Reviewerly/exHarmony huggingface dataset:
Twitter bot detection using supervised machine learning

Abstract In the world of Internet and social media, there are about 3.8 billion active social media users and 4.5 billion people accessing the internet daily. Every year there is a 9% growth in the number of users and half of the internet traffic consists of mostly bots. Bots are mainly categorized into two categories: good and bad bots; good bots consist of web crawlers and chat bots whereas bad bots consist of malicious bots which make up 20% of the traffic, the reason they are not good is that they are used for nefarious purposes, they can mimic human behavior, they can impersonate legal traffic, attack IoT devices and exploit their performance. Among all these concerns, the primary concern is for social media users as they represent a large group of active users on the internet, they are more vulnerable to breach of data, change in opinion based on data. Detection of such bots is crucial to prevent further mishaps. We use supervised Machine learning techniques in this paper such as Decision tree, K nearest neighbors, Logistic regression, and Naïve Bayes to calculate their accuracies and compare it with our classifier which uses Bag of bots’ word model to detect Twitter bots from a given training data set.
A new clear sky solar irradiance model

A new broadband parametric clear-sky solar irradiance model is reported in this paper. Constructed as a trade-off between accuracy and accessibility, the model holds some innovative elements, such as: (1) the atmospheric transmittances are inferred by integration of Leckner's spectral ones within three disjointed spectral bands; (2) within each band, the specific aerosol transmittance is expressed as an exponential-decay equation, where the aerosol optical depth formally preserves the Ångström power law, but written at a fixed wavelength; (3) targeting a better capture of the peculiarities of solar radiation extinction by different types of aerosols, the Ångström exponent is innovatively introduced as an input parameter; (4) the directional scattering characteristics of aerosols are considered empirically by including the Ångström parameters in the equation of the downward fraction. The new model was tested against two challenging data sets collected from two stations located in Europe and the United States. The results show that the proposed model gives good estimates, comparable to those of REST2, a current benchmark in clear-sky solar irradiance modeling.
Efficient Information-Theoretic Multi-party Computation over Non-commutative Rings

We construct the first efficient, unconditionally secure MPC protocol that only requires black-box access to a non-commutative ring R. Previous results in the same setting were efficient only either for a constant number of corruptions or when computing branching programs and formulas. Our techniques are based on a generalization of Shamir's secret sharing to non-commutative rings, which we derive from the work on Reed Solomon codes by Quintin, Barbier and Chabot (IEEE Transactions on Information Theory, 2013). When the center of the ring contains a set $$A = \{\alpha _0, \ldots , \alpha _n\}$$ such that $$\forall i \ne j, \alpha _i \,-\, \alpha _j \in R^*$$ , the resulting secret sharing scheme is strongly multiplicative and we can generalize existing constructions over finite fields without much trouble. Most of our work is devoted to the case where the elements of A do not commute with all of R, but they just commute with each other. For such rings, the secret sharing scheme cannot be linear "on both sides" and furthermore it is not multiplicative. Nevertheless, we are still able to build MPC protocols with a concretely efficient online phase and black-box access to R. As an example we consider the ring $$\mathcal {M}_{m\times m}(\mathbb {Z}/2^k\mathbb {Z})$$ , for which when $$m > \log (n+1)$$ , we obtain protocols that require around $$\lceil \log (n+1)\rceil /2$$ less communication and $$2\lceil \log (n+1)\rceil $$ less computation than the state of the art protocol based on Circuit Amortization Friendly Encodings (Dalskov, Lee and Soria-Vazquez, ASIACRYPT 2020). In this setting with a "less commutative" A, our black-box preprocessing phase has a less practical complexity of $$\mathsf {poly}(n)$$ . We fix this by additionally providing specialized, concretely efficient preprocessing protocols for $$\mathcal {M}_{m\times m}(\mathbb {Z}/2^k\mathbb {Z})$$ that exploit the structure of the matrix ring.
Computer-Vision based Face Mask Detection using CNN

The global pandemic due to the novel corona virus, covid-19 has affected millions of lives across the globe. It has also disturbed economy, environment and social norms leading to many problems and giving birth to different rules and laws in order to ensure public safety. Wearing masks is one of the most important and primitive precautionary measures along with safe social distancing as advised by the World Health Organization. To manually monitor people not wearing face mask in order to ensure public safety is definitely a strenuous task. Therefore, this research work proposes a real time face mask detection system by applying computer vision and machine learning concepts like convolution neural networks and refined MobileNetV2 architecture to ease the deployment of proposed model in embedded devices with limited computational capacity. The dataset utilized here is available on Kaggle as Face mask detection dataset. The model is trained using Adam Optimizer algorithm which is best suited for deep learning models and is built using Keras, TensorFlow and OpenCV. The proposed model touches 99% accuracy under various training to testing ratios like 70% training and 30% testing,50% training and 50% testing etc. Precision, recall, f-score and support are calculated for all trials. This means that the system is computationally effective and could potentially be used in places like railway stations, airports or any other public places to detect people not wearing face mask and ensure safety to certain extent during this pandemic times.
Application of Chatbot for consumer perspective using Artificial Intelligence

