Reviewerly/exHarmony huggingface dataset:
Secure autonomous charging stations: machine-to-machine authentication framework

The advent of autonomous vehicles requires machine-to-machine interaction to support services for charging the vehicle, running on designated/programmed routes, and following the rules and regulations. The charging/refueling stations are emerging-industry that is compelling service-markets to enable the machine-to-machine interaction. That in turn necessitates supporting the standardized features such as socket-outlets and the power to enable electric vehicles to be charged. This economic shift also introduces vulnerabilities that could result in security and safety concerns; e.g., for the computing on wheels platforms and communications modules with the support infrastructure. In this paper, we consider the security vulnerabilities, e.g., hardware and embedded security primitives that can enforce secure authentication in fog computing environment. That in turn enables secure interactions between the adapting network of vehicles and charging stations. The paper discusses the use of hardware security and trust primitives and authentication protocol for enabling secure autonomous machine-to-machine framework.
Chemical oceanography examples

In this chapter, you will find R and Python scripts for vertical profiles, time-series, barplots, boxplots, pie charts, 3d scatter, 3d surface, and ternary plots commonly used in chemical oceanography.
First-Order Optimization Techniques

With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology. Over 300 color illustrations are included and have been meticulously designed to enable an intuitive grasp of technical concepts, and over 100 in-depth coding exercises (in Python) provide a real understanding of crucial machine learning algorithms. A suite of online resources including sample code, data sets, interactive lecture slides, and a solutions manual are provided online, making this an ideal text both for graduate courses on machine learning and for individual reference and self-study.
Multiframe Super-resolution Reconstruction Based on Information Fusion

Multiframe super-resolution reconstruction (SRR) can obtain higher resolution images regardless of limitations of physical sensors, which is widely applied in various fields. Most of the existing algorithms solve the under-determined SRR problem by adding prior information, of which the accuracy easily affects the quality of obtained high-resolution (HR) images. A SRR process through information fusion is proposed in this paper based on multiple low-resolution (LR) images with half-pixel displacements. HR images are generated by fusing the low-frequency estimation and extracted high-frequency information, and the optimal fusion factor are obtained resorting to one-dimensional search. The proposed process realizes more realistic image reconstruction due to the extraction of accurate HR gradient information instead of possibly inaccurate prior information and achieves excellent performance in objective quality. The simulation results show the proposed method achieved improvements of up to 9.2 dB, 18.1% and 7.9% in noise level, structure and feature indicators, respectively.
Reinforcement-based heterogeneous ensemble for anomaly detection in streaming environment

Intrusion detection in networks is a challenging process, mainly due to huge amount of data and the imbalanced nature of the data. Further, the ever-changing transmission patterns introduce concept drift, which also exhibits a huge challenge. This work presents a heterogeneous ensemble based prediction model to detect anomalies in the network environment. The major goal of the proposed model is to provide faster, more efficient real-time predictions and to enhance the reliability of the model by providing an iterative mechanism to handle concept drifts. The ensemble is created using three varied base learners and the results are aggregated using a voting combiner to provide results. Decision tree, random forest, and gradient boosting trees are used as the base learners. The varied nature of the learners enables effective performances in models. Further reinforcement and an iterative training component is introduced into the model to handle concept drift. Experiments were performed on benchmark intrusion detection data and the results indicate the high performing nature of the model. Comparisons were performed with recent state-of-the-art models in literature and they indicate improved performances of the proposed model, indicating the high performing nature of the proposed ensemble model.
Social Microbial Prosthesis - Towards Super-Organism Centered Design

In this paper, I speculate on a future where our need to socialize physically is solely to exchange bacteria.With our biological data being in the hands of private companies and governments and our environments' microbiomes becoming less diverse, our social systems, social identities and social interactions are redefined and reinvented to adapt to this new reality.In this world, everyone wears a "social microbial prosthesis" that analyzes their microbial composition from their breath and reveals sensitive information on their chest.The microbial prosthesis would be able to give off information not only on the microbial composition but also on the mental and physical health of a person.This second skin plays a role in controlling communication and interaction between people, where one is able to, by inspecting surrounding people's prosthesis, take careful considerations of who to interact with and who to avoid.Social Microbial Prosthesis is a critique of the race of private companies and governments to collect our biological data, the role of commercializing such data in shaping and changing our social identities and a response to the loss of microbial diversity in our environments due to our modern lifestyles and surroundings.
Book review

