Reviewerly/exHarmony huggingface dataset:
The Implementation of Facial Symmetry Assessment Before and After Orthognathic Surgery Using Transfer Learning

In recent years, deep learning has been widely used in medical care. The implementation of deep learning technology in medical care makes the judgment of medical results more accurate and saves evaluation time. For example, the preoperative evaluation of orthognathic surgery requires a lot of time to observe the facial symmetry of the clinical patient. This paper, in cooperation with Chang Gung Memorial Hospital, studies to judge the degree of facial symmetry through migration learning. The system uses contour map as the training data, and construct the system to classify and score the symmetry of the face. Therefore, doctors can quickly and easily assess the status of clinical patients and save evaluation time. In this case, the contour map of the face from 71 patients provided by the doctor is classified into four equal parts according to the scores given by the doctors. Machine learning is used to extract sample features and a set of trained module. For user interface, the doctor uploads the image through the webpage, and the system outputs the classified result to provide a reference for the doctor so that the doctor can further understand the part of the patient that needs correction.
Ranking-Based Fuzzy Min-Max Classification Neural Network

The performance of fuzzy min-max classification neural network (FMM) is affected by the input sequence of training set patterns. This paper proposes a ranking-based fuzzy min-max Classification Neural Network (RFMM) to overcome this shortcoming. RFMM improves FMM through the following three aspects. First, RFMM ranks the input order of the training set patterns according to their membership degree to the center point of same class, so that the finally constructed network is fixed and does not depend on the input order of the training set. Second, a new membership function based on Manhattan distance is constructed, which overcomes the problem that the membership degree obtained by the membership function in the FMM will not decrease steadily with the increase of the distance between the input pattern and the hyperbox. At last, RFMM uses the method based on individual contour coefficient to classify the patterns in overlapping regions, which overcomes the problem that when the FMM eliminates the overlapping region by shrinking hyperboxes, the membership degree of the patterns in the contracted region to the class they belong is changed. Experimental results show that RFMM has better learning ability, and compared with other FMM methods, RFMM shows higher classification accuracy and lower network complexity.
Dialogue Act Classification for Virtual Agents for Software Engineers during Debugging

A "dialogue act" is a written or spoken action during a conversation. Dialogue acts are usually only a few words long, and are often categorized by researchers into a relatively small set of dialogue act types, such as eliciting information, expressing an opinion, or making a greeting. Research interest into automatic classification of dialogue acts has grown recently due to the proliferation of Virtual Agents (VA) e.g. Siri, Cortana, Alexa. But unfortunately, the gains made into VA development in one domain are generally not applicable to other domains, since the composition of dialogue acts differs in different conversations. In this paper, we target the problem of dialogue act classification for a VA for software engineers repairing bugs. A problem in the SE domain is that very little sample data exists - the only public dataset is a recently-released Wizard of Oz study with 30 conversations. Therefore, we present a transfer-learning technique to learn on a much larger dataset for general business conversations, and apply the knowledge to the SE dataset. In an experiment, we observe between 8% and 20% improvement over two key baselines.
Review on Breast Cancer Disease Predictive Modelling using Swarm Intelligence

Breast Cancer (BC) is one among the most critical diseases affecting women across the world mostly between the ages 35-55 and is growing every year in a capricious rate. By identifying the disease in its early stages, we can control the occurrence of BC. The manual approach used by radiologists has failed since they are similar in the appearance of micro-calcifications, and segmentation of breast images are complicated process. Hence there is a need of automated systems for detection of the disease in early-stage which helps to assist radiologists for diagnosing the disease in a precise manner and make necessary decisions for patients' treatment in the future. For classifying and predicting the disease, swarm intelligence (SI) plays a major role in terms of obtaining an optimal solution and reducing its training time. This paper presents study on review and analyses the performance of breast cancer disease predictive modelling using swarm intelligence. Also, it recommends the solution for forthcoming exertion to predict the location of the tumour in an efficient manner.
Introduction of Video Analysis Technology in Basketball Teaching Under the Background of “Internet +”

The basketball teaching methods of frontline teachers are traditional and lack of scientific, innovative, and attractive. As the science of basketball teaching video analysis technology software can let the student see own technique and tactical implementation effect, precise analysis of the information in a timely manner, and makes the language training guidance into a visual image, under the background of Internet + to realize on-site communication between teachers and students, training for the basketball teaching provides an effective auxiliary means, greatly improves the effect of physical education teaching.
k-eNSC: k-estimation for Normalized Spectral Clustering

