intelligent systems.
The Role of Design Thinking for Achieving Leadership in the Digital Transformation of Business

The implementation of the technologies of the fourth industrial revolution is widely discussed both in the scientific community and among business leaders.New digital technologies provide a real opportunity for companies to achieve leadership by changing their business model, focusing on customer experience, using big data technologies, increasing flexibility, and changing methods of managing their assets.Such disruptive changes require an understanding of the role of the organization's culture in the process of change and attention to it.However, technology alone cannot lead to the new business successes.People who use these technologies should be prepared to change regular business models and business processes in order to move to a new quality.Moreover, people quickly accept changes and participate in their implementation if they were involved in the process of developing these changes.One of the most sought-after qualities in the era of the digital revolution is the creativity of thinking.Design thinking has occupied an important place in the practice of teaching in leading universities and business schools in the world.Thanks to this approach, a person gets the opportunity to overcome t he accumulated stereotypes and patterns.Companies of various sizes and activities actively apply design thinking in the process of transforming their business.This paper identifies areas of application of design thinking in various areas of company management in the process of digital transformation in order to achieve leadership in business.
FDHelper: Assist Unsupervised Fraud Detection Experts with Interactive Feature Selection and Evaluation

Online fraud is the well-known dark side of the modern Internet. Unsupervised fraud detection algorithms are widely used to address this problem. However, selecting features, adjusting hyperparameters, evaluating the algorithms, and eliminating false positives all require human expert involvement. In this work, we design and implement an end-to-end interactive visualization system, FDHelper, based on the deep understanding of the mechanism of the black market and fraud detection algorithms. We identify a workflow based on experience from both fraud detection algorithm experts and domain experts. Using a multi-granularity three-layer visualization map embedding an entropy-based distance metric ColDis, analysts can interactively select different feature sets, refine fraud detection algorithms, tune parameters and evaluate the detection result in near real-time. We demonstrate the effectiveness and significance of FDHelper through two case studies with state-of-the-art fraud detection algorithms, interviews with domain experts and algorithm experts, and a user study with eight first-time end users.
An Overview on Document Summarization Techniques

The increase in the performance and fast accessing of web resources has made a new challenge of browsing among huge data on Internet.Since digitally stored information is more and more available, users need suitable tools able to select, filter, and extract only relevant information.Text summarization can be classified into two approaches: extraction and abstraction.This paper focuses on extraction approach.The goal of text summarization based on extraction approach is sentence selection.One of the methods to obtain the suitable sentences is to assign some numerical measure of a sentence for the summary called sentence weighting and then select the best ones.Therefore, text summarization techniques are currently adopted in several fields of information retrieval and filtering, such as, information extraction, text mining, document classification, recommender systems, and contextual advertising.A summary text is a derivative of a source text condensed by selection and/or generalization on important content.Query-focused summaries enable users to find more relevant documents more accurately, with less need to consult the full text of the document.Extractive summarization methods try to find out the most important topics of an input document and select sentences that are related to these chosen concepts to create the summary.This paper is a Comparative study of four techniques used for extractive summarization, namely, Neural Network, Graph Theoretic, Fuzzy based method and Cluster based method
Two Stream Networks for Self-Supervised Ego-Motion Estimation

Learning depth and camera ego-motion from raw unlabeled RGB video streams is seeing exciting progress through self-supervision from strong geometric cues. To leverage not only appearance but also scene geometry, we propose a novel self-supervised two-stream network using RGB and inferred depth information for accurate visual odometry. In addition, we introduce a sparsity-inducing data augmentation policy for ego-motion learning that effectively regularizes the pose network to enable stronger generalization performance. As a result, we show that our proposed two-stream pose network achieves state-of-the-art results among learning-based methods on the KITTI odometry benchmark, and is especially suited for self-supervision at scale. Our experiments on a large-scale urban driving dataset of 1 million frames indicate that the performance of our proposed architecture does indeed scale progressively with more data.
Accuracy Level of Diagnosis of ENT Diseases in Expert System

