Reviewerly/exHarmony huggingface dataset:
Aspect Sentiment Classification Based on Sequence to Sequence Reinforced Learning

The task of aspect sentiment classification (ASC) is a fundamental task in sentiment analysis. Given an aspect and a sentence, the task classifies the sentiment polarity expressed on the target in the sentence. Previous work usually distinguish the sentiment based on one-way LSTM, which are often complicated and need more training time. In this paper, motivated by the BERT from Google AI Language, we propose a novel two-way encoder-decoder framework that automatically extracts appropriate sentiment information according to sequence to sequence reinforced learning. We use reinforcement learning to explore the space of possible extractive targets, where useful information provided by earlier predicted antecedents could be utilized for making later coreference decisions. The experiments on SemEval datasets demonstrate the efficiency and effectiveness of our models.
Securing Communication 2FA Using Post-Quantic Cryptosystem

Many financial institutions interact with their customers via short message services (SMS), which is today one of the fastest and most powerful means of communicating information around the world. This information can sometimes be an access code such as the unique password (OTP) for two-factor authentication (2FA) or banking information and personal identities. All this data is confidential, and it is a major disadvantage to send them since an SMS service does not provide data encryption during network transmission and on mobile. Recently, OTPs via SMS have suffered from strong attacks that intercept messages. In order to avoid attacks and offer effective content security to 2FA credentials sent via SMS, the authors propose an SMS encryption mechanism using a post quantic cryptosystem quasi-cyclic MDPC and an electronic signature of the OTPs. Finally, this article performs an implementation and a security analysis of the proposal.
A Novel Framework for Spam Hunting by Tracking Concept Drift

In mid seventies a new method of exchanging messages between electronic devices originated which revolutionized the global community into a new world of computer networks called internet. The users identified the potential usage of this method presently known as email and started using it as the means of communication and marketing. But the competence of this method was lessened by the wide spread proliferation of spam. Researchers have come up with many proposals and tools to fight against spam. But the dynamic nature of spam makes the tools ineffective and raises the requirement for developing a filter that is to be successful over time in identifying spam. Hence spam filtering is a particularly exigent machine learning task as the data distribution and concept being learned changes over time. This paper explores this phenomenon called concept drift seen in email datasets and proposes a new framework in identifying the strategies for developing spam detection systems.
Training UUV Navigation and Contact Avoidance with Reinforcement Learning

In the last decade, reinforcement learning algorithms have achieved exceptional performance on tasks involving planning, scene interpretation, and exploration. This progress has made the challenging problems of UUV navigation and contact avoidance accessible to a reinforcement learning approach. UUV navigation tasks assume very limited information is available to an agent about its environment. We designed a simulation that realistically restricts the information available to a UUV about its environment, and trained the agent to navigate to a target while avoiding contacts. We found that a Deep Q-Learning with Experience Replay agent was able to learn contact avoidance in an information-limited environment while successfully navigating to an objective.
Beauty Attracts the Eye but Character Captures the Heart: Why Personality Matters in Chat Bot Design

Why does personality matters in chat bot design? People project human traits onto everything and now, with the proliferation of conversational user interfaces these objects can talk back. That is why is it important to create a personality for your chat bot that is relatable and believable and conveys appropriate meaning and emotion in a style and language that is congruent with your end users expectations. This paper describes how a variety of user-centred design techniques, including psychometric profiling, observation and discourse analysis were used to design a personality for a chat bot for use in military settings.
Psyncgan for Data Generation Application of Partially Syncronized GAN to Image and Caption Data Generation

A common problem that is seen in most practical applications of the general machine learning algorithms is the insufficient amount of data that has been already tagged for the application in hand. The abundance of unsyncronized (untagged) data is growing exponentially, yet its useability for practical applications is limited due to the amount of time it would take to process each data entry and classify it for each specific task. PSyncGAN is a novel approach that is able to suppliment existing sets of data that are either incomplete or insufficient for practical big data and machine learning applications. In our model, we are solving two multi-modal generative problems at the same time, Image captioning and Image Generation from natural language description with a limited amount of synchronized textual descriptions and images. The benefit of such a model is that there is no strict requirement of having synchronized corpora of text and image, but can actually make heavy use of the large amounts of singular data freely available on the internet, thus to be able to train on an almost infinite amount of data, including the data generated by the model itself. The results described below are indeed very promising, and could constitute a deeper research direction. Besides being able to train on the limited amount of training data, in this paper we also show that this model can be used as a basis for various other deep generative methods that are learning data distribution correlations (both symmetric and asymmetric) as a training data extension, by providing endless training examples for each proposed training data pair.
Web-Based Responsive Mobile Learning (M-Learning) Design

