Reviewerly/exHarmony huggingface dataset:
Constructions for Quantum Indistinguishability Obfuscation

An indistinguishability obfuscator is a probabilistic polynomial-time algorithm that takes a circuit as input and outputs a new circuit that has the same functionality as the input circuit, such that for any two circuits of the same size that compute the same function, the outputs of the indistinguishability obfuscator are indistinguishable. Here, we study schemes for indistinguishability obfuscation for quantum circuits. We present two definitions for indistinguishability obfuscation: in our first definition (qiO) the outputs of the obfuscator are required to be indistinguishable if the input circuits are perfectly equivalent, while in our second definition (qiOD), the outputs are required to be indistinguishable as long as the input circuits are approximately equivalent with respect to a pseudo-distance D. Our main results provide (1) a computationally-secure scheme for qiO where the size of the output of the obfuscator is exponential in the number of non-Clifford (T gates), which means that the construction is efficient as long as the number of T gates is logarithmic in the circuit size and (2)a statistically-secure qiOD, for circuits that are close to the kth level of the Gottesman-Chuang hierarchy (with respect to D); this construction is efficient as long as k is small and fixed.
Efficient IoT Data Management for Geological Disasters Based on Big Data-Turbocharged Data Lake Architecture

Multi-source Internet of Things (IoT) data, archived in institutions’ repositories, are becoming more and more widely open-sourced to make them publicly accessed by scientists, developers, and decision makers via web services to promote researches on geohazards prevention. In this paper, we design and implement a big data-turbocharged system for effective IoT data management following the data lake architecture. We first propose a multi-threading parallel data ingestion method to ingest IoT data from institutions’ data repositories in parallel. Next, we design storage strategies for both ingested IoT data and processed IoT data to store them in a scalable, reliable storage environment. We also build a distributed cache layer to enable fast access to IoT data. Then, we provide users with a unified, SQL-based interactive environment to enable IoT data exploration by leveraging the processing ability of Apache Spark. In addition, we design a standard-based metadata model to describe ingested IoT data and thus support IoT dataset discovery. Finally, we implement a prototype system and conduct experiments on real IoT data repositories to evaluate the efficiency of the proposed system.
Robust Strong-Coupling Architecture in Circuit Quantum Electrodynamics

We report on a robust method to achieve strong coupling between a superconducting flux qubit and a high-quality quarter-wavelength coplanar waveguide resonator. We demonstrate the progression from the strong to ultrastrong coupling regime by varying the length of a shared inductive coupling element, ultimately achieving a qubit-resonator coupling strength of 655 MHz, $10\mathrm{%}$ of the resonator frequency. We derive an analytical expression for the coupling strength in terms of circuit parameters and also discuss the maximum achievable coupling within this framework. We experimentally characterize flux qubits coupled to superconducting resonators using one- and two-tone spectroscopy methods, demonstrating excellent agreement with the proposed theoretical model.
The Development of a Simulation Model for Assessing the CI/CD Pipeline Quality in the Development of Information Systems Based on a Multi-Agent Approach

The need for the development of fast and efficient software requires the automation during the build, test and release stages. Thus, a methodology of the continuous integration and continuous delivery (CI/CD) emerges which using a continuous integration server which makes easier automatical build, test, and package of the new feature settings. Poor understanding and execution of CI/CD operations can slow down and even stop the pace of a software project. Many bottlenecks in the CI/CD pipeline can arise from misconfiguration, in other words, an inadequate level of automation, inappropriate agent characteristics, and a suboptimal queuing strategy. On the other hand, the finding a way to analyze and improve the settings of the CI/CD pipeline brings great management and economic benefits, since an optimal configuration ultimately implies high efficiency. In this regard, this study attempts to develop a simulation model that will provide a testing environment for a variety of factors that affect CI/ CD performance. The present study, therefore, aims to provide a pipeline simulation model based on the logic of a queuing system, which is based on a multi-agent approach and an experimental environment that allows you to test different settings and scenarios.
A Taxonomy of Social Roles for Agents in Games

Social agents have been used in games often, for example, to create a social dimension (e.g. the inhabitants of a village) or to provide challenges to players (e.g. the opponents players face). These agents have an essential role in the players' experience, and, as such, their creation needs to carefully considered. In this paper we propose a taxonomy of social roles that agents can play in games as a step towards the formalization of the problem of the creation of social agents in games. We believe that this taxonomy can help researchers to reach some common ground on the subject and, therefore, promote common views of the research problems involved in the design and development of social agents for games. We discuss several open challenges in the creation of social agents for games and discuss some future directions of research that can be grounded on the analysis of the taxonomy. For instance, many of the social roles proposed are played by agents that do not have much agency or autonomy. Also, there is a large number of under-explored social roles in games at the moment. The taxonomy serves as inspiration to guide game design involving social interactions with game actors, promoting new kinds of gameplay built on the interactive space afforded by the social agents.
ElGA

