Reviewerly/exHarmony huggingface dataset:
Success Prediction of Leads – A Machine Learning Approach

Competition between organizations urges the need of using new approaches. A lead is described by an individual or organization with interest in the products or services that an entity sells, and Machine Learning appears to be a very effective way of improving the performance of the corporate leads. Predicting the success of new leads is a critical aspect to enhance the effectiveness and efficiency of marketing, and to tackle this problem, we propose in this paper a solution supported in the Machine Learning approach. This solution was implemented to increase the winning probability of proposals. Preliminary results also suggest that by using this solution, managers could improve the quality of business decisions.
A Live Emotions Predictor System Using Convolutional Neural Networks

Facial expression recognition has been an active research area, with an increasing number of applications like avatar animation, cyber security, and neuromarketing. The use of neural networks and data science are having a strong growth in research centers and universities; the field of machine learning is booming because it is a strong tool and it has an immense amount of applications. The purpose of this paper is the development of a Live Emotions Predictor using Convolutional Neural Networks, this was developed in different sections, the part of data processing and its own training using a Convolutional Neural Network (CNN) that generates accurate and precise predictions of the 5 main emotions in a graphical way. For the processing part it is important to have data that can be trained, preprocessing, and thus be able to have better results. The data generated by iMotion® are CSV files and the first part was to be able to have a clean database for its training. In the training part, the challenge was to generate a sufficiently robust CNN so we can obtain highly reliable "accuracy's" (percentages greater than 88%), determining the main architecture and all its layers to obtain these results.
Similarity Measures and Text Documents Classfication Accuracies Using Benchmark Datasets

The total amount of text data information available on the web has been remarkably increasing the data accumulating and augmenting each day. Data and information that are available in high volume are not represented in a structured form that remains suitable for text processing. Text data mining is a subfield of data mining which aims at exploring the useful information from the recorded resources. Text data mining has the following key challenges namely high dimensionality, distance measures between data, achieving quality and classifier accuracies. The research work has attempted to addresses the key text data mining challenges by proposing high-dimensionality reduction novel techniques based on feature similarity functions. In the proposed design, the feature similarity measures are used to group features into clusters. From these feature clusters, an optimal transformation matrix is obtained using which the high dimension text corpus is projected to its equivalent low dimension. This low dimensionality text corpus can be used to implement text clustering and text classification efficiently.
Impact of the new Software development methodologies in Puerto Rico: An exploratory survey

Currently in Puerto Rico, there is no scientific literature that allows to know what are the organizational schemes that predominate in software development companies, nor is it easy to visualize how the interactions of the working groups within these companies are. Twenty years after the promulgation of the Agile Manifesto, after several years of execution and extensive literature represented in articles, papers in specialized conferences, websites and textbooks, associated with each particular agile methodology; In Puerto Rico it is still unknown what the degree of adoption of the new paradigms is and it is necessary to review more information to guide developers on the benefits of choosing a particular software development based on a methodology, facilitated by the use of tools and using usability criteria throughout the process, all these ideals adjusted to their particular work context. This exploratory study seeks to evaluate, synthesize and present the empirical findings on software development in Puerto Rico (PR), this research is of great importance to know what type of applications are made in the Island, to identify the work methodologies that are used investigate whether agile methods are used, determine the role that users play in the process and analyze how usability techniques adjust to the software life cycle; These aspects are the first step to propose to software developers an integration framework that facilitates their work and allows them to meet the needs of their customers, without compromising the quality of their products.
A Blockchain-Based Approach for Cross-Ledger Reconciliation

Technologies available to implement cross-ledger reconciliation processes are centralized due to the strong necessity of consistency. These solutions fail to guarantee data integrity since data and business rules are handled separately, and the data is processed in batch, delaying the results. This article proposes a decentralized approach to the reconciliation processes using a private Blockchain based on the Ethereum platform. Consistency will be assured by using an Authority Round algorithm and the data will be processed in real-time, showing the results as soon as the transactions are processed. Since the records in the Blockchain are immutable, all the transactions will be traceable, allowing auditability and maintaining the link between original and generated data.
Laplacian Generative Adversarial Networks for Multi-Scale Super-Resolution