The mobile initiative is changing education and social life worldwide. The question is how to offer the same user-friendly, aesthetically pleasing mobile learning courses on different mobile devices. Developing effective web sites courses and content isn't only about creating a course with solid material. Managers and course authors are concerned about adapting different courses to a variety of learning styles and individual needs and keeping users interested and engaged. A good place to start is to organize thoughts and figure out exactly what is wanted to be included in the web sites course. Responsive design gives the ability to create a single m-learning course that automatically adapts to any browser or device. This allows the target audience to benefit from the courses without having to worry about screen size and resolution. In this chapter, m-learning is discussed in a responsive design context that can be used across platforms and offers the design of courses according to m-learning instructional strategies.
Explore the Influence of Politics on Cultural Attention Based on Sentiment Analysis of Social Network Data

With the increasingly frequent cultural exchanges between China and foreign countries, more and more Chinese people advocate western festivals. Foreign culture has invaded China and our traditional culture has been neglected seriously, which results in the more servere sense of loss of traditional culture. Therefore, Chinese government has stepped up efforts to spread traditional culture and issued relevant documents to make the public pay more attention to the inheritance and development of traditional culture.
Multiobjective evolutionary algorithm (MOEA)-based sparse clustering

Abstract Multiobjective evolutionary algorithm (MOEA) is a nature-inspired population based algorithm, it has attracted much attention from the researches and made a great progress since MOEA can generate a set of nondominated solutions in a single run. However, there are some challenges for practical application in the aspect of constraint handing, encoding scheme, the evolutionary operators design and Pareto solution selection. Hence, in order to overcome the above bottle-neck problems, this chapter will present three algorithms to discuss the application of MOEA on constrained multiobjective optimization problems, clustering learning, classification learning and sparse clustering.
Navigating the cyber-insurance landscape to protect MSPs and their clients

The level and severity of data breaches and cyber attacks occurring today continues to focus the minds of organisations across the world. According to Absolute Market Insights: “It is estimated that over the next five years, cybercrime could potentially cost companies $5.2tr every year.” 1 Moreover, according to a study by IBM and the Ponemon Institute, the average cost of a data breach in 2018 reached $3.86m, up 6.4% from the year before. 2 There is a growing awareness among enterprises that having cyber insurance is essential. And so we are seeing an escalating interest in this kind of insurance. Managed service providers (MSPs) are likely to be especially attracted to this proposition as their strategic focus is on dealing with networks and they can take on the role of a trusted consultant for their clients. The knowledge MSPs typically have of their clients’ network infrastructure, and their understanding of the cyber security market, allows them to pinpoint the bigger threats, argues Michael Mittel of RapidFire Tools.
Privacy of Real-Time Pricing in Smart Grid

Installing smart meters to publish real-time electricity rates has been controversial while it might lead to privacy concerns. Dispatched rates include fine-grained data on aggregate electricity consumption in a zone and could potentially be used to infer a household's pattern of energy use or its occupancy. In this paper, we propose Blowfish privacy to protect the occupancy state of the houses connected to a smart grid. First, we introduce a Markov model of the relationship between electricity rate and electricity consumption. Next, we develop an algorithm that perturbs electricity rates before publishing them to ensure users' privacy. Last, the proposed algorithm is tested on data inspired by household occupancy models and its performance is compared to an alternative solution.
Digital Steganography Security

Steganography has been considered as a major instrument used for an unauthorized and destructive purpose such as crime and warfare, and forensics has been used for a constructive purpose such as crime detection and fraud detection. Hence, the combination of both steganography and forensics plays a major role in the present internet era for information exchange between two parties. It has been propelled to the forefront of the current security techniques. The main objective of the technique is to provide an imperceptible way of transferring secret messages to the recipient. Another issue to be noted is that the term steganography completely differs from cryptography. The above-stated analysis is used in digital forensics. There are many steganography software tools available for ordinary computer users.
A Novel Image Segmentation Algorithm Based on Active Contour Model and Retinex Model

