Reviewerly/exHarmony huggingface dataset:
A probabilistic approach towards an unbiased semi-supervised cluster tree

Conventionally, it is a prerequisite to acquire a good number of annotated data to train an accurate classifier. However, the acquisition of such dataset is usually infeasible due to the high annotation cost. Therefore, semi-supervised learning has emerged and attracts increasing research efforts in recent years. Essentially, semi-supervised learning is sensitive to the manner how the unlabeled data is sampled. However, the model performance might be seriously deteriorated if biased unlabeled data is sampled at the early stage. In this paper, an unbiased semi-supervised cluster tree is proposed which is learnt using only very few labeled data. Specifically, a K-means algorithm is adopted to build each level of this hierarchical tree in a decent top-down manner. The number of clusters is determined by the number of classes contained in the labeled data. The confidence error of the cluster tree is theoretically analyzed which is then used to prune the tree. Empirical studies on several datasets have demonstrated that the proposed semi-supervised cluster tree is superior to the state-of-the-art semi-supervised learning algorithms with respect to classification accuracy.
A Minimal Training Strategy to Play Flappy Bird Indefinitely with NEAT

A large number of algorithms to generate behaviors of game agents have been developed in recent years. Most of them are based on artificial intelligence techniques that need a training stage. In this context, this paper proposes a minimal training strategy to develop autonomous virtual players using the NEAT neuroevolutionary algorithm to evolve an agent capable of playing the Flappy Bird game. NEAT was used to find the simplest neural network architecture that can perfectly play the game. The modeling of the scenarios and the fitness function were set to ensure adequate representation of the problem compared to the real game. The fitness function is a weighted average based on multiple scenarios and scenario-specific components. Coupling the minimal training strategy, a representative fitness and NEAT, the algorithm had a short convergence time (around 20 generations), with a low complexity network and achieved the perfect behavior in the game.
Application of Chicken Swarm Optimization in Detection of Cancer and Virtual Reality

Cancer is a very common type of disease occurring amongst people and it is also amongst the main causes of deaths of humans around the world. Symptom awareness and needs of screening are very essential these days in order to reduce its risks. Several machine learning models have already been proposed in order to predict whether cancer is malignant or benign. In this paper, we have attempted to propose a better way to do the same. Here we discuss in detail about how we have applied the chicken swarm Optimisation as a feature selection algorithm to the cancer dataset of features in order to predict if the cancer is malignant or benign. Here we also elucidate how the Chicken Swarm Optimization provides better results than several other machine learning models such as Random Forest, k-NN, Decision Trees and Support Vector Machines. Feature Selection is a technique used to eliminate the redundant features from a large dataset in order to obtain a better subset of features to use for processing. In order to achieve this, we have used Chicken Swarm Optimization. The chicken swarm optimization algorithm is a bio-inspired algorithm. It attempts to mimic the order of hierarchy and the behavior of chicken swarm in order to optimize the problems. On the basis of these predictions we can also provide quick treatment by using virtual reality simulators that can be helpful for complex oncological surgeries. The results shown by this are better than the other models as this model achieves a very high accuracy as compared to the others discussed in the paper.
Regression Tree Role for Interpret Monetizing of Game Live Streaming

Abstract Game Live Streaming is streamers activity who play games with a purpose to monetizing. The running game recorded into a streaming statistic. Therefore, streamers can monitor it. The company of Game Live Streaming evaluated whether they are eligible for compensation to make it easier to interpret monetizing, a decision tree is needed. However, the dataset obtained from statistical streaming is numeral; the calculation uses Standard Deviation Reduction for construct Regression Tree which the concept is similar to ID3 Algorithm. The Regression stop constructs Tree when the CV is zero, or the number of instances is the least. Also, the mean reached dataset homogeneously.
Heterogeneous differential privacy for vertically partitioned databases

Summary Existing privacy‐preserving approaches are generally designed to provide privacy guarantee for individual data in a database, which reduces the utility of the database for data analysis. In this paper, we propose a novel differential privacy mechanism to preserve the heterogeneous privacy of a vertically partitioned database based on attributes. We first present the concept of privacy label, which characterizes the privacy information of the database and is instantiated by the classification. Then, we use an information‐based method to systematically explore the dependencies between all attributes and the privacy label. We finally assign privacy weights to every attribute and design a heterogeneous mechanism according to the basic Laplace mechanism. Evaluations using real datasets demonstrate that the proposed mechanism achieves a balanced privacy and utility.
Online assessment system based on IMS-QTI specification

