Reviewerly/exHarmony huggingface dataset:
A Pattern Matching-Based Framework for Quantum Circuit Rewriting

The realization of quantum algorithms relies on specific quantum compilations according to the underlying quantum processors. However, there are various ways to physically implement qubits in different physical devices and manipulate those qubits. These differences lead to different communication methods and connection topologies, with each vendor implementing its own set of primitive gates. Therefore, quantum circuits have to be rewritten or transformed in order to be transplanted from one platform to another. We propose a pattern matching-based framework for rewriting quantum circuits, called QRewriting. It takes advantage of a new representation of quantum circuits using symbol sequences. Unlike the traditional way of using directed acyclic graphs, the new representation allows us to easily identify the patterns that appear non-consecutively but reducible. Then, we convert the problem of pattern matching into that of finding distinct subsequences and propose a polynomial-time dynamic programming-based pattern matching and replacement algorithm. We develop a rule library for basic optimizations and use it to rewrite the Arithmetic and Toffoli benchmarks from the $G_{IBM}$ gate set to the $G_{Sur}$ gate set. Compared with the existing tool PaF, QRewriting obtains an improvement of reducing depths (resp. gate counts) by 29\% (resp. 14\%).
Gym Attendance Management System

Gym attendance management system deals with the maintenance of the Gym member attendance details. It is generates the attendance of the Gym member on basis of daily presenty.It is maintained on the daily basis of their attendance. The Gym member will be provided withthe separate user id to make the Gym member’s status. The Admin handling the particular attendance for all Gym member. Only if the Gym member present on that particular day, the attendance will be calculated. The Gym members attendance reports based on packages and consolidate will be generated.
Visual communication design as a form of mindfulness-based intervention

The paper explores the effects of mindfulness-based design interventions to help children better self-regulate their emotions.It attempts to ascertain if the use of prototype designs could further enhance the children's awareness or mindfulness, and emotional wellbeing.Children's emotional and behavioural problems have become a growing concern.These problems are related to the difficulties children face in controlling their emotions, which may lead to psychological problems in adulthood.This indicates the need to help children understand feelings and how to self-regulate emotions.A pilot design project was conducted to explore the connection between mindfulness-based practice and emotion regulation in children to develop usable mindfulness-based prototype designs.Preliminary findings indicate storytelling to be a mindfulness-based prototype design that can benefit children in terms of emotional regulation.To fully realize the potential of mindfulness-based design interventions may require the development of more robust and rigorous activities and thorough analysis.
Efficient Security Sharing Scheme Using Block chain in Internet of Things

Abstract Recently, the organizations and different institutions are utilized cloud service providers to save the information for sharing information with the consideration of Internet of Things (IoT). In this storage, third party access is a biggest issue for reducing unwanted access and data theft. So, this paper, Block chain based Security Sharing scheme with Data Access Control (BSSDAC) is developed for empowering the access control and secures operation of data transaction. The major main of this research towards empower the security and data access control in the IoT applications. Cipher text-Policy Attribute-Based Encryption (CP-ABE) can be developed to enhance the security of personal data. In the CP-ABE, the key generation is achieved by Aquila Optimization Algorithm (AOA). To maintain the owner's security, the Block chain based Data Access Control (DAC) can be designed. To maintain secure data storage in addition sharing, the Block chain based Cipher text-Policy Attribute-Based Encryption (CP-ABE) is designed. The data owner is empowering the security and data access control with the consideration of Block chain technology. Finally, the security of the data, access control of the data owner can be achieved through the Block chain based approach. The proposed method is implemented in MATLAB and performances are evaluated. The proposed technique is contrasted with the existing technologies such as Elliptic Curve Cryptography (ECC) in addition Rivest–Shamir–Adleman (RSA) respectively.
Experimental-schemes-based non-Gaussian operations in continuous  variable quantum teleportation

We undertake a comprehensive study of quantum teleportation using non-Gaussian entangled resource states produced by performing non-Gaussian operations on a two-mode squeezed vacuum (TMSV) state. These non-Gaussian operations include photon subtraction, photon addition, and photon catalysis and are performed via heralding schemes using photon number resolving detectors. Since these schemes produce non-Gaussian states probabilistically, it is necessary to consider the success probability. We first derive a unified expression for the Wigner characteristic function describing the photon subtracted, added, and catalyzed TMSV states. We then utilize it to obtain the fidelity of teleporting an input coherent and squeezed vacuum state. Considering the success probability allows us to find the most advantageous non-Gaussian operations along with the associated optimal squeezing and beam splitter transmissivity parameters. It turns out that the symmetric one-photon subtracted TMSV state is most advantageous for teleporting an unknown input coherent and squeezed vacuum state. We expect the derived Wigner characteristic function to be useful in the state characterization and other quantum protocols.
An Evaluation of OCR on Egocentric Data

