Reviewerly/exHarmony huggingface dataset:
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Abstract We describe the design, analysis, implementation, and evaluation of P irsona , a digital content delivery system that realizes collaborative-filtering recommendations atop private information retrieval (PIR). This combination of seemingly antithetical primitives makes possible—for the first time—the construction of practically efficient e-commerce and digital media delivery systems that can provide personalized content recommendations based on their users’ historical consumption patterns while simultaneously keeping said consumption patterns private. In designing P irsona , we have opted for the most performant primitives available (at the expense of rather strong non-collusion assumptions); namely, we use the recent computationally 1-private PIR protocol of Hafiz and Henry (PETS 2019.4) together with a carefully optimized 4PC Boolean matrix factorization.
Sustainable territorial development in the context of higher education structure

The paper analyzes the impact of the higher education system on territorial development on the basis of modern statistical data. The assessment of the current state of affairs brings out a significant differentiation of regions by the number of universities and students who study there. The formed architecture of the higher education structure does not meet the modern requirements of the spatial development of Russia, contributing to the outflow of talented young people from peripheral regions, provoking uncompensated migration, causing a decrease in the equal accessibility of higher education and the effectiveness of meeting the demand for qualified personnel in regional labor markets. Given the results of the analysis, we draw a conclusion that it is necessary to intensify scientific research in the field in order to develop the latest theory and methodology for synchronizing and harmonizing the interests of the country and its territories.
UniParma at SemEval-2021 Task 5: Toxic Spans Detection Using CharacterBERT and Bag-of-Words Model

With the ever-increasing availability of digital information, toxic content is also on the rise.Therefore, the detection of this type of language is of paramount importance.We tackle this problem utilizing a combination of a stateof-the-art pre-trained language model (Charac-terBERT) and a traditional bag-of-words technique.Since the content is full of toxic words that have not been written according to their dictionary spelling, attendance to individual characters is crucial.Therefore, we use Char-acterBERT to extract features based on the word characters.It consists of a Character-CNN module that learns character embeddings from the context.These are, then, fed into the well-known BERT architecture.The bagof-words method, on the other hand, further improves upon that by making sure that some frequently used toxic words get labeled accordingly.With a ∼4 percent difference from the first team, our system ranked 36 th in the competition.The code is available for further research and reproduction of the results 1 .
DeepBlueAI at SemEval-2021 Task 7: Detecting and Rating Humor and Offense with Stacking Diverse Language Model-Based Methods

This paper describes the winning system for SemEval-2021 Task 7: Detecting and Rating Humor and Offense.Our strategy is stacking diverse pre-trained language models (PLMs) such as RoBERTa and ALBERT.We first perform fine-tuning on these two PLMs with various hyperparameters and different training strategies.Then a valid stacking mechanism is applied on top of the fine-tuned PLMs to get the final prediction.Experimental results on the dataset released by the organizer of the task show the validity of our method and we win first place and third place for subtask 2 and 1a.
Application of the Level-$2$ Quantum Lasserre Hierarchy in Quantum Approximation Algorithms

The Lasserre Hierarchy is a set of semidefinite programs which yield increasingly tight bounds on optimal solutions to many NP-hard optimization problems. The hierarchy is parameterized by levels, with a higher level corresponding to a more accurate relaxation. High level programs have proven to be invaluable components of approximation algorithms for many NP-hard optimization problems. There is a natural analogous quantum hierarchy, which is also parameterized by level and provides a relaxation of many (QMA-hard) quantum problems of interest. In contrast to the classical case, however, there is only one approximation algorithm which makes use of higher levels of the hierarchy. Here we provide the first ever use of the level-$2$ hierarchy in an approximation algorithm for a particular QMA-complete problem, so-called Quantum Max Cut. We obtain modest improvements on state-of-the-art approximation factors for this problem, as well as demonstrate that the level-$2$ hierarchy satisfies many physically-motivated constraints that the level-$1$ does not satisfy. Indeed, this observation is at the heart of our analysis and indicates that higher levels of the quantum Lasserre Hierarchy may be very useful tools in the design of approximation algorithms for QMA-complete problems.
A Qualitative Report on Diffusion based Image Inpainting Models

Diffusion equations have been successfully applied in the field of digital image processing for the past twenty years, describing the random motion of the particles in physics.Image inpainting is a significant research problem in the image processing.Its main intent is to complete the unknown parts of the image from the knowledge of known parts of the image.This research problem can be used to restore damaged photograph, random loss of wavelet coefficients during transmission, superimposed text, noise, and/or blur.According to available models on digital image inpainting, this paper attempts to make an outline of state-of-the-art diffusion based image inpainting models with corresponding mathematical representation.We also compared the state-of-the-art diffusion based inpainting techniques in terms of its main idea, type of distortion, strengths, and weaknesses.
A Privacy Preservation Model for RFID Data-Collections is Highly Secure and More Efficient than LKC-Privacy