ChatBot can be described as software that can chat with people using artificial intelligence. A chatbot is a PC program that reenacts human discussion through voice orders or text visits or both. Chatbot, short for chatter-bot, is a man-made reasoning (AI) include that can be implanted and utilized through any significant informing applications. This software are recycled to achieve tasks such as rapidly responding to consumers, informing them, assisting to purchase products and providing better service to customers. This research paper showing how a chatbot can be created through demonstration as well as their applications of chatbots in numerous areas such as traveling, banking, health, customer call centers and e-commerce. Additionally, the results of an example chatbbot for online food order service developed in the e-commerce domain is presented using Dialog-flow engine and Facebook Messanger. This research work represents the Chat flow, components of a chatbot, Implementation using Dialogflow.
Empowering Knowledge Distillation via Open Set Recognition for Robust 3D Point Cloud Classification

Real-world scenarios pose several challenges to deep learning based computer vision techniques despite their tremendous success in research. Deeper models provide better performance, but are challenging to deploy and knowledge distillation allows us to train smaller models with minimal loss in performance. A model also has to deal with open set samples from classes outside the ones it was trained on and should be able to identify them as unknown samples while classifying the known ones correctly. Finally, most existing image recognition research focuses only on using two-dimensional snapshots of the three-dimensional real world objects. In this work, we attempt to bridge these three research fields, which have been developed independently until now, despite being deeply interrelated in practice. We propose a joint knowledge distillation and open set recognition training methodology for three-dimensional object recognition. We demonstrate the effectiveness of the proposed method via various experiments on how it allows us to obtain a much smaller model, which takes a minimal hit in performance while being capable of open set recognition for 3D point cloud data.
Multi-theorem Designated-Verifier NIZK for QMA

We present a designated-verifier non-interactive zero-knowledge argument system for QMA with multi-theorem security under the Learning with Errors Assumption. All previous such protocols for QMA are only single-theorem secure. We also relax the setup assumption required in previous works. We prove security in the malicious designated-verifier (MDV-NIZK) model (Quach, Rothblum, and Wichs, EUROCRYPT 2019), where the setup consists of a mutually trusted random string and an untrusted verifier public key. Our main technical contribution is a general compiler that given a NIZK for NP and a quantum sigma protocol for QMA generates an MDV-NIZK protocol for QMA.
Differential Privacy Location Protection Method Based on the Markov Model

Location-based services (LBS) have become an important research area with the rapid development of mobile Internet technology, GPS positioning technology, and the widespread application of smart phones and social networks. LBS can provide convenience and flexibility for the users’ daily life, but at the same time, it also brings security risks to the users’ privacy. Untrusted or malicious LBS servers can collect users’ location data through various ways and disclose it to the third party, thus causing users’ privacy leakage. In this paper, a differential privacy location protection method based on the Markov model for user’s location privacy is proposed. Firstly, the transition probability matrix between states of the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>n</mi> </math> -order Markov model is used to predict the occurrence state and development trend of events; thereby, the user’s location is predicted, and then a location prediction algorithm based on the Markov model (LPAM) is proposed. Secondly, a location protection algorithm based on differential privacy (LPADP) is proposed, in which location privacy tree (LPT) is constructed according to the location data and the difficulty of retrieval, the two nodes with the largest predicted value of LPT are allocated with a reasonable privacy budget, and Laplace noise is added to protect location privacy. Theoretical analysis and experimental results show that the proposed method not only meets the requirements of differential privacy and protects location privacy effectively but also has high data availability and low time complexity.
Multi-criteria recommender system model for lockdown decision of Covid-19

A lockdown is an appropriate method to suppress the spread of COVID-19 in a region. However, apart from the spread of COVID-19, it also affects various aspects and sectors. Therefore, a lockdown decision for a region must go through a careful planning before being finalized. There are many criteria to be considered in a lockdown decision, among them are the number of positive cases, health facilities and demographic situation in an area. This article will discuss a recommender system model that may assist the lockdown decision in a region by considering several criteria originating from multiple databases. in this model had two kinds of inputs, namely internal and external databases. In this case, Internal database is reproduction number of covid-19 case in West Java. External Database is elderly age number ratiom health facility number ratio, nurse ratio and medical worker ratio (specialists and general practitioners), beds in health facilities and population density. The outputs of this method were decisions whether to implement lockdown or new normal for a region in accordance of predetermined parameters. The results of this model can be used for the government in formulating strategies to reduce the number of spread of Covid-19 in terms of determining which areas should apply lockdown.
Formal concept analysis for the generation of plural referring expressions

Referring Expression Generation (REG) is one of the most relevant and complex tasks in Natural Language Generation. Plural referring expressions are linguistic expressions that intend to identify a set of objects in a certain context. In this paper we formulate for the first time plural referring expressions and the elements of the associated REG problem in terms of the well known Formal Concept Analysis (FCA) mathematical framework. We provide theoretical results and general algorithms for computing the whole collection of referable sets in the context under analysis, as well as a condensed representation of all referring expressions for each referable set.
California's COVID-19 Virtual Training Academy: Rapid Scale-Up of a Statewide Contact Tracing and Case Investigation Workforce Training Program

Case investigation (CI) and contact tracing (CT) are key to containing the COVID-19 pandemic. Widespread community transmission necessitates a large, diverse workforce with specialized knowledge and skills. The University of California, San Francisco and Los Angeles partnered with the California Department of Public Health to rapidly mobilize and train a CI/CT workforce. In April through August 2020, a team of public health practitioners and health educators constructed a training program to enable learners from diverse backgrounds to quickly acquire the competencies necessary to function effectively as CIs and CTs. Between April 27 and May 5, the team undertook a curriculum design sprint by performing a needs assessment, determining relevant goals and objectives, and developing content. The initial four-day curriculum consisted of 13 hours of synchronous live web meetings and 7 hours of asynchronous, self-directed study. Educational content emphasized the principles of COVID-19 exposure, infectious period, isolation and quarantine guidelines and the importance of prevention and control interventions. A priority was equipping learners with skills in rapport building and health coaching through facilitated web-based small group skill development sessions. The training was piloted among 31 learners and subsequently expanded to an average weekly audience of 520 persons statewide starting May 7, reaching 7,499 unique enrollees by August 31. Capacity to scale and sustain the training program was afforded by the UCLA Extension Canvas learning management system. Repeated iteration of content and format was undertaken based on feedback from learners, facilitators, and public health and community-based partners. It is feasible to rapidly train and deploy a large workforce to perform CI and CT. Interactive skills-based training with opportunity for practice and feedback are essential to develop independent, high-performing CIs and CTs. Rigorous evaluation will continue to monitor quality measures to improve the training experience and outcomes.
Constructing Locally Leakage-Resilient Linear Secret-Sharing Schemes

Innovative side-channel attacks have repeatedly falsified the assumption that cryptographic implementations are opaque black-boxes. Therefore, it is essential to ensure cryptographic constructions' security even when information leaks via unforeseen avenues. One such fundamental cryptographic primitive is the secret-sharing schemes, which underlies nearly all threshold cryptography. Our understanding of the leakage-resilience of secret-sharing schemes is still in its preliminary stage. This work studies locally leakage-resilient linear secret-sharing schemes. An adversary can leak m bits of arbitrary local leakage from each n secret shares. However, in a locally leakage-resilient secret-sharing scheme, the leakage's joint distribution reveals no additional information about the secret. For every constant m, we prove that the Massey secret-sharing scheme corresponding to a random linear code of dimension k (over sufficiently large prime fields) is locally leakage-resilient, where $$k/n > 1/2$$ is a constant. The previous best construction by Benhamouda, Degwekar, Ishai, Rabin (CRYPTO–2018) needed $$k/n > 0.907$$ . A technical challenge arises because the number of all possible m-bit local leakage functions is exponentially larger than the number of random linear codes. Our technical innovation begins with identifying an appropriate pseudorandomness-inspired family of tests; passing them suffices to ensure leakage-resilience. We show that most linear codes pass all tests in this family. This Monte-Carlo construction of linear secret-sharing scheme that is locally leakage-resilient has applications to leakage-resilient secure computation. Furthermore, we highlight a crucial bottleneck for all the analytical approaches in this line of work. Benhamouda et al. introduced an analytical proxy to study the leakage-resilience of secret-sharing schemes; if the proxy is small, then the scheme is leakage-resilient. However, we present a one-bit local leakage function demonstrating that the converse is false, motivating the need for new analytically well-behaved functions that capture leakage-resilience more accurately. Technically, the analysis involves probabilistic and combinatorial techniques and (discrete) Fourier analysis. The family of new "tests" capturing local leakage functions, we believe, is of independent and broader interest.
Quantum reinforcement learning: the maze problem

Abstract Quantum machine learning (QML) is a young but rapidly growing field where quantum information meets machine learning. Here, we will introduce a new QML model generalising the classical concept of reinforcement learning to the quantum domain, i.e. quantum reinforcement learning (QRL). In particular, we apply this idea to the maze problem, where an agent has to learn the optimal set of actions in order to escape from a maze with the highest success probability. To perform the strategy optimisation, we consider a hybrid protocol where QRL is combined with classical deep neural networks. In particular, we find that the agent learns the optimal strategy in both the classical and quantum regimes, and we also investigate its behaviour in a noisy environment. It turns out that the quantum speedup does robustly allow the agent to exploit useful actions also at very short time scales, with key roles played by the quantum coherence and the external noise. This new framework has the high potential to be applied to perform different tasks (e.g. high transmission/processing rates and quantum error correction) in the new-generation noisy intermediate-scale quantum (NISQ) devices whose topology engineering is starting to become a new and crucial control knob for practical applications in real-world problems. This work is dedicated to the memory of Peter Wittek.
User Response to Extended Reality Sponsorship Activations on Social Media: Exploring Impressions of GoPro’s Use of 360° Video in Marketing

(2021). User Response to Extended Reality Sponsorship Activations on Social Media: Exploring Impressions of GoPro's Use of 360° Video in Marketing. Journal of Interactive Advertising: Vol. 21, Digital and Interactive Marketing Communications in Sports, pp. 93-107.
Analyze the Datasets of Software Effort Estimation With Particle Swarm Optimization

Software Effort Estimation is a software estimation process as an essential process in software projects. Many studies have been carried out regarding software estimation with various methods, including machine learning methods or non-machine learning methods. This research was conducted using a public data set and carried out through a Particle Swarm optimization attribute selection experiment on the project parameters using the K-NN algorithm as an estimate. The software estimation effort dataset used in this study is Desharnais, Maxwell, Kitchenham CSC, and Kamrer. The results of the study indicate that the Particle Swarm optimization feature selection can reduce the RMSE value and show a significant decrease in the RMSE value. This shows that the lower the RMSE value, the more precise the estimated value will be. This research is useful where it can be used as information for developers in estimating the time to build an application so that it is in accordance with the initial plan.
Kindergarden quantum mechanics graduates ...or how I learned to stop gluing LEGO together and love the ZX-calculus

This paper is a 'spiritual child' of the 2005 lecture notes Kindergarten Quantum Mechanics Coecke (2005) [24], which showed how a simple, pictorial extension of Dirac notation allowed several quantum features to be easily expressed and derived, using language even a kindergartner can understand. Central to that approach was the use of pictures and pictorial transformation rules to understand and derive features of quantum theory and computation. However, this approach left many wondering 'where's the beef?' In other words, was this new approach capable of producing new results, or was it simply an aesthetically pleasing way to restate stuff we already know? The aim of this sequel paper is to say 'here's the beef!', and highlight some of the major results of the approach advocated in Kindergarten Quantum Mechanics, and how they are being applied to tackle practical problems on real quantum computers. Toward that end, we will focus mainly on what has become the Swiss army knife of the pictorial formalism: the ZX-calculus, a graphical tool for representing and manipulating complex linear maps on 2N dimensional space. First we look at some of the ideas behind the ZX-calculus, comparing and contrasting it with the usual quantum circuit formalism. We then survey results from the past few years falling into three categories: (1) completeness of the rules of the ZX-calculus, (2) state-of-the-art quantum circuit optimisation results in commercial and open-source quantum compilers relying on ZX, and (3) the use of ZX in translating real-world stuff like natural language into quantum circuits that can be run on today's (very limited) quantum hardware. We also take the title literally, and outline an ongoing experiment aiming to show that ZX-calculus enables children to do cutting-edge quantum computing stuff. If anything, this would truly confirm that 'kindergarten quantum mechanics' wasn't just a joke.
Text Region Conditional Generative Adversarial Network for Text Concealment in the Wild

Textual information appearing on the captured image may contain personal information. In various circumstances, publishing such images in the public domain may create a threat of privacy leak. To avoid these situations, we propose a text concealment method. To accomplish this task, we have used a conditional generator, which is a text region conditioned concealment network. The text regions predicted by the detector network are used as a conditioning criterion for the concealment process. The text region prediction in the form of a word-level bounding box may contain stroke pixels as well as the background pixels. However, to reduce the number of background pixels for proper conditioning of text concealment network, a character level annotation is used for the generator in place of word-level bounding box annotation. It helps to focus more on strokes as compared to the background pixels. A character-level symmetric line representation of text has been proposed to obtain finer level text region prediction as compared to the character-level bounding box. The proposed model is trainable from end-to-end. The text region conditioned generator is trained from the loss of global and local discriminators. The proposed method is validated on public scene text image datasets such as ICDAR 2015, COCO-Text and Synthesis dataset. The proposed architecture shows competitive results as compared to other state-of-the-art approaches.