Reviewerly/exHarmony huggingface dataset:
Interactive Fiction Creation in Villanelle: Understanding and Supporting the Author Experience

Villanelle is an interactive fiction authoring tool designed to support autonomous non-player characters, or “char-acter AI.” Character AI is notoriously challenging for interactive fiction authors to develop, especially for authors approaching interactive fiction from a writing rather than programming background. This paper describes a participatory design process in which we assess the author experience with Villanelle and build a new tool iteration to support their needs. The results of our first user study demonstrate the strong potential of Villanelle's incorporation of behavior trees as an easy-to-Learn computational model for character AI, but they also indicate syntax challenges for inexperienced programmers. Consequently, we developed a block-based programming interface for Villanelle and recruited a new set of study participants to evaluate this iteration using the same study instruments. The results indicate improvements in Villanelle's usability and creativity support for inexperienced programmers.
Simulation of Electromagnetic Emanation of Cryptographic ICs: Tools, Methods, Problems

Cryptographic algorithms implemented in hardware have to be resistant against many physical and side-channel analysis (SCA) attacks. Electromagnetic analysis attacks are based on the exploitation of measurable electromagnetic emanation of cryptographic chips. Detailed knowledge about electromagnetic radiation is necessary for the evaluation of the resistance of chips against the attacks during the early design phase, i.e. before the manufacturing and measurements. Therefore simulation of EM radiation of cryptographic chips is required. In this paper we discuss the possibility of simulation of electromagnetic traces for elliptic curve cryptography based on the publications we found regarding simulations of EM emanation of cryptographic integrated circuits.
A CCA-Full-Anonymous Group Signature with Verifiable Controllable Linkability in the Standard Model

Group signatures allow users to anonymously sign messages on behalf of a group. In this paper, we construct a group signature with verifiable controllable linkability in a generic way, where a linking authority (LA) can link two signatures to determine whether they are generated by the same unknown signer or not. Our core building block is a structure-preserving public key encryption with equality test, where public keys, plaintexts, and ciphertexts are all group elements, encryption, decryption, and test algorithms only consist of group and pairing operations. Due to its structure-preserving property, our scheme is easy to combine with non-interactive zero-knowledge proofs on bilinear groups and hence make this combination more efficient than the most CCA-full-anonymous GSS-VCL constructions in the standard model.
Enhancing Decision Trees for Data Stream Mining

Data stream gained obvious attention by research for years.Mining this type of data generates special challenges because of their unusual nature.Data streams flows are continuous, infinite and with unbounded size.Because of its accuracy, decision tree is one of the most common methods in classifying data streams.The aim of classification is to find a set of models that can be used to differentiate and label different classes of objects.The discovered models are used to predict the class membership of objects in a data set.Although many efforts were done to classify the stream data using decision trees, it still needs a special attention to enhance its performance, especially regarding time which is an important factor for data streams.This fast type of data requires the shortest possible processing time.This paper presents VFDT-S1.0 as an extension of VFDT (Very Fast Decision Trees).Bagging and sampling techniques are used for enhancing the algorithm time and maintaining accuracy.The experimental result proves that the proposed modification reduces time of the classification by more than 20% in more than one dataset.Effect on accuracy was less than 1% in some datasets.Time results proved the suitability of the algorithm for handling fast stream mining.
APPLICATION OF THE GRAVITY SEARCH METHOD TO MINIMIZE THE COST OF CONDUCTING A MULTIFACTOR EXPERIMENT

Experimental research methods are increasingly used in industry in the optimization of production processes. Experiments, as a rule, are multifactorial and are connected with optimization of quality of materials, search of optimum conditions of carrying out technological processes, development of the most rational designs of the equipment, etc. The use of experimental planning makes the behavior of the experimenter purposeful and organized, significantly increases productivity and reliability of the results. An important advantage is its versatility, suitability in the vast majority of research areas. When implementing an industrial experiment, the main task is to obtain the maximum amount of useful information about the influence of individual factors of the production process, provided that the minimum number of expensive observations in the shortest period of time. Therefore, it is important to increase the efficiency of experimental research with minimal time and cost. For this purpose, it is expedient to develop systems of automation of experiments which will allow to reduce terms of carrying out experimental researches and to reduce expenses for them. Object of research: processes of optimization of plans of multifactor experiment on cost and time expenses. Subject of research: an optimization method developed on the basis of the gravitational search algorithm, which consists in comparing the force of gravity (cost) of the first row of the planning matrix of the experiment to the next rows of the matrix. In the study of photoelectric transducers of angular displacements, the efficiency and effectiveness of the gravitational search method were analyzed in comparison with previously developed methods: analysis of line permutations, particle swarm, taboo search. The cost of carrying out the experiment plan and the efficiency for solving optimization problems in comparison with the original plan and the implementation of the above methods are shown.
Demands, challenges and solutions for Irandoc to improve and expand activities of academic and research libraries (ARLs)

Purpose Iranian Research Institute for Information Science and Technology (Irandoc) undertaken activities to improve and expand Academic and Research Libraries (ARLs). However, it is necessary to identify demands, challenges and solutions for Irandoc to improve and extend activities of these libraries. Design/methodology/approach In this study, documents of Science &amp; Technology Information System (STIS) commission analyzed in order to investigate the most important steps taken by Irandoc. The results of this part of research used to conduct interviews with administrators and experts in four groups including thirteen ARL managers, four middle and upper level managers at Irandoc, two deputies in Ministry of Science, Research and Technology (MSRT), and four experts in this field. Therefore, the present study was qualitative with content analysis approach. The credibility of the research findings promoted by a peer debriefing and member checking methods. Findings The results showed that strengthening of STIS commission, reinforced Irandoc's authoritative duties in MSRT, establishing a network of ARLs, promoting Irandoc's relationship with ARLs, institutional mapping among organizations in the field of libraries and information, updating of Irandoc's policies and statutes regarding ARLs, completing library statistics and information, standardizing, monitoring and evaluating ARLs, developing Continuous Professional Development (CPD) programs, and future study of ARLs were among the most important operational and executive strategies for improving and expanding ARLs' activities. Originality/value Organizational structure of the relationship among MSRT, Irandoc, ARLs and the types of services and resources ARLs provide in six levels, as well as conceptual model of Irandoc's position in this field presented.
Region Adaptive Single Image Dehazing

Image haze removal is essential in preprocessing for computer vision applications because outdoor images taken in adverse weather conditions such as fog or snow have poor visibility. This problem has been extensively studied in the literature, and the most popular technique is dark channel prior (DCP). However, dark channel prior tends to underestimate transmissions of bright areas or objects, which may cause color distortions during dehazing. This paper proposes a new single-image dehazing method that combines dark channel prior with bright channel prior in order to overcome the limitations of dark channel prior. A patch-based robust atmospheric light estimation was introduced in order to divide image into regions to which the DCP assumption and the BCP assumption are applied. Moreover, region adaptive haze control parameters are introduced in order to suppress the distortions in a flat and bright region and to increase the visibilities in a texture region. The flat and texture regions are expressed as probabilities by using local image entropy. The performance of the proposed method is evaluated by using synthetic and real data sets. Experimental results show that the proposed method outperforms the state-of-the-art image dehazing method both visually and numerically.
Towards a New Epistemology for Technology

This article proposes a new epistemology of technology, based on the concept of interface, taking into account the transformations that technology has caused in society, people and things, creating new connections, interactions and networks, different from traditional science, and therefore from traditional epistemology. The concepts of epistemology, technique, method, opinion and interface are examined. As a branch of philosophy, epistemology is purely theoretical. For this reason the present study does not envisage any practical or empirical application, except as regards the possible idea of modelling and perhaps semantic deduction.
SBTHCT: Segmentation of Brain Tissues using Hybrid Clustering Technique

Because of the complex brain tumour structure, boring bodies and external factors like noise, brain magnet resonance imaging data have difficulty in influencing the tumour and oedema. Apart from the morphological operations, application of an effective hybrid clustering algorithm to segment brain tumors in this project is suggested to ease noise sensitivity and increase segmentation stability. Vienna adaptive filtration is particularly used for denoise and for the removal of non brain tissue morphology, thereby effectively reducing process sensitivity to noise. The most important contributions are: Second, the K-Man++ and Gaussian C-Fuzzy cluster refers to the algorithm of the segment images. This consolidation not only enhances the reliability and sensitivity of the algorithm. The tumor pictures removed are eventually processed after morphological procedure and median filtering in order to achieve correct brain tumor representation. The algorithm proposed was compared to other existing segmentation algorithms. The results show that the proposed algorithm is better accurately, sensitively, specifically and performance retrieval.
FDEA: Face Dataset with Ethnicity Attribute

Face attributes play an important role in face-related applications. However, existing face attributes (such as expression, age, and skin color) are subject to change. The ethnicity attribute is precious due to its invariance over time, but has not been developed well. This is partly because there is no large enough dataset and labeled accurately with ethnicity attribute. This paper proposes a new Face Dataset with Ethnicity Attribute (FDEA), intended for ethnicity recognition benchmark. For this purpose, we first collect an initial face dataset from CelebA and LFWA [], MORPH [], UTKFace [], FairFace [], and the web. The samples extracted from CelebA are not labeled with ethnicity attribute. To this end, we employ nine annotators to label these samples from CelebA, while cleaning the remaining samples manually. Finally, our FDEA contains 157,801 samples and is divided into three classes: Caucasian (54,438), Asian (61,522), and African (41,841). Moreover, we carry out a benchmark experiment by testing eight mainstream backbones on the proposed FDEA. The baseline results of the three-classification accuracy are all over 0.92. FDEA is publicly available at https://github.com/GZHU-DVL/FDEA.
Efficient Cipher Scheme for Hybrid Models with Internal Structure Modification

Data confidentiality (DC) became an essential security service required during data transmission or storing sensitive data over the network in every application.A rise in security attacks causing several security services are required to be implemented for preventing both passive and active attacks.DC security service is most common and based on a complex reversible mathematical calculation recognized as cipher algorithm.The confidentiality of information kept in digital mode, as well as its simple accessing at any moment, has become a significant phenomenon.However, many applications put constrained to develop cipher models that can provide better performance while maintaining minimum security level.In this regard, several hybrid models have been developed and discussed in the literature based on cascading which provides enhanced security but increases system performance due to the running of multiple algorithms.The aim of the proposed scheme is to improve the system performance of the cascaded hybrid model by doing modifications in the internal structure of each algorithm used in the model by reducing the number of rounds required for doing encryption and decryption.Each algorithm used in cascading in hybrid model uses a different key generated using the SHA256 algorithm which helps to maintain the security level of the hybrid cryptography model.Finally, we conduct the performance analysis of the existing hybrid cryptography model with the proposed cipher scheme having a reduced number of rounds.The proposed scheme shows better performance of the hybrid model compared with its corresponding existing model.
On the Development of Foreground Detection under Complex Background

Foreground detection is a prime task in the field of computer vision for targeting the emerging applications like video surveillance, object tracking, action recognition, scene analysis. For moving object detection, it is always desirable to accurately extract the foreground under complex background conditions with less computational overhead. In this work, we propose a multifeature-based moving object detection scheme, where the feature vector for each pixel constitutes gray level intensity value and extended scale-invariant local ternary pattern (E-SILTP) over a local region. Further, to improve the detection accuracy with minimum computational cost, extended Canberra distance is employed for similarity distance between model and current pixel instead of popular Mahalanobis distance and Forstner distance. The experimental results are validated using some standard data sets and shows superior performance than that of the benchmark schemes.
Clustering based on Preferences with K-modes using Categorical Variables

In a globalized market environment with continuously changing conditions and customer requirements, international and cross-border co-operations gain an increasing importance for different actors in order to establish adequate networks. This can highly contribute to the sustainable, more effective operation of market actors in the long-run that can even become more resilient in case of turbulent events. 51 actors of the Central European region with different organisational types were selected and their preferences were asked in a survey about their interests regarding up-to-date technologies in which they would like to work together with other partners and they were ready to broaden the limits of their present knowledge. In this paper, based on the received answers a clustering of the members is performed with the K-modes algorithm and a well-interpretable profiling of the resulted groups is provided, which may serve as a tool to assist more beneficial network formations within the Central European region.
Node2Grids

Graph Convolutional Network (GCN) has been widely used in graph learning tasks. However, GCN-based models (GCNs) are inherently coupled training frameworks repetitively conducting the recursive neighborhood aggregation, which leads to high computational and memory overheads when processing large-scale graphs. To tackle these issues, we present Node2Grids, a cost-efficient uncoupled training framework that leverages the independent mapped data for obtaining the embedding. Instead of directly processing the coupled nodes as GCNs, Node2Grids supports a more efficacious method in practice, mapping the coupled graph data into the independent grid-like data which can be fed into the uncoupled models as Convolutional Neural Network (CNN). This simple but valid strategy significantly saves memory and computational resources while achieving comparable results with the leading GCN-based models. Specifically, in order to support a general and convenient mapping approach, Node2Grids selects the most influential neighborhood with central node fusion information to construct the grid-like data. To further improve the downstream tasks' efficiency, a simple CNN-based neural network is employed to capture the significant information from the mapped grid-like data. Moreover, the grid-level attention mechanism is implemented, which enables implicitly specifying the different weights for the extracted grids of CNN. In addition to the typical transductive and inductive learning tasks, we also verify our framework on million-scale graphs to demonstrate the superiority of cost performance against the state-of-the-art GCN-based approaches. The codes are available on the GitHub link.
Performance Analysis of Deep Neural Maps

Deep neural maps are unsupervised learning and visualization methods that combine autoencoders with self-organizing maps. An autoencoder is a deep artificial neural network that is widely used for dimension reduction and feature extraction in machine learning tasks. The self-organizing map is a neural network for unsupervised learning often used for clustering and the representation of high-dimensional data on a 2D grid. Deep neural maps have shown improvements in performance compared to standalone self-organizing maps when considering clustering tasks. The key idea is that a deep neural map outperforms a standalone self-organizing map in two dimensions: (1) better convergence behavior by removing noisy/superfluous dimensions from the input data and (2) faster training due to the fact that the cluster detection part of the DNM deals with a lower dimensional latent space. Traditionally, only the basic autoencoder has been considered for use in deep neural maps. However, many different kinds of autoencoders exist such as the convolutional and the denoising autoencoder, and here we examine the effects of various autoencoders on the performance of the resulting deep neural maps. We investigate five types of autoencoders as part of our deep neural maps using three different data sets. Overall we show that deep neural maps perform better than standalone self-organizing maps both in terms of improved convergence behavior and faster training. Additionally we show that deep neural maps using the basic autoencoder outperform deep neural maps based on other autoencoders on nonimage data. To our surprise, we found that deep neural maps based on contractive autoencoders outperformed deep neural maps based on convolutional autoencoders on image data.
Do Sarcastic News and Online Comments Make Readers Happier?

Newsvendors cultivate online communities to encourage online users’ comments worldwide. However, the incivility of online commenting is an important issue for both researchers and practitioners. This study focuses on the impact of news with and without online comments on readers’ emotions. An online experiment was designed with news sarcasm (sarcastic vs. neutral) and comments (civil, uncivil, and none) to examine participants’ emotions. Two pretests were administered to determine the target news and the incivility of online comments. Five hundred and twenty-nine subjects took part in the formal online experiment, and the results demonstrated both sarcasm in news and incivility in comments made readers significantly unhappy. The interaction effect between sarcasm in news and incivility in comments was also significant, implying that news might form a “frame” in readers’ minds and influence how they judge comments and emotions. Implications and discussions are also included.
Simplicial complexes from finite projective planes and colored configurations

In the 7-vertex triangulation of the torus, the 14 triangles can be partitioned as T1⊔T2, such that each Ti represents the lines of a copy of the Fano plane PG(2,F2). We generalize this observation by constructing, for each prime power q, a simplicial complex Xq with q2+q+1 vertices and 2(q2+q+1) facets consisting of two copies of PG(2,Fq). Our construction works for any colored k-configuration, defined as a k-configuration whose associated bipartite graph G is connected and has a k-edge coloring χ:E(G)→[k], such that for all v∈V(G), a,b,c∈[k], following edges of colors a,b,c,a,b,c from v brings us back to v. We give one-to-one correspondences between (1) Sidon sets of order 2 and size k+1 in groups with order n, (2) linear codes with radius 1 and index n in the lattice Ak, and (3) colored (k+1)-configurations with n points and n lines. (The correspondence between (1) and (2) is known.) As a result, we suggest possible topological obstructions to the existence of Sidon sets, and in particular, planar difference sets.
Privacy-centric AI-based real-time storage-less edge computing approaches for passenger counting and action classification on public transport vehicles

Public transport operators aspire to provide the mobility solutions of the future. They attempt to ensure the safety and comfort of passengers in public transportation by analyzing their behavior. In this paper, we propose a novel approach for human action classification, falling detection, and passenger counting using a stream of 2D video frames. This could help the transport operators in planning transportation networks, improving the design of their vehicles, and enhancing passengers' flow throughout the day. Also, the falling detection could be used to ensure the passenger's safety onboard. This approach uses the body geometry and the rate of change in human body joints across consecutive frames to classify whether a person is either sitting or standing. In addition, the geometric model uses the previously predicted action for each person until the model detects a certain decrease or increase in the joints position of the human. Previously this was done by training models for classifying actions. These models were dependent on the environment they were used in and the dataset used for model training. On the other hand, upon comparing both our presented geometric approach and the classifier approach in terms of speed and accuracy, our approach superseded as there is no machine learning model used in the geometric approach other than the pose estimation model. Using our approach, we were able to develop a privacy-aware system that does not record anything, upload any user data to a server, or aggregate user information.