Reviewerly/exHarmony huggingface dataset:
Digitale Annotation alchemischer Decknamen

This chapter aims to show the uses of modelling alchemical terms in a digital thesaurus using the example of Michael Maier's (1568Maier's ( -1622) ) writings.Alchemical language is supposed to be full of secrets and it is indeed full of ambiguities.They are revealed to initiated adepts ('experts') who are familiar with the underlying semiotic codes of analogy.Its allegories have wrongly brought alchemy the miscredit of being known as an 'esoteric pseudo-chemistry', which recent studies have proven wrong.Alchemical language is an example of scientia poetica; its Decknamen are coded, ornate and unstable.Computational methods like Natural Language Processing (NLP), Named Entity Recognition (NER) and knowledge representation technologies, for example using thesauri of terms of alchemy in XML, allow us to handle the typical ambiguity of alchemical data.We can make implicit instances of knowledge explicit in a digital thesaurus while the linking of a concrete word (a string or label) in a text to the thesaurus remains loose enough to allow for imprecise poetic language.Computational models are "temporary states in a process of coming to know", in which computers are not "knowledge jukeboxes" but "representation machines" (McCarty).They create a systematic approximation of reality, and from its shortcomings we learn about the reality we aimed to model.This might be a viable and helpful new approach in research on alchemy, a field which has shown a great reluctance to make meaning explicit in the past.But it also comes with the responsibility of ensuring that annotation remains as objective as possible, open to uncertainty and not too concrete in case of ambiguities.The main challenges are linking concepts and labels, and avoiding interpretation in the process of making information explicit, since the annotation will be done automatically.
USE OF WEB 2.0 IN CULTURAL HERITAGE

As example of digital humanities in this paper the presence of institutional websites for heritage assets on relevant social media and Web 2.0 tools was analysed (social tools, Wikipedia and blogs).It was studied if new forms of cultural communication are being used as actual communication ways.The presence of institutional profiles was analysed on the following social networks: Facebook, Twitter, Google+, YouTube, Vimeo, Instagram, Flickr, Pinterest, TripAdvisor and FourSquare.Furthermore, it was identified the presence of heritage assets in the "Google Arts & Culture" project of the Google Cultural Institute as a new digital cultural resource.Results heterogeneous have been obtained and have been detected lacks in the adaption web 2.0.Wikipedia is the web 2.0 tool more used and only the 50% of the cases use Facebook or Twitter -most of them governmental institutions which manage cultural assets-.Image-based social networks are used but not in a usual way.
Manual differential count and automated differential leukocyte count in normal individuals: a comparative study

Introduction: Differential leukocyte is a frequently ordered laboratory test. It is the percentage distribution of types of leukocytes on a stained film. There are two methods to determine differential leukocyte count which are manual and automated. Manual method is considered the gold standard and is used to validate differential counts obtained by automated method. The comparative knowledge about the different methods to determine differential leukocyte count may guide us to use of proper method to determine the differential count more accurately and in a shorter time. The study aims to compare differential leukocyte count of normal blood samples by manual and automatic methods. Materials and methods: A cross-sectional, analytical study was conducted in laboratory of Kasturba Hospital, Manipal. A total of 347 blood samples of adults that did not show abnormalities in automatic analyzer were included in the study by purposive sampling method. Blood smears for manual count were prepared by Leishman’s stain. Results: A significant difference was observed between manual and automated leukocyte differential counts in 100 and 200 cells per specimen in neutrophils, lymphocytes, monocytes, eosinophils and basophils. Conclusions: The study concludes the need of improving accuracy and reliability of the automated methods. A reference range generated by further studies could help provide more accurate determination of differential leukocyte count.
Implication of Landscape Changes Using Google Earth Historical Imagery in Yenagoa Bayelsa State, Nigeria

Assessment and impact on the environment of dumpsites investigated between 1990 and 2015 and 2015 to 2020 in Yenagoa, Bayelsa State, Nigeria, using Remote Sensing and Geographical Information Systems (GIS) are used to evaluate landscape change from Google Earth Historical Imagery (GIS). Historical Google Earth imagery shows an increase from 1900 to 2015, and again from 2015 to 2020, due to an increase in built-up areas, which resulted in illegal dumpsites in the environment, implying an increase in population due to migration and industrial activities in the area. Six soil samples were collected from illegal dumpsites and one from a control location and analysed for heavy metal ions. The Fe, Mn, Cr, Cu, Zn, Ni, Cd, and Pb concentrations were determined using the atomic absorption spectrometer. All of the illegal dumpsites were discovered to be located along the market area, close to Epie Creek and the pound. Furthermore, the pollution load index (PLI) and contamination factor (CF) results show that heavy metal pollution is present in the area. As a result, it is recommended that urban growth be tracked in order to plan ahead of time for various dumpsite locations, and that site suitability studies for landfill selection sites be made available using GIS and remote sensing-based Multi-Criteria Evaluation. Combining remote sensing and GIS, according to the study, could be an effective approach to landscape planning and environmental improvement.
AUTOMATIC LABELING FOR FASHION DATASETS