Modern graphs are not only large, but rapidly changing. The rate of change can vary significantly along with the computational cost. Existing distributed graph analysis systems have largely been designed to operate on static graphs. Infrastructure changes in these systems need to occur when the system is idle, which can result in significant wasted resources or the inability to cope with changes.
Arabic and American Sign Languages Alphabet Recognition by Convolutional Neural Network

Hearing loss is a common disability that occurs in many people worldwide.Hearing loss can be mild to complete deafness.Sign language is used to communicate with the deaf community.Sign language comprises hand gestures and facial expressions.However, people fi nd it challenging to communicate in sign language as not all know sign language.Every country has developed its sign language like spoken languages, and there is no standard syntax and grammatical structure.The main objective of this research is to facilitate the communication between deaf people and the community around them.Since sign language contains gestures for words, sentences, and letters, this research implemented a system to automatically recognize the gestures and signs using imaging devices like cameras.Two types of sign languages are considered, namely, American sign language and Arabic sign language.We have used the convolutional neural network (CNN) to classify the images into signs.Diff erent settings of CNN are tried for Arabic and American sign datasets.CNN-2 consisting of two hidden layers produced the best results (accuracy of 96.4%) for the Arabic sign language dataset.CNN-3, composed of three hidden layers, achieved an accuracy of 99.6% for the American sign dataset.
Establishing a Baseline of Romanian Speech-to-Text Models

With the increasing usage of Natural Language Processing to facilitate the interactions between humans and machines, automatic speech recognition systems have become increasingly popular as a result of their utility in a wide range of applications. In this paper we explore well-known open-source speech-to-text engines, namely CMUSphinx, DeepSpeech, and Kaldi, to build a baseline of models to transcribe Romanian speech. These engines employ various underlying methods from hidden Markov models to deep neural networks that also integrate language models, thus providing a solid baseline for comparison. Unfortunately, Romanian is still a low-resource language and six datasets of various qualities were merged to obtain 104 hours of speech. To further increase the size of the gathered corpora, our experiments consider data augmentation techniques, specifically SpecAugment, applied on the most promising model. Besides using existing corpora, we publicly release a dataset of 11.5 hours generated from Governmental transcripts. The best performing model is obtained using the Kaldi architecture, considers a hybrid structure with a Deep Neural Network, and achieves a WER of 3.10% on the test partition.
Predictive structure or paradigm size? Investigating the effects of i-complexity and e-complexity on the learnability of morphological systems

Research on cross-linguistic differences in morphological paradigms reveals a wide range of variation on many dimensions, including the number of categories expressed, the number of unique forms, and the number of inflectional classes. However, in an influential paper, Ackerman &amp; Malouf (2013) argue that there is one dimension on which languages do not differ widely: in predictive structure. Predictive structure in a paradigm describes the extent to which forms predict each other, called i-complexity. Ackerman &amp; Malouf (2013) show that although languages differ according to measure of surface paradigm complexity, called e-complexity, they tend to have low i-complexity. They conclude that morphological paradigms have evolved under a pressure for low i-complexity, such that even paradigms with very high e-complexity are relatively easy to learn so long as they have low i-complexity. While this would potentially explain why languages are able to maintain large paradigms, recent work by Johnson et al. (submitted) suggests that both neural networks and human learners may actually be more sensitive to e-complexity than i-complexity. Here we will build on this work, reporting a series of experiments under more realistic learning conditions which confirm that indeed, across a range of paradigms that vary in either e- or i-complexity, neural networks (LSTMs) are sensitive to both, but show a larger effect of e-complexity (and other measures associated with size and diversity of forms). In human learners, we fail to find any effect of i-complexity at all. Further, analysis of a large number of randomly generated paradigms show that e- and i-complexity are negatively correlated: paradigms with high e-complexity necessarily show low i-complexity.These findings suggest that the observations made by Ackerman &amp; Malouf (2013) for natural language paradigms may stem from the nature of these measures rather than learning pressures specially attuned to i-complexity.
Decomposition of high-rank factorized unitary coupled-cluster operators using ancilla and multiqubit controlled low-rank counterparts