Anecdotal evidence suggests that some students, in some of Indonesia's Islamic boarding schools (pesantren), harbor negative attitudes towards English, prompting resistance to learning the language at individual and institutional levels. This paper reports on an empirical study investigating this issue in three pesantren in Java. Employing Dörnyei’s (2005) L2 Motivational Self System as the main theoretical framework, a questionnaire consisting of well-established and context-specific constructs was designed to measure students' motivation for learning English (n = 376). The one-way MANOVA performed suggested that religious factors do play a role in shaping learners' motivation. To further examine this, a follow-up qualitative study involving class observations and student interviews was conducted. This revealed that the participants relate their motivation to their spiritual vision, i.e. using English mainly as a means of da'wah (Islamic propagation) and for communicating with other Muslims worldwide. However, this da'wah motive does not appear to generate much learning effort, and is possibly a way of overcoming the dissonance they feel in studying the non-believer language. The paper concludes by emphasizing the need for L2 researchers to further examine religious attitudes and religiosity as consequential socio-cultural aspects of learning English with pedagogical implications for institutions in many global contexts.
Book review

The business world is fast entering a virtual environment where technology is applied in all areas to improve efficiency. For example, web-based synchronous chat has become a popular way of communication for customer support. However, we note that business managers and customers bemoan the amount of time taken to serve customers via webchat, caused in some part perhaps by the textual route to get to a resolution using this new media. This has received little attention from the research world. We therefore collected 15 customer service webchat exchanges from an international online retail company and explored the challenges by looking at the generic structural moves, their impact on the time to resolution and the linguistic affordances of webchat. It was found that webchat moves highly depend on the type of business, company policies and division of labor in contact centre. Some webchat affordances such as allowing for URLs to lengthy webpages and long lists of instructions can lead to communication breakdowns. The results of this analysis can be a valuable resource for the pre-employment training and coaching of online customer service representatives as well as for business communication courses in higher education where little attention is given to such genre.
Authenticity Data through Digital Signature Technique with MD5 Algorithm

Data entry must be done by a professional person because this process requires a high level of accuracy. If you enter incorrectly, the results given will also be inaccurate and can even cause misunderstanding. To maintain the authenticity of the data, digital signature techniques are used by using the MD5 algorithm as a hash function that is widely used in cryptographic applications are MD5 and SHA.
Wearable technology and antibiotic resistance (ABR) working parties

This abstract relates to the following papers: Spender, A., Bullen, C., Altmann-Richer, L., Cripps, J., Duffy, R., Falkous, C., Farrell, M., Horn, T. and Wigzell, J. Wearables and the Internet of things: considerations for the life and health insurance industry. British Actuarial Journal , 24. doi: 10.1017/S1357321719000072 .
Session Hijacking Over Cloud Environment

Cloud computing is a technology that offers an enterprise model to provide resources made available to the client and network access to a shared pool of configurable computing resources and pay-for-peruse basis. Generally, a session is said to be the collective information of an ongoing transaction. This package is typically stored on the server as a temporary file and labeled with an ID, usually consisting of a random number, time and date the session was initiated. That session ID is sent to the client with the first response, and then presented back to the server with each subsequent request. This permits the server to access the stored data appropriate to that session. That, in turn allows each transaction to be logically related to the previous one. Session hijacking is the common problem that is experienced in the cloud environment in which the session id is gained and information is gathered using the session ID compromising its security. This chapter covers session hijacking and the countermeasures to prevent session hijacking.
New Graphical Password Scheme Containing Questions-Background-Pattern and Implementation