Online examination is an important and powerful phase of the online education. Although e-Assessment for e-Learning is widely realized nowadays. However, there still is a lack of interoperable, shareable, reusable, and open source tools that affords much motivation to teachers. Therefore, in this paper, we describe a prototype assessment authoring tool to assist instructors in creating test question items in accordance with the latest version of QTI specification (Question and Test Interoperability) proposed by IMS Global Learning Consortium. We adopt this specification to build assessment content that can be shared, reused and access across distributed compliant systems. Our system is composed of three main components: The first component is a quiz authoring tool which is intended to create and edit tests and questions. The second represents a repository for quiz items and tests. The third component is an assessment engine for interpreting and displaying QTI XML documents.
Development and Testing of Microcontroller-Based Learning Media for the Internet of Things Lab Work

Abstract These days, the Internet of things (commonly abbreviated as IoT) has been applied in various industrial sectors such as manufacturing, health, restaurant, electricity generation, transmission, and distribution, etc. Therefore, IoT learning materials need to be included in the learning process both at the college and high school level. The most common obstacle is the lack of learning media about IoT. This paper reports on the process of developing an IoT laboratory work (lab work) learning media based on the Arduino UNO and NodeMCU microcontroller. Furthermore, this paper also discusses the results of the feasibility test on learning media. The stages of the research are need assessment, planning, development and implementation, and also evaluation. Outcomes of this development process are the Arduino and NodeMCU-based IoT lab work modules, short manuals and lab sheets. Product testing with student respondents shows that the Arduino and NodeMCU-based IoT lab work modules, manuals and lab sheets are considered to be very feasible for use in learning. The test results show students considered this module can increase learning interest and make it easier for them to understand IoT learning materials.
Automated Logic-Mathematical Modeling of Railway Automation Devices Technical Condition

The article is devoted to the development of automated systems for computer modeling and diagnostics of the automation devices technical condition in the railway transport in order to improve the safety of train traffic and increase the operational reliability of devices by reducing failures. As a mechanism for derivation multilevel mathematical models of the technical condition of railway automation dynamic systems, a method of functional grammars is proposed, which has elements of artificial intelligence. To compile knowledge bases of automated systems that formalize decision-making processes in the diagnosis and monitoring of railway automation devices, a fuzzy logic apparatus is used. A mathematical model of decision-making process on a diagnostic object technical condition based on diagram of the changing diagnostic parameter function is shown. The functioning of the model on a real example is demonstrated.
When Interactive Graphic Storytelling Fails

Many people are unaware of the digital dangers that lie around each cyber-corner. Teaching people how to recognize dangerous situations is crucial, especially for those who work on or with computers. We postulated that interactive graphic vignettes could be a great way to expose professionals to dangerous situations and demonstrate the effects of their choices in these situations. In that way, we aimed to inoculate employees against cybersecurity threats. We used the Comic-BEE platform to create interactive security awareness vignettes and evaluated for how employees of a major industrial company perceived them. For analysing the potential of these comics, we ran an evaluation study as part of a capture-the-flag (CTF) event, a interactive exercise for hacking vulnerable software. We evaluated whether the comics fulfilled our requirements based on the responses of the participants. We showed the comics, on various cybersecurity concepts, to 20 volunteers. In the context of a CTF event, our requirements were not fulfilled. Most participants considered the images distracting, stating a preference for text-only material.
Data Mining Techniques for Predicting User Interest in Facebook Pages: A Comparison

Now-a-days the use of social media (e.g; facebook, twitter etc.) is increased rapidly. It is rational to exploit interest of users based on their facebook-page activities. However, it cannot be easily predicted manually as there are number of attributes including social interactions. The aim of this paper is to do analysis of 7 data mining algorithms and their variants for determining the best appropriate prediction of people interest in facebook pages. For this, we will analyze and compare various algorithms such as KStar, LWL, IBK, J48, Naive Bayes, Bayes Net, Decision Table. Their performances have been evaluated by using a dataset with 10172 instances of 6 attributes each. Among all the utilized algorithms Bayes Net demonstrates the best execution in regard to the accuracy. However, in respect to the computational time KStar demonstrates the best execution instead of Bayes Net.
Playful design, empathy and the nonhuman turn