Purpose of this study is to test the accuracy of ear, nose and throat (ENT) diseases with an expert system. The expert system is designed to help people make early detection of illnesses so that the possibility of delay in treatment can be minimized. The method used is Naive Bayes with Forward Chaining Inference for 14 types of diseases with 42 symptoms originating from ENT specialists. The method was tested on 25 patients who used an expert system and adjusted the results of expert diagnoses. The test results are influenced by the number of symptoms, so that the accuracy obtained is only 88%. So this research is needed to be further developed to find a more reliable expert system in diagnosing ENT diseases.
DesignDiff: Continuously Modeling Software Design Difference from Code Revisions

The design structure of a system continuously evolves as the consequence of fast-paced code revisions. Agile techniques, such as continuous testing, ensures the function goals of a system with every code revision. However, there lacks an efficient approach that can continuously model the design difference resulting from every single code revision to facilitate comprehension and ensure the design quality. This paper contributes a novel design modeling approach, called Design Differencing (DESIGNDIFF), that models and visualizes the highlevel design differences resulting from every code revision. This paper defines a complete and general set of 17 design change operators to capture the design difference from any code revision. We evaluated the potential of DESIGNDIFF in three aspects. First, a user study of 10 developers indicated that DESIGNDIFF can help practitioners to faster and better understand high-level design differences from real-life software commits. Second, DESIGNDIFF analyzed 14,832 real-life commits in five real-life projects: finding 4,189 commits altered the software design, 855 commits introduced and 337 commits eliminated design flaws. The latency between the flaw introduction and elimination is on average 2 months to 2 years! With an affordable performance overhead, DESIGNDIFF has great potential to benefit practitioners in more applications.
Data-Driven Parametric Text Normalization: Rapidly Scaling Finite-State Transduction Verbalizers to New Languages

This paper presents a methodology for rapidly generating FST-based verbalizers for ASR and TTS systems by efficiently sourcing language-specific data. We describe a questionnaire which collects the necessary data to bootstrap the number grammar induction system and parameterize the verbalizer templates described in Ritchie et al. (2019), and a machine-readable data store which allows the data collected through the questionnaire to be supplemented by additional data from other sources. This system allows us to rapidly scale technologies such as ASR and TTS to more languages, including low-resource languages.
Citizen Seismology in Taiwan: Development, Outreach, and Formative Assessment of Near-Real Time Earthquake Game Competition Activities

Citizen seismology encourages the public involvement in data collection, analysis, and reporting, and has the potential to greatly improve the emergency response to seismic hazard. We believe in the value of citizen seismology and started with distributing Quake-Catcher Network (QCN) sensors at schools in Taiwan. Unlike most of western countries, Earth Sciences is a required course in junior and senior high schools of Taiwan (Ministry of Education, 2014). Low-cost seismometer is potentially a powerful tool in classrooms to teach earthquake science, which makes school teachers our targeted users. We work closely with school teachers and students to understand how can a citizen seismology project can help them to encourage successful engagement. In this study, we establish the achievements gained and problems encountered in different phases of the project since 2013. The main tasks carried out here include (1) refinement of Citizen Seismology Literacy (CSL) into three dimensions: Awareness, and Willingness, and Technology fluency (2) development of an interactive competition platform called the Near-Real Time Earthquake Game Competition (EGCnrt) that allows citizen seismologists to report earthquake information by processing P- and S-wave arrivals, peak ground motion, and first motion of P-waves for every inland M≥4 earthquakes in Taiwan; (3) development of the formative assessment for the 1.5-hour-long, non-lecture-based game activities. Based on 565 student surveys completed after our 2016 summer outreach activity, we found that all three CSL dimensions have an influence on students’ score in the training activity. The final score in EGCnrt is also found to highly correlate with the performance in the three training sections. We therefore, propose that the CSL can act as a powerful indicator for the performance and engagement in earthquake learning activities. The game-based, non-lecture-based learning approach can be effective in promoting citizen seismology in the future.