Reviewerly/exHarmony huggingface dataset:
Patient Identification using Facial Recognition

The medical history of patients and previous meetings to a doctor have been traditionally recorded and maintained using paper and pen. This research is done with an aim to throw some light on the field of Computer Vision that can potentially revolutionise the way that prescriptions are given to patients since ages. In this paper, an implementable idea of using the advanced Computer Vision Technology to eliminate printed prescriptions and physical components like RFID, Record Files has been proposed. The results of having a Universal Medical Face Identification for every patient have been properly showcased that take several factors into consideration like reducing the inefficiency, amount of time taken at reception and efforts of medical staff in recognising a patient and drawing out subtleties of the patient's medical history, previous visits to the specialist, and prescriptions. A database linked to the patient's face image can be deployed on a secure platform that can be updated from time to time and will be universally considered as the basis of the identity of patients available to the doctors at all certified medical centres for studying and taking immediate action. Facial recognition can be utilized in hospitals for staff and patient tracking efficiently and practically faster than the current record based approach. Finally, the comparison of wait time and technical challenges of current methods, implementation and privacy are highlighted.
The Dynamic Signature Verification Using population-Based Vertical Partitioning

The dynamic signature is an attribute used in behavioral biometrics for verifying the identity of an individual. This attribute, apart from the shape of the signature, also contains information about the dynamics of the signing process described by the signals which tend to change over time. It is possible to process those signals in order to obtain descriptors of the signature characteristic of an individual user. One of the methods used in order to determine such descriptors is based on signals partitioning. In this paper, we propose a new method using a population-based algorithm for determining vertical partitions of the signature and its descriptors. Our method uses a Differential Evolution algorithm for signals partitioning and an authorial one-class fuzzy classifier for verifying the effectiveness of this process. In the simulations, we use a commercial BioSecure DS2 dynamic signature database.
Comparison of Perfect and Quasi Werner States

In this paper, we investigate comparatively the behaviors of quantum discord and concurrence for Werner states based on two bipartite entangled squeezed states. The maximally entangled squeezed states are regarded as a perfect-Werner states, while the non-maximally entangled squeezed states are called quasi-Werner states. We show that, the behavior of the quantum correlations keep unchanged for these two types of states. However, the quantum correlations amount presents in perfect-Werner states is very higher than that presents in quasi-Werner ones. Furthermore, we show that for large values of squeezed parameter the quasi-Werner states approaches the perfect-Werner states qualitatively and quantitatively.
An Exploration of Methods for Identifying Conversation Participants

In this paper, we investigate the use of a number of methods for identifying the participants of conversations. These methods include: Twitchell's Speech Acts Profiling, five well-known authorship attribution methods, a technique that has been used to identify co-authors of collaborative written text, a neural network and a deep neural network. Our results suggest that for written text, most of the methods perform well. However, they perform poorly on transcripts of spoken dialogue, such as the Friends sitcom transcripts and chat text. Our results show that as the number of conversation turns increases, so does the accuracy of identifying conversation participants. However, this comes at a cost of a reduced number of conversations within the dataset.
On the Complexity of Attacking Commercial Authentication Products

In this paper we discuss the difficulties of mounting successful attacks against crypto implementations if essential information is missing. We start with a detailed description of our attack against our own design, to highlight which information is needed to increase the success of an attack, i.e. we use it as a blueprint to the following attack against commercially available crypto chips. We would like to stress that our attack against our own design is very similar to what happens during certification e.g. according to the Common Criteria Standard as in those cases the manufacturer needs to provide detailed information. If attacking commercial designs without signing NDAs, we were forced to intensively search the Internet for information about the designs. We were able to reveal information on the processing sequence during the authentication process even as detailed as identifying the clock cycles in which the individual key bits are processed. But we could not reveal the private keys used by the attacked commercial authentication chips 100% correctly. Moreover, as we did not knew the used keys we could not evaluate the success of our attack. To summarize, the effort of such an attack is significantly higher than the one of attacking a well-known implementation.
‘Totaled City’: The Postdigital Poetics of Ben Lerner’s 10:04