In machine learning, the power of an approach is measured by its capability to be adapted for different applications and using different formats of data. Spectral Clustering is an unsupervised method that can be adopted for many research fields in and beyond computer science. In this paper, we present and analyze the existing algorithms of spectral clustering, and based on their limits we propose our modified version to deal with the most common challenge in this context which is the dynamic estimation of the output number of clusters.
Analysis of Solar Power and Energy Variability Through Site Adaptation of Satellite Data With Quality Controlled Measured Solar Radiation Data

Abstract Satellite-based solar power data is becoming more and more important because of its continuous temporal and spatial availability. However, its reliability can be enhanced through quality control and calibration against ground-based measurement data. Here, a holistic methodology is employed for the adaptation of satellite-based data for estimating solar energy. For the purpose, high-quality ground-based measurement data and satellite-based datasets are assessed across 12 sites in three small islands located in the Indian Ocean. Initially, both datasets go through a rigorous quality control process. A quantitative analysis of irradiance and insolation data is then conducted. Eventually, site adaptation of satellite-based data is performed using bias removal technique and statistical analysis of datasets. A set of seven statistical performance indicators is used to support the assessment. Analysis of datasets shows that adaptation of peak values should be performed separately. Results showed that despite the small surface areas of the islands studied, a spatial variation of insolation can be depicted. A temporal variation of insolation is also noted with a peak in the summer and low insolation levels in winter. Peak irradiance values tend to exceed solar constant for all sites. Variations of peak irradiance can only be noticed in ground-based measurement data. While insolation levels are comparable in the summer season for all the sites, insolation levels in the winter season are higher in the sites with lower latitudes. Calibration factors for peak irradiance, monthly and annual average irradiance as well as yearly insolation are presented.
How Much Support Can API Recommendation Methods Provide for Component-Based Synthesis?

Program synthesis is one of the key research areas in software engineering. Many approaches design domain-specific language to constrain the program space to make the problem tractable. Although these approaches can be effective in certain domains, it is still a challenge to synthesize programs in generic programming languages. Fortunately, the component-based synthesis provides a promising way to generate generic programs from a component library of application programming interfaces (APIs). However, the program space constituted by all the APIs in the library is still very large. Hence, only small programs can be synthesized in practice. In recent years, many approaches of API recommendation have been proposed, which can recommend relevant APIs given some specifications. We think that applying this technique to component-based synthesis is a feasible way to reduce the program space. And we believe that how much support the API recommendation methods can provide to component-based synthesis is also an important criterion in measuring the effectiveness of these methods. In this paper, we investigate 5 state-of-the-art API recommendation methods to study their effectiveness in supporting component-based synthesis. Besides, we propose an approach of API Recommendation via General Search (ARGS). We collect a set of programming tasks and compare our approach with these 5 API recommendation methods on synthesizing these tasks. The experimental results show that the capability of these API recommendation methods is limited in supporting component-based synthesis. On the contrary, ARGS can support component-based synthesis well, which can effectively narrow down the program space and eventually improve the efficiency of program synthesis. The experimental results show that ARGS can help to significantly reduce the synthesis time by 86.1% compared to the original SyPet.
A Literature Review on Feature Selection using Evolutionary Algorithms

Feature Selection is an optimization problem, where a subset of relevant features are derived from a set of features. It's a pre-processing technique performed before training an algorithm. Features/Attributes provide information about the labels/targets, so we may think that more attributes means more information about the target, but this is not the case always. Initially, the algorithm's performance may go up, but gradually it may come down; this is because of the irrelevant and redundant attributes present in the dataset. This phenomenon is called a Curse of Dimensionality. Feature Selection problem can be optimized using Evolutionary algorithms. This paper emphasizes on use of Evolutionary algorithms in optimizing the feature selection problem. This is a review paper where all the works relating to the application of Evolutionary algorithms in the field of Feature Selection are reviewed and presented.
An Approach for Embedding Intelligence in a System for Automatic Test Generation and a 3D Result Model

This paper presents a logical approach for assessing the student's success in a testing system. The aim is to develop an approach for an intelligent transition from one level of complexity to another. Proposed approach adds intelligence algorithms into an existing system for automatic test generation. The hypothesis says: the precise determination of the number of questions at each level and for each tested increases the competence of the learner. Adding intelligence to a system for automatic e-test generation can satisfy the aim. Two algorithms for automatic transition from one level to another based on the learner's result are presented. A puzzle method with a curiosity function is applied for logical data processing and evaluation of the success of the learner (called trainee). A 3-dimension (3D) model has been created to visualize the result of the two algorithms.
Ambient-Oriented Modelling in an Intelligent Agriculture Infrastructure