Abstract In the context of interspecies play involving humans, we find limitations when it comes to understanding most species. One reason for this may be the fact that we tend to anthropomorphize the other to be able to empathize with it. In light of this, how can we infer communication signs of other species so we are able to connect with the nonhuman world? We look for answers to this question by adopting a phenomenological approach that allows us to decentre from the anthropocentric perspective. We highlight animal studies, especially those that extend to them the notion of play, and the studies on interspecies playful interaction conducted in the context of animal–computer interaction. In addition, we propose considering empathy as an interspecies dialogical bridge with nonhumans. Finally, we argue about an expansion of the field of interaction design as an approach to the connection with the nonhuman world.
High-Performance Solution of the Two-Class SVM Problem for Big Data Sets by the Mean Decision Rule Method

Support Vector Machines (SVM) is one of the most convenient and reliable tools for solving the two-class recognition problem. But it has very high computational complexity in the case of large training sets. In our previous paper we proposed a simple approach, named Mean Decision Rule (MDR) method, that allows to quickly enough find an approximate (but not very different from the exact) solution of the SVM problem in linear feature space for single computer even for large enough training sets. In this paper we propose a parallel version of MDR method. Experiments with real data sets show, that it has quasilinear acceleration of training stage and jointly with the proposed optimized data strategy allows to additionally increase the computation performance.
Pre-trained Affective Word Representations

Learning word representations from large corpora relies on the distributional hypothesis that words present in similar contexts tend to have similar meanings. Recent work has shown that word representations learnt in this manner lack sentiment information which, can be introduced using external knowledge. Our work addresses the question: Can affect lexica improve word representations learnt from a corpus ? In this work, we propose techniques to incorporate affect lexica, which capture fine-grained information about a word's psycholinguistic and emotion orientation, into the training for Word2Vec SkipGram, Word2Vec CBOW, and GloVe using a Joint Learning approach. We use affect scores from the Warriner's affect lexicon to regularize the vector representations learnt from an unlabeled corpus. Our proposed method outperforms previously methods on standard tasks for word similarity detection, outlier detection, and sentiment analysis. We also show the usefulness of our approach for the prediction of formality, frustration, and politeness in text.
A Transparent and Privacy-Aware Approach Using Smart Contracts for Car Insurance Reward Programs

Car insurance companies worldwide have launched reward programs that provide benefits (e.g., cash-back) to good drivers. However, two issues may arise from these programs. First, drivers cannot easily verify whether their insurer is properly following the program rules upon computing their rewards. The second issue is that privacy can be violated when sensing data is collected from policyholders’ cars to identify whether they are good drivers. This paper proposes a smart contract-based solution that trades off user privacy for reward transparency. A smart contract computes rewards based on sensing data policyholders provide to the Ethereum blockchain. To preserve privacy, a policyholder can (i) select what sensing data is sent to the blockchain, (ii) use distinct pseudonyms to hide his or her real identity, (iii) choose what accuracy sensing data has, and (iv) verify whether his or her sensing data allows him or her to remain indistinguishable from other drivers whose data has been already disclosed in the blockchain.
Efficient License Management Based on Smart Contracts Between Software Vendors and Service Providers

In a fully interconnected world where even network-related services are becoming more dependent on software, the management of license agreements is critical for the business of any software vendor and communication provider. Building, managing and protecting the infrastructure to handle software license validation and scalability for the provider and, on the other hand, assessing the correct use of the software licenses for the vendor can become an expensive part of the relationship costs. We propose a novel approach for decentralized software licensing that leverages blockchain and smart contracts as fundamental enabling technologies. Our proposal guarantees a secure and inexpensive system with no central point of failure that can regulate the relations among untrusted parties. We describe the main design choices and present a prototype experimentation that demonstrates the benefits of the proposal in the context of virtualized network infrastructures.
A Practical Grafting Model Based Explainable AI for Predicting Corporate Financial Distress

Machine learning and deep learning are all part of artificial intelligence and have a great impact on marketing and consumers around the world. However, the deep learning algorithms developed from the neural network are normally regarded as a black box because their network structure and weights are unable to be interpreted by a human user. In general, customers in the banking industry have the rights to know why their applications have been rejected by the decisions made by black box algorithms. In this paper, a practical grafting method was proposed to combine the global and the local models into a hybrid model for explainable AI. Two decision tree-based models were used as the global models because their highly explainable ability could work as a skeleton or blueprint for the hybrid model. Another two models including the deep neural network and the k-nearest neighbor model were employed as the local models to improve accuracy and interpretability respectively. A financial distress prediction system was implemented to evaluate the performance of the hybrid model and the effectiveness of the proposed grafting method. The experiment results suggested the hybrid model based on the terminal node grafting might increase the accuracy and interpretability depending on the chosen local models.
Orthogonal Least Squares Based Incremental Echo State Networks for Nonlinear Time Series Data Analysis