The factorized form of the unitary coupled-cluster (UCC) approximation is one of the most promising methodologies to prepare trial states for strongly correlated systems within the variational quantum eigensolver framework. The factorized form of the UCC Ansatz can be systematically applied to a reference state to generate the desired entanglement. The difficulty associated with such an approach is the requirement of simultaneously entangling a growing number of qubits, which quickly exceeds the hardware limitations of today's quantum machines. In particular, while circuits for singles and double excitations can be performed on current hardware, higher-rank excitations require too many gate operations. In this work, we propose a set of schemes that trade off using extra qubits for a reduced gate depth to decompose high-rank UCC excitation operators into significantly lower depth circuits. These results will remain useful even when fault-tolerant machines are available to reduce the overall state-preparation circuit depth.
Detection of phishing attacks using the anti-phishing framework

. In the area of computer security, phishing is a form of criminal activity that involves obtaining sensitive data, such as access data for banking applications, e-commerce applications (such as eBay or PayPal) or credit card information, using techniques to manipulate the identity data of a person or institution. A phishing attack consists of sending the attacker an electronic message, using instant messaging software or telephone, in which the user is advised to give his confidential data to win certain prizes, or is informed that they are prizes. necessary due to technical errors that led to the loss of the original data. According to the reports of the AntiPhishing Working Group (APWG) published in December 2018, phishing against banking and the payment processor was high. Almost all phishy URLs use HTTPS and use redirects to avoid detection. This paper is a comparative survey of the available methods for detecting phishing sites. There has been a comparative study of anti-phishing tools and their limitations have been found. An anti-phishing model, a framework to help detect these attacks, is discussed. The identification of this type of attack is a very important problem considering that, at present, people carry out many online transactions regarding money transfer, payment of bills or purchases.
A comparative analysis of classification techniques for human activity recognition using wearable sensors and smart-phones

INTRODUCTION: In these days, the usage of smart-phones and wearable sensors have increased at an exceptional rate.These smart devices are equipped with different sensors such as gyroscope, accelerometer and GPS.By using these sensors to analyze the activity of the end-user, behavioural characteristics of the user can be captured.OBJECTIVES: Although smart-phone and wearable devices provide a platform for conducting social, psychological and physical studies, they still have several limitations and challenges.METHODS: This paper provides a comparative analysis of different classical Machine Learning and Deep Learning algorithms and discusses their accuracy and efficiency for human activity recognition (HAR).RESULTS and CONCLUSION: The paper has primarily used the data captured using wireless sensor devices placed on different parts of a human body, and then compared the results for different classifiers.The conclusion shows that Deep learning schemes are extremely accurate and efficient in comparison with classical machine learning techniques.
Development of a corporate university institution: strategic initiatives

This article is devoted to the study of the concept, methodological approach and problems of formation and development of the corporate university system. The article reveals the prerequisites for the creation of corporate universities, presents the Russian and international experience. The analysis of the current state of development of the corporate university system and existing strategic initiatives for the development of corporate universities is carried out. The authors of the article paid attention to the formation of a roadmap for the development of the institution of corporate universities, taking into account the impact of strategic initiatives using the advantages of the Learning management system.
Probabilistic Couplings from Program Logics

Probabilistic couplings are a powerful abstraction for analysing probabilistic properties. Originating from research in probability theory, a coupling is a distribution over pairs that relates – or couples – two given distributions. If we can find a coupling with certain properties, then we can conclude properties about the two related distributions. In this way, probabilistic relational properties – properties comparing two executions of a probabilistic program – can be established by building a suitable coupling. Couplings have also been explored in the logic and verification literature. For example, probabilistic bisimulation asserts that there exists a coupling; in this way, couplings can be used to verify equivalence of finite state probabilistic transition systems. However, their use in mathematics suggests that couplings can prove more sophisticated properties for richer probabilistic computations, such as imperative programs and infinite state systems. Furthermore, we can borrow a tool from probability theory, called proof by coupling, to construct couplings in a compositional fashion. This chapter describes how coupling proofs can be naturally encoded in pRHL, a relational program logic originally designed for verifying cryptographic protocols. Several examples are presented, showing how to use this proof technique to verify equivalence, stochastic domination and probabilistic convergence.
LSTM Derin Öğrenme Yaklaşımı ile Covid-19 Pandemi Sürecinde Twitter Verilerinden Duygu Analizi

