Reviewerly/exHarmony huggingface dataset:
Actualizing distributed knowledge in bounded groups

Abstract The idealizations resulting from the use of Kripke semantics in Epistemic Logic are inherited by formalizations of group epistemic notions. For example, distributed knowledge is often taken to reflect the potential knowledge of a group: what agents would know if they had unbounded means of communication and deductive ability. However, this does not specify whether/how this potential can be actualized, especially since real people are not unbounded reasoners. Inspired by experiments on group reasoning, we identify two dimensions of actualizing distributed knowledge: communication and inference. We build a dynamic framework with effortful actions accounting for both, combining impossible-worlds semantics and action models, and we provide a method for extracting a sound and complete axiomatization.
Yak Object Detection Based on Data Augmentation of Style Transfer Method

To address the issue of complex yak data collection in the plateau area, as well as a lack of data, which leads to the limitation of the object detection model, a data enhancement method based on style transfer is used to increase the number of Tibetan plateau yak samples and improve object detection accuracy. In this study, we examine the results of several generative adversarial networks using the cycle generative adversarial network technique of alternate insertion residual network. Extend the original 450 yak data set by two times, manually generate four different data sets, compare the accuracy of different data sets using the YOLOv3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</sup> object detection model, and verify that the alternating insertion residual network recurrent generation counter network improves the data effect. The test results suggest that this strategy may significantly enhance item detection accuracy in small samples.
RSFAD: A Large-Scale Real Scenario Face Age Dataset in the wild

Age estimation is a hot and challenging research topic in the computer vision community. Several facial datasets annotated with age and gender attributes became available in recent years. However, the statistical information of these datasets reveal the unbalanced label distribution which inevitably introduce bias during model training. In this work, we manually collect and label a large-scale age dataset called Real Scenario Face Age Dataset (RSFAD) which contains 85,044 facial images captured from surveillance cameras in the wild. Due to the COVID-19, we not only label the apparent age group and gender but also label the breathing mask, and the label distribution of RSFAD dataset is almost uniform which is the first age dataset to the best of our knowledge. In addition, we investigate the impact of age, gender and mask distribution on age group estimation by comparing GDEX CNN model trained on several different datasets. Our experiments show that the RSFAD dataset has good performance for age estimation task and also it is suitable for being an evaluation benchmark.
The Maximum Euclidean Distance in a Class Defines the Boundary of Neighborhood and Leads to a New Machine Learning Algorithm

Classification occurs when we are given a data set in which each data point is assigned a class based on the values and or characteristics of attributes. The k-Nearest Neighbor (kNN) algorithm in machine learning is a very simple and powerful tool for doing this. It is based on the idea that data points of a certain class are neighbours to one another. To find the class to which a given test data or unknown data belongs, one measures the Euclidean distances of the test data or unknown data from all the data points of all the classes in the training data in kNN.&nbsp; Then, out of the k nearest distances, where k is any number greater or equal to 1, the class to which test data or unknown data is closest its most number of times is the class assigned to the test data or unidentified data. In this chapter, I propose a variation of kNN, which I call the ANN method (Alternative Nearest Neighbor) to distinguish it from kNN. The definition of neighbour is the distinguishing feature of ANN that distinguishes it from kNN. In ANN, the class to which the unknown data is neighbour is the class whose maximum Euclidean distance from its data points is less than or equal to the maximum Euclidean distance between all of the class's training data points.As a result, ANN will always provide a unique solution to each unknown data. In contrast, the solution in kNN may vary depending on the value of the number of nearest neighbours k. So, in kNN, as k is varied the performance may vary too. However, this is not the case with ANN; its performance for a specific training dataset is unique. The main motivation behind finding the ANN machine learning method has been to modify the conventional kNN method in such a way that it is independent of the parameter k and the user of the method need not make a choice of k neighbors based on experience or other criteria. For the training data [1] considered in this paper, the ANN gives 100% accurate result.
The Language Lab

The language lab accompanies the course Practical Phonetics and Oral Communication Skills 1 (PPOCS 1) taught in the English Language Competence programme at the Department of English and American Studies at the University of Vienna (see Richter, “Practical Phonetics and Oral Communication Skills,” this volume). Building upon knowledge in phonetics and phonology acquired in introductory linguistics lectures, it should be taken in the third or fourth semester as recommended by the curricula of the Bachelor of Education or the Bachelor of Arts in English and American Studies (University of Vienna, 2013; University of Vienna, 2016). Both programmes require students to have completed the Integrated Language and Study Skills module (see Martinek & Savukova, this volume).
Designing an Architecture for Structuring Didactic Concepts, Methods and Tools