The article discusses an adaptation of ViPS to build an intelligent infrastructure for agriculture in the Plovdiv region termed "Agriculture 2.0 - Plovdiv". The main challenge is the virtualization of "things" from the physical world, as well as related events, time and spatial aspects. To present the spatial aspects of things we use an Ambient-Oriented Modeling approach. By adapting of ambient-oriented interpreter we created an animated simulator for modelling of some scenarios implementing basic services in the space. The infrastructure is developed together with two agricultural institutes in the region of Plovdiv - the first deals with cereals, and the second with vegetable crops.
Autonomous Robot Exploration and Measuring in Disaster Scenarios using Behavior Trees

Autonomous exploration and environmental measuring using robotic systems is highly relevant in natural or industrial post-disaster scenarios. Challenging conditions and presence of unknown hazardous substances might disturb rescuers, affecting their performance or even endanger their lives. Under these circumstances, robotic systems can be deployed to reduce the workload of rescuers and improve the situational awareness of emergency services. To accomplish this, several algorithms need to be synchronized on the robots. Behavior trees offers a simple and modular structure for task execution, able to provide a deep insight of complex task flow at first glance. To cover the scenario described in this work, we combine robot navigation, exploration and complete coverage planning, 2D/3D obstacle mapping and localization of hazard substances. Our main contribution is a behavior tree-based robot execution frame, which coordinates the sub-tasks of all these applications. Our system was successfully tested at the European Robotics Hackathon 2019, where a hazardous materials incident response operation was staged. With this implementation, we aim to simplify the system design and application of robotic systems used for autonomous operation.
Tamperproof Evidence with Blockchain

There is a lot of buzz around the market regarding crypto currencies but no one hardly knows what’s the technology behind it so we are talking about blockchain or the Next Generation Internet as many professionals refers it. Blockchain the recent revolutionizing technology of the IT sector right now and experts are taking a lot of interest in knowing this technology and the reason for the interest in Blockchain is its central attributes that provide security, anonymity and data integrity without any third party organization in control of the transactions, and therefore it creates interesting research areas, especially from the perspective of technical challenges and limitations finding its application all over the different industries. Although it has many applications in different fields such as Banking, Government and Healthcare etc. but we will be focusing on how blockchain can help us in transforming our legal system. In this paper we will discuss some of the basic problems that are faced by the system and due to which many persons face serious problems which in turn are really affecting their lives. So we are proposing an idea that how with the help of blockchain we can somehow lessen the burden of some authorities and helping them in a way possible we have built a small prototype which will basically explain what we are trying to do in this system.
VMI-PSL: Visual Model Inspector for Probabilistic Soft Logic

Hybrid recommender systems achieve state-of-the-art performance by integrating several different information sources along with multiple recommendation approaches. Probabilistic Soft Logic (PSL) has been shown to be an accessible and effective means of creating extensible hybrid recommenders [11]. PSL allows users to easily create intuitive models that incorporate background information and capture complex interactions. However these complex interactions can sometimes make PSL models difficult to inspect, debug, and understand. In this paper, we present a generic visual model inspector for PSL, and show how our inspector can be used on a hybrid recommender system to: debug errors in the model, analyze the performance of individual components of the model, and explain recommendations made by the model.
Decentralized Application for Two-Factor Authentication with Smart Contracts

Nowadays, the safety of a mobile phone cannot be assured with the password of the owner. Humans generally do not remember passwords which can be simpler to recollect and additionally use the identical password across different applications. It becomes less-secured if an intruder gains access to that particular mobile and they can easily crack the OTP sent by the officials. In this paper, we proposed a new method of securing the OTP with the help of blockchain using tokens generated by Ethereum. In this approach, the application has an authentication model using an email and password, and another one is using the Ethereum account address.
How Can We Manage the Blackboard Learning Management System to Enhance Student's Learning?