Single-image super-resolution, aims to reconstruct images from low-resolution to high-resolution and super-high-resolution, so as to improve image clarity and visual effects. Based on the principles of generating adversarial networks and image pyramids, a LapSRGAN is proposed to compromise the merits of Laplacian Pyramid Generative Adversarial Network(LapGAN) and Laplacian Pyramid Super-Resolution Network(LapSRN). LapSRGAN is an end-to-end image reconstruction network, which can achieve double and quadruple high-quality, high-resolution reconstruction of the original image. The proposed LapSRGAN is trained with multiscale discriminators and perceptual loss which calculated on feature maps of image. Extensive quantitative and qualitative evaluations on bench mark datasets confirm the effectiveness of the proposed algorithm.
Personalization, Bias and Privacy

Personalization can be seen as a positive bias towards each user. However, it also has negative consequences such as privacy loss as well as the filter bubble or echo chamber effect due to the feedback-loop that creates. In addition, the web system itself can bias the user interaction distorting the data used for personalization. In this presentation we discuss the interaction of these three elements: personalization, bias and privacy.
Persona Prototypes for Improving the Qualitative Evaluation of Recommendation Systems

The majority of existing research in the field of recommendation systems is aimed at optimizing accuracy metrics for given datasets, which leads to an algorithm-driven design of resulting solutions. Given a lack of understanding of the dataset characteristics and insufficient diversity of represented individuals, such approaches lead to amplifying the hidden data biases and existing disparities. In this research, we address this problem by proposing a Persona Prototyping approach that selects a set of the most representative user individuals to help in understanding the complex distribution of user interests and performing a proper qualitative evaluation of recommendation algorithms. A hierarchical density-based clustering technique is applied to distinguish diverse user groups and select their prototypes. Each of the selected representatives is presented in an easily understandable form of a textual user story describing the prototype behaviors, inspired by the concept of persona from the interaction design. We evaluated the diversity and representativeness of selected individuals and the results show that the proposed method is capable of identifying diverse interest archetypes and can be used to improve the qualitative analysis of recommendations and to test how well they respond to the diversity of user needs.
An Ontological Model for Courses and Academic Profiles Representation: A case study of King Abdulaziz University

Educational data is growing continuously. This huge amount of data that is scattered and come from different resources with different formats usually is noisy, duplicated, inconsistent, and unorganized. These data can be more efficient and usable when it is processed using semantic web technologies that can transfer data to be readable, understandable, processable by machines and executed to produce accurate decisions that can solve many educational challenges. This study concentrated on the challenge of allocating the best appropriate academic teacher to teach a specific course based on the research area of interests. A university ontology is developed to model the semantics of the courses and academic profiles to solve the chosen challenge.
Best Practices on Metamodel-Based Photovoltaic Monitoring System with Prediction Method for Photovoltaic Power Generation

HS Solar Energy Company Inc. in Sejong city, Korea, has a big problem on how to monitor heterogeneous inverters with different protocols. Still a current photovoltaic power plant with different inverters, it has attracted significant attention to its experience of difficulties in monitoring integrated power generation. To solve this problem for the company, we adapt a metamodel mechanism to easily manage and integrate heterogeneous data into a metamodel-based data format. The existing metamodel-based photovoltaic monitoring system (M-PVMS) of the HS solar energy company also needs to simply predict the photovoltaic power generation in a day for small farm owners in the countryside. Therefore, we propose a method for predicting the power generation of M-PVMS panels using the gated recurrent unit (GRU) algorithm, which supports real-time learning to predict the photovoltaic system behavior that rapidly accumulates data in real time. As a result, we can predict the power generation for small farm owners with a probability of 96.353%.
Demo: The Neural Network Verification (NNV) Tool

NNV (Neural Network Verification) is a framework for the verification of deep neural networks (DNNs) and learning-enabled cyber-physical systems (CPS) inspired by a collection of reachability algorithms that make use of a variety of set representations such as the star set. NNV supports exact and over-approximate reachability algorithms used to verify the safety and robustness of feed-forward neural networks (FFNNs). These two analysis schemes are also used for learning enabled CPS, i.e., closed-loop systems, and particularly in neural network control systems with linear models and FFNN controllers with piecewise-linear activation functions. Additionally, NNV supports over-approximate analysis for nonlinear plant models by combining the star set analysis used for FFNNs with the zonotope-based analysis for nonlinear plant dynamics provided by CORA. This demo paper demonstrates NNV's capabilities by considering a case study of the verification of a learning-enabled adaptive cruise control system.
Identify Education Quality Based on Islamic Senior High School Data in Kompetisi Sains Madrasah Using Fuzzy C-Means Clustering

