Reviewerly/exHarmony huggingface dataset:
An adaptive RBF hidden layer generation algorithm

Abstract We presents an adaptive learning algorithm of RBF neural network. This algorithm uses an adaptive splitting operation based on network sensitivity and sample density to dynamically change the number of nodes in the hidden layer of RBF network. At the same time, a refactoring operation based on energy consumption is proposed, and the connection weights of the hidden layer and the output layer are obtained by using the least square method. In the experiment of iris sample classification, the recognition rate of the model is 95%.
Co-Displayed Items Aware List Recommendation

Existing recommender systems usually generate personalized recommendation lists based on the estimation of the preference scores over user-item pairs, while ignoring the impacts of the entire display list that plays a central part in the decision making process of a user. This leaves us an opportunity to generate better recommendation results by considering the impacts of all offered choices. However, such an extension cannot be handled efficiently by traditional top-k list recommendation methods, due to the entire list dependency issue which means a complete list of items is needed before we can precisely measure any item preference among the list. In this paper, we propose a Co-displayed Items Aware (CDIA) list generation approach, which is based on the reinforcement learning architecture, and can efficiently generate high-utility lists. Specifically, we propose CDIA-Sim to predict users' preferences, which considers the impacts of the co-displayed items. Then, to overcome the entire list dependency issue in the list recommendation task, we utilize the reinforcement learning technique and design CDIA-RL to generate high-utility lists. Experimental results show that CDIA-Sim achieves significant improvements in modeling user-item preferences, and CDIA-RL can yield lists efficiently and effectively, illustrating better performance than other competitors.
The biology high school text-book’s errors on fern material

The teacher’s explaining and the text-book can contribute the student’s comprehension, but the explaining that is accepted by students from teachers or text-books is not consistent with the scientific concept, or there is misconception. Ferns are one of the materials that is learned by students of grade X in the even semester. The method that is used in this research is using descriptive approach. The purpose of this research is to search the errors on high school biology text-books, on ferns material that are used by teachers and high school students in Malang city. The result of this research show that from 3 text-books that are researched, there are 10 concept mistakes, 7 principal errors, 5 the writing errors of taxon name, and 2 the language writing errors. So it is recommended, in writing book of fern material, the writer should understand the right concept so there will not be errors that contribute to be misconception.
Hierarchical neural network for facial attribute transfer

Abstract The facial attribute transfer refers to generating a face image with desired attributes while preserving other details. In the existing methods, some of the them consider the independence among attributes but neglect the integrity. It may result in information loss and lead to distorted generation. The others ensure the integrity at the cost of insufficient independence limits. The generated images will be partially blurred. In this paper, GAN and Variational Autoencoders (VAE) structure are incorporated to preserve the details while attribute transfer occurs. The concept of hierarchical latent representation is introduced to realize the attribute independence. These methods work with each other forming an effective network for facial attribute transfer, referred as Hi-GAN. Experiments on the CelebA dataset show that our model can output clearer and more realistic images on facial attribute transfer.
Network Traffic Text Classification Based on Multi-instance Learning and Principal Component Analysis

Network traffic text classification plays an important role in network security. Traditional classification methods based on machine learning, such as supervised learning algorithms and semi-supervised algorithms, are insufficient: classification mode is too simple, unable to adapt to diverse classification requirements; text feature selection method is simple, text classification lacks diversity, and classification accuracy is low. And the classification speed is slow, not suitable for environments with high traffic and real-time. Multi-instance learning classification can describe the characteristics of the sample more accurately and comprehensively, and can improve the classification effect. In this paper, we combined the multi-instance learning classification with principal component analysis (PCA) to select text features of data sets, and removed the redundant and uncorrelated features in the original data, obtained a better classification accuracy.
The Theory of Traces for Systems with Nondeterminism, Probability, and Termination

This paper studies trace-based equivalences for systems combining nondeterministic and probabilistic choices. We show how trace semantics for such processes can be recovered by instantiating a coalgebraic construction known as the generalised powerset construction. We characterise and compare the resulting semantics to known definitions of trace equivalences appearing in the literature. Most of our results are based on the exciting interplay between monads and their presentations via algebraic theories.
Inferring types and effects via static single assignment

The static single assignment form which is used by compilers of imperative programming languages is, as noted by Kelsey, a functional language. Local mutability is removed by renaming variables, and control flow is split into basic blocks, which are, in turn, a set of mutually recursive lambda abstractions. This suggests that such constructs could be used as the foundation for a functional language. In this paper, we present the core of a purely functional programming language with an imperative look-and-feel and with a type and effect system based on that of Leijen's Koka. We also discuss our initial results with a prototype implementation and a type and effect inference algorithm.
Path Planning for Messenger UAV in AGCS with Uncertainty Constraints

This paper mainly solves a path planning problem of messenger UAV in an air-ground collaborative system which is composed of a fixed-wing unmanned aerial vehicle (UAV) and multiple unmanned ground vehicles (UGVs). The UGVs play the role of mobile actuators, while the UAV serves as a messenger to achieve information sharing among the UGVs. The UAV needs to fly over each UGV periodically to collect the information and then transmit the information to the other UGVs. The path planning problem for the messenger UAV can be modeled as a Dynamic Dubins Traveling Salesman Problem with Neighborhood (DDTSPN). The goal of this problem is to find a shortest path which enables the UAV to access all the UGVs periodically. In the paper, we proposes a solution algorithm for the UAV’s path planning with uncertainty constraints which means the UAV doesn’t know the UGVs’ motion parameters. The algorithm is based on the idea of decoupling: firstly the sequence for the UAV to access the UGVs are determined by the genetic algorithm (GA), and then a reasonable prediction mechanism are proposed to determine the access locations of the UAV to the UGVs’ communication neighborhoods. Then the theoretical analysis of the effectiveness for the UAV’s path planning strategy is emphasized. At last, the effectiveness of the proposed approach is corroborated through computational experiments on several different scale instances.
Optimization Algorithms for Tibetan-Chinese Neural Machine Translation

Abstract Tibetan-Chinese neural machine translation (NMT) is facing serious resource scarcity problem. This paper compares the application effect of multiple neural network optimization algorithms under the condition of resource scarcity, and proposes an optimization method which is suitable for resource scarcity languages. Then, by which it improving the performance of the Tibetan-Chinese NMT. Experimental results show that when choosing a suitable optimization algorithm, the Tibetan-Chinese NMT can still exceed the traditional statistical machine translation (SMT) and achieve better translation performance.
INCLUSIVE ENVIRONMENT: DEVELOPING INTEGRATED DEFINITION

The article highlights the problem of the absence of an inclusive environment for people with disabilities as a factor in the declarative nature of inclusive reforms. Actual barriers and problems that prevent people with disabilities from participating actively in society are described. These barriers require an integrated approach to develop practical solution and minimize them. Pedagogical aspect of terms "inclusion" and "inclusive environment" is the most developed (right now). Nevertheless, the main idea of the pedagogical concept of inclusion is the creation of a "school for all" that meets the common European standards and the mission of creating a society for all. So the idea of inclusivity at this point has a broader meaning than simply creating the right environment at school. The key elements of social and pedagogical aspects are recognition of diversity, elimination of inequalities, minimization of barriers and creation of conditions in which any exclusion in all spheres of life is impossible. Psychological aspect of inclusion is to understand psychological well-being as an important element of it. Psychological conditions of an inclusive environment should contribute to the individual's experience of subjective well-being. Based on the etymology of the word, you can describe inclusion as the process of forming a single whole by combining different parts. Having analyzed the social, pedagogical and psychological aspects of defining this concept, we believe that it is only whole environment in which inclusion must be realized. Thus, an inclusive environment is a human environment that provides conditions for inclusion of all persons into the spheres of society through the elimination of any barriers, taking into account the diversity of people. The integrated concept gives the opportunity to scientifically substantiate the design of new projects, programs and creation of inclusive social services.
OVJESMO Methodology: Data Collected for Forensic Analysis in Hard Disk Drives Applying the ISO / IEC Standard

The present research designed or a methodology for handling digital evidence contained in storage units, with emphasis on hard drives, applying ISO 27037. The methodology proposed consists of six phases that include: the location, the assurance of the scene of the events, the identification, collection, preservation and analysis of the digital evidence. As conclusions of the study the proposed methodology allows to improve the quality of digital evidence to be admitted in judicial proceedings due to compliance with the principles of relevance, reliability and sufficiency.
Elements of Zuboff’s Surveillance Capitalism

Shoshana Zuboff’s 'The Age of Surveillance Capitalism' (Public Affairs, New York, 2019, 690 pgs) has, familiarised those who are interested in data protection to the term ‘surveillance capitalism’, coined by her in 2015 to describe a new stage of capitalism. It is the most ambitious analysis of the dominant form of post-industrial capitalism that has emerged since the turn of the century, attempting the scale of analysis that Adam Smith, Karl Marx and others applied to industrial capitalism in all its dimensions: moral, economic and political. It is also a call to arms: an exhortation to rise up against and destroy this mutant version of information capitalism before it destroys us, but without attempting a blueprint for how this is to be done, or what should replace it.This brief article neither endorses nor critiques Zuboff’s analysis, but has the more limited aim of explicating what I see as key points of her analysis, and the sometimes unfamiliar concepts and terminology she utilises. I summarise her analysis of the logic of surveillance capitalism in ten points, and her sixteen answers to the question ‘How did they get away with it?’. How do these many disparate contributing elements change the distribution of power in our societies? Zuboff finds that it occurs in three main way: (i) an asymmetric division of learning in society; (ii) application of a radical behaviourist ideology of instrumentarianism; and (iii) a consequent anti-democratic seizure of social power, a ‘coup from above’.My only conclusion in this article is simple: read, don’t ignore, and don’t resign hope to technological determinism. Zuboff is not a fatalist: surveillance capitalism is not a technology, but a market form. Its logic of accumulation is currently dominant, but neither inevitable, nor impervious to destruction. Her analysis of and case against surveillance capitalism, and her arguments and terminology will be with us for some time, and deserve to be.
Locality Sensitive Batch Selection for Triplet Networks

Triplet networks are deep metric learners which learn to optimise a feature space using similarity knowledge gained from training on triplets of data simultaneously. The architecture relies on the triplet loss function to optimise its weights based upon the distance between triplet members. Composition of input triplets therefore directly impacts the quality of the learned representations, meaning that a training scheme which optimises their formation is crucial. However, an exhaustive search for the best triplets is prohibitive unless the search for triplets is confined to smaller training regions or batches. Accordingly, current triplet mining approaches use informed selection applied only to a random minibatch, but the resulting view fails to exploit areas of complexity in the feature space. In this work, we introduce a locality-sensitive batching strategy, which uses the locality of examples to create batches as an alternative to the commonly adopted randomly minibatching. Our results demonstrate this method to offer better performance on three image and two text classification tasks with statistical significance. Importantly most of these gains are incrementally realised with as little as 25% of the training iterations.
Author Difference and Consistency in Writing Styles: Analysis of Academic Papers by Hierarchical Bayesian Models

This study examined the difference between authors and the consistency in each author's writing styles, both were the basis of authorship verification. We analyzed 88 academic papers on psychology written by 22 authors and focused on the rates of “non-content words”, “bigram of parts-of-speech”, “bigram of postpositional particles”, “positioning of commas”, ”words before period”, and “Kanji, Hiragana, and Katakana” in the papers. Next, symmetric Kullback-Leibler divergence distances between the papers were calculated. To examine the author differences in writing styles, using hierarchical Bayesian modeling, we compared the distances between papers written by the same author with those by different authors. Furthermore, to examine author consistency in writing styles, we compared the distances of short durations (under five years) between papers written by the same author with the longer durations (over five years). These results supported the hypothesis that there exist author differences and consistency in writing styles.
Nonparametric Versus Parametric Reasoning Based on Contingency Tables

This paper proposes scenarios of generating two-way and three way contingency tables (CTs). A concept of probability flow parameter (PFP) plays a crucial role in these scenarios. Additionally, measures of untruthfulness of H0 are defined. The power divergence statistics and the |X| statistics are used. This paper is a simple attempt to replace a nonparametric statistical inference from CTs by the parametric one. Maximum likelihood method is applied to estimate PFP and instructions of generating CTs according to scenarios in question are presented. The Monte Carlo method is used to carry out computer simulations.
Evaluate Software Quality by Learning from Historical Data

This paper proposes a method to evaluate software quality by learning from historical data. Quantitative evaluation of software quality is not an easy issue. But historical software can provide us with a lot of software quality information. We present a data acquiring model to guide the data collection from historical software. Then machine learning algorithm that support incremental training is applied to learn the relationship between software quality and software metrics from the data. As a case study, we collected the data of 82 aviation embedded software in an institute, and trained a k-Nearest Neighbors (k-NN) classification model optimized by genetic algorithms for evaluating the software reliability.
SANOM-HOBBIT: simulated annealing-based ontology matching on HOBBIT platform

Abstract Ontology alignment is an important and inescapable problem for the interconnections of two ontologies stating the same concepts. Ontology alignment evaluation initiative (OAEI) has been taken place for more than a decade to monitor and help the progress of the field and to compare systematically existing alignment systems. As of 2018, the evaluation of systems is partly transitioned to the HOBBIT platform. This paper contains the description of our alignment system, simulated annealing-based ontology matching (SANOM), and its adaption into the HOBBIT platform. The outcomes of SANOM on the HOBBIT for several OAEI tracks are reported, and the results are compared with other competing systems in the corresponding tracks.
Source Code Indexing for Component Reuse

The momentum of the open-source community has been constantly increasing, thus leading to numerous tools for writing, maintaining, and sharing source code. Several code search engines have been developed to support development tasks and facilitate reuse either directly or by functioning as information sources for code recommenders. In this chapter, we present AGORA, a code search engine that facilitates reuse in component level, snippet level, and project level. Through its Elasticsearch index, AGORA fosters advanced queries (syntax-aware, regular expressions), while the engine also integrates with popular code hosting repositories and offers a well-designed API. We provide representative examples and a usage scenario to illustrate the functionality of AGORA, and perform a comparative analysis in a code reuse context, which indicates that AGORA provides an efficient alternative to current solutions.
Cloud computing is a cost saver only if...

Cloud computing is one of the most booming trends in the ICT fields. It is promoted as a cost saver as it moves the burden of provisioning capital IT investments to the side of the cloud provider. However, there are no criteria or guidelines to help cloud customers get a full picture of what is required from them before going to the cloud. The aim of this research is to provide a base for a cost estimation model for the cloud customers. This research adopted a 360-degree industry view to study the total cost incurred for cloud customers. Expert interviews from the vendor, customer and consultancy sides were used. Using the TCO approach and TCT, the findings show more costs areas that are not communicated by the vendor in advance. These cost areas include delivery, communication costs, service, management and quality. As well, the prices offered by the vendors are not as straightforward as expected.
A New Nonlinear Method for Calculating the Error of Passive Location

The existing literatures usually adopt the linear method to obtain the location error which is usually evaluated by geometrical dilution of precision (GDOP) in nonlinear passive location systems. However, the linear method is not always suitable in any condition, and the obtained GDOP may deviate the true values of location errors severely when the location system is of severe nonlinearity. Thus, a new nonlinear method for the calculation of location Error based on unscented transformation (UT) is proposed and verified in this paper.
Aircraft Scheduling Problems Based on Genetic Algorithms

The problem of aircraft scheduling is a typical scheduling problem, by abstracting the problem into a typical personnel on duty, the support aircraft is regarded as a person who need to be laid off, requiring that at any time, there are sufficient number of support aircraft in the patrol area to ensure the safe flight of special aircraft, but also to meet the constraints. In order to solve the problem of shift discharge efficiently, an adaptive genetic algorithm is proposed. Especially in the algorithm, the two-layer coding method of individual coding is introduced innovatively, in which the first layer of coding represents the take-off airport serial number and the second layer of coding represents the take-off aircraft serial number. The traditional individual coding method directly composes the take-off time into chromosomes, which makes it difficult to know the number of schedules, increases the complexity of calculation, and the direct use of take-off time coding cannot guarantee that the initialized individual meets the constraints, and too much non-feasible generation will seriously reduce the efficiency of iterative evolution. The problem can be effectively avoided by using the two-layer coding method of individual coding.
Improving the Performance of Video Content Genuineness Using Convolution Neural Network

Video searching in search engines uses metadata information to find the relevant videos according to the search queries. Metadata information mainly comprises the title and description of the video. The major drawback of this approach is that it overlooks whether or not the content of the video is genuine or not. Since the metadata information is provided by the uploader, the person may provide false information about it. Therefore, there is a need of improving the results of video searched. The proposed work classifies the video in different categories and then compares the tag provided to each video with the tags that were extracted from the metadata of the video. The other factor like views count, likes and dislikes, comments is also considered for the ranking of the video searched. It improves the genuineness of the content of the video searched.
RAM Effects on Hadoop Performance

Hadoop is a distributed processing system with parallel processing paradigm. Instead of buying high-end server computers, we can connect a few commodity machines to solve big problems. In this paper, the effect of the type of RAM installed in each computer on the overall Hadoop performance was experimented. Dual channel RAM showed better performance than single channel RAM.
Interval Type 2 Fuzzy Fireworks Algorithm for Clustering

This chapter presents Interval Type 2 Fuzzy Fireworks Algorithm for clustering (IT2FWAC). It is an optimization method for finding the optimal number of clusters based on the centroid features which uses the Fireworks Algorithm (FWA), but with a dynamic adjustment of parameters using an Interval Type 2 Fuzzy Inference System (IT2FIS). Three variations of the IT2FWAC are proposed to find the optimal number of clusters for different datasets: IT2FWAC -I, IT2FWAC -II, and IT2FWAC –III. They are explained in detail.
Virtual Machine Incremental Backup Method for Bitmap Characteristics

Based on the QEMU underlying kernel state environment, this paper proposed a secure and efficient incremental backup method for virtual machines, which was oriented to the format of Qcow2 image files and had the characteristics of bitmap. Aiming at the disaster recovery backup problem of KVM virtual machine, this method supported users to make full and incremental remote and local backup according to backup strategy. Experiments showed that this method could efficiently generate backup files and save storage disk space.