Reviewerly/exHarmony huggingface dataset:
AffectON: Incorporating Affect Into Dialog Generation

Due to its expressivity, natural language is paramount for explicit and implicit affective state communication among humans. The same linguistic inquiry (e.g., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">How are you?</i> ) might induce responses with different affects depending on the affective state of the conversational partner(s) and the context of the conversation. Yet, most dialog systems do not consider affect as constitutive aspect of response generation. In this article, we introduce <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AffectON</i> , an approach for generating affective responses during inference. For generating language in a targeted affect, our approach leverages a probabilistic language model and an affective space. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AffectON</i> is language model agnostic, since it can work with probabilities generated by any language model (e.g., sequence-to-sequence models, neural language models, n-grams). Hence, it can be employed for both affective dialog and affective language generation. We experimented with affective dialog generation and evaluated the generated text objectively and subjectively. For the subjective part of the evaluation, we designed a custom user interface for rating and provided recommendations for the design of such interfaces. The results, both subjective and objective demonstrate that our approach is successful in pulling the generated language toward the targeted affect, with little sacrifice in syntactic coherence.
Review on Machine learning and its application in Atmospheric science and Human Behavior Recognition

Machine learning is a very popular research field, almost every industry is using machine learning to improve the data analysis ability of the industry. In this paper, we mainly analyze the application of machine learning in behavior recognition, meteorology and other industries, and summarize the common methods and development status of machine learning in these two industries.
A cloud software life cycle process (CSLCP) model

Small to medium-sized enterprises take advantage of the strengths and opportunities of cloud computing. These enterprises require a well-defined software process model to produce reliable and quality cloud software, given their limited resources. Existing related work is surveyed, and the needed missing features are determined. A cloud software life cycle process model is proposed, validated, and verified to handle the shortcomings of existing cloud software process models. A case study is used to illustrate all the activities required throughout the software life cycle of the proposed model. The proposed cloud software life cycle process model is a cyclic iterative prototyping model. It is compatible with levels two and three of the capability maturity model integration and extends the Egyptian software process improvement model to fit the cloud environment. The model helps small enterprises develop quality, maintainable, and sustainable cloud software at a reasonable cost.
An Attention-based Approach to Detect Emotion from Tweets

In today's world, social networks are the place where user share their views, emotions in their way. Social media, such as Twitter, Instagram, Facebook, etc. where millions of people express their views in their daily day-to-day life, which can be their sentiments and opinions or expressing emotions about a particular thing or their own. This gave researchers an outstanding opportunity to analyze the emotions of users' activities on social networks. These massive digital data contain people's day to day life sentiments, opinions, and showing emotions. Over the years there have been different research on emotion analysis of the social platform. As people tend to have different thoughts, analyzing the right emotion from social data is becoming a challenge. This clearly states that there is a need for an attempt to work towards these problems and it has opened up several opportunities for future research for hidden emotion identification, users' emotions about a particular topic, etc. Detecting emotion from text is one of the toughest challenges in natural language processing. Developing a system that can detect emotion from social media is a crying need as people are sharing more of their thoughts here. In this research work, to learn the representation of the tweets, we propose an attention-based model. The proposed model has been divided into different components and subcomponents consisting of ID Convolution, Bidirectional LSTM, and Attention mechanism. We create a new dataset from SemEval Affect in Tweets dataset and then conduct experiments for the best outcomes. Our model achieves up to 79% accuracy in this task.
The Eighteenth Century

We shall next declare the occasion and the cause which moved our poet to this particular work. He lived in those days, when (after Providence had permitted the invention of printing as a scourge for the sins of the learned) paper also became so cheap, and printers so numerous, that a deluge of authors covered the land: […] At the same time, the licence of the press was such, that it grew dangerous to refuse them either [applause or money]; for they would forthwith publish slanders unpunished, the authors being anonymous, and skulking under the wings of publishers. (Alexander Pope, “Martinus Scriblerus of the Poem,” The Dunciad, 1728)
From Reward to Histone: Combining Temporal-Difference Learning and Epigenetic Inheritance for Swarm's Coevolving Decision Making