Due to the Covid-19 pandemic, education was digitized massively and in a very short time. A vast variety of didactic concepts, methods and tools came into use, quite often as an ad-hoc solution that coped with a specific need. However, the extreme circumstances of the pandemic left little to no room to reflect in depth on how these different elements integrate into a big picture. As a consequence, all stakeholders involved in the educational process need to gain orientation within these new settings and possibilities, as a prerequisite for taking suitable decisions within their respective scope. To support this need for orientation, we introduce an architectural model that structures didactic concepts, methods and tools into a big picture, which is formulated using the notation of class diagram as known from software and requirements engineering.
aDCF Loss Function for Deep Metric Learning in End-to-End Text-Dependent Speaker Verification Systems

Metric learning approaches have widely expanded to the training of Speaker Verification (SV) systems based on Deep Neural Networks (DNNs), by using a loss function more consistent with the evaluation process than the traditional identification losses. However, these methods do not consider the performance measure and can involve high computational cost, for example, the need for a careful pair or triplet data selection. This paper proposes the approximated Detection Cost Function (aDCF) loss, which is a loss function based on the measure of the decision errors in SV systems, namely the False Rejection Rate (FRR) and the False Acceptance Rate (FAR). With aDCF loss as the training objective function, the end-to-end system learns how to minimize decision errors. Furthermore, we replace the typical linear layer as the last layer of DNN by a cosine distance layer, which reduces the difference between the metric in the training process and the metric during evaluation. aDCF loss function was evaluated in RSR2015-Part I and RSR2015-Part II datasets for text-dependent speaker verification. The system trained with aDCF loss outperforms all the state-of-the-art functions employed in this paper in both parts of the database.
Investigations of multiple quantum NMR dynamics of spin dimer on quantum computer

We propose the algorithm for the quantum computer on the IBM Q platform. By this algorithm one can obtain the multiple-quantum NMR dynamics of the dimer with spins 1/2. Our algorithm includes preparation of thermodynamic equilibrium state as initial conditions for dimer and circuit for evolution under the Hamiltonian of multiple-quantum NMR experiment. Multiple-quantum coherences arise in multiple-quantum NMR experiment. Ones are connected with solid body structure and get a lot of research consequently. Intensities of these coherences are measured at end of experiment. In our algorithm the intensities of multiple-quantum coherences are evaluated by tomography of diagonal elements of final density matrix for dimer.
Auto Encoder Based Image Inpainting Model Using Multi Layer Latent Representations

Image inpainting is used in computer vision to reconstruct images after removal of unwanted objects and in the construction of damaged images in a visually acceptable manner. Use of this technique is mainly found in the areas of image editing. Although many algorithms have been developed by researchers over the years, these mainly fall into the reconstruction of small regions or objects with less structural complexities. With the advancement of machine learning techniques, innovative ideas have emerged which led to the development of mechanisms to reconstruct more complex structural variations in large regions of the images. In this research, a considerably large region of a damaged image has been inpainted using a convolutional auto encoder with an encoder-decoder combination. It is equipped with a novel approach to modify the latent space of the input image. These latent representations are created from multiple layers of the encoder to form a Multi Layer Latent Representation (MLLR). This MLLR is fed to the decoder which generates the image by applying the transpose convolution operation. The quality of the inpainted images generated from our model is compared with the images generated from the model having a single latent representation without the MLLR. Peak Signal to Noise Ratio (PSNR) and Structured Similarity Index Metrics (SSIM) are used in the evaluation. Empirical analysis indicate that the model is able to provide SSIM values over 0.9 for the reconstructed images with damaged areas that consist of 12% of the image surface.
Covidseeker: A Geospatial Temporal Surveillance Tool

Geospatial temporal data derived from smartphones traditionally used for purposes of navigation may offer valuable information for public health surveillance and locational hot spotting. Our objective was to develop a web-based application, called Covidseeker, that captures continuous fine-grained geospatial temporal data from smartphones and leverages these data to study transmission patterns of COVID-19.This report describes the development of Covidseeker and the process by which it utilizes geospatial temporal data from smartphones and processes it into a usable format to study geospatial temporal patterns of COVID-19. We provide an overview of the design process, the principles, the software architecture, and the dashboard of the Covidseeker application and consider key challenges and strategic uses of capturing geospatial temporal data and the potential for future applications in outbreak surveillance.A resource such as Covidseeker can support situational awareness by providing information about the location and timing of transmission of diseases such as COVID-19. Geospatial temporal data housed in smartphones hold tremendous potential to capture more depth about where and when transmission occurs and the patterns of human mobility that lead to increases in risk of COVID-19.An enormous and highly rich source of geospatial temporal information about human mobility can be used to provide highly localized discrete information that is difficult to capture by traditional sources. The architecture of Covidseeker can be applied to help track COVID-19 and should be integrated with traditional disease surveillance practices.
How Do Mental Models Actually Exist in the Brain: On Context-Dependent Neural Correlates of Mental Models

