Reviewerly/exHarmony huggingface dataset:
Biomorphic Artificial Intelligence: Achievements and Challenges

In this chapter, the modern advances in the field of artificial intelligence development and their biological meaning are considered. Critical cell reconstruction of the brain by the BlueBrain project is criticized. The direct presentation of some methods of artificial intelligence as biological processes is also formulated, which, possibly, will make it possible in the future to improve these methods, or to make standard those modifications of which we will discuss. Using the terms of teaching neural networks, the teacher expresses the complex internal mechanisms of intuition and unconscious information processing. In addition, as a “game with reality” according to the Monte Carlo method, conscious thinking is modeled, including common sense and planning. Based on the conducted analogies, improvements in the artificial intelligence are proposed that will make the complexity of its thinking similar in complexity to human one and lead to the creation of the strong artificial intelligence. These ideas may be useful to develop new approach to the artificial intelligence theory and practice.
Multiple Fusions Approach for Keystroke Dynamics Verification System with Soft Biometrics

Abstract Computer security is a process that controls the entire information system, including network, system and hardware. Important information that must be controlled in a system is the data or information contained in a system. Various methods have been used to ensure that only users with legitimate access to data can use a system. Usernames and passwords have been a common practice by many systems as the first requirement to be fulfilled to access the system, but some systems use the secondary verification for additional confirmation. In this article, Keystroke Dynamics has been used as the user’s second level authentication for the systems that use the keyboard to login into a system. A common problem of system intrusions is that the system fails to identify the user who signs in using the keyboard when the login is correct. There is a possibility that someone else tries to break into the system. To ensure and improve users’ recognition who use the keyboard to enter their logins into the system, Keystroke Dynamics is used as a next-level verification if the login is correct. Soft biometrics is used in the user authentication process using KD method in this study. The soft biometric elements used in this study are culture, gender, educational level (CGPA - Cumulative Grade Point Average) and region of birth (ROB). All of these four soft biometric elements are expected to enhance capabilities in the user authentication process.
Geometric Properties of Adversarial Images

Machine learning models are now widely used in a variety of tasks. However, they are vulnerable to adversarial perturbations. These are slight, intentionally worst-case, modifications to input that change the model's prediction with high confidence, without causing a human eye to spot a difference from real samples. The detection of adversarial samples is an open problem. In this work, we explore a novel method towards adversarial image detection with linear algebra approach. This method is built on a comparison of distances to the centroids for a given point and its neighbors. The method of adversarial examples detection is explained theoretically, and the numerical experiments are done to illustrate the approach.
On the use of the policy gradient and Hessian in inverse reinforcement learning

Reinforcement Learning (RL) is an effective approach to solve sequential decision making problems when the environment is equipped with a reward function to evaluate the agent’s actions. However, there are several domains in which a reward function is not available and difficult to estimate. When samples of expert agents are available, Inverse Reinforcement Learning (IRL) allows recovering a reward function that explains the demonstrated behavior. Most of the classic IRL methods, in addition to expert’s demonstrations, require sampling the environment to evaluate each reward function, that, in turn, is built starting from a set of engineered features. This paper is about a novel model-free IRL approach that does not require to specify a function space where to search for the expert’s reward function. Leveraging on the fact that the policy gradient needs to be zero for an optimal policy, the algorithm generates an approximation space for the reward function, in which a reward is singled out employing a second-order criterion. After introducing our approach for finite domains, we extend it to continuous ones. The empirical results, on both finite and continuous domains, show that the reward function recovered by our algorithm allows learning policies that outperform those obtained with the true reward function, in terms of learning speed.
Recommendations as Graph Explorations

We argue that most recommendation approaches can be abstracted as a graph exploration problem. In particular, we describe a graph-theoretic framework with two primary parts: (a) a recommendation graph, modeling all the elements of an (application) domain from a recommendation perspective, including the subjects and objects of recommendations as well as the relationships between them; (b) a set of path operations, inferring new edges, i.e., implicit or unknown relationships, by traversing and combining paths on the graph. The resulting path algebra model provides an abstraction and a common foundation that is beneficial to three aspects of recommendations: (a) expressive power - expression and subsequent use of several significantly different, existing but also novel recommendation approaches is reduced to parameterizing a unique model; (b) usability - by capturing part of the recommendation mechanisms in the underlying path algebra semantics, specification of recommendation approaches becomes easier and less tedious; (c) processing speed - implementing recommender systems on top of graph engines opens up the door for several optimizations that speed up execution. We demonstrate the above benefits by expressing several categories of recommendation approaches in the path algebra model and benchmarking some of them in a recommender system implemented on top of Neo4J, a widely used graph system.
Mesh-based scale-invariant feature transform-like method for three-dimensional face recognition under expressions and missing data