In this paper, we evaluate state-of-the-art OCR methods on Egocentric data. We annotate text in EPIC-KITCHENS images, and demonstrate that existing OCR methods struggle with rotated text, which is frequently observed on objects being handled. We introduce a simple rotate-and-merge procedure which can be applied to pre-trained OCR models that halves the normalized edit distance error. This suggests that future OCR attempts should incorporate rotation into model design and training procedures.
Memory-Based Model Editing at Scale

Even the largest neural networks make errors, and once-correct predictions can become invalid as the world changes. Model editors make local updates to the behavior of base (pre-trained) models to inject updated knowledge or correct undesirable behaviors. Existing model editors have shown promise, but also suffer from insufficient expressiveness: they struggle to accurately model an edit's intended scope (examples affected by the edit), leading to inaccurate predictions for test inputs loosely related to the edit, and they often fail altogether after many edits. As a higher-capacity alternative, we propose Semi-Parametric Editing with a Retrieval-Augmented Counterfactual Model (SERAC), which stores edits in an explicit memory and learns to reason over them to modulate the base model's predictions as needed. To enable more rigorous evaluation of model editors, we introduce three challenging language model editing problems based on question answering, fact-checking, and dialogue generation. We find that only SERAC achieves high performance on all three problems, consistently outperforming existing approaches to model editing by a significant margin. Code, data, and additional project information will be made available at https://sites.google.com/view/serac-editing.
Automatic compile-time synthesis of entropy-optimal Boltzmann samplers

We present a famework for the automatic compilation of multi-parametric Boltzmann samplers for algebraic data types in Haskell. Our framework uses Template Haskell to synthesise efficient, entropy-optimal samplers generating random instances of user-declared algebraic data types. Users can control the outcome distribution through a pure, declarative interface. For instance, users can control the mean size and constructor frequencies of generated objects. We illustrate the effectiveness of our framework through a prototype generic-boltzmann-brain library showing that it is possible to control thousands of different parameters in systems of tens of thousands of ADTs. Our prototype framework synthesises Boltzmann samplers capable of rapidly generating random objects of sizes in the millions.
Confidence Score for Source-Free Unsupervised Domain Adaptation

Source-free unsupervised domain adaptation (SFUDA) aims to obtain high performance in the unlabeled target domain using the pre-trained source model, not the source data. Existing SFUDA methods assign the same importance to all target samples, which is vulnerable to incorrect pseudo-labels. To differentiate between sample importance, in this study, we propose a novel sample-wise confidence score, the Joint Model-Data Structure (JMDS) score for SFUDA. Unlike existing confidence scores that use only one of the source or target domain knowledge, the JMDS score uses both knowledge. We then propose a Confidence score Weighting Adaptation using the JMDS (CoWA-JMDS) framework for SFUDA. CoWA-JMDS consists of the JMDS scores as sample weights and weight Mixup that is our proposed variant of Mixup. Weight Mixup promotes the model make more use of the target domain knowledge. The experimental results show that the JMDS score outperforms the existing confidence scores. Moreover, CoWA-JMDS achieves state-of-the-art performance on various SFUDA scenarios: closed, open, and partial-set scenarios.
A Layered Reference Model for Penetration Testing with Reinforcement Learning and Attack Graphs

This paper considers key challenges to using reinforcement learning (RL) with attack graphs to automate penetration testing in real-world applications from a systems perspective. RL approaches to automated penetration testing are actively being developed, but there is no consensus view on the representation of computer networks with which RL should be interacting. Moreover, there are significant open challenges to how those representations can be grounded to the real networks where RL solution methods are applied. This paper elaborates on representation and grounding using topic challenges of interacting with real networks in real-time, emulating realistic adversary behavior, and handling unstable, evolving networks. These challenges are both practical and mathematical, and they directly concern the reliability and dependability of penetration testing systems. This paper proposes a layered reference model to help organize related research and engineering efforts. The presented layered reference model contrasts traditional models of attack graph workflows because it is not scoped to a sequential, feed-forward generation and analysis process, but to broader aspects of lifecycle and continuous deployment. Researchers and practitioners can use the presented layered reference model as a first-principles outline to help orient the systems engineering of their penetration testing systems.
Natural Language processing based Automatic Text Summarization