In this chapter, the concept of context-dependent realisationRealisation of mental modelsMental model is introduced and discussed. Literature from neuroscienceNeuroscience is discussed showing that different types of mental modelsMental model can use different types of brainBrain areas. Moreover, it is discussed that the same occurs for the formation and adaptationAdaptation of mental modelsMental model and the controlControl of these processes. This makes that it is hard to claim that all mental modelsMental model use the same brainBrain mechanisms and areas. Instead, the notion of context-dependent realisationRealisation is proposed here as a better manner to relate neural correlatesNeural correlate to mental modelsMental model. It is shown in some formal detail how this context-dependent realisationRealisation approach can be related to well-known perspectives based on bridge principle realisationBridge principle realisation and interpretation mappingInterpretation mapping realisationRealisation.
Consortium Shared Identity Over an Enterprise Blockchain

The current onboarding and offboarding processes for production crews involve a set of often inconsistent and manual activities, which are repetitive and inefficient, leading to various issues such as longer delays in bringing people on to a production project, inconsistent permission enforcement when a project is over, and higher overall cost.
Application of Blended Teaching into the Course of Comprehensive English

This study, incorporating the quantitative and qualitative research methods, has explored the application of the blended teaching model in Comprehensive English for English majors. It adopts the experimental research method and selects two classes without significant difference before the experiment as research subjects. Questionnaires and interviews are conducted for students from the pilot class to investigate their viewpoints on the efficiency of the blended teaching as well as their attitudes towards this model. In addition, an independent samples test is conducted on the final exam results at the end of the semester to investigate the efficiency of blended teaching in this course. It is proved that this teaching model is instrumental to students' English learning and most students appreciate it. The researcher has reflected on the problems existing the application and come up with some solutions so as to make full use of this model and further improve the students' learning efficiency.
Comments On The Cryptanalysis Of An Identity-Based Strong Designated Verifier Signature Scheme

In this paper, we explain that the proposed modification to an identity-based strong designated verifier signature scheme (Lecture Notes in Electrical Engineering, vol. 164, no. 1, Springer-Verlag, 2012) is not secure. This scheme has a short signature size, low communication cost, and low computational cost. But, it does not satisfy the required authentication property in a designated verifier signature scheme. In particular, we introduce an attack that enables anybody to verify the signature.
Analyzing and Improving the Robustness of Tabular Classifiers using Counterfactual Explanations

Recent studies have revealed that Machine Learning (ML) models are vulnerable to adversarial perturbations. Such perturbations can be intentionally or accidentally added to the original inputs, evading the classifier’s behavior to misclassify the crafted samples. A widely-used solution is to retrain the model using data points generated by various attack strategies. However, this creates a classifier robust to some particular evasions and can not defend unknown or universal perturbations. Counterfactual explanations are a specific class of post-hoc explanation methods that provide minimal modification to the input features in order to obtain a particular outcome from the model. In addition to the resemblance of counterfactual explanations to the universal perturbations, the possibility of generating instances from specific classes makes such approaches suitable for analyzing and improving the model’s robustness. Rather than explaining the model’s decisions in the deployment phase, we utilize the distance information obtained from counterfactuals and propose novel metrics to analyze the robustness of tabular classifiers. Further, we introduce a decision boundary modification approach using customized counterfactual data points to improve the robustness of the models without compromising their accuracy. Our framework addresses the robustness of black-box classifiers in the tabular setting, which is considered an under-explored research area. Through several experiments and evaluations, we demonstrate the efficacy of our approach in analyzing and improving the robustness of black-box tabular classifiers.
Knowledge-Based Algorithms for BDI Agents

A multiagent algorithm is a knowledge-based distributed algorithm that solves some problem via cooperative work of agents. A BDI agent is an agent with beliefs, desires, and intentions. From the point of view of an individual agent, a multiagent algorithm is an algorithm based on this agent’s knowledge and beliefs, aimed at achieving this agent’s desires by sequentially acting on its intentions. We consider agents to be reactive, proactive, and rational. This paper proposes and studies two knowledge-based multiagent algorithms. In particular, one of the proposed algorithms solves the following resource renting problem: there is a system of agents that arrive at a resource center one by one, in random order, with the goal to rent some available resource. The available resources are passive and form a cloud. If there is no competition for an available resource, it is lend on demand and is returned to the cloud after use. The agents also form a cloud, but leave the resource center immediately after renting the desired resource. The goal is to design a knowledge-based multiagent algorithm with the following correctness property: each agent that arrives at the resource center eventually rents some desired resource at a moment when there is no competition for that resource.
A Noisy-Labels Approach to Detecting Uncompetitive Auctions