Applying intelligence to a group of simple robots known as swarm robots has become an exciting technology in assisting or replacing humans to fulfil complex, dangerous and harsh missions. However, building a strategy for a swarm to thrive in a dynamic environment is challenging because of control decentralisation and interactions between agents. The decision-making process in a robotic task commonly takes place in sequential stages. By understanding the subsequent action-reaction process, a strategy to make optimal decisions in a respective environment can be learnt. Hence, using the concept of epigenetic inheritance, novel evolutionary-learning mechanisms for a swarm will be discussed in this paper. Reinforcement evolutionary learning using epigenetic inheritance (RELEpi) is proposed in this article. This method utilizes reward, temporal difference and epigenetic inheritance to approximate optimal action and behaviour policies. The proposed method opens possibilities to combine reward-based learning and evolutionary methods as a stacked process where histone value is used rather than fitness function. The formulation consists of methylation and epigenetic mechanisms, inspired by the epigenome studies. The methylation process helps the accumulation of the reward to histone value of the gene. Epigenetic mechanisms give the ability to mate genetic information along with their histone value.
A Broad Perspective On Integrating Internet Of Things With Blockchain

Blockchain is an incredible concept that creates trust and maintains a distributed environment without the demand of any third party. This concept has disrupted many industries including IoT. Researchers have found that blockchain has the proficiency to provide a solution for IoT software-related security issues including improper authentication of a device, unauthorized connection with a device, physical and hardware security flaws. The major challenge lies in integrating Blockchain with resource-constrained devices of IoT at the thing layer. This paper provides a broad perspective of integrating these two technologies. It highlights the guidelines for the necessity of blockchain for IoT based applications and a summary of the related work carried out on the integration of Blockchain with IoT. A successful experimental setup was done to implement blockchain on IoT based Smart Laboratory system to show the feasibility of the integration.
Collaborative prediction of web service quality based on user preferences and services

The prediction of web service quality plays an important role in improving user services; it has been one of the most popular topics in the field of Internet services. In traditional collaborative filtering methods, differences in the personalization and preferences of different users have been ignored. In this paper, we propose a prediction method for web service quality based on different types of quality of service (QoS) attributes. Different extraction rules are applied to extract the user preference matrices from the original web data, and the negative value filtering-based top-K method is used to merge the optimization results into the collaborative prediction method. Thus, the individualized differences are fully exploited, and the problem of inconsistent QoS values is resolved. The experimental results demonstrate the validity of the proposed method. Compared with other methods, the proposed method performs better, and the results are closer to the real values.
Development Effort Estimation Practices in Small Software Companies: An Exploratory Study

The software engineering research recognizes that small software companies are different to medium-sized and large organizations. Therefore, they require particular implementations of already known software practices to support their developments; this includes the estimation of the software development effort. The literature reports that estimation techniques based on expert judgement are preferred by the software industry in general, however, it is not clear what practices are used by small software companies and why they use them. Trying to clarify these aspects, particularly for small software companies in Chile, this article presents an empirical study based on semi-structured interviews that explores the effort estimation practices used by ten business stable organizations. The study results show the practices preferred by them, the reasons behind their preferences, and the expected accuracy of their estimates. The study also shows that using an appropriate estimation technique is not enough to keep the estimates under control.
Using Additive Bayesian Networks and Association Rules in Antimicrobial Resistance Analysis

The aim of this paper is to analyze the effectiveness of combining two data-driven approaches, additive Bayesian networks (ABN) and association rules mining (ARM), in identifying relevant patterns of antimicrobial resistance (AMR). The main idea is to use information provided by ARM as prior knowledge in the inference of ABNs describing relationships between antimicrobials involved in resistance patterns. The results obtained for a dataset containing Escherichia coli isolates illustrate that by combining the two approaches one can better explain AMR patterns of association than by using only one of the methods.
Ensemble Labeling Towards Scientific Information Extraction (ELSIE)