The education system in a country certainly has goals to be achieved. Many attempts were made to achieve the success of an education system that was created. This case causes competition between educational institutions and between students. The results of the competition can also represent schools that need special attention to improving learning systems. The learning system in Indonesia needs evaluation because of educational quality not perfectly. In this research, the fuzzy c-means (FCM) method is used to cluster the quality of existing Kompetisi Sains Madrasah (KSM) participant data to find schools that have good quality education. Next, a cluster evaluation is performed to determine the success of the cluster using silhouettes. Based on 4 trials, that are 3 clusters, 4 clusters, 5 clusters, and 6 clusters, the best results are on 3 clusters with the best silhouette value of 0.6499 with a standard deviation value of 0.2419. In the identification process, the results obtained that SMA has the best education quality in KSM.
Real-Time Motion-Planning in Dynamic Environments via Enhanced Velocity Obstacle

Freely networked production lines are the essence of the factories of the future. In this context, the mobility of the robotic agents (i.e. mobile manipulators) and logistic units for transferring the goods and the products in a highly dynamic environment shall be considered as a challenging problem. The challenge lies mainly on the recognition of the changes in the environment and fast proper reaction to these changes. The available solutions to the problem are mostly based on the information from embedded local sensoric systems of the mobile units. In this paper, we present a solution based on a global networked sensoric system. We use the depth cameras to monitor the environment of a population of the robotic agents. We apply enhanced method of the Velocity Obstacle to perform a collision free path planning for mobile agents. To save space and computation time, we enhance the collision cones of the obstacle velocities by defining oblique ellipsoidal cones instead of straight circular cones. The aim is to make the process for real-time scenarios as simple, quick and practical as possible. The described methods will be implemented in a dynamic laboratory and the results will be discussed.
SABRES - A Proof of Concept for Enhanced Cloud Qualified Electronic Signatures

Public Key Infrastructure (PKI) is an essential technology that enables secure communications over the Internet. Trust Service Providers (TSPs) have a crucial role in assuring the binding of the owner's identity to the public key through the digital certificates. Therefore, any breach may lead to major security and privacy issues. Since 2013, proposed solutions aim to improve the security of PKIs, many of them by using blockchain technology. This way a distributed trust is obtained. But the majority of the proposed solutions are not compliant with the working standards and therefore the adoption process is very difficult. Offering cloud Qualified Electronic Signatures (QES) services puts even more pressure on the TSPs, which have to ensure the sole control of the user over the private keys stored server-side. The solutions proposed so far to assure this sole control of the user have been focused on proposing remote signature protocols that guarantee the exclusive access of the user to the private keys. The problem is that this exclusiveness cannot be formally proved to the user. Therefore, in the end, this goes down to just trusting the TSP. In this context, introducing a distributed trust source would significantly grow the reliability and security of the cloud QES services. The current paper proposes a new solution that uses blockchain to improve trust in QES performed in the cloud, but not through a signature protocol, but through a distributed means of logging and verifying all accesses to one's private key. The proposed solution is fully compliant with all working standards for cloud QES and combines PKI and blockchain advantages: interoperability with current working systems and distributed trust.
Interactions between Inter- and Intra-Individual Effects on Gaze Behavior

Previous work has shown that gaze behavior can vary not only as a function of stimuli and task but also as a function of the observer. Thereby stable inter-individual differences have been shown as well as changes in gaze behavior due to an observer's internal state. How such intra-individual differences interact with inter-individual variations in gaze behavior has not been studied explicitly before. Here, we tackle this question by analyzing fixation statistics and scan path representations in a visual comparison task inducing different observer states. Results show that reliable inter-individual differences exist in fixations statistics and for scan path representations that allow for modeling a longer temporal horizon. Changes in observer state affected the data, but a substantial amount of variance between observers remained stable. We discuss the results in the light of making personalized gaze-based applications more robust against changes in context.
An Improved AlexNet Model with Multi-channel Input Images Processing for Human Face Feature Points Detection

We present an improved AlexNet model with multi-channel input images for face feature point detection. Faces are rich expressions, they are the most direct vehicle for expressing the diversity of human inner emotions, and they are also the most important tools for communication between people. We focus on the detection of face feature points based on convolutional neural networks. We use the Caffe framework to train and test the improved network structure. The model adopts the AlexNet, we divide the face image into 3 sub-images that overlap and each have a colour channel, The weighted average of the results obtained by inputting 3 channel sub-pictures into the model. Finally, we get the feature point coordinates of the human face. We verified our method on multiple face datasets and compare the differences between the traditional face feature detection methods and the current mainstream deep learning face feature point detection methods and their respective performance characteristics. Our network runs at the speed of 80 FPS (frame per second) which is faster than DCNN counterpart and our method gets better detection performance.
Acoustic Emanation of Haptics as a Side-Channel for Gesture-Typing Attacks

In this paper, we show that analysis of acoustic emanations recorded from haptic feedback during gesture-typing sessions is a viable side-channel for carrying out eavesdropping attacks against mobile devices. The proposed approach relies on acoustic emanation resulting from haptic events, namely the buzz of a small vibration motor as the finger initiates the gesture-typing of a work in a sentence. By analysing time between haptic feedback events, it is possible to identify the text that a user enters via the soft keyboard on their device. The attack requires no prior interaction or need to install software on the target device (unlike similar works); only the ability to record audio within the vicinity. We present an experimental framework to illustrate the feasibility of the attack. In the experiments we show that sentences can be detected with an accuracy of 70% with some sentences identified with up to 95% accuracy. The attack can be conducted with minimal computation and on non-specialist consumer equipment. The paper concludes by proposing a number of countermeasures that mitigate the ability of an attacker to successfully intercept keyboard input.
Research and Implementation of Network Public Opinion Rrediction System

Abstract The Internet has become a distribution center of ideological and cultural information and an amplifier of public opinion. To dig, analyze and study hot public opinions on the Internet is an important means to fully understand what netizens are thinking and doing. This paper analyzes the function of public opinion system, and introduces the key technology to realize the prediction of network public opinion, which can well realize the prediction function of public opinion.
Elementary-base cirquent calculus II: Choice quantifiers

Abstract Cirquent calculus is a novel proof theory permitting component-sharing between logical expressions. Using it, the predecessor article ‘Elementary-base cirquent calculus I: Parallel and choice connectives’ built the sound and complete axiomatization $\textbf{CL16}$ of a propositional fragment of computability logic. The atoms of the language of $\textbf{CL16}$ represent elementary, i.e. moveless, games and the logical vocabulary consists of negation, parallel connectives and choice connectives. The present paper constructs the first-order version $\textbf{CL17}$ of $\textbf{CL16}$, also enjoying soundness and completeness. The language of $\textbf{CL17}$ augments that of $\textbf{CL16}$ by including choice quantifiers. Unlike classical predicate calculus, $\textbf{CL17}$ turns out to be decidable.
Research on ESP Hybrid Teaching Innovation based on the Computer Information Technology

Abstract The rapid development of the educational information age has brought changes and challenges to the field of English education. The development of computer information technology has led to the reform of teaching methods. Under the guidance of constructivism theory, around the teaching concept of “teachers’ leading role and students as the main body, and the conductor combination” [1] . In this paper, based on English majors offered English course as an example, analyzed the computer information technology platform based on hybrid English teaching, in order to provide useful reference for the teaching reform of basic courses for English majors.
Automatic Attribute Learning for Person Re-Identification

Abstract Attribute based person re-identification is more robust to image appearance changes than low-level visual feature based methods. However, manual person attribute annotation has low efficiency, poor scalability and poor discrimination. Therefore, a scalable automatic attribute learning method for person re-identification is proposed. Firstly, the mapping matrix of person attributes is designed and generated automatically according to the principles of discernibility, sharing and low redundancy. Then the visual features are extracted using convolutional neural network and attribute classifiers are trained to detect person attributes, and the attribute based representation of person is generated. Finally, the person re-identification is carried out by comparing the similarity of the attribute based representations. Extensive experiments have been carried out on two common datasets Market-1501 and Duke MTMC-reID with the comparison to the Sate-of-the-Arts methods, the method achieved the best performance, which proves the superiority and effectiveness of the method.
Logically automorphically equivalent knowledge bases models

We study knowledge bases as a certain algebraic and model-theoretical structure. Algebraic model of a knowledge base allows to distinguish some algebraic and logical invariants of knowledge bases. In particular, our interest is to find out how these invariants are related to the informational equivalence of knowledge bases.