It is very important to understand people's thoughts regarding social events occurring in the world and to make some inferences by analyzing these thoughts.With these analysis and inferences, various projects can be initiated and decision-making processes can be formed.One of the procedures used for these purposes is the sentiment analysis which is performed by classifying text with various computer algorithms.The methods used to perform sentiment analysis are generally categorized as dictionary-based methods and machine learning approaches.In this paper, a sentiment analysis study has been carried out by considering a number of frequently spoken terms on the Twitter social media platform regarding the coronavirus (Covid-19) pandemic, which has affected the world and is still ongoing.For this, some Turkish titles related to the subject were collected and sentiment analysis was conducted by classifying these titles as positive and negative thoughts.For this analysis, a system using a Long Short-Term Memory (LSTM) structure, which is one of the deep learning methods, was proposed.The proposed system was applied on the obtained data sets and a maximum 97% accuracy was achieved.
SEFPN: Scale-Equalizing Feature Pyramid Network for Object Detection

Feature Pyramid Network (FPN) is used as the neck of current popular object detection networks. Research has shown that the structure of FPN has some defects. In addition to the loss of information caused by the reduction of the channel number, the features scale of different levels are also different, and the corresponding information at different abstract levels are also different, resulting in a semantic gap between each level. We call the semantic gap level imbalance. Correlation convolution is a way to alleviate the imbalance between adjacent layers; however, how to alleviate imbalance between all levels is another problem. In this article, we propose a new simple but effective network structure called Scale-Equalizing Feature Pyramid Network (SEFPN), which generates multiple features of different scales by iteratively fusing the features of each level. SEFPN improves the overall performance of the network by balancing the semantic representation of each layer of features. The experimental results on the MS-COCO2017 dataset show that the integration of SEFPN as a standalone module into the one-stage network can further improve the performance of the detector, by ∼1AP, and improve the detection performance of Faster R-CNN, a typical two-stage network, especially for large object detection APL∼2AP.
A Literature Review of Knowledge Tracing for Student Modeling : Research Trends, Models, Datasets, and Challenges

Modeling students' knowledge is a fundamental part of online learning platforms. Knowledge tracing is an application of student modeling which renowned for its ability to trace students' knowledge. Knowledge tracing ability can be used in online learning platforms for predicting learning performance and providing adaptive learning. Due to the wide uses of knowledge tracing in student modeling, this study aims to understand the state-of-the-art and future research of knowledge tracing. This study focused on reviewing 24 studies published between 2017 to the third quarter of 2021 in four digital databases. The selected studies have been filtered using inclusion and exclusion criteria. Several previous studies have shown that there are two approaches used in knowledge tracing, including probabilistic and deep learning. Bayesian Knowledge Tracing model is the most widely used in the probabilistic approach, while the Deep Knowledge Tracing model is the most popular model in the deep learning approach. Meanwhile, ASSISTments 2009–2010 is the most frequently tested dataset for probabilistic and deep learning approaches. In the future, additional studies are required to explore several models which have been developed previously. Therefore this study provides direction for future research of each existing approach.
The Influence of Embodiment as a Cartoon Character on Public Speaking Anxiety

Virtual Reality can be used to embody people in different types of body—so that when they look towards themselves or in a mirror they will see a life-sized virtual body instead of their own, and that moves with their own movements. This will typically give rise to the illusion of body ownership over the virtual body. Previous research has focused on embodiment in humanoid bodies, albeit with various distortions such as an extra limb or asymmetry, or with a body of a different race or gender. Here we show that body ownership also occurs over a virtual body that looks like a cartoon rabbit, at the same level as embodiment as a human. Furthermore, we explore the impact of embodiment on performance as a public speaker in front of a small audience. Forty five participants were recruited who had public speaking anxiety. They were randomly partitioned into three groups of 15, embodied as a Human, as the Cartoon rabbit, or from third person perspective (3PP) with respect to the rabbit. In each condition they gave two talks to a small audience of the same type as their virtual body. Several days later, as a test condition, they returned to give a talk to an audience of human characters embodied as a human. Overall, anxiety reduced the most in the Human condition, the least in the Cartoon condition, and there was no change in the 3PP condition, taking into account existing levels of trait anxiety. We show that embodiment in a cartoon character leads to high levels of body ownership from the first person perspective and synchronous real and virtual body movements. We also show that the embodiment influences outcomes on the public speaking task.
Connecting AI-based Oracles to Blockchains via an Auditable Auction Protocol

The use of Artificial Intelligence (AI), in particular sequential decision-making (SDM) algorithms, in blockchains can greatly improve their autonomy and general features. In this context, this paper proposes the protocol CONDOR (Connecting ai-based Oracles to blockchaiN via an auDitable auctiOn pRotocol) between a smart contract and some off-chain AI-based oracles to enable the smart contract to choose among AI solution proposals while inciting those oracles to provide non-forged results. The main principle is to consider the AI outcomes as auctions that can be challenged and audited through a dispute phase. An Ethereum implementation of the aforementioned protocol was built in order to assess its scalability and time performances.