Security of authentication is needed to be provided superlatively to secure users' personal and exchange information, since online information exchange systems have been developed according to internet speed.Therefore, aim of the chapter is to develop current graphical password scheme based on recall, create and implement a new graphical password scheme composed of three layer verification.We programmed our scheme in order to use in section of anonymous information exchange system and user's registration of trading chat room.While we conducted survey on user by accessing participant to our system lied in participants' local network and we analyzed in accordance with the average length of their created password and statistical significant of entropy bit.From the survey of total participants, our scheme has statistical significance, furthermore it was proved that it can secure form a variety of attacks as entropy bit was high.
Decision Support System for Assigning Members to Agile Teams

The use of agile methodologies like SCRUM is seen by companies in the software engineering field as a strategic necessity for their competitiveness, which makes them more reactive and dynamic in an increasingly demanding and competitive international market. One of the critical factors in the implementation of a SCRUM environment is the set-up of teams that are simultaneously homogeneous and composed of the best collaborators for each SCRUM role. In this sense, this study describes the modeling process and presents the implementation of a decision support system that can contribute to improving the process of assigning an agile team simultaneously considering the technical and social skills of employees. The results of the study allowed testing the application considering different competencies associated to each Agile position, the impact that the attribution process suffers from oscillations in the process of evaluation and self-evaluation, and the impact in terms of the performance of the inclusion of new collaborators and criteria comparison.
An Improved Teaching-Learning Optimization Algorithm based on Morlet Wavelet Variation

In order to overcome the weaknesses of the Teaching and Learning optimization (TLBO) algorithm in solving function optimization problems, such as easy to fall into local optimum, slow convergence at the later stage, and low solution accuracy, an improved algorithm with dynamic adaptive teaching factors and Morlet wavelet variation-based algorithms is proposed. Firstly, the improved algorithm introduces a nonlinear dynamic teaching factor to adjust the influence of teachers on students in the iterative optimization process. Secondly, in order to avoid algorithm trapped in local optimum, using Morlet wavelet to the implementation of global extreme value of each dimension in each generation wavelet disturbance, disturbance and the result was recognized as a certain probability is selected for the new position of the individual, make full use of the advantage of global extremum information guide populations to be near optimal solution quickly, by fine-tuning characteristics of wavelet functions help population out of local minima. The simulation results on 18 classical test functions show that the improved algorithm has better performance than TLBO, SLTLBO, CSA and BOA algorithms, and is suitable for solving function optimization problems. It is applied to engineering practice to solve PID parameter optimization and obtains good results.
Robot Task Planning in Deterministic and Probabilistic Conditions Using Semantic Knowledge Base

A new method is proposed to increase the reliability of generating symbolic plans by extending the Semantic-Knowledge Based (SKB) plan generation to take into account the amount of information and uncertainty related to existing objects, their types and properties, as well as their relationships with each other. This approach constructs plans by depending on probabilistic values which are derived from learning statistical relational models such as Markov Logic Networks (MLN). An MLN module is established for probabilistic learning and inference together with semantic information to provide a basis for plausible learning and reasoning services in support of robot task-planning. The MLN module is constructed by using an algorithm to transform the knowledge stored in SKB to types, predicates and formulas which represent the main building block for this module. Following this, the semantic domain knowledge is used to derive implicit expectations of world states and the effects of the action which is nominated for insertion into the task plan. The expectations are matched with MLN output.
File Encryptor

In today’s world, security is one of the major challenges which are faced by the people. For securing our confidential data, encryption is done. Encryption is the process that protects our important information from unwanted people accessing or manipulating it. Cryptography is one of the methods for encryption and decryption of data. With the evolution of human intelligence, cryptographic forms have become more complex in order to have our files and information more secure, and arrays of encryption systems are being used for protecting our data from tampering by various organizations. In this paper, our strategy used is to apply the encryption methods using Advanced Encryption Standard (AES) in order to proceed on both encryption and decryption.
Sentiment analysis of movie reviews based on pretraining and dual branch coding

Due to the high dimensionality and sparsity of text, and to the complex semantics of the natural language, sentiment analysis of movie reviews presents difficult challenges. In o-rder to solve these problems, a novel architecture which conta-ins a robustly optimized bidirectional encoder representations from transformers pretraining approach (RoBERTa), a bidir-ectional long short-term memory (BiLSTM), a temporal conv-olutional network (fCN), a convolutional layer and an attenti-on mechanism is proposed in this paper. The proposed archit-ecture is called dual branch feature coding network based on RoBERTa (DBN-Ro). In DBN-Ro, the RoBERTa pretraining model is used to obtain the word embedding and the dual bra-nch network is used to extract the contextual semantic and multi-level representations. The convolutional layer is used to perform dimensionality compression on the stitched vectors. The attention mechanism is introduced to highlight important features. The DBN-Ro is experimentally validated on two mov-ie reviews, the results shows that the it can achieve better pre-diction results in sentiment analysis of movie reviews tasks.
On the Analysis of Mutation Operators in Multiobjective Cartesian Genetic Programming for Designing Combinational Logic Circuits

Approximate Computing is an emerging paradigm that takes advantage of inherently error resilient digital circuits to design circuits with higher energetic efficiency, lower delay, or a smaller occupied area on the chips. Traditional approaches do not handle multiple objectives and metaheuristics appear as a proper alternative. In particular, Multiobjective Cartesian Genetic Programming (MOCGP) can find good solutions to the design and optimization of approximate circuits. The performance of CGP depends on the mutation adopted, as normally CGP only uses mutation for creating new solutions. However, to the best of our knowledge, just the traditional point mutation (PM) was used by the previously proposed MOCGP. Thus, the literature lacks an analysis of the best mutation operators of MOCGP. We propose here the analysis of PM and Single Active Mutation(SAM) on the multiobjective optimization of 15 heterogeneous combinational logic circuits from scratch and starting with a feasible solution. The results indicate that SAM obtained better results than PM.
Print Journals: Off Site? Out of Site? Out of Mind?

With paper journals seemingly decreasing in importance, libraries need to consider issues of space allocation and storage of journal volumes. Space solutions may include shelving in open, closed, and/or compact shelving, mass storage in off-site, or onsite, facilities, or even discarding the paper and relying on electronic access. Any solution involving the current paper collections will have a serious impact on technical services. This workshop explores these issues in light of the experiences of Appalachian State University, which is in the planning stages of a new library. [Article copies available for a fee from The Haworth Document Delivery Service: 1-800-HAWORTH. E-mail address: &lt;docdelivery@haworthpress.com&gt; Website: &lt;http://www.HaworthPress.com&gt;]
An Enhanced Expression Recognition Based on Sparse Representation Using Data Normalization

As a hot issue of visual recognition and the premise of human-computer intelligent interaction, emotion recognition from facial expression is highly recognized and widely used. However, face images collected in the natural environment tend to have different head postures and intensities of illumination, which interfere with the discrimination effect of the facial expression. In this paper, an enhanced facial expression recognition model based on sparse representation using data normalization is designed, which can boost the recognition performance in an unconstrained environment compared with the original model. Multi-task Convolutional Neural Networks (MTCNN) is used for facial landmark detection along with affine transformation for aligning all the faces. Meanwhile, histogram equalization is employed to normalize the gray-scale. Finally, the test image is classified by sparse coding. The real effect of the proposed model is verified by experiments on the CK+ and Oulu-CASIA database, and the accuracy is improved to 51.33% and 60.76% respectively. Besides, the amount of data is greatly reduced by sparse coding.
Short Text Generation Based on Adversarial Graph Attention Networks

Text generation has attracted more and more attention in the field of natural language. Recently, GAN (Generative Adversarial Networks) have been widely used in text generation, among which the GAN-based models, such as SeqGAN and SentiGAN, have shown remarkable effects in text generation. However, previous text generation models simply use CNN (Convolutional Neural Networks) as discriminators and ignore relationships between the same-label texts. Meanwhile, most models only consider using a single generator to generate a single species text, not for multispecies texts. To meet the requirements, in this paper, we propose a novel framework model-SGATGAN, which applies GAT (Generative Attention Nets) as the discriminator to establish the connection between the texts of the same type. It also provides a method of generating multispecies texts using a single generator. In this model, the graph attention neural network is used as the discriminator via the feedback to guide the generator in a specific location to generate a specific type of short text. Experimental results on two benchmarks show that our model significantly outperforms previous methods, giving state-of-the-art results in short text generation.