RFID is a smart label technology that is used in several real-life applications such as inventory management, asset tracking, personnel tracking, controlling access to restricted areas, ID badging, supply chain management, counterfeit prevention (e.g., in the pharmaceutical industry), and smart farming. Generally, the data collection of RFIDs consists of the users’ visited locations and their visiting time, so called as trajectory datasets. Aside from applications, trajectory datasets can also be released for public use. For this reason, they could lead to being privacy violation issues. To address these issues in trajectory datasets, LKC-Privacy is proposed. Unfortunately, in this work, we demonstrate that LKC-Privacy still has a serious vulnerability that must be improved. To rid the demonstrated vulnerability of LKC-Privacy, a privacy preservation model is proposed in this work. Furthermore, the proposed mode is evaluated by extensive experiments. From the experimental results, they indicate that the proposed model is highly secure and more efficient than LKC-Privacy.
Residue-Free Computing

Abstract Computer applications often leave traces or residues that enable forensic examiners to gain a detailed understanding of the actions a user performed on a computer. Such digital breadcrumbs are left by a large variety of applications, potentially (and indeed likely) unbeknownst to their users. This paper presents the concept of residue-free computing in which a user can operate any existing application installed on their computer in a mode that prevents trace data from being recorded to disk, thus frustrating the forensic process and enabling more privacy-preserving computing. In essence, residue-free computing provides an “incognito mode” for any application. We introduce our implementation of residue-free computing, R esidue F ree , and motivate R esidue F ree by inventorying the potentially sensitive and privacy-invasive residue left by popular applications. We demonstrate that R esidue F ree allows users to operate these applications without leaving trace data, while incurring modest performance overheads.
PROMOTING DIGITAL AWARENESS AT SCHOOL: A THREE-YEAR INVESTIGATION IN PRIMARY AND SECONDARY SCHOOL TEACHERS

PROMOTING DIGITAL AWARENESS AT SCHOOL: A THREE-YEAR INVESTIGATION IN PRIMARY AND SECONDARY SCHOOL TEACHERS
THE OCCURRENCE OF CODE-SWITCHING AMONG MALAYSIAN UNDERGRADUATES ON WHATSAPP: REVIEW OF THE LITERATURE

This research proffers a critical overview of the theoretical and analytical occurrence of code-switching by reviewing a range of empirical and relevant studies. In particular, the dominating and governing factor of computer-mediated communication and code-switching. This research also probes the pertinent concepts, focusing on the types of code-switching and its correlation with computer mediated communication. Subsequently, it reviews the structural features of WhatsApp, deliberating the occurrence of code-switching among UiTM undergraduates in Malaysia, noting the social motivation of style-shifting. The research concludes with recommendations for future research, emphasising on the issue of its applicability to the analysis of second language acquisition and learning.&#x0D; &#x0D; &lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/edu_01/0950/a.php" alt="Hit counter" /&gt;&lt;/p&gt;
A general sample complexity analysis of vanilla policy gradient

We adapt recent tools developed for the analysis of Stochastic Gradient Descent (SGD) in non-convex optimization to obtain convergence and sample complexity guarantees for the vanilla policy gradient (PG). Our only assumptions are that the expected return is smooth w.r.t. the policy parameters, that its $H$-step truncated gradient is close to the exact gradient, and a certain ABC assumption. This assumption requires the second moment of the estimated gradient to be bounded by $A\geq 0$ times the suboptimality gap, $B \geq 0$ times the norm of the full batch gradient and an additive constant $C \geq 0$, or any combination of aforementioned. We show that the ABC assumption is more general than the commonly used assumptions on the policy space to prove convergence to a stationary point. We provide a single convergence theorem that recovers the $\widetilde{\mathcal{O}}(\epsilon^{-4})$ sample complexity of PG to a stationary point. Our results also affords greater flexibility in the choice of hyper parameters such as the step size and the batch size $m$, including the single trajectory case (i.e., $m=1$). When an additional relaxed weak gradient domination assumption is available, we establish a novel global optimum convergence theory of PG with $\widetilde{\mathcal{O}}(\epsilon^{-3})$ sample complexity. We then instantiate our theorems in different settings, where we both recover existing results and obtain improved sample complexity, e.g., $\widetilde{\mathcal{O}}(\epsilon^{-3})$ sample complexity for the convergence to the global optimum for Fisher-non-degenerated parametrized policies.
Phishing Website Detection Using ML

Phishing is one kind of cyber-attack , it is a most dangerous and common attack to retrieve personal information, account details, credit card credentials, organizational details or password of a client to conduct transactions. Phishing websites seem to like the relevant ones and it is difficult to differentiate among those websites. It is one of the most threatening that every individuals and organization faced. URLs are known as web sites are by which users locate information on the internet. The review creates warning of phishing attacks, detection of phishing attacks and motivate the practice of phishing prevention among the readers. With the huge number of phishing emails or messages received now days, companies or individuals are not able to find all of them.
Lotus at SemEval-2021 Task 2: Combination of BERT and Paraphrasing for English Word Sense Disambiguation

In this paper, we describe our proposed methods for the multilingual word-in-Context disambiguation task in SemEval-2021.In this task, systems should determine whether a word that occurs in two different sentences is used with the same meaning or not.We proposed several methods using a pre-trained BERT model.In two of them, we paraphrased sentences and add them as input to the BERT, and in one of them, we used WordNet to add some extra lexical information.We evaluated our proposed methods on test data in SemEval-2021 task 2.
Evaluating Foveated Video Quality Using Entropic Differencing

Virtual Reality is regaining attention due to recent advancements in hardware technology. Immersive images / videos are becoming widely adopted to carry omnidirectional visual information. However, due to the requirements for higher spatial and temporal resolution of real video data, immersive videos require significantly larger bandwidth consumption. To reduce stresses on bandwidth, foveated video compression is regaining popularity, whereby the space-variant spatial resolution of the retina is exploited. Towards advancing the progress of foveated video compression, we propose a full reference (FR) foveated image quality assessment algorithm, which we call foveated entropic differencing (FED), which employs the natural scene statistics of bandpass responses by applying differences of local entropies weighted by a foveation-based error sensitivity function. We evaluate the proposed algorithm by measuring the correlations of the predictions that FED makes against human judgements on the newly created 2D and 3D LIVE-FBT-FCVR databases for Virtual Reality (VR). The performance of the proposed algorithm yields state-of-the-art as compared with other existing full reference algorithms. Software for FED has been made available at: http://live.ece.utexas.edu/research/Quality/FED.zip
Heralded preparation of polarization entanglement via quantum scissors

Quantum entanglement is at the heart of quantum information sciences and quantum technologies. In the optical domain, the most common type of quantum entanglement is polarization entanglement, which is usually created in a postselection manner involving destructive photon detection and thus hindering further applications which require readily available entanglement resources. In this work, we propose a scheme to prepare multipartite entangled states of polarized photons in a heralded manner, i.e., without postselection. We exploit the quantum scissors technique to truncate a given continuous-variable entanglement into the target entangled states which are of hybrid discrete-continuous or solely discrete types. We consider two implementations of the quantum scissors: one modified from the original quantum scissors [Pegg et al., Phys. Rev. Lett. 81, 1604 (1998)] using single photons and linear optics and the other designed here using a type-II two-mode squeezer. We clarify the pros and cons of these two implementations as well as discussing practical aspects of the entanglement preparation. Our work illustrates an interface between various types of optical entanglement and the proposed quantum scissors techniques could serve as alternative methods for heralded generation of polarization entanglement.
Evaluation of the Bubble view Metaphor for the Crowdsourcing Study of Visual Attention Deployment in Tone-Mapped Images

Attention is an important attribute of human vision for study of user's quality of experience (QoE). The attention information collection from eye tracking is impossible in the current scenario of Covid-19. Different mouse metaphors have been proposed to study visual attention without eye tracking equipment. These methods have shown promising results on different types of images (visualizations, natural images and websites) with well-identified regions of interest. However, they have not been precisely tested for QoE applications, where natural images are processed with different algorithms (compression, tone-mapping, etc.) and visual content can induce more exploratory behavior. This paper studies and compares different configurations of bubble view metaphors for the study of visual attention in tone-mapped images.
Transformation of Mobility Industry by Advanced Digital Technologies

Abstract The world of mobility has always been a vanguard of scientific discovery. Artificial intelligence (AI) has already been used in motorsports, making the car more reliable and advanced. The digital transformation using AI and Machine learning in the automotive industry is fast, creating huge monetization opportunities. Autonomous cars, sophisticated self-control, safe, secure, and high-performance vehicles, and even flying cars may be the future of mobility. This research paper discusses how AI and Robotics will transform the mobility sector. It discusses the use of AI and Machine Learning (ML)in driverless cars. It also studies AI and Robotics’ convergence, creating a platform and ecosystems for human–robot partnerships, making humans free for more critical tasks. The digital technology disruption of the mobile industry is not free from challenges. The paper also discusses challenges, possible solutions, and how the transformation can benefit humanity.
Accent labeling algorithm based on morphological rules and machine learning in English conversion system

Abstract The dependency of a speech recognition system on the accent of a user leads to the variation in its performance, as the people from different backgrounds have different accents. Accent labeling and conversion have been reported as a prospective solution for the challenges faced in language learning and various other voice-based advents. In the English TTS system, the accent labeling of unregistered words is another very important link besides the phonetic conversion. Since the importance of the primary stress is much greater than that of the secondary stress, and the primary stress is easier to call than the secondary stress, the labeling of the primary stress is separated from the secondary stress. In this work, the labeling of primary accents uses a labeling algorithm that combines morphological rules and machine learning; the labeling of secondary accents is done entirely through machine learning algorithms. After 10 rounds of cross-validation, the average tagging accuracy rate of primary stress was 94%, the average tagging accuracy rate of secondary stress was 94%, and the total tagging accuracy rate was 83.6%. This perceptual study separates the labeling of primary and secondary accents providing the promising outcomes.
RG PA at SemEval-2021 Task 1: A Contextual Attention-based Model with RoBERTa for Lexical Complexity Prediction

In this paper we propose a contextual attentionbased model with two-stage fine-tune training using RoBERTa.First, we perform the firststage fine-tune on corpus with RoBERTa, so that the model can learn some prior domain knowledge.Then we get the contextual embedding of context words based on the tokenlevel embedding with the fine-tuned model.And we use Kfold cross-validation to get K models and ensemble them to get the final result.Finally, we attain the 2nd place in the final evaluation phase of sub-task 2 with pearson correlation of 0.8575.
The Design of Outpatient Services in Children’s Hospitals Based on the Double Diamond Model

With the opening of the two-child policy, the user base of children’s hospitals continues to expand. However, in the service environment of children’s hospitals, the service targets are not single; their direct users are children, but parents also play a key role. Therefore, this paper redesigns the children’s hospital outpatient service system based on the service design process under the double diamond model in four stages: discover, define, develop and deliver. In this paper, we optimize both the service system and the service touchpoints. At the service system level, the design of the consultation app simplifies part of the consultation process and improves the consultation efficiency, thus saving parents’ time and relieving children’s anxiety; at the service touch point level, the use of a guide robot instead of a manual contact saves labor costs and optimizes the interaction form of the robot to improve children’s consultation experience.
Deciding EA-equivalence via invariants

Abstract We define a family of efficiently computable invariants for ( n , m )-functions under EA-equivalence, and observe that, unlike the known invariants such as the differential spectrum, algebraic degree, and extended Walsh spectrum, in the case of quadratic APN functions over $\mathbb {F}_{2^n}$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow> <mml:mi>F</mml:mi> </mml:mrow> <mml:mrow> <mml:msup> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> <mml:mrow> <mml:mi>n</mml:mi> </mml:mrow> </mml:msup> </mml:mrow> </mml:msub> </mml:math> with n even, these invariants take on many different values for functions belonging to distinct equivalence classes. We show how the values of these invariants can be used constructively to implement a test for EA-equivalence of functions from $\mathbb {F}_{2}^{n}$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msubsup> <mml:mrow> <mml:mi>F</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> <mml:mrow> <mml:mi>n</mml:mi> </mml:mrow> </mml:msubsup> </mml:math> to $\mathbb {F}_{2}^{m}$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msubsup> <mml:mrow> <mml:mi>F</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> <mml:mrow> <mml:mi>m</mml:mi> </mml:mrow> </mml:msubsup> </mml:math> ; to the best of our knowledge, this is the first algorithm for deciding EA-equivalence without resorting to testing the equivalence of associated linear codes.
PERSONNEL POTENTIAL OF TEACHERS OF RUSSIAN UNIVERSITIES IN THE CONDITIONS OF DIGITALIZATION

L. Voronina 1, E. Zaitseva 1, E. Gaisina 2, N. Izmozherova 2 1Ural Federal University (RUSSIAN FEDERATION) 2Ural State Medical University (RUSSIAN FEDERATION)
Search Engine Optimization – Study and Analysis

Search Engine Optimization is about understanding what people are searching for online, the answers they are seeking, the words they are using, and the type of content they wish to consume. Knowing the answers to these questions will allow you to connect to the people who are searching online for the solutions you offer. Building the onsite and offsite page tactics will help us to build a better site that ranks higher in search and also with better click through rates. Understanding what the users or consumers need and producing the same to them is one of the major factor in the Digital Marketing. Using Analytics tool we can analyze how our site is doing well or not with our intended action will help us to understand our audience better.