Text summary is a well-known technique for distilling a document's primary points. Beginning with data extraction from a website link, the recommended approach for text summarizing and keyword extraction continues through a number of steps. It will help with the creation of a machine learning and natural language processing solution to replace the existing product evaluation process (NLP). The next major one is Automatic Text Summarization (ATS), which may simply summarize the source data and provide us with a condensed form that preserves the content and general meaning. Automatic Text Summarization was proposed in the 1950s, but the discipline is still in its infancy. Undirected graphs, weighted graphs, keyword extraction, and sentence extraction are all used in the Text Rank algorithm. In this paper, we will look upon. The proposed solutions for text summarization and keyword extraction passes through a series of processes, beginning with information or data extraction through the website link, eliminating outliers and improper information, and constructing a summary of the extracted information or data. It will assist in the development of a machine learning and natural language processing solution to the traditional product review procedure (NLP). Key Words: Text Summarization, relevant Information, summary, Natural language processing (NLP), Text Summarization.
The Influence of the Role of Parents and Peers on the Self-Concept of High Grade Students at SD N Klumprit 1

The purpose of this study was to determine: to analyze the influence between the role of parents on the self-concept of high grade students at SD N Klumprit 1, to analiyze the influence between peers on the self-concept of high-class students at SD N Klumprit 1, to analyze the influence between the role of parents and peers on the self-concept of high-class students at SD N Klumprit 1. The method used in this study is quantitative with measuring instruments in the form of self-concept scale, parental social support scale, and peer interaction scale. The results showed that the analysis of the parental role variable with the self-concept variable seen from the correlation value (r) = 0.696, it can be said that there is a positive influence between the role of parents on self-concept, analysis of peer variables with self-concept variables seen from the correlation value (r) = 0.725, it can be said that there is a positive influence between peers on self-concept. The F statistical value of the influence of the role of parents and peers on students' self-concept is greater than F table (3.15) which is 56.957, it can be said that there is a significant influence of the role of parents and peers on the self-concept of class students height SD N Klumprit 1.
Continual-Learning-as-a-Service (CLaaS): On-Demand Efficient Adaptation of Predictive Models

Predictive machine learning models nowadays are often updated in a stateless and expensive way. The two main future trends for companies that want to build machine learning-based applications and systems are real-time inference and continual updating. Unfortunately, both trends require a mature infrastructure that is hard and costly to realize on-premise. This paper defines a novel software service and model delivery infrastructure termed Continual Learning-as-a-Service (CLaaS) to address these issues. Specifically, it embraces continual machine learning and continuous integration techniques. It provides support for model updating and validation tools for data scientists without an on-premise solution and in an efficient, stateful and easy-to-use manner. Finally, this CL model service is easy to encapsulate in any machine learning infrastructure or cloud system. This paper presents the design and implementation of a CLaaS instantiation, called LiquidBrain, evaluated in two real-world scenarios. The former is a robotic object recognition setting using the CORe50 dataset while the latter is a named category and attribute prediction using the DeepFashion-C dataset in the fashion domain. Our preliminary results suggest the usability and efficiency of the Continual Learning model services and the effectiveness of the solution in addressing real-world use-cases regardless of where the computation happens in the continuum Edge-Cloud.
Improving Accuracy of Interpretability Measures in Hyperparameter Optimization via Bayesian Algorithm Execution

Despite all the benefits of automated hyperparameter optimization (HPO), most modern HPO algorithms are black-boxes themselves. This makes it difficult to understand the decision process which leads to the selected configuration, reduces trust in HPO, and thus hinders its broad adoption. Here, we study the combination of HPO with interpretable machine learning (IML) methods such as partial dependence plots. These techniques are more and more used to explain the marginal effect of hyperparameters on the black-box cost function or to quantify the importance of hyperparameters. However, if such methods are naively applied to the experimental data of the HPO process in a post-hoc manner, the underlying sampling bias of the optimizer can distort interpretations. We propose a modified HPO method which efficiently balances the search for the global optimum w.r.t. predictive performance \emph{and} the reliable estimation of IML explanations of an underlying black-box function by coupling Bayesian optimization and Bayesian Algorithm Execution. On benchmark cases of both synthetic objectives and HPO of a neural network, we demonstrate that our method returns more reliable explanations of the underlying black-box without a loss of optimization performance.
Dialogic Design of Accessible Digital Musical Instruments: Investigating Performer Experience