Biometric identification from three-dimensional (3-D) face surface characteristics has become popular. Traditional face recognition methods have achieved very high recognition accuracy under controlled environments. However, 3-D face recognition technology still faces a great challenge for facial expressions and missing data caused by pose variations or occlusions. The mesh-based scale-invariant feature transform (SIFT)-like method is one of the most effective methods to deal with these issues. However, eliminating edge response has not been considered in this kind of method, which affects recognition performance and computation cost. To address this problem, a mesh edge point filter is proposed to remove edge keypoints. For feature description, a local descriptor that is composed of the histogram of geometric shapes and the histogram of shape index is designed to describe local shapes of keypoints. A complete mesh-based SIFT-like framework for 3-D face recognition is also presented. Results from experiments based on the Bosphorus and face recognition grand challenge v2.0 databases show that the proposed method is effective for expression variations, occlusions, and pose variations. Compared with several state-of-the-art methods, the proposed method has obtained a great compromise between computational complexity and recognition performance.
DRCS-SR: Deep Robust Compressed Sensing for Single Image Super-Resolution

Compressed sensing (CS) represents an efficient framework to simultaneously acquire and compress images/signals while reducing acquisition time and memory requirements to process or transmit them. Specifically, CS is able to recover an image from a random measurements. Recently, deep neural networks (DNNs) are exploited not only to acquire and compress but also for recovering signals/images from a highly incomplete set of measurements. Super-resolution (SR) algorithms attempt to generate a single high resolution (HR) image from one or more low resolution (LR) images of the same scene. Despite the success of the existing SR networks to recover HR images with better visual quality, there are still some challenges that need to be addressed. Specifically, for many practical applications, the original images may be affected by various transformation effects including rotation, scaling, and translation. Moreover, in real-time transmissions, image compression is carried out first, followed by acquisition time reduction. To address this problem, we propose a novel robust deep CS framework that is able to mitigate the geometric transformation and recover HR images. Specifically, the proposed framework is able to perform two tasks. First, it is able to compress the transformed image with the help of an optimized generated measurement matrix. Second, the proposed framework is able not only to recover the original image from the compressed version but also to mitigate the transformation effects. The simulation results reported in this article show that the proposed framework is able to achieve high level of robustness against different geometric transformations in terms of peak signal-to-noise-ratio (PSNR) and similar structure index measurements (SSIM). For the convenience of dissemination, we make our source codes available at GitHub <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</sup> .
Keyword Search over Federated RDF Systems

In this paper, we study the problem of keyword search over federated RDF systems. We utilize the full-text search interfaces provided by SPARQL endpoints and the authoritative documents of URIs to map keywords to the classes, and generates SPARQL queries by exploring the schema graph. Then, we send the generated queries to the SPARQL endpoints and evaluate these queries. Experiments show that our approaches are effective and efficient.
A Discourse-Informed Approach for Cost-Effective Extractive Summarization

This paper presents an empirical study that harnesses the benefits of Positional Language Models (PLMs) as key of an effective methodology for understanding the gist of a discursive text via extractive summarization. We introduce an unsupervised, adaptive, and cost-efficient approach that integrates semantic information in the process. Texts are linguistically analyzed, and then semantic information, specifically synsets and named entities, are integrated into the PLM, enabling the understanding of text, in line with its discursive structure. The proposed unsupervised approach is tested for different summarization tasks within standard benchmarks. The results obtained are very competitive with respect to the state of the art, thus proving the effectiveness of this approach, which requires neither training data nor high-performance computing resources.
Research on Application of Internet of Things Information Security Using Blockchain Technology

The blockchain is based on a distributed security network. It has a unique data trust protection mechanism and strong data security protection and tamper protection capabilities. It meets the traditional physical security network's physical decentralized data trust and other network data trust for other information security application technologies. Technical requirements also have natural technological advantages in the application of information security technologies such as the mobile Internet of Things cloud. Therefore, this article starts with several related basic conceptual issues related to blockchain security technology, focusing on research and analysis of its technical advantages in the development of IoT security applications and the widespread application and commercial promotion of blockchain security technology in the future. Refer to decision recommendations.
Modelling Distributed Fault-Tolerant High Availability Storage Cluster Based on Block-Chain Concepts for Tracking Scientific-Research Progress

The aim of the paper is to outline the prerequisites for the introduction of a model for integrating an intended interface platform for storing information regarding results and background information from scientific research, over a blockchain network. The state of the art will describe the actual concept of the technology and the main article will introduce the in-depth architecture of a software prototype GenBankChain, a distributed blockchain network for storing results of experiments and research data. The project is based over a research and the development of a concept for utilizing the existing infrastructure of the Institute for Plant Genetic Resources "K. Malkov", Sadovo, but the described model is based over existing concepts from the Block-chain research community and the resulting architecture can be used for building a class of applications based over this concept. The main purpose of the article is to address a practical approach for implementing a theoretical concept and to propose an implementation solution for a real-world problem.
Intuitionistic Fuzziness and Modality

Ever since the first publication over intuitionistic fuzzinsess, the two standard modal operators "necessity" ( □ ) and "possibility" (◇) are introduced over intuitionistic fuzzy sets. In the years, these two operators are object of a lot of extensions and modifications. In the present paper, a new direction of tow modifications of the two standard modal operators is discussed. We introduce four new operators and study their properties.
Temporal Logic with Recursion.

We introduce extensions of the standard temporal logics CTL and LTL with a recursion operator that takes propositional arguments. Unlike other proposals for modal fixpoint logics of high expressive power, we obtain logics that retain some of the appealing pragmatic advantages of CTL and LTL, yet have expressive power beyond that of the modal μ-calculus or MSO. We advocate these logics by showing how the recursion operator can be used to express interesting non-regular properties. We also study decidability and complexity issues of the standard decision problems.
A Supervised Multiclass Classifier as an Autocoding System for the Family Income and Expenditure Survey

Coding is a task that classifies an object to a corresponding code (or class). This is often required for survey data processing in the field of official statistics. Since the governmental survey has large number of objects and codes (or classes), and the release time of the survey result has to be strictly observed, the autocoding system is a key solution for improving data processing. For this autocoding system, mainly two types of methodologies have been developed. One is the use of the supervised classification methods including machine learning techniques and the other is rule-based methods. For the supervised classification method, we have developed a supervised multiclass classifier using machine learning which has the advantages of simplicity and practical calculation time. In this paper, we present an application of the proposed method for the Family Income and Expenditure Survey in Japan with a comparison of the accuracy and the efficiency of the rule-based method.
Navigation System Research and Design Based on Intelligent Image Classification Algorithm of Extreme Learning Machine

The Navigation System discussed in the paper can recognize ripe fruit automatically for agricultural harvesting machinery by machine vision technology, and then achieve the purpose of autonomous positioning and navigation through autonomous path planning. In order to achieve this process, agricultural machinery positioning and navigation system must have high precision and fast image processing algorithm. Based on this, this paper introduces the extreme learning machine algorithm into the agricultural machinery navigation system, combined with BP neural network algorithm, through the determination of the image coordinates of ripe fruit and fruit tree, to achieve the rapid navigation of agricultural machinery operation. In order to verify the feasibility of the scheme, the computational efficiency and precision of the algorithm are counted. The experimental results show that the picking efficiency has been improved obviously and the picking accuracy has been improved by using the extreme learning machine, which can meet the design requirements of modern agricultural machinery and equipment.
Who talks to whom: an evaluation of a call log visualization

Abstract Adding temporal information to social network visualizations is still a challenging task despite previous research efforts. Visualizing call logs on an event-based level can show various attributes of a connection. The dimension time is of great interest to analysts as it offers insights into trends and patterns such as changing relationships between different actors or economic opportunities for businesses. Yet current approaches suffer from limitations that can be improved with the visualization design presented in this work. Our presented visualization was developed considering aesthetic criteria and characteristics of adjacency matrices and node-link diagrams. A heuristic evaluation according to these criteria was conducted. In a formative evaluation process, an artificial dataset was specifically created to examine dynamic social networks. A qualitative user study with observation and think-aloud protocols was conducted and analyzed with regard to the user’s strategies, limitations of the visualization and potential additional features. The visualization appears to be suitable for all of the evaluated network tasks; however, path-related tasks were more challenging than other tasks. Graphical abstract
Research and Design of Personalized Recommendation System Model for Course Learning Based on Deep Learning in Grid Environment

With the development of online courses and MOOCs, the traditional course learning recommendation platform can no longer meets the individual needs of learners at different levels. After careful analysis of the current recommendation methods, the grid environment is proposed based on the grid environment. A deep learning-based curriculum learning personalized recommendation system model, which collects basic data, professional basic data, and curriculum basic data for a large number of students, establishes a personalized mathematical model for curriculum recommendation, and trains learning models and student data. According to the results, the training parameters are continuously adjusted to accurately recommend the course learning resources for students, thereby reducing resource processing and retrieval time, and improving students’ efficiency in course learning.
Research on the Knowledge and Ability dual-driven Teaching Model for the Course of Compilers Principles

In this paper, the key and difficult problems in the teaching process of the course of Compilers Principles are studied, as well as the theory and practice teaching models are discussed. Then a Knowledge and Ability dual-driven Teaching Model for the course of Compilers Principles is proposed, that is to let students understand and master the compilers theory and technique through the course teaching; in addition, it takes compilation teaching as an approach to reveal and analyze the problem solving rules of "problem--formalized description -- computerization", and systematically trains and improves students' ability to use language and language processors to solve problems and even to solve complex engineering problems. We have tried and popularized this teaching model in the multiple rounds of teaching practices, and embodied this teaching idea in the study and development of the course standard for the course of Compilers Principles.
Crossroads of seeing: about layers in painting and superimposition in Augmented Reality

Abstract Augmented Reality (AR) is itself a technology in which two ways of seeing are crossed. Our field of vision is thereby superimposed with digital information and images. But before this, the real environment is already perceived by machine seeing, it is redoubled by a 3D-model, scanned, located and linked. In this brief investigation, I will face the way of seeing in AR with traditional procedures, like ‘trompe-l'œil’ and the so-called ‘velo’, to distinguish between what remains classic and what has changed. It is important to examine this as layering, because it is a very thin stack of techniques, technology, materials and media, we seek to watch through. Subsequently, I shall analyze a painting of the contemporary artist Laura Owens in which both ways are crossed, the traditional one and the one concerning AR.
Motion Control of a Mobile Robot Based on a Chaotic Iterative Map

In this work, the problem of designing an efficient chaotic path planning generator for exploring an area is studied. To achieve so, a Chaotic Random Bit Generator (CRBG) is first constructed. The randomness of the generator was tested and verified by the FIPS statistical package. Then, the proposed CRBG is utilized to produce the chaotic motion commands for an autonomous mobile robot moving in a 2D area, in four or eight different directions. A series of simulations have been performed in Matlab, in order to evaluate the efficiency of the proposed method and the results show that the motion in eight directions gives better results with respect to the coverage rate while also the multiple visits to the same cells have been decreased significantly.
32‐2: Improvement in Directional Cubic Convolution Image Interpolation

Compared with traditional methods, edge‐based interpolation algorithm can generate high‐resolution images with rare edge artifacts. However complex textured regions produce morphological distortion at the same time, resulting in a HD image looks unnatural. In this paper, we analyze the disadvantages of the best performing edge‐based interpolation (DCCI) algorithm and propose a novel adaptive edge‐based interpolation algorithm, which use both 2‐D texture entropy and directional gradient to eliminate the distortion. It turns out that this method can effectively improve the image quality.
Trust and autonomy for regulating the users’ acceptance of IoT technologies

The success of IoT technologies is undeniable. They are entering more and more in our lives, carrying out increasingly complex tasks. However, there are still a few problems we need to face and solve. For instance, it is not given that the users will be prepared to afford all the automation that IoT devices will offer or that it will be compatible with the users’ cognitive attitudes and its actual and real goals. Within this work, we start analyzing which reasons undermine the acceptance of IoT systems and then we propose a possible solution, tanking into account not just the user-device interaction, but also how this affects the device-device interaction. Since the complexity of the tasks the user asks may require the cooperation of some devices to be realized, the regulation of this relationship represents a necessary step for this technology. The first contribution of this work is the level characterization of the autonomy a user can grant to an IoT device. The second contribution is a theoretical model to deal with users and to stimulate users’ acceptance, taking also into account a possible, collaborative organizational structure, to manage the creation of groups and the partners’ selection process.