Creating image Datasets is a time-consuming step and a challenge for specific classification problems.The dilemma of this task concerns the manual annotation of training images.Thus, this paper presents an automatic labeling method without humans in the loop for Fashion datasets.First, we introduce a new descriptor for texture segmentation called LNLBP (Large Neighbourhood Local Binary Pattern) which is able to capture both micro-structure and macro-structure texture information.Then, using a Fuzzy logic system, the proposed method detects the change in texture, intensity and human pose in order to localize correctly the position of the fashion item.Finally, we generate the 'xml' files containing the coordinates and the labels in order to use them afterward for the training with the Google Tensorflow object detection API: for real-time clothes classification.The resulting dataset will be made publicly available for research.
Gender Affect on Investment Behaviour

Decision making is an action which results in a certain type of action from a set of alternate choices. The selected choice may be a rational or an irrational decision. Individual or small investors do not have the advantage of big and small firms regarding invest decision making. Various factors both external and internal, are behind investment decisions. The attitudes of both gender are similar of different with regard to various factor influences. This research endeavor attempts to identify the significant factors which influence decision making regarding investments with a focus on gender. Here for this study 14 attributes were selected to identify the underlying factors and their influences in decision making. For this purpose tools of statistics such as independent t-test and mean scores on a likert scale was utilized for this research study. The study determines there are notable differences between genders on certain variables which play an influencing role when it comes to equity investment decision making. On the majority of the variables both genders share similar influencing variables with regard to investment decisions in equities.
Enhancing entanglement and total correlation dynamics via local unitaries

The interaction with the environment is one of the main obstacles to be circumvented in practical implementations of quantum information tasks. The use of local unitaries, while not changing the initial entanglement present in a given state, can enormously change its dynamics through a noisy channel, and consequently its ability to be used as a resource. In this way, local unitaries provide an easy and accessible way to enhance quantum correlations in a variety of different experimental platforms. Given an initial entangled state and a certain noisy channel, what are the local unitaries providing the most robust dynamics? In this paper we solve this question considering two-qubit states, together with paradigmatic and relevant noisy channels, showing its consequences for teleportation protocols and identifying cases where the most robust states are not necessarily the ones imprinting the least information about themselves into the environment. We also derive a general multipartite law relating the interplay between the total correlations in the system and environment with their mutual information built up over the noisy dynamics. Finally, we employ the IBM Quantum Experience to provide a proof-of-principle experimental implementation of our results.
Adversarial Attacks on Neural Network with Batch Dimensions Perturbation and Manhattan-Distance Constraints

In recent years, with the development of deep learning technology, neural networks play an increasingly important role in more and more fields.However, research shows that neural networks are vulnerable to the attack of adversarial examples.The purpose of this paper is to study the principle of adversarial examples generation and propose a new method of generating adversarial examples.Compared with existed methods, our method achieves better deception rate and perturbs less pixels of images.During an epoch in batch dimension iteration, multiple pixels are perturbed while Manhattan-Distance constraints are added to them.Our algorithm performs well in experiments.Compared with Carlini-Wagner method, only 60 more dimensions are perturbed, which indicates that the computation cost of our algorithm is completely acceptable.Besides, compared with FGSM algorithm, the deception rate increases by 12% while the generation times of them are almost same.
Key Exchange Using Tree Parity Machines: A Survey

Secure key exchange is an important step to secure a communication. When multiple parties are using a symmetric key encryption protocol, they need a secret key to exchange encrypted messages. If the key is compromised, their whole communication gets exposed. While many techniques are based on mathematics for their design, another trend is using Artificial Intelligence (AI) to build a model that can learn to exchange keys securely. One of the most famous AI-based techniques is the Tree Parity Machine Key Exchange Model. Although it has been broken shortly after its introduction, it has seen many improvements during the last decade. In this paper, we will conduct a survey on this model to how it works and how it was broken. Then, we will survey the improvements it has seen and tell if there is still a possibility to use it in real world applications.
Improving the Performance of Reputation Evaluation by Combining a Network Structure With Nonlinear Recovery