The chapter argues that Ben Lerner’s 10:04 is a response to the new ontological condition of metamodernism. Not only is 10:04 deliberately playful in its mash-up of fiction and autobiography; it also encapsulates extreme levels of anxiety concerning the way twenty-first-century cityspace and technology are now indelibly intertwined. The chapter argues that 10:04 is a kind of metamodernist thought-experiment, an attempt to formulate new ways by which prose writing can address the contemporary need for meaningful connection and affect. In 10:04, Lerner argues for new forms of creative hybridity, new assemblages of digital and non-digital entanglement, what I term postdigitality, as a means by which human dignity in a post-crash, post-covid city can be reclaimed.
Performance Analysis of Small Files in HDFS using Clustering Small Files based on Centroid Algorithm

In day to day life, a lot of files are generated from various areas, due to the rapid development of technologies. Storing these files consumes a lot of memory space. Large-sized files are not only represented as Big Data. Large numbers of small files are also considered as big data. To process large-sized file Hadoop is used. Processing small files in Hadoop is not easy, because it holds memory space of size 128MB separately for each and every dataset. To overcome this, Clustering Small Files based on Centroid (CSFC) approach is used to place the related files in a cluster. If the fetched data is not related to any other files they knew its different and a cluster will be generated. The combined files are forwarded to HDFS for further processing. The Name node holds metadata and Data Node hold the dataset. The data set can be fetched directly from the Data node in HDFS efficiently.
A capsule-unified framework of deep neural networks for graphical programming

Recently, the growth of deep learning has produced a large number of deep neural networks. How to describe these networks unifiedly is becoming an important issue. To make difference from capsule networks, we first formalize neuronal (plain) networks in a mathematical definition, give their representational graphs, and prove a generation theorem about the induced networks of the graphs. Then, we extend plain networks to capsule networks, and set up a capsule-unified framework for deep learning, including a mathematical definition of capsules, an induced model for capsule networks and a universal backpropagation algorithm for training them. Moreover, we present a set of standard graphical symbols of capsules, neurons, and connections for application of the framework to graphical programming. Finally, we design and implement a demo platform to show the graphical programming practicability of deep neural networks in mouse-click drawing experiments.
Engineering Reliable Interactions in the Reality-Artificiality Continuum

Milgram's reality-virtuality continuum applies to interaction in the physical space dimension, going from real to virtual.However, interaction has a social dimension as well, that can go from real to artificial depending on the companion with whom the user interacts.In this paper we present our vision of the Reality-Artificiality bidimensional Continuum (RAC), we identify some challenges in its design and development and we discuss how reliable interactions might be supported inside RAC.1 https://www.sciencenews.org/article/coronavirus-covid-19-social-distancing-psychological-fallout, https://www.nationalgeographic.com/history/2020/04/psychologists-watching-coronaviru s-social-distancing-coping/,
Research on Heat Conduction Model of Special Equipment Based on Difference Equation

Abstract Special equipment plays an important role in the life safety of special operators. With the development of economy and society, the performance requirements of special equipment are higher and higher. In this paper, the fire-proof clothing in special equipment is taken as an example to study the internal temperature change of the fire-proof clothing in high temperature environment and improve the design of the fire-proof clothing. Through Newton’s law of cooling and Fourier’s law, combined with the relevant physical parameters, we establish the heat conduction model of the fire suit, discretize the differential equation in the model, use the chasing and driving method to solve the tridiagonal matrix after discretization, and transform the unknown parameters (heat extraction coefficient) in the model into a two-parameter optimization problem, combining with the classical optimization algorithm—The genetic algorithm is solved. Experiments show that our model can better solve this kind of problems, and the fitting accuracy is increased by 10%. Through the fitting image of the experimental results, we can see the improvement of the accuracy directly. Through the improvement, our model can improve the fitting accuracy, which is of great significance for the design and research of special equipment Fire clothing, and also has certain reference value for solving similar problems.
Enhancement of Efficiency of Military Cloud Computing using Lanchester Model

Cloud computing is a technology that uses centrally processed computing resources over the Internet by a large number of users. Because many requests are concentrated on cloud servers, they must be properly distributed to avoid degradation of quality. Load balancing categorizes requests from users according to established algorithms and assigns appropriate virtual machines. Because load balancing algorithms are developed according to the cloud's usage environment, various algorithms are being utilized. Recently, government agencies are also interested in introducing cloud technologies beyond private sectors. Many militaries have selected Cloud as its basic task to apply new technologies such as AI to military operations. However, there is no precedent for military cloud development, and the lack of doud technology research considering the operational environment has delayed the progress of cloud adoption. The algorithm presented by this paper makes the combat power, which varies according to the importance of the operation, an important variable. This variable makes each user's access to computing resources different. Although similar to other dynamic algorithms, the impact of priorities is so big that the degree of imbalance between tasks was higher.
Modulation Classification in Successive Relaying Systems with Interference

Modulation classification plays an important role in various civil and military applications. In this paper, we study the modulation classification for successive relaying multiple-input multiple-output (MIMO) systems with interference. To mitigate the effect of interference from another relay node, we adopt a method that jointly identify the modulation types of the source node and the relay node. Specifically, higher order moments (HOMs) and higher order cumulants (HOCs) of the received signal are used as the discriminating features in random forest (RF) to classify the modulation types. Extensive simulations verify the advantages of the proposed scheme over the traditional scheme that treats the interference as colored noise.
A Proposed Code Inspection Model using Program Slicing Technique

Code inspection is the way to ensure the quality and the control of products and software by detecting, correcting or reducing defects. Nowadays, several methods of code inspection emerged, most of which consisted of processing, preparation, analysis, meetings, and reform. However, each code inspection method may face problems in terms of number of developers, whether large or small, ease of attendance at meetings, and defects coverage. Moreover, some uses of the programs do not tolerate errors during implementation. In this paper, we will propose a new code inspection model that integrates the program slicing technique with code inspection process. We create reports that facilitate the implementation of the code review process and reduce its time, costs, and team size. The proposed model supposed to be more accurate detection and resolution of existing defects.
Deep Convolutional Network for Spatially Correlated RAYLEIGH Noise Suppression on TerraSAR-X Images

The task of design of deep convolutional network for denoising of single-look amplitude images of TerraSAR-X spaceborne remote sensing system is considered. The images are distorted by spatially correlated Rayleigh noise which is difficult to remove. Noise suppression on TerraSAR-X images is complicated also by necessity of improved preservation of fine details and textures on the images. A deep convolutional network structure is proposed as well as custom loss function for its training providing for this network a detail preserving optimization. A comparative analysis of the proposed network training with different loss functions and different training test sets is carried out. It is shown that the proposed network provides PSNR for denoised images in average on 1.3 dB better than well known DCT based filter adapted for the noise. It is shown also that usage of the proposed custom loss function allows to provide weighted PSNR value on 0.8 times better than usage of conventional loss function, and on 2 dB better than DCT based filter. Results of processing of real TerraSAR-X images are presented.
Speaker Identification in Different Emotional States

A major challenge degrading the robustness of speaker-recognition systems is the variation in the emotional state of speakers. In this study, we propose a speaker recognition system in an emotional state for two languages, Arabic and English. In addition, cross-language speaker recognition was applied. Convolutional neural network (CNN) and long short-term memory (LSTM) models were used to design a convolutional recurrent neural network (CRNN) main system. The overall CRNN system exhibited an accuracy as high as 97.5% and 97.18% for Arabic and English emotional speech inputs. For the cross-language program, the overall accuracy was as high as 91.83%.
Harmony search algorithm for chemical kinetics optimization problems

This paper analyses the literature on the application of Harmony Search algorithm and discusses the application of the harmony search algorithm to calculate the kinetic parameters of the reaction. A mathematical model is constructed for solving inverse kinetic problems for determining parameters taking into account the physical and chemical aspects of the process of the transformation of the pentane-hexane fraction catalytic isomerization, which allows predictive calculations.
Multi-level Predictive with Training Framework (MP with TF) for ranking machine learning algorithms

Machine learning algorithms differ from conventional ones in a way that they adjust themselves to perform better when exposed to surplus data. Huge volumes of data were pipelined from the grades scored by student over the last `n' years. Among the data earned from these, `k' years were populated as trained set and `(n-k)' as test sets. For the proposed Multi-level Predictive with Training Framework (MP with TF), five AI based approaches such as k-nearest neighbors, neural networks, Logistic regression, Naive Bayes and Random forest were considered to predict the target variable set. The performance of these machine learning algorithms were implemented using the actual grades scored by the students in their End Examination through a data science toolkit. Furthermore, the performances of these algorithms were ranked based on the accuracy of predicting the grades scored by the students.
A Computational Intelligence Perspective on Multimodal Image Registration for Unmanned Aerial Vehicles (UAVs)

Remote Sensing (RS) applications generally require robustness, stability, accuracy, promptness, and a high autonomy level to simplify the Big Data (BD) processing in real-time. Image Registration (ImR) is among the most employed RS tasks. ImR transforms different groups of images into a coordinate system that allows overlaying two or more images from the same scene acquired with various sensors and/or taken at different times and angles. The original imageries must be normalized and geometrically aligned to create an ample image containing information from all the separate images. ImR is a crucial step when one has several views and a myriad of sensors that must be fused. The BD aspect of Multimodal Image Registration (MIR) is related to the idea of multispectral and hyperspectral imaging, which involve a vast amount of frequency bands. BD from different sources assist the decision-making processes and create additional more massive datasets for the long-term tracking of various phenomena. This chapter focuses on the MIR from infrared and optical sensors relying on the Particle Swarm Optimization (PSO) class of algorithms. These computational intelligence procedures circumvent problems related to multiresolution methods and the high computational cost of hard optimization methods.
A Stochastic Approach for an Enhanced Trust Management in a Decentralized Healthcare Environment

Medical institutions are increasingly adopting IoT platforms to share data, communicate rapidly and improve healthcare treatment abilities. However, this trend is also raising the risk of potential data manipulation attacks. In decentralized networks, defense mechanisms against external entities have been widely enabled while protection against insider attackers is still the weakest link of the chain. Most of the platforms are based on the assumption that all the insider nodes are trustworthy. However, these nodes are exploiting of this assumption to lead manipulation attacks and violate data integrity and reliability without being detected. To address this problem, we propose a secure decentralized management system able to detect insider malicious nodes. Our proposal is based on a three layer architecture: storage layer, blockchain based network layer and IoT devices layer. In this paper, we mainly focus on the network layer where we propose to integrate a decentralized trust based authorization module. This latter allows updating dynamically the nodes access rights by observing and evaluating their behavior. To this aim, we combine probabilistic modelling and stochastic modelling to classify and predict the nodes behavior. Conducted performance evaluation and security analysis show that our proposition provides efficient detection of malicious nodes compared to other trust based management approaches.
Epistemic predicates in the arithmetical context

Abstract In this paper, we investigate epistemic predicates in extensions of arithmetic. We use as our case study Kurt Gödel’s 1951 thesis that either the power of the human mind surpasses that of any finite machine or there are absolutely unsolvable problems. Because Gödel also claimed that his disjunction was a mathematically established fact, we must ask the following: what sort of syntactical object should formalize human reason? In this paper, we lay the foundations for a predicate treatment of this epistemic feature. We begin with a very general examination of the Gödel sentence in the arithmetical context. We then discuss two systems of modal predicates over arithmetic. The first, called coreflective arithmetic or ${\textsf{CoPA}}$, extends ${\textsf{PA}}$ with a coreflective modal predicate but does not contain a consistency statement. The second, called doxastic arithmetic or ${\textsf{DA}}$, has as its characteristic feature the consistency statement but does not contain coreflection or its instance, the ${\textsf{4}}$ axiom. We examine the logical properties of, motivations for and criticisms of both systems. We close with a brief comparison of the systems in the context of Gödel’s disjunction.
On the Construction of Web NER Model Training Tool based on Distant Supervision

Named entity recognition (NER) is an important task in natural language understanding, as it extracts the key entities (person, organization, location, date, number, etc.) and objects (product, song, movie, activity name, etc.) mentioned in texts. However, existing natural language processing (NLP) tools (such as Stanford NER) recognize only general named entities or require annotated training examples and feature engineering for supervised model construction. Since not all languages or entities have public NER support, constructing a tool for NER model training is essential for low-resource language or entity information extraction. In this article, we study the problem of developing a tool to prepare training corpus from the Web with known seed entities for custom NER model training via distant supervision. The major challenge of automatic labeling lies in the long labeling time due to large corpus and seed entities as well as the concern to avoid false positive and false negative examples due to short and long seeds. To solve this problem, we adopt locality-sensitive hashing (LSH) for various length of seed entities. We conduct experiments on five types of entity recognition tasks, including Chinese person names, food names, locations, points of interest (POIs), and activity names to demonstrate the improvements with the proposed Web NER model construction tool. Because the training corpus is obtained by automatic labeling of the seed entity–related sentences, one could use either the entire corpus or the positive only sentences for model training. Based on the experimental results, we found the decision should depend on whether traditional linear chained conditional random fields (CRF) or deep neural network–based CRF is used for model training as well as the completeness of the provided seed list.