While it is accepted that accessible digital musical instruments (ADMIs) should be created with the involvement of targeted communities, participatory design (PD) is an unsettled practice that gets defined variously, loosely or not at all. In this paper, we explore the concept of dialogic design and provide a case study of how it can be used in the design of an ADMI. While a future publication will give detail of the design of this instrument and provide an analysis of the data from this study, in this paper we set out how the conversations between researcher and participant have prepared us to build an instrument that responds to the lived experience of the participant.
How to Agree to Disagree: Managing Ontological Perspectives using Standpoint Logic

The importance of taking individual, potentially conflicting perspectives into account when dealing with knowledge has been widely recognised. Many existing ontology management approaches fully merge knowledge perspectives, which may require weakening in order to maintain consistency; others represent the distinct views in an entirely detached way. As an alternative, we propose Standpoint Logic, a simple, yet versatile multi-modal logic "add-on" for existing KR languages intended for the integrated representation of domain knowledge relative to diverse, possibly conflicting standpoints, which can be hierarchically organised, combined and put in relation to each other. Starting from the generic framework of First-Order Standpoint Logic (FOSL), we subsequently focus our attention on the fragment of sentential formulas, for which we provide a polytime translation into the standpoint-free version. This result yields decidability and favourable complexities for a variety of highly expressive decidable fragments of first-order logic. Using some elaborate encoding tricks, we then establish a similar translation for the very expressive description logic SROIQb_s underlying the OWL 2 DL ontology language. By virtue of this result, existing highly optimised OWL reasoners can be used to provide practical reasoning support for ontology languages extended by standpoint modelling.
COVID-19 Impact Sentiment Analysis on a Topic-based Level

Last December 2019, health officials in Wuhan, a province from China, identified a novel coronavirus called SARS-CoV-2 causing pneumonia. In March 2020, World Health Organization (WHO) declared COVID-19 disease being a pandemic. During quarantine periods, people all over the globe were living under severe and overwhelming circumstances and expressing feelings of loneliness, dread, and anxiety. The pandemic has had a significant impact on the labor markets. As a result, several employees have lost their jobs while others are in grave danger to lose their positions the next day. In this paper, we developed a hybrid approach integrating sentiment analysis combined with topic modeling to analyze the impact of the COVID-19 pandemic on Moroccan citizens. The data used in this study includes comments collected from a well-known news website in Morocco called Hespress. Our approach follows a two-step process. In the first step, we implement a topic modeling method to analyze and extract topics from Arabic comments, and in the second step, we perform topic-based sentiment analysis to classify people’s feedback on extracted topics. The final results revealed that the expressed sentiments regarding all the topics are highly negative.
Detecting and Describing the Real World Objects from the Given Images

In this paper we are gone see how to describe the physical appearance or characteristics of the real world objects to visually impaired persons, from the captured images and how to find the exact size of the objects, and to calculate the distance of the object from the device or the user. Another aim of this model is to give the exact information of the images to the users.
Distribution of Quantum Circuits Over General Quantum Networks

Near-term quantum computers can hold only a small number of qubits. One way to facilitate large-scale quantum computations is through a distributed network of quantum computers. In this work, we consider the problem of distributing quantum programs represented as quantum circuits across a quantum network of heterogeneous quantum computers, in a way that minimizes the overall communication cost required to execute the distributed circuit. We consider two ways of communicating: cat-entanglement that creates linked copies of qubits across pairs of computers, and teleportation. The heterogeneous computers impose constraints on cat-entanglement and teleportation operations that can be chosen by an algorithm. We first focus on a special case that only allows cat-entanglements and not teleportations for communication. We provide a two-step heuristic for solving this specialized setting: (i) finding an assignment of qubits to computers using Tabu search, and (ii) using an iterative greedy algorithm designed for a constrained version of the set cover problem to determine cat-entanglement operations required to execute gates locally. For the general case, which allows both forms of communication, we propose two algorithms that subdivide the quantum circuit into several portions and apply the heuristic for the specialized setting on each portion. Teleportations are then used to stitch together the solutions for each portion. Finally, we simulate our algorithms on a wide range of randomly generated quantum networks and circuits, and study the properties of their results with respect to several varying parameters.