Characterizing the reputation of an evaluator is particularly significant for consumers to obtain useful information from online rating systems. Furthermore, overcoming the difficulties of spam attacks on a rating system and determining the reliability and reputation of evaluators are important topics in the research. We have noticed that most existing reputation evaluation methods rely only on using the evaluator’s rating information and abnormal behaviour to establish a reputation system, which disregards the systematic aspects of the rating systems, by including the structure of the evaluator-object bipartite network and nonlinear effects. In this study, we propose an improved reputation evaluation method by combining the structure of the evaluator-object bipartite network with rating information and introducing penalty and reward factors. The proposed method is empirically analyzed on a large-scale artificial data set and two real data sets. The results have shown that this method has better performance than the original correlation-based and IARR2 in the presence of spamming attacks. Our work contributes a new idea to build reputation evaluation models in sparse bipartite rating networks.
Retraction: Feature Specific Hybrid Framework on composition of Deep learning architecture for speech emotion recognition (J. Phys.: Conf. Ser. 1916 012094)

This article (and all articles in the proceedings volume relating to the same conference) has been retracted by IOP Publishing following an extensive investigation in line with the COPE guidelines. This investigation has uncovered evidence of systematic manipulation of the publication process and considerable citation manipulation. IOP Publishing respectfully requests that readers consider all work within this volume potentially unreliable, as the volume has not been through a credible peer review process. IOP Publishing regrets that our usual quality checks did not identify these issues before publication, and have since put additional measures in place to try to prevent these issues from reoccurring. IOP Publishing wishes to credit anonymous whistleblowers and the Problematic Paper Screener [1] for bringing some of the above issues to our attention, prompting us to investigate further. [1] Cabanac G, Labbé C and Magazinov A 2021 arXiv: 2107.06751v1 Retraction published: 23 February 2022
Segmentation of renal cell carcinoma using stain normalization

Renal cell carcinoma is a malignant tumor of the urinary system with high mortality. Early detection and therapy can improve the survival rate of patients. Therefore, the computer-aided diagnostic system has attracted wide research attention. This paper proposed a novel stain normalization method to normalize the histological renal cell carcinoma images, which suffer from color variations commonly caused by different dying conditions. To demonstrate the effectiveness of the proposed method, we designed two contrastive testing scenarios. Specifically, we trained two nearly the same U-Net models, except that one was trained based on the image patches after stain normalization using our method. In contrast, the other one was trained based on the image patches after several standard data augmentation methods. The segmentation results of the two models showed that the model trained after stain normalization achieved a better average Dice coefficient by 6.81%. It proved that the proposed stain normalization method is effective.
A Novel Initialization Approach for Fuzzy C-Means algorithm using Unsupervised Random Forest Method

The Fuzzy C-Means algorithm is a Clustering algorithm that allows a data point to belong two or more clusters partially. It makes use of fuzzy matrix which contains the information about the membership of the data points to every cluster. It takes the number of clusters and the initialized fuzzy matrix as input to generate the fuzzy matrix which then is used to partition the data. There are two major challenges, a) to choose the initial fuzzy matrix for fast convergence and b) the optimum number of clusters. In this paper, Unsupervised random forest method has been used to derive both, the optimum number of clusters and the initialized fuzzy matrix. The advantage of proposed approach is validated by the number of iterations required to converge, and hence reduce the computation power. Also, proposed approach identifies optimum number of clusters separately. The method is validated on well known UCI datasets and found satisfactory performance.
BUKU DIGITAL INTERAKTIF SISTEM AIR CONDITIONING (AC): UPAYA MEMBELAJARKAN SISWA PADA MASA PANDEMI COVID-19 DI DAERAH YANG MISKIN SINYAL SELULER.

Indonesia in the 2019 PISA report for reading scores has fallen far from the previous, which is ranked 72 out of 79 countries (Hewi &amp; Shaleh, 2020). One of the external factors that influence is the learning process. Interactive Digital Book (BUDIN) is an alternative media that can be used to increase students' reading interest (Rosyadi &amp; Santosa, 2019). The research objective was to determine the increase in the reading intensity of students through the BUDIN air conditioning system as well as an alternative learning process during the Covid-19 pandemic that could be used in areas with poor cellular signal. The method in this research is using survey explanatory method. Data analysis was carried out by descriptive analysis (A. Muhidin and Maman A, 2007: 53). BUDIN air conditioning system is intended for students in class XI automotive engineering expertise program.From the research results, it was found that independent learning using BUDIN increased reading intensity from before. It can be seen that after using BUDIN, the reading intensity has increased. The reading intensity of the very high category rose to 16%, then the high category increased to 47%. While the reading intensity of the moderate category decreased to 25% of students, as well as students in the low category, it decreased to 12% of students from the previous 38% of students.
How Do Rationalism and Empiricism Provide Sound Foundations?

