Reviewerly/exHarmony huggingface dataset:
Building Qutrit Diagonal Gates from Phase Gadgets

Phase gadgets have proved to be an indispensable tool for reasoning about ZX-diagrