Reviewerly/exHarmony huggingface dataset:
Affect-Aware Conversational Agent for Intelligent Tutoring of Students in Nursing Subjects

In many social professions employees require skills in affect- and situation-aware social interaction. One option for teaching and training such social interaction skills by computer-based training methodology is the use of dialogue simulations. Here, a student interacts with a simulated dialogue partner and the dialogue flow explores specific interaction situations and affectual settings. Conversational agents provide a basic technology for creating such dialogue simulations. However, they usually lack a means for managing affect-related dialogue state. In this paper we propose an approach to integrate affective reasoning into a conversational agent for intelligent tutoring applications in order to improve the agent's ability to recognise dialogue intents, generate emotionally aligned responses, and provide a metric for evaluating student performance.
CBAG: Conditional biomedical abstract generation

Biomedical research papers use significantly different language and jargon when compared to typical English text, which reduces the utility of pre-trained NLP models in this domain. Meanwhile Medline, a database of biomedical abstracts, introduces nearly a million new documents per-year. Applications that could benefit from understanding this wealth of publicly available information, such as scientific writing assistants, chat-bots, or descriptive hypothesis generation systems, require new domain-centered approaches. A conditional language model, one that learns the probability of words given some a priori criteria, is a fundamental building block in many such applications. We propose a transformer-based conditional language model with a shallow encoder "condition" stack, and a deep "language model" stack of multi-headed attention blocks. The condition stack encodes metadata used to alter the output probability distribution of the language model stack. We sample this distribution in order to generate biomedical abstracts given only a proposed title, an intended publication year, and a set of keywords. Using typical natural language generation metrics, we demonstrate that this proposed approach is more capable of producing non-trivial relevant entities within the abstract body than the 1.5B parameter GPT-2 language model.
Principled quality diversity for ensemble classifiers using MAP-Elites

For many supervised learning tasks, ensemble classifiers - which make predictions by combining multiple simple models - outperform single model classifiers. While genetic programming can be used to evolve populations of simple classifiers, it tends to produce populations of highly similar models. In this work we propose Neuro MAP-Elites (NME) as a method for evolving populations of high performing models which produce diverse predictions, making them suitable for constructing ensembles.
Sentiment Analysis of Healthcare Big Data: A Fundamental Study

Healthcare sentiment research is structured to determine patients’ diagnoses of healthcare-related concerns. It requires the views of patients into consideration to devise strategies and improvements that may resolve their concerns directly. Sentiment analysis is seen to considerable success for commercial goods and is applied to other fields of use. Sentiment research included in numerous methods, including evaluations of goods and services. In health care too, there are vast volumes of knowledge regarding health care that can be accessed electronically, such as personal journals, social media, and on the medical condition rating pages. Analyzes of emotions provide a range of advantages, such as the strongest outcome to enhance standards of treatment through diagnostic knowledge. In the aspect of a healthcare study, the health facilities and therapies are not only prescribed but are often distinguished by their strong characteristics. Machine learning methods are used in evaluating and ultimately producing an effective and correct judgment to millions of analysis papers. The techniques under surveillance are extremely effective, but cannot be applied to unknown places, while unattended techniques are poor. More analysis is required to increase the precision of the unattended strategies so in this time of the knowledge flood they are more realistic. This presents a fundamental thesis that actually gives a short analysis of the sector, the research context and relevant problems/challenges and also dealt with the various challenges in the field with possible solutions to identified problems.
Study on the Construction of Big Data and Valorization Services of Intelligent Water

Data resources are the basis and guarantee for carrying out various operations of intelligent water conservancy, but there are many kinds of data in water conservancy industry, and the integration of resources is difficult. In view of the complexity of water resources data, business application needs, the existing data center data resources and the information data of the new acquisition network for data processing, the use of data extraction, data conversion, data replication, data sharing, data integration and other technologies to achieve multi-source information fusion of water resources. By using component technology to develop data fusion components, and by integrating local data sources, heterogeneous data sources, distributed heterogeneous data sources, component libraries and user relationships, this paper proposes a service-oriented componentized big data construction model to provide water resources data value services for the water industry.
Multi-UAV Task Allocation Method Based on Improved Bat Algorithm

At present, as a method of solving large-scale complex optimization problems, bionic algorithms are widely used in multi-UAV task allocation. Bat algorithm, as a kind of bionic algorithm, has problems such as long flying distance of UAV in the process of task allocation. In this paper, an improved bat algorithm for multi-UAV task allocation method is proposed. On the basis of the bat algorithm, the comparison of the number of drones and target points and the calculation of the flight distance of the drone are added. The experimental results show that the improved algorithm can reduce the calculation time and the flight time of the UAV, shorten the flight distance of the UAV, and improve the efficiency of the algorithm.
Build-and-Touch: A Low-Cost, DIY, Open-Source Approach Towards Touchable Virtual Reality

Virtual Reality (VR) is attracting more and more attention from academic research and practical application with the current availability of low-cost and end-user friendly devices. In terms of haptic (rather than visual) interaction, however, this technology is still in its infancy and there are few devices that are inexpensive and technologically simple to operate, respectively to procure. In this context, we present a concept of how haptic interaction with VR data gloves can also succeed by means of a commercially available web cam, sophisticated tracking software, and homemade low-cost hardware. All hardware and software components are to be obtained inexpensively or are open-source in order to achieve the greatest possible dissemination potential. With this work, we intend to provide an important trigger for future improvements and dissemination in terms of both technology and areas of application.
Analysis of Features of Different Activation Functions

Inspired by the mechanisms of biological neural cells, artificial neural network (ANN) has a wide application over many areas for its predictive performances of data analysis. Devised as the mathematic “gate”, activation function plays a key role in ANN to determine the degree of activation of each neuron unit. The choice of activation function usually has a strong influence on optimization and model accuracy. In this paper, several mathematical properties of activation functions are analyzed through the studies of relating literatures. It's concluded that the selection of activation function is not an achievement at one stroke but what should take considerations from different aspects.
GemiN’ I: Seamless Skin Interfaces Aiding Communication through Unconscious Behaviors

Voice command is the primary way of interacting with smart home devices and virtual assistants. However, being vocal is not always feasible or appropriate. This project aims to explore the potential of on-skin interfaces in social settings by discreetly communicating to a smart home device when enacting nearly unconscious behaviors. GemiN’I is a Beauty Technology that aesthetically and inconspicuously embeds sensors in face jewels to detect facial muscle movements, which then signals smart home devices without vocal commands, allowing the user and observers to interact without vocal interrupts. Our design rationale consists on: a) the form factor of a commercially available product for face decoration such as facial gems, b) novel discrete interfaces by making the technology invisible(circuits hidden within the gems), and the interaction by triggering devices when enacting unconscious behaviors (frowning), and c) seamlessly triggering a device in presence of an observer.
A Novel GPU Implementation for Image Stripe Noise Removal

Image processing is a class of procedures very helpful in several research fields. In a general scheme, a starting image generates a output image, or some image features, whose values are composed by using different methods. In particular, among image processing procedures, image restoration represents a current challenge to address. In this context the noise removal plays a central role. Here, we consider the specific problem of stripe noise removal. To this aim, in this paper we propose a novel Gaussian-based method that works in the frequency domain. Due to the large computational cost when using, in general, Gaussian related methods, a suitable parallel algorithm is presented. The parallel implementation is based on a specific strategy which relies the newest powerful of graphic accelerator such as NVIDIA GPUs, by combining CUDA kernels and OpenACC’s routines. The proposed algorithm exhibits good performance in term of quality and execution times. Tests and experiments show the quality of the restored images and the achieved performance.
VPN: Overview and Security Risks

Virtual Private Networks (VPN) gives anonymity and online security whilst using the web by creating a private network from our public internet connection. VPNs cloak Internet Protocol addresses such that our online activities cannot be traced. Initially, VPNs were only being utilized by huge corporate organizations, which could bear its cost, to safely and privately share records between workplaces situated in various parts of the world. Currently in 2020, now over a quarter of the world's population uses VPN to access the internet anonymously. This paper presents an introduction to VPN technology, its working, its types, some of the VPN Protocols, comparison between free and paid VPN services, and security issues associated with VPN use.
How Developers and Tools Categorize Sentiment in Stack Overflow Questions - A Pilot Study

The paper presents results from a pilot questionnaire-based study on ten Stack Overflow (SO) questions. Eleven developers were tasked with determining if the SO question sentiment was positive, negative or neutral. The results from the questionnaire indicate that developers mostly rated the sentiment of SO questions as neutral, stating that they received little or no emotional feedback from the questions. Tools that were designed to analyze Software Engineering related texts (SentiStrength-SE, SentiCR, and Senti4SD) were on average more closely aligned with developer ratings for a majority of the questions than general purpose tools for detecting SO question sentiment. We discuss cases where tools and developer sentiment differ along with implications of the results. Overall, the sentiment tool output on the question title and body is more aligned with the developer rating than just the title alone. Since SO is a very common medium of technical exchange, we also report that adding code snippets, short titles, and multiple tags were top three features developers prefer in SO questions in order for it to be answered quickly.
An Association Mining Rules Implemented in Data Mining

Data mining has created a great impact on the business organizations in different ways. The main purpose of mining is nothing but extracting useful and interesting knowledge-based patterns from large amount of data or information which is present in the data warehouses. The mining is nothing but identifying the frequent itemsets purchased by a customer from the store and by comparing the relationship between the itemsets that the analysis is been done. In order to identity the relationship, we introduce different association rule-based algorithms. We concentrate more on promising studies about the latest trends and technologies which are been implemented in the association mining rule. In this paper, we focus on association rule by using different algorithm to justify the rules.
Secure and Efficient Key Hierarchical Management and Collaborative Signature Schemes of Blockchain

As the core of the blockchain, reliable key management is the necessary guarantee for the security of the blockchain business. However, existing key management methods of blockchain mainly include local storage, offline storage, and wallet storage, and cannot meet the requirements of security, ease of use, and cost at the same time. Moreover, the key management scheme of the blockchain itself lacks a secure and efficient solution to manage the user’s key. In addition, the situation of collaborative signatures should be considered in the blockchain to meet the needs of users. To this end, secure multi-party computation (SMPC) is the most used technology, and the security of these schemes can be further improved. In order to improve the security and efficiency of blockchain key management and meet the needs of collaborative work, key hierarchical management and collaborative signature schemes incorporating SMPC and feature encryption are proposed. In this scheme, the keys are divided into three layers, and the keys of the upper layer are used to encrypt the keys of the lower layer, thus ensuring the security of all keys. Moreover, the processes of key generation, recovery, revocation and update, and multi-party signature are designed in detail. Compared with other schemes through theory analysis and experiment, our schemes can reduce the file-sharing time and the storage overhead of the user. This can improve the utilization of system resources and system efficiency, while ensuring a high level of security.
A Comparative Approach to Social Media Extreme Speech: Online Hate Speech as Media Commentary

By exploring lessons learned from Ethiopia and Finland, this article challenges two assumptions about online hate speech research. First, it challenges the assumption that the best way to understand controversial concepts such as online hate speech is to determine how closely they represent or mirror some underlying set of facts or state of affairs online or in social media. Second, it challenges the assumption that academic research should be seen as separate from the many controversies that surround online hate speech debates globally. In its place, the article proposes the theory of “commentary” as a comparative research framework aimed at explaining how the messy and complex world of online and social media practices is articulated as hate speech over other ways of imagining this growing problem in global digital media environments.
Shellfish Recognition Based on Gabor Transformation and Principal Component Analysis

In this paper, a method of shellfish recognition based on Gabor transform and two-dimensional image principal component analysis (2DPCA) is studied. Gabor transform is used to extract the image features and determine the dimension of the image features; The 2DPCA method is used to reduce the dimension of the transformed features. Compared with BP neural network and support vector machine (SVM) experiments, the results shows that the extreme learning machine classifier is very fast, which has good generalization and high accuracy.
DiKT: Dichotomous Knowledge Tracing

Knowledge tracing models the cognitive process of skill acquisition of a student to predict the current knowledge state. Based on cognitive processing theory, we regard student knowledge state in dichotomous view in alignment with Performance Factor Analysis (PFA). Assuming that a student’s correct and incorrect responses are fundamentally different for modeling a student’s knowledge state, we propose a Dichotomous Knowledge Tracing (DiKT), a novel knowledge tracing network with a dichotomous perspective on a student’s knowledge state. We modify the network’s value memory by dividing it into two memories, each encoding recallable and unrecallable knowledge to precisely capture the student knowledge state. With the proposed architecture, our model generates a knowledge trajectory that instantly and accurately portrays a student’s knowledge level based on learning history. Empirical evaluations demonstrate that our proposed model achieves comparable performance on benchmark educational datasets.
Compositional Sonification of Cybersecurity Data in a Baroque Style

Compositional sonification is a musical mapping algorithm that transforms a dataset into a soundscape for humans to hear numbers within a musical structure. In this study, we used three methods to sonify data: 1) a Baroque-style gestural mapping algorithm that emphasized timing and rhythm using an analog method of handwritten composition, 2) an electronic online pitch sequencer, and 3) the classical instrument Harp performing notated music. We investigated related sound mapping algorithms and the sensitivity of pattern representation of cybersecurity data, including the malware distribution network datasets. Our preliminary experiments showed that a Baroque-inspired timing element based on gestures plays a critical role in aural sonification. Using a sequencer with visualizers helped listeners grasp the patterns more effectively since they were able to watch and hear the results simultaneously. Performing the sonified data live on the harp helped make the compositions more relatable and thus connected with a broader audience. The harp also added a visual element which helped listeners identify patterns. We found that listeners were able to accurately identify the sonified data patterns in both the baroque-style analog handwritten compositions performed on harp and the compositions produced by the Online Sequencer. While non-musicians were able to answer questions about the data patterns they heard with a high percentage of accuracy, their results improved when visual elements were added.
Combining formal and machine learning techniques for the generation of JML specifications

Producing maintainable programs is a big challenge for the software industry as it requires solid Engineering skills and efficient CASE tools. Often, industrial programs are of a very large size (more than 1M SLOC), use high-level programming languages to their full extent (e.g. C++20, Ada 2005 or Java 16), are provided with scarce and often outdated documentation partially written in natural language. Maintenance engineers are therefore in need to understand the application at hand starting from the material left behind by the developers. The European H2020 Project DECODER (https://www.decoder- project.eu) addresses this problem by proposing to combine Natural Language Processing techniques and Formal Methods to turn as best as possible code artifacts into formal data allowing to reduce the maintenance costs and thus the total costs of ownership. In this context, we will show how to generate JML annotations using a combination of 1) automatic generation of minimal predicates, 2) Natural Language Processing (NLP) based predicates generator, and 3) manual refinement and correction, to instrument and enhance code and documentation. We will illustrate it on code samples from the MyThaiStar (https://github.com/devonfw/my- thai-star) application developed with the CASE tool devonfw by CAP GEMINI, and the Joram JMS implementation (https://gitlab.ow2.org/joram/joram) from OW2 code base.
SDSV: Angle Measurement for Supervised Classification

Data classification is one of the most fundamental tasks that can be accomplished by supervised machine learning. There exists a lot of algorithms, and they have the specific case of uses. Different classification methods follow different techniques to map the relationship between input and output. This article proposes an angle measurement-based classification technique called Support Directional Shifting Vectors (SDSV) to segment a spectral domain into regions with a very effective solution for classification problems. This method introduces two shifting vectors, named Support Direction Vector (SDV) and Support Origin Vector (SOV). These vectors are formed as a linear function to measure cosine-angle by the dot product of two separated data classes, named target data points and non-target data points. Considering the target class samples, the vectors get aligned in a way that the angle with the target class gets minimized, while the angle with the non-target class gets maximized. The error in the position of the linear function has been modeled as the loss function. Then, the vector position is updated iteratively by optimizing this loss function using a gradient descent algorithm. We have used this model to classify data from two different machine learning datasets to evaluate the performance of the proposed method. Finally, we have carefully examined the results and compared them with the other standard classification algorithms. In summary, the proposed SDSV algorithm has shown a comparable accuracy compared to different standard algorithms.
Bitrate Adaptive Scheme in Real-time Video Conference System

With the popularization of mobile communication technology, the network conditions of users are diverse, so one video stream cannot meet the needs of different receivers. Senders need to adjust bitrate of sent video streams according to the bandwidth of receivers. It is necessary to take into account that the increase in transmission data rate will increase the power consumption of the device. This paper proposes a cost calculation method combines bandwidth loss and power consumption to determine the sender's bitrate adaptation scheme. The goal is to find the lowest-cost bitrate adaptive scheme to achieve a compromise between bandwidth loss and power consumption. In practical application scenarios where the power consumption caused by data transmission is as low as possible, the cost calculation method proposed in this paper can provide the optimal bitrate adaptation scheme.
Variational auto-encoder

The Variational Auto-Encoder (VAE) has proven successful in various domains and tasks such as dimensionality reduction, information retrieval, and so on. In this chapter, we first present the Auto-Encoder, which used to learn compressing the input data and minimize errors in reconstructing them. Next, we introduce and explain VAE, which is an advanced version of AE. Finally, we provide an example program of building, training, and testing the VAE model on the MNIST handwritten digit dataset for compressing and reconstructing images.