Abstract The contrast between AI and HCAI is a continuation of the 2,000-year-old clash between Aristotle’s rationalism, based on logical analyses, and Leonardo da Vinci’s empiricism, based on sensory exploration of the world. Both are valuable and worthy of understanding. Both philosophies, rationalism and empiricism, offer valuable insights, so I apply rational thinking for its strengths, but I know that balancing it with an empirical outlook helps me see other possibilities, which use observational strategies. Watching users of technology has always led me to fresh insights, so I am drawn to usability studies, interviews, naturalistic observations, and repeated weeks-long case studies with users doing their work to complement the rationalist approach of controlled experiments in laboratory settings.
RAISING THE VOICE: EXPLORING ANTECEDENTS OF VOICE-ACTIVATED ASSISTANT USAGE INTENTION

Voice-activated assistants have become popular devices for a hands-free interaction with information systems.Despite a growing body of research, comprehensive models on usage intention especially in a commercial context have been under-researched.This research in progress explores antecedents of attitude and usage intention of voice-activated assistants through multiple theoretical lenses, including the Technology Acceptance Model, the Unified Theory of Acceptance and Use of Technology, Social Exchange Theory, and privacy concerns.The result is a conceptual framework consisting of six propositions on the impact of perceived usefulness, perceived ease of use, social influence, trust, and privacy concerns on attitude towards voice-activated assistants and their usage intention.
Security Concerns of Virtualization in Cloud Computing

Due to the advancements in computer technology, there is an unprecedented growth in cloud computing technology. Cloud computing is a scalable and collective resource sharing framework with the aid of virtualization. Cloud computing helps in the virtualization of the resources to provide software as a service, infrastructure as a service, and platform as a service. Virtualization is a technical innovation that distinguishes functions from the underlying hardware and enables one to construct a useful environment from abstract tools. Attackers may breach VM infrastructures and get access to different VMs on the alike system. Attackers may get control of host VMs and perform malicious activity. This chapter gives an overview of the importance of virtualization in cloud computing, various security challenges in virtualization, and different types of security in virtualization. A hypervisor is a software program that exists between host virtual machines and hardware components. This chapter describes the cloud-based intrusion on the virtualized infrastructure and the best practices to follow in order to safeguard the computing resources and the user data from the attacker.
Securing Hidden Message Communication in Social Media using Stenography

Internet is a vital factor for information technology and communication for secured information.From the evolution of internet, providing security to information that are transmitted between nodes in network has become one of the most important aspect in networking.As millennials have made abundantly clear, social networks are often worn as an outlet for straight forward texts, used as an outlet for sending sensitive information without getting much attention from people around.Users don't comprehend their susceptibility.Social media offer a means to chat, share photos and uphold causes.They underestimate personage presence and significance to outsiders.So, a thing turns sloppy.All the personal contact details are very easily accessed by the hackers.Similarly, all of the browsing history, shopping record or other services are also hacked by the hackers with the help of social media connection.In order to prevent this from happening, this paper aims to provide a technique to hide the necessary information.The technique used is AES encryption to encrypt text files and LSB Stenography technique to hide these encrypted text files in the images.Images are trendy wrap stuff worn intended for Stenography.In the sphere of digital imagery, many unlike image file formats, exist, for specific applications.
Detail preserving depth prediction from a single image based on multi-scale deep network and gradient network

redicting a convincing depth map from a monocular RGB image is a daunting task in the field of computer vision. An efficient detail preserving depth prediction algorithm on the basis of multi-scale deep network and gradient network is presented in this paper. Specifically, the proposed method leverage the designed multi-scale deep network to obtain the global depth image from training datasets. Moreover, a depth gradient generation strategy on the basis of gradient network, which permits us to obtain the local depth detail information of the scene is developed. In the end, the reliable depth map with better details could be reconstructed via merging the depth gradient and depth information on the basis of the optimization algorithm. Experimental results evaluated from the qualitative and quantitative perspective on the Make 3D and NYUv2 datasets indicate that the designed depth prediction scheme is superior to several depth estimation approaches, and can reconstruct plausible depths
S-type speed control curve based on the number of pulses

Abstract The stepper motor is a kind of motor that converts electrical pulse signals into corresponding angular displacements or linear displacements, with high accuracy and easy control. According to the characteristics of stepper motor controlled by pulses, this paper designs an S-type speed control method based on the number of pulses, then tests and analyzes it in a motion system which controlled by STM32. The results show that: the change of the speed for the motion system is compared. It is stable and basically conforms to the designed quadratic function curve, and there is no out-of-step phenomenon in the test. While retaining the great stability and acceleration performance of S-type acceleration and deceleration, this control method greatly reduces the calculation difficulty. It is more suitable for microcontrollers and embedded control systems.