Blackboard is an important learning management system by many institutions. Even though student's interactions and engagements with Blackboard is an essential process that can enhance their learning, it is important to know how students interact with the Blackboard and the resources needed to improve its usage. It was observed that the average student's login onto Blackboard was high at certain periods within the week and besides the students use other online resources in addition to Blackboard. The result is very informative and guides the lecturer to make timely interactive updates on the Blackboard. The results suggest that other online resources must be embedded or linked to Blackboard to enhance wider student's engagements.
E-Voting System Evaluation Based on the Council of Europe Recommendations: nVotes

E-voting implantation has been facing important challenges in recent years. Several incidents, together with a lack of evaluation methodologies social and cultural customs hinder a broader application. In this work, the authors aim to contribute to a safer introduction of e-voting tools by applying a practical evaluation framework strongly based on the security requirements issued by the Council of Europe (CoE) in 2017 to nvotes, a system that has been utilized to cast over 2 million votes over the last 6 years.
Learning Environment Containerization of Machine Leaning for Cybersecurity

Machine learning plays a critical role in detecting and preventing in the field of cybersecurity. However, many students have difficulties on configuring the appropriate coding environment and retrieving datasets on their own computers, which, to some extent, wastes valuable time for learning core contents of machine learning and cybersecurity. In this paper, we propose an approach with learning environment containerization of machine learning algorithm and dataset. This will help students focus more on learning contents and have valuable hand-on experience through Docker container and get rid of the trouble of configuration coding environment and retrieve dataset. This paper provides an overview of case-based hands-on lab with logistic regression algorithm for credit card fraud prediction.
High value passenger identification research based on Federated Learning

Nowadays, airlines are facing increasingly fierce market competition while ushering in development opportunities. Many scholars researched on airline passenger value using data mining approaches, but the evaluation index of air passenger value in the existing research is based on internal data sources. It is of great importance to blend the external data from third-party under the premise of safe and legal data privacy disclosure to extend the characteristic dimension of their customers. Therefore, this research proposes a novel model that can blend multi-source big data to enrich airline passengers' feature dimensions under the premise of ensuring passengers' information privacy security, and establish the user profile of passengers for accurately identifying the high-value passengers. It is proved that our proposed novel model has better performance compared with the results of the traditional model that only use one party data in terms of Area Under Curve (AUC) and Kolmogorov-Smirnov (KS) value.
Increasing Efficiency in Maintenance Processes Through Modular Service Bundles

Increasing complexity in production and factory automation represents a significant challenge in context of maintenance. One approach to address complexity is the implementation of automated industrial service bundles, which resemble complex business processes. To enable efficient usage of service bundles, their individual components are designed as functional modules in the form of independent micro services and are compatible with the paradigm of service-oriented architectures. For ubiquitous communication and data exchange between service entities, message-oriented middleware provides an adequate solution. This paper presents an approach for increasing efficiency in maintenance using a service bundle. The use case demonstrates the automated creation of service tickets enriched with necessary information from various sources.
On the Modeling and Predication of Teaching Effectiveness with Machine Learning

The nature of teaching lies in the instructive interactions between a teacher and a student. Consequently, the quality of teaching (or teaching effectiveness) is decided by the characteristics of both the teacher and the student. We try to build a fundamental modeling framework that captures this nature of teaching and makes it possible the formal modeling as well as the calculation and predication of teaching effectiveness. Moreover, leveraging on machine learning methods, application of the framework does not require full implementations of its models.
Supporting viewpoints to review the lack of requirements in space systems with machine learning

Identifying the insufficient requirements, such as missing or lacking requirements, is important to prevent serious accidents for CPSs (Cyber-Physical Systems), such as launch vehicles and spacecrafts which often required to be self-adaptive. In JAXA (Japan Aerospace Exploration Agency), several review boards in place to verify the requirements toward space systems from various viewpoints which often derived from the reviewer's experience about anomalies. However, the impossibility of assigning a well-experienced reviewer to all review opportunities highlights the importance of sharing their viewpoints. In this paper, we aimed to extract and exploit associations between development documents and archived anomaly reports about space systems, thereby identifying the lacking requirement in the review. An association between these two documents can be treated as a viewpoint of the review, which contributes to preventing previously experienced anomalies. To cope with this problem, we propose a CNN (Convolutional Neural Network) model that predicts the correlation of documents. A collection of judgments about meaningfully related pairs of text was prepared to train the proposed model to contribute to detect the lack of requirements. Experimental results have shown that the performance of the proposed method is significantly better than that of baseline methods (i.e., 71.0% in F-measure). Also, further investigation has shown that not only the word similarity, but different attributes are necessary to solve our problem.