Reviewerly/exHarmony huggingface dataset:
Exploring human genome feature for improving genetic algorithm performance

Genetic algorithm (i.e., GA) has longtermly obtained an extensive recognition for solving the optimization problem. Its pipelines process, which involves several operations, has been applied in many NP-hard problems, including the transportation network design problem (i.e., TNDP). As part of evolutionary computation methods, GA is inspired by Darwinian evolution, which is relied on the genetic operators (i.e., recombination, and mutation). On other side, the considerably achievement has been acquired by the genome researches, which offers an opportunity to deeply explore the recombination and mutation processes. This paper then presents variants of GA, which are inspired by the recent genome evidence of genetic operators. This exploration expectantly extends the benefit of evolution-based algorithm, which has been shown by the previous finding of GA. For examining the performance of proposed GA, the numerical experiment is involved for solving the TNDP. The performance comparisons show that the variation of crossover rate within a certain group of population provide better result than the standard GA.
‘Back to the future’? From engravings to 3D printing – implications for UK copyright law

This chapter explores the protection of 3D models and 3D CAD files from the perspective of copyright law and considers the copyright issues associated with 3D scanning. The chapter begins with a brief overview of the protection of 3D models as artistic works before moving to a detailed consideration of the protection of CAD files from the perspective of copyright law. In doing so, the chapter explores the current debate on whether it is possible to distinguish between instructions, aesthetics and functions in copyright law, also known as ‘separability’. The EU Software Directive as well as UK cases such as Abraham Moon & Sons Ltd v Andrew Thornber and Others and the US case of Star Atheltica LLC v Varsity Brands Inc et al are drawn upon in seeking an answer. Having done so, the final section of the chapter considers the implications for copyright law as a result of 3D scanning. Arguing that the future potential of 3D scanning will only grow, the chapter ends with suggestions for the future, particularly from the perspective of enforcement.
MIXTURES OF REGRESSION CURVE MODELS FOR ARABIC CHARACTER RECOGNITION

In this paper, we demonstrate how regression curves can be used to recognize 2D non-rigid handwritten shapes.Each shape is represented by a set of non-overlapping uniformly distributed landmarks.The underlying models utilize 2nd order of polynomials to model shapes within a training set.To estimate the regression models, we need to extract the required coefficients which describe the variations for a set of shape class.Hence, a least square method is used to estimate such modes.We proceed then, by training these coefficients using the apparatus Expectation Maximization algorithm.Recognition is carried out by finding the least error landmarks displacement with respect to the model curves.Handwritten isolated Arabic characters are used to evaluate our approach.
Localization of Copy-Move Forgery in Digital Images through Differential Excitation Texture Features

In this digital age, the extensive usage of digital devices and availability of open source image editing software leads to the easy manipulation of digital images.Copy-Move Forgery (CMF) is a guileless and widespread approach to hide or duplicate a certain portion of the image without leaving visual clues.Thus, it is difficult to detect the copy-move forgeries and there is a need for forensic experts to rely on an effective approach for CMF detection for forensic analysis.Hence, an efficient passive block based approach to detect and localize CMF is proposed.In the proposed method, texture features are extracted from Differential Excitation Component on the overlapping blocks of the image.Similarity measure is performed for block matching and mapping is done to identify the duplicated regions.Evaluation is performed qualitatively and quantitatively on CoMoFoD dataset; true detection rate of 0.99 and false detection rate of 0.08 has been achieved.Evaluation validates that proposed method out performs the other existing methods with regard to detection accuracy.
A Data Representation Approach to Support Imbalanced Data Classification Based on TWSVM

Imbalance classification requires to represent the input data adequately to avoid biased results towards the class with the greater samples number. Here, we introduce an enhanced version of the famous twin support vector machine (TWSVM) classifier by incorporating an extended dual formulation of its quadratic programming optimization. Besides a centered kernel alignment (CKA)-based representation is used to avoid data overlapping. In particular, our approach, termed enhanced TWSVM (ETWSVM), allows representing the input samples in a high dimensional space (possibly infinite) after reformulation of the TWSVM dual form. Obtained results for binary classification demonstrate that our ETWSVM can reveal relevant data structures diminishing overlapping and biased classification results under imbalance scenarios. Moreover, ETWSVM notably adopts the lowest computational cost for training in comparison to state-of-the-art methods.
Pedestrian Similarity Extraction to Improve People Counting Accuracy

Current state-of-the-art single shot object detection pipelines, composed by an object detector such as Yolo, generate multiple detections for each object, requiring a post-processing Non-Maxima Suppression (NMS) algorithm to remove redundant detections. However, this pipeline struggles to achieve high accuracy, particularly in object counting applications, due to a trade-off between precision and recall rates. A higher NMS threshold results in fewer detections suppressed and, consequently, in a higher recall rate, as well as lower precision and accuracy. In this paper, we have explored a new pedestrian detection pipeline which is more flexible, able to adapt to different scenarios and with improved precision and accuracy. A higher NMS threshold is used to retain all true detections and achieve a high recall rate for different scenarios, and a Pedestrian Similarity Extraction (PSE) algorithm is used to remove redundant detentions, consequently improving counting accuracy. The PSE algorithm significantly reduces the detection accuracy volatility and its dependency on NMS thresholds, improving the mean detection accuracy for different input datasets.
Quiz Training Program through iSpring Suite 8.0 to Junior High School Teachers Tuban

The purpose of this paper is for teachers to create their own teaching and evaluation materials using iSpring. This ability is very necessary considering that students are now a generation that is already familiar with the internet since they were born. While the teachers are a part of the digital immigrant generation, which was born before the internet developed rapidly. The method used in the form of training and mentoring was carried out by two English Education Lecturers and a Lecturer from the Faculty of Engineering which was attended by 36 junior high school teachers from both the city of Tuban and from other sub-districts around Tuban and held for four meetings. To find out the level of satisfaction of the training, participants were given a quiz and interview. The results of this training showed that most participants felt very enthusiastic, and could make their own material and evaluation in teaching English. After that they also managed to publish it in a format compatible with Android. This is very important because it is a combination of CALL (Computer Assisted Language Learning) and MALL (Mobile Assisted Language Learning), which means that this project uses computer devices and the results can be applied in the form of mobile (HP).
A Heuristic Transformation in Discriminative Dictionary Learning for Person Re-Identification

Person re-identification (ReID) is an important technology for target association in surveillance applications. Recently, sparse representation-based classification has been applied to person ReID with the advantage of discriminative feature extraction and has produced excellent results. The dictionary learning (DL) method is vital to the sparse representation, and the discriminative power of the learned dictionary determines the performance of ReID. Unlike previous approaches that only added constraints in DL, we propose a discriminative dictionary learning model (DDLM) that learns the discriminative dictionary by transforming the dictionary representation space in the training process. We determine the statistical distribution from the training data and divide the data into two categories according to the contribution for sparse representation: the high-contribution data and low-contribution data. Then, we extend the information space that contains the most high-contribution data and shrink the remaining parts. As the representation space of the dictionary is transformed, the solving process is modified accordingly. The experiments on the benchmark datasets (CAVIAR4REID, ETHZ, and i-LIDS) demonstrate that the proposed model outperforms the state-of-the-art approaches.
Heterogeneous System GI/GI(n)∕∞ with Random Customers Capacities

In the paper, we consider a queuing system with n types of customers. We assume that each customer arrives at the queue according to a renewal process and takes a random resource amount, independent of their service time. We write Kolmogorov integro-differential equation, which, in general, cannot be analytically solved. Hence, we look for the solution under the condition of infinitely growing a service time, and we obtain multi-dimensional asymptotic approximations. We show that the n-dimensional probability distribution of the total resource amounts is asymptotically Gaussian, and we look at its accuracy via Kolmogorov distance.
Recognizing Textual Entailment based on Deep Learning Approach

Textual entailment (TE) is a relation that holds between two pieces of text where one reading the first piece can conclude that the second is most likely true.This paper proposes new model based on deep learning approach to recognize textual entailment.The deep learning approach is based on syntactic structure [Holder-Relation -Target] [1] which contains all lexical, syntactic and semantic information about the input text.The proposed model constructs deep leaning neural networks, which aims at building deep and complex encoder to transform a sentence into encoded vectors.The experimental results demonstrate that proposed technique is effective to solve the problem of textual entailment recognition
The effects of information quality of Facebook and perceived ease of use and usefulness on users’ satisfaction and use intention

This study investigates the relationships among information quality of Facebook, perceived usefulness, perceived ease of use, users" satisfaction, and use intention of students who apply for airline service major using structural equation modeling approach. To achieve this objective, an online survey was conducted to students who had experience to apply airline service major of college or university. The results of this study reveal that timeliness, playfulness, and simplicity had positive effects on users‘ satisfaction. Results also show perceived ease of use influenced a positive impact on perceived usefulness which, in turn, influenced users’ satisfaction. Furthermore, results indicate that users‘ satisfaction had a positive effect on use intention. The findings of this study provide implications to not only students who apply for airline service major, but also college or university administrators who try to attract students in the major.
Siamese Discourse Structure Recursive Neural Network for Semantic Representation

Finding a highly informative, low-dimensional representation for texts, specifically long texts, is one of the main challenges for efficient information storage and retrieval. This representation should capture the semantic and syntactic information of the text while retaining relevance for large-scale similarity search. We propose the utilization of Rhetorical Structure Theory (RST) to consider text structure in the representation. In addition, to embed document relevance in distributed representation, we use a Siamese neural network to jointly learn document representations. Our Siamese network consists of two sub-networks of recursive neural networks built over the RST tree. We examine our approach on two datasets, a subset of Reuters's corpus and BBC news dataset. Our model outperforms latent Dirichlet allocation document modeling on both datasets. Our method also outperforms latent semantic analysis document representation has been beaten by our method by 3% and 6% on the BBC and Reuters datasets, respectively. The proposed method also outperforms TF _ IDF representations by 11 % and 15% and the word embedding averaging representation by 6% and 7% in precision at k retrieved documents on Reuters and BBC datasets, respectively.
Correction to: Multilayer Partially Homomorphic Encryption Text Steganography (MLPHE-TS): A Zero Steganography Approach

The full name of the first author’s affiliation was not shown in the original publication. It is correctly shown here.
When Expert Opinion Evidence Goes Wrong

This paper combines three computational argumentation systems to model the sequence of argumentation in a famous murder trial and the appeal procedure that followed. The paper shows how the argumentation scheme for argument from expert opinion can be built into a testing procedure whereby an argument graph is used to interpret, analyze and evaluate evidence-based natural language argumentation of the kind found in a trial. It is shown how a computational argumentation system can do this by combining argument schemes with argumentation graphs. Frighteningly, it is also shown by this example that when there are potentially confusing conflicting arguments from expert opinion, a jury can only too easily accept a conclusion prematurely before considering critical questions that need to be asked.
Transfer and Extraction of the Style of Handwritten Letters using Deep Learning

How can we learn, transfer and extract handwriting styles using deep neural networks? This paper explores these questions using a deep conditioned autoencoder on the IRON-OFF handwriting data-set. We perform three experiments that systematically explore the quality of our style extraction procedure. First, We compare our model to handwriting benchmarks using multidimensional performance metrics. Second, we explore the quality of style transfer, i.e. how the model performs on new, unseen writers. In both experiments, we improve the metrics of state of the art methods by a large margin. Lastly, we analyze the latent space of our model, and we show that it separates consistently writing styles.
Assured Delegation on Data Storage and Computation via Blockchain System

With the widespread of cloud computing, the delegation of storage and computing is becoming a popular trend. Concerns on data integrity, security, user privacy as well as the correctness of execution are highlighted due to the untrusted remote data manipulation. Most of existing proposals solve the integrity checking and verifiable computation problems by challenge-response model, but are lack of scalability and reusability. Via blockchain, we achieve efficient and transparent public verifiable delegation for both storage and computing. Meanwhile, the smart contract provides API for request handling and secure data query. The security and privacy issues of data opening are settled by applying cryptographic algorithms all through the delegations. Additionally, any access to the outsourced data requires the owner's authentication, so that the dat transference and utilization are under control.
The ontology of rock music: Recordings, performances and the synthetic view

This paper discusses the state-of-the-art dispute over the ontological question of rock music: what is the work of art, or the central work-kind, of rock music, if any? And, is the work of rock music ontologically distinct from the work of classical music, which is the only musical tradition whose ontology is vastly studied? First, I distinguish between two levels of inquiry in musical ontology: the fundamental level and the higher-order level, in which comparative ontology - the project in which someone engages by considering that there is ontological variety among works of distinct musical traditions - falls. After addressing two general questions about rock music, I turn to Theodore Gracyk?s ontological account of rock music, according to which the primary focus of critical attention in rock music are recordings, or recorded tracks. This view has the consequence that ?recordings? is a fundamental concept of philosophy of music, necessary for us to understand rock music. Stephen Davies objected that Gracyk?s account fails to assign appropriate value to a valuable practice with which rock audiences are committed, live performance, and argued that the works of rock music are of the ontological kind for studio performance. Finally, Andrew Kania synthetized both views: rock recorded tracks are at the centre of rock as an art form, thus being the rock works.
Fine-Gained Location Recommendation Based on User Textual Reviews in LBSNs

As user-generated reviews from Location Based Social Networks (LBSNs) are becoming increasingly pervasive, exploiting sentiment analysis based on user’s textual reviews for location recommendation has become a popular approach due to its explainable property and high prediction accuracy. However, the inherent limitations of existing methods make it difficult to discover what aspects that a user cared most about when visiting a location. In this study, we propose a fine-gained location recommendation model by jointly exploiting user’s textual reviews and ratings from LBSNs, which considers not only the direct rating that a user would score on a location but also the compatibility between user’s interested features and location’s high-quality features. Specifically, the proposed recommendation model consists of three steps: (1) extracting feature-sentiment pairs from user’s textual reviews; (2) learning to rank features using an Elo-based scheme; (3) making fine-gained location recommendation. Experiment results demonstrate that our proposed model can improve the recommendation performance compared with several state-of-the-art methods.
Design Guidelines for Feature Model Construction: Exploring the Relationship between Feature Model Structure and Structural Complexity

Software Product Lines (SPLs) play an important role in the context of large-scale production of software families. Feature models (FMs) are essential in SPLs by representing all the commonalities and variabilities in a product line. Currently, several tools support automated analysis of FMs, such as checking the consistency of FMs and counting the valid configurations of a product line. Although these tools greatly reduce the complexity of FM analysis, FM design is often performed manually, thus being prone to bad design choices in the domain analysis phase. This paper reports on our work to improve FM qualities from the exploration of the relationship between FM structure and structural complexity. By performing two common operations (i.e., consistency checking and counting valid configurations on FMs with different sizes and structures), we collected the time that an automated tool needs to finish these operations. Then, we applied data mining approaches to explore the relationship between FM structure and structural complexity. In addition, we provide guidelines for designing FMs based on our observations.
Some problems of realization of the scientific capacity of the higher school in the context of the relationship of education and science

The article discusses the driving forces and stages of relationship science, education and industry, revealed features of civilization knowledge and education, problems of realization of the scientific capacity of the higher school and optimization factors of scientific and educational activities in higher education under the conditions of modern Russia.
Traditional and digital genres – problems and prospects of research

The article focuses on the theoretical issues of studying the genre system of the language.The problems of choosing a term for typical forms of literary works (genres); a term for a crop of speech acts (speech genres) as well as terms for texts in Internetcommunication (digital genres and Internet-genres) are analyzed.The issues of the development of digital genres classifications, based on different criteria, are considered.The most common of these criteria are the concept of the author and the addressee, the theme and purpose of the statement, the place and time of communication, the linguistic representation of the genre, etc.One of the most convenient classifications of digital genres is the one based on the criterion of technology influence.
A Pragmatic Analysis of the Language of Facebook Posts and Status Updates

This study investigates the communicative functions of Facebook posts and status updates. It depicts the formal features and the functions of digital discourse. On account of that, posts and status updates are analyzed through Searle’s Speech Act framework, together with Grice's theory of cooperative maxims and implicature. The sample data comprises data from males and females in the age between 21-35 years. Ten statuses and posts with a number of randomly chosen comments are chosen for analysis and then, the data are categorized according to their semantic domain.The results of the analysis show that digital discourse has some characteristics that are classified into formal features, which are related to the form of the post, and functional manifestations, which are associated with the function of the post. Besides, it is observed that people tend to use indirect speech acts more often than using direct ones. Some of the functions of digital discourse that are derived from the data include: preaching, prayers, supplications, spreading consciousness among people, greeting, revealing constructed online identities and addressing target audience. As for the formal features, mingling verbal and pictorial elements is the most dominant and explicit digital formal feature that is extracted. Furthermore, there are some pragmatic features related to meaning, such as the various inferences that can be drawn from a very few amount of given information.  .