The algorithm of active contour model is an image segmentation method based on curve evolution theory, which have great flexibility, adaptability and separation accuracy. Accurate segmentation of inhomogeneous image targets has always been a difficult issue in image segmentation field. In this paper, an improved Chan-Vese model based on local information is proposed, which utilizes both global and local image information. Combining the local binary fitting (LBF) model with the retinex model, this paper redefines the fit of the Chan-Vese model. And adding a weight coefficient, so that the fitting term adaptively calculates the respective weights of the global and local information. The experimental results on various image data show that the proposed method can achieve more accurate segmentation results.
Optimal Distributed Fusion Filtering with the Mean and Variance Information of the State

The distributed optimal (linear minimum variance) fusion filtering problem for multi-sensor discrete-time stochastic systems is considered. Based on the projection theory, a new fusion algorithm is presented. The mean and variance information of the state can be computed off-line. The local filters are Kalman filters using local measurement data. After obtaining the local filtering error variances and cross covariances, using the new fusion algorithm, the distributed optimal fusion filter is derived. Commonly, this fusion filter is better than (at least, as good as) the fusion filter weighted by matrices. A numerical example shows the effectiveness of the new fusion algorithm.
A New Partially Segment-Wise Coupled Piece-Wise Linear Regression Model for Statistical Network Structure Inference

We propose a new non-homogeneous dynamic Bayesian network with partially segment-wise sequentially coupled network parameters. The idea is to infer the segmentation of a time series of network data using multiple changepoint processes, and to model the data in each segment by linear regression models. The conventional uncoupled models infer the network interaction parameters for each segment separately, without any systematic information-sharing among segments. More recently, it was proposed to couple the network interaction parameters sequentially among segments. The idea is to enforce the parameters of any segment to stay similar to those of the previous segment. This coupling mechanism can be disadvantageous, as it enforces coupling and does not feature any options to uncouple. We propose a new consensus model that infers for each individual segment whether it should be coupled to (or better should stay uncoupled from) the preceding one.
Reasoning Based Virtual Machine Mapping Toward Physical Machine

Cloud computing is an arising paradigm to run and hosts a number of applications and services. These computing services are accommodated by a set of virtual machines. These virtual machines are an abstraction of real servers or physical machines. A physical machine can hosts a number of virtual machines, depending on its capacity. Virtual machine placement has a direct effect on the quality of the services for both end-users and cloud service providers. In this study, we address the problem of virtual machine placement and migration for minimization of resources and power consumption. We formulate this problem as a multi-objective optimization and propose a resource-aware reasoning based scheme with state-of-the-art solutions. The simulations results with real-world traces show that the integrated schemes use a fewer number of physical servers to accommodate more virtual machines.
A Discrete Event Approach for Scene Generation conditioned on Natural Language

Recognizable scene synthesis conditioned on natural language (NL) descriptions has many applications. The success of existing methods is limited to either clipart scenes or single objects. In this paper, a novel approach to generate realistic complex scenes is proposed. Instead of using a local image asset library or direct text-to-pixel mappings, this method deploys knowledge retrieved from the Internet, which helps to generate diverse and creative scenes. The proposed approach allows to modify the synthesized scene via NL. This helps to generate more complex scenes, and to correct training bias or errors. We validate the proposed approach on challenging MS-COCO dataset and present that our method can improve the semantic alignment to the input language descriptions, and the interpretability and quality of synthesized scenes.
Knowledge Upload Service Using Semantic Based Categorization

AI (Artificial Intelligence) is one of the hot keywords in Computer Science research and development these days. Applications of AI are very diverse that include but not limited to image recognition, voice recognition, industry manufacturing, auto driving and human interaction for any service industry. Here we propose a knowledge upload system utilizing different AI techniques. The knowledge recording part of the system works in this way. First the human voice is captured using microphone. Later on the captured voice is converted into text using voice recognition techniques. Finally, the recognized text is categorized into personal or general category using semantic techniques. The knowledge retrieval part of the system on the other hand helps in retrieving the thoughts of a person whose knowledge has been uploaded regarding some particular subject. A knowledge upload service can be beneficial for each generation as the people of that generation can benefit from the thoughts of people of earlier generation. In this way precious human knowledge can be saved for the benefit of mankind. Normally AI is general which is not specific to a person but the system proposed here can be termed as a step towards PAI (Personal Artificial Intelligence).
Infinite Kernel Extreme Learning Machine