This paper develops an incremental randomized learning method for an extended Echo State Network (φ-ESN), which has a reservoir with random static projection, to better cope with non-linear time series data modelling problems. Although the typical ESN can effectively improve the prediction performance of the network by extending a random static nonlinear hidden layer, since the input weights and biases of the hidden neurons in the extended static layer are randomly assigned, some neurons have little effect on reducing the model error, resulting in high model complexity, poor generalization and large performance fluctuation. A constructive incremental randomized learning method termed OLS-φ-ESN is proposed for generating the nodes of the extended static nonlinear hidden layer. Two-step training paradigm is adopted, namely, randomly assigning the input weights and biases of the hidden neurons in the extended static layer according to a supervisory mechanism and solving output weights by least squares algorithm. Based on Orthogonal Least Squares (OLS) search algorithm, the proposed supervisory mechanism is designed where an adaptive threshold is also set to better control the compactness of the generated learner model. Simulation results concerning both nonlinear time series prediction and system identification tasks indicate some advantages of our proposed OLS-φ-ESN in terms of more compact model and sound generalization.
A method of perspective normalization for video images based on map data

Objective perspective distortion is a problem that needs to be solved by video surveillance analysis. Compared with the street scene method, which depends on prior knowledge of the scene or 3D scene of the dedicated hardware recovery scene, the commonly used perspective distortion correction method is based on the linear relationship to monitor a video image in perspective normalization. However, the distortion caused by perspective imaging is nonlinear, and the linear perspective normalization model cannot guarantee the accuracy of the correction in the scene where the perspective phenomenon is evident. An image normalization method based on map data is proposed to solve this problem. A nonlinear perspective correction model is introduced by establishing a single relation between video image space and map space. With selected control points between image and map, we can calculate homography matrix in order to build the perspective correction model, which is computed to know the single pixel real size in map. The proposed perspective correction model is applied to the moving target detection. The results of the linear correction model and the proposed nonlinear correction model prove the validity and practicability of the method.
Comparative Analysis of Speaker Recognition System Based on Voice Activity Detection Technique, MFCC and PLP Features

Due to rapid advancement in technology, speaker recognition systems become more robust and user friendly. The idea is to study and analyse the speech signal based on feature extraction method. This paper compares the performance of Mel-Frequency Cepstral Coefficient (MFCC) and PLP feature extraction with voice activity detection (VAD) technique. Vector Quantisation approach is used for features matching to select the combination which gives highest accuracy.
An idea based on sequential pattern mining and deep learning for text summarization

Abstract One of the Natural Language Processing (NLP) studies that has been widely researched is automatic text summarization. There are a lot of techniques and methods that are proposed for text summarization. However, not much attention has been given on the coherence and cohesion in text. The aim of this study is to present an idea to combine Sequential Pattern Mining (SPM) and Deep Learning (DL) for better text summarization process and result. In text summarization, it is important to produce understable and readable summary, and SPM as text representation extracting algorithm is capable to maintain the meaning of text by giving attention of the order of words appearance. Whereas DL is a popular and powerful machine learning technique widely used recently in various data mining studies. This study uses descriptive research methodology that collects all of the facts and information which are related to SPM and DL for text summarization, where NLP as the body of knowledge, SPM and DL as the method, and text summarization as the domain problem that need to be solved. The findings of the study are presented as a logical design and mapping of current text representation that can be implemented to further improve automatic text summarization results, in particular, to improve its coherence and cohesion.
Action based Video Summarization

The enormous growth of digital multimedia has resulted in increased complexity of time and data management. Many researchers have developed techniques for managing this tremendous amount of data or longer videos effectively. Video summarization is one of the most important research areas for effective comprehension of video content by selecting the informative frames of the video as representatives. This paper presents two-step action based video summarization approach. First, video frames are extracted and content based key frames are selected. Second, video summary is generated by removing redundant frames by employing feature extraction and classification technique. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) methods are used for feature extraction. Convolution Neural Network (CNN) is used for classification of frames and video summary is generated using K-Means clustering and Bayesian model. Experiments are conducted on SumMe [1] video dataset by varying feature extraction, classification and summarization methods. Summary of the video is compared with summarized video time and number of reduced frames. Summary is generated by retaining key features of the video.