Despite several rounds of institutional reform starting from 2005, the public procurement process in Russia remains marred by low competitiveness and inefficiency. In the years 2014–2018, almost half of the studied auctions failed to attract more than a single-bidder. But are single-bidder auctions necessarily uncompetitive? The auction format we study is a sealed-bid auction, where a bidder is unaware of who else is participating. If they were to submit a bid with the expectation of competition that fails to materialise, for all means and purposes the auction can be said to be competitive. More importantly, the presence of many bidders does not guarantee competition – we could be facing a cartel, or the restriction of competition via a corrupt procurer, such as the case of bid-leakage. We assume that bids in multi-bidder auctions are predominantly competitive while bids in single-bidder auctions are not, and apply generalized confident learning, a method for classification in the presence of noisy labels, to attempt to separate competitive and uncompetitive bids. This allows us to identify behaviour patterns resembling monopolists and cartels.
Multi-face Recognition Systems Based on Deep and Machine Learning Algorithms

In this paper, we consider two multi-face recognition systems using deep and machine learning algorithms. Specifically, one is based on the Haar cascade algorithm coupled with the Local Binary Patterns (LBP) classification approach and the other one on the Histogram of Orianted Gradients (HOG) descriptors coupled with the Convolutional Neural Network (CNN) algorithm. We also carry out an exhaustive comparison between the two systems. The simulation results show clearly that both systems have high face detection rates. However, the deep learning-based system outperforms the machine learning-Based system in the face recognition task.
Virtual reality is as real as physical reality, but just different

What can virtual reality tell us about real reality and the nature of existence? David Chalmers reveals all to Richard Webb
A Study on the Data Quantification of Weapon System RAM Objective Setting Using Evidence Theory

When setting the RAM objectives, various data such as expert opinions and the historical data of similar types of equipment are used. However, many times subjectivity is involved in the process of merging and utilizing data, and there are many cases where some information is omitted or an ambiguous method is used. Most of the previous work focused only on the process or method of calculating values using well-organized data rather than manipulating raw data. But if the data manipulation process is not objective, it is difficult to guarantee the accuracy of the results even if the calculation logic and method are accurate. This study proposes a systematic data merging process used to set the RAM objectives using the evidence theory. The proposed method can be used to avoid information loss and merge the data objectively. Moreover, contribute to improving the accuracy of setting the RAM objectives in the future.
Reinforcement Learning based Load Balancing in a Distributed Heterogeneous Storage System

With the growing demand for big data storage and processing, distributed storage systems with heterogeneous devices have become the majority in cloud data centers. However, hardware heterogeneity and workload variety make it challenging to maintain optimal performance in those storage systems. In this work, we present a learning-based control method that optimizes the performance of a distributed storage system. Specifically, to provide automatic parameter tuning upon dynamic workload patterns on a tiered storage architecture, we employ deep reinforcement learning (RL) and implement a simulation environment for a Ceph storage system. Through simulation tests, we demonstrate our RL method shows better performance than other heuristic approaches for a task of load balancing based on the primary affinity settings in Ceph.
Innovative applications and reflections on nontouch design in the prevention and control of epidemics

With the launch of new pneumonia prevention and control efforts, the concept of nontouch design was born, the principle of which is to design high-tech devices that can be operated and performed without touching through the use of various new technological innovations. Nontouch design is only a breaking point, but what is more noteworthy is the radical change in people’s lifestyles. This paper explains the feasibility of innovative applications of nontouch design, analyzes the advantages and shortcomings of current nontouch design devices in current applications, and thus combines theory and practice and proposes innovative points of nontouch design based on design principles from practical, esthetic, and social functions.
Redefining Family: Introducing the Case for Humanoid Robotics in Family Law Jurisprudence in Central Asia

In the era of 4th industrial revolution, fascinating innovations can be seen in the field of robotics. The emotional intelligence of robots as well as human sympathy towards them is increasing. A human-robot marriage in future would pose challenges to lawmakers including whether human-robot marriage has a legal worth? and how these families will deal with children, property and issues related to liabilities. This article is the very first in Central Asia to consider the family law implications of the romantic relationships that applied scientists are working to develop. It argues that the advancements in humanoid robotics may pose challenges to the current understanding of family law in Central Asia.