This paper addresses the analysis of the problem of combining Infinite Kernel Learning (IKL) approach and Extreme Learning Machine (ELM) structure. ELM represents a novel and promising alternative to Neural Networks, for its simplicity in implementation and high efficiency, especially concerning convergence and generalization performance. A currently underdeveloped topic concerning ELM implementation is given by the optimization process of base kernels: choosing different kernel combinations may lead to very dissimilar performance results. An innovative ELM approach using a combination of multiple kernels has been proposed in Liu et al. As a change of paradigm, we are interested in using an infinite set of base kernels, defining in this way an original ELM based algorithm called Infinite Kernel Extreme Learning Machine (IK-ELM). About that, a novel 3-step algorithm combining IKL and ELM is proposed. Finally, a brief analysis about further possible directions is discussed.
FPGA Implementation of Binarized Perceptron Learning Hardware Using CMOS Invertible Logic

This paper introduces FPGA implementation of learning hardware for a neural network. The proposed learning hardware is designed using CMOS invertible logic that realizes probabilistic bidirectional (forward and backward) operations with basic CMOS logic gates. The backward operation based on CMOS invertible logic makes hardware-based learning possible because the loss function is not required. For a simple case study, the proposed learning hardware trains using simplified a MNIST data set for a 25-input binarized perceptron. Our FPGA implementation on Digilent Genesys 2 achieves around 100 × faster operating speed than that using a traditional learning algorithm on software while maintaining the same recognition accuracy of 99%.
Quadcopter Flights in a Variety of Atmospheres

Quadcopters, or drones, have come into wide use recently for a variety of applications including video inspection of otherwise inaccessible areas, real estate sales, and entertainment. Students are interested in them and in some cases own their own. The combination of high mobility, relatively low price, and autonomous operation is quite compelling. Besides just flying around on Earth, could drones be useful in planetary exploration? Being able to avoid getting stuck (as the Mars rover Spirit did, although after far exceeding its life expectancy) while covering far more of a planet than a ground-based rover would be attractive.
EDGEGAT: An Approach to Add External Knowledge for Semantic Matching

Natural Language Inference (NLI) is one of NLP tasks to deduce, given a premise, whether a relevant hypothesis should be declared true or false. In view of the performance of previous models, the improvement provided by external knowledge is substantial. Inspired by it, we propose a new mechanism for introducing external knowledge, i.e., adding the graph convolutional network (EDGEGAT) we designed to the NLI model. Unlike previous external knowledge methods, EDGEGAT can easily be combined with NLI models and automatically learn to filter and utilize the knowledge graph during training. Using this mechanism we show significant improvement in performance for previous NLI models with Stanford Natural Language Inference (SNLI) dataset. Moreover, we demonstrate that EDGEGAT is effective and more suitable for knowledge graphs than the previous graph convolution network.
Supervised Learning Problem Formulation

In machine learning we distinguish various approaches between two extreme ones: unsupervised and supervised learning. The task of unsupervised learning consists in grouping similar data points in the description space thus inducing a structure on it. Then the data model can be expressed in terms of space partition. Probably, the most popular of such grouping algorithms in visual content mining is the K-means approach introduced by MacQueen as early as in 1967, at least this is the approach which was used for the very popular Bag-of-Visual Words model we have mentioned in Chap. 1. The Deep learning approach is a part of the family of supervised learning methods designed both for classification and regression. In this very short chapter we will focus on the formal definition of supervised learning approach, but also on fundamentals of evaluation of classification algorithms as the evaluation metrics will be used further in the book.
Domain-Camera Adaptation for Unsupervised Person Re-Identification

Although supervised person re-identification (Re-ID) performance has been significantly improved in recent years, it is still a challenge for unsupervised person Re-Iddue to its absence of labels across disjoint camera views. On the other hand, Re-Idmodels trained on source domain usually offer poor performance when they are tested on target domain due to inter-domain bias e.g. different classes and intra-domain difference e.g camera variance. To overcome this problem, given a labeled source training domain and an unlabeled target training domain, we propose an unsupervised transfer method, Domain-Camera Adaptation model, to generate a pseudo target domain by bridging inter-domain bias and intra-domain difference. The idea is to fill the absence of labels in target domain by transferring labeled images of source domain to target domain across cameras. Then we propose a cross-domain classification loss to extract discriminative representation across domains. The intuition is to think of unsupervised learning as semi-supervised learning in target domain. We evaluate our deep model on Market-1501 and DukeMTMC-reID and the results show our model outperforms the state-of-art unsupervised Re-ID methods by large margins.
Protecting children online? Cyberbullying policies of social media companies

"Protecting children online? Cyberbullying policies of social media companies." Information, Communication & Society, 23(4), pp. 630–631