Extracting scientific facts from unstructured text is difficult due to challenges specific to the complexity of the scientific named entities and relations to be extracted. This problem is well illustrated through the extraction of polymer names and their properties. Even in the cases where the property is a temperature, identifying the polymer name associated with the temperature may require expertise due to the use of complicated naming conventions and by the fact that new polymer names are being “introduced” into the lexicon as polymer science advances. While domain-specific machine learning toolkits exist that address these challenges, perhaps the greatest challenge is the lack of—time-consuming, error-prone and costly—labeled data to train these machine learning models. This work repurposes Snorkel, a data programming tool, in a novel approach as a way to identify sentences that contain the relation of interest in order to generate training data, and as a first step towards extracting the entities themselves. By achieving 94% recall and an F1 score of 0.92, compared to human experts who achieve 77% recall and an F1 score of 0.87, we show that our system captures sentences missed by both a state-of-the-art domain-aware natural language processing toolkit and human expert labelers. We also demonstrate the importance of identifying the complex sentences prior to extraction by comparing our application to the natural language processing toolkit.
Extracting Relations Between Organizational Patterns Using Association Mining

Patterns are powerful when used in combinations. Identifying relationships between patterns is challenging. The existing approaches and pattern formats reflect the relationships with other patterns in a very informal and traditional way. We are proposing an automatic approach which discover such relationships from the patterns descriptive text using text mining and natural language processing techniques. In this work, we demonstrate how it contributes in inference of relationships and its strength among patterns.
A Duality Based Approach For Fractional Order Tv-Model In Optical Flow Estimation

In this paper, a fractional order total variation (TV) model is presented for optical flow estimation in the image sequences. In particular, the proposed model is introduced by generalizing an integer order total variation model formed by using a combination of total variation and quadratic regularization terms. However, it is difficult to solve this minimization problem due to the non-differentiability nature of fractional order TV-term. The fractional order derivative is discretized using the Grünwald-Letnikov derivative. The resulting formulation is solved by using the primal-dual algorithm. The experimental results on different datasets demonstrate the effectiveness of the proposed model against outliers and provide a dense and discontinuity preserving optical flow.
Deep Layer Convolutional Neural Network (CNN) Architecture for Breast Cancer Classification Using Histopathological Images

In recent years, there are various improvements in computational image processing methods to assist pathologists in detecting cancer cells. Consequently, deep learning algorithm known as Convolutional Neural Network (CNN) has now become a popular method in the application image detection and analysis using histopathology image (images of tissues and cells). This study presents the histopathology image related to breast cancer cells detection (mitosis and non-mitosis). Mitosis is an important parameter for the prognosis/diagnosis of breast cancer. However, mitosis detection in histopathology image is a challenging problem that needs a deeper investigation. This is because mitosis consists of small objects with a variety of shapes, and is easily confused with some other objects or artefacts present in the image. Hence, this study proposed four types of deep layer CNN architecture which are called 6-layer CNN, 13-layer CNN, 17-layer CNN and 19-layer CNN, respectively in detecting breast cancer cells using histopathology image. The aim of this study is to detect the breast cancer cell which is called mitosis from histopathology image using suitable layer in deep layer CNN with the highest accuracy and True Positive Rate (TPR), and the lowest False Positive Rate (FPR) and loss performances. The result shows a promising performance for deep layer CNN architecture of 19-layer CNN is suitable for this MITOS-ATYPHIA and AMIDA13 dataset.
rcGAN: Learning a Generative Model for Arbitrary Size Image Generation

We introduce rcGAN, a new generative method that is capable of synthesising arbitrary sized, high-resolution images derived from a single reference image used to train our model. Our two-steps method uses a randomly conditioned convolutional generative adversarial network (rcGAN) trained on patches obtained from a reference image. It can capture the reference image internal patches distribution and then produce high-quality samples that share with this image the same visual attributes. After training, the rcGAN generates recursively an arbitrary number of samples which are then stitched together to produce an image whose size is determined by the number of samples that are used to synthesise it. Our proposed method can provide a practically infinite number of variations of a single input image that offers enough variability while preserving the essential large scale constraints. We experiment with our two-steps method on many types of models, including textures, building facades and natural landscapes, comparing very positively against others methods.
Development of a System for Correlating Ocular Biosignals to Achieve the Movement of a Wheelchair

The command and control of the movement of a wheelchair can be done manually, semi-automatically or automatically in full, depending on the degree of disability of the patient. Most often, these controls are made mechanically or electromechanically in order not to complicate the construction and to create easy-to-move facilities, but in more severe cases biosignals of the patient can be used. In the first part of the paper a series of aspects related to eye tracking devices and also analyzes the areas of applicability of eye tracking devices of any shape are presented. The second part of the paper presents theoretical and practical aspects of the blinking process and, respectively, of monitoring eye movements (corneal reflex, pupil position) and attention, as control elements of the wheelchair. The third part of the research presented in this paper describes the experimental system and how to use it on a mobile that performs the received commands. The paper ends with the presentation of the results and conclusions derived from the interpretation of the recorded data and, respectively, the applications of the installation.
Parameters of Hulls of Primitive BCH Codes of Length <i>q</i> <sup>3</sup> – 1

Let <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(q,n,δ)</sub> be the primitive BCH code over <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</b> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> of length n=q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> -1 with designed distance δ, where <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</b> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> is the finite field of order q. In this letter, we investigate the parameters of the codes <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(q, n, δ)</sub> and their hulls for 2 ≤ δ ≤ n. We also present a sufficient and necessary condition on the designed distance such that the hull has the largest dimension.
Online education: steps into the future

Abstract This article reveals the essence, perspectives and role of online education. Such a concept as EdTech (Educational Technologies) is considered. The stages of development of education built by means of information technologies are presented. The concepts of VR, AR, MR and their possibilities in online learning are also discussed. The problems of traditional education, the advantages and disadvantages of online education and its possibilities are revealed.
A Detection Algorithm of Giant Panda in Wild Video Image Based on Wavelet-SSD Network

The image and video of the giant panda captured by the infrared camera in the field are of great significance to the research and protection of the giant panda. However, there are few pandas in the field and many useless data are also captured by the infrared camera, which brings many difficulties to the detection of the panda image. In order to quickly find the panda image from a large number of images, this paper proposes a special method of fast detection of giant panda based on the WL-SSD (wavelet feature single shot multibox detection) network using Spatial domain and frequency domain feature. The algorithm extracts the frequency domain features of the giant panda image through wavelet feature network. The experimental results show that the giant panda detection algorithm proposed in this paper better use of the high frequency characteristics of the giant panda texture feature, and it can find the panda image in high accuracy and detection rate from a large number of images.
Computational Complexity of View Synthesis with the Number of Selected Images using Array Cameras

This paper proposes the approach to view synthesis using array cameras. 25 cameras of 5 by 5 array capture a target scene and the calibrated cameras provide rectified images. Since computational complexity increases according to a number of images used for view synthesis, this paper proposes to selection of proper viewpoints instead of using 25 images captured by array cameras. Randomly chosen viewpoints are used for the synthesis and computational complexity is calculated with a number of images. Qualitative results of the synthesis are also provided so that one can determine the optimal number of images for reliable result of the synthesis.
Wavelet Filter Banks Using Allpass Filters

Allpass filter is a computationally efficient versatile signal processing building block. The interconnection of allpass filters has found numerous applications in digital filtering and wavelets. In this chapter, we discuss several classes of wavelet filter banks by using allpass filters. Firstly, we describe two classes of orthogonal wavelet filter banks composed of two real allpass filters or a complex allpass filter, and then consider design of orthogonal filter banks without or with symmetry, respectively. Next, we present two classes of filter banks by using allpass filters in lifting scheme. One class is causal stable biorthogonal wavelet filter bank and another class is orthogonal wavelet filter bank, all with approximately linear phase response. We also give several design examples to demonstrate the effectiveness of the proposed method.
Detect and Depornize Pornographic Images Using Pre-trained CNN Models

The ubiquitous Internet has achieved great transparency of information; however, it also becomes the media of negative information, especially pornographic content. Classifying and filtering out such content are of high demand on the social networks. CNN models can be used to classify pornographic images with good flexibility and high accuracy. However, building CNN models from scratch is complex and computationally expensive. Fortunately, training pre-trained CNN models using the technique of transfer learning takes relatively less time to develop and can still deliver great performances. In this paper, we show that the most recent pretrained CNN models can be re-trained to be pornographic image detectors using the technique of transfer learning. Furthermore, considering the practical applications in social media and entertainment, this paper introduces a depornize algorithm to cover these sensitive and pornographic content. Experimental results demonstrate the efficiency of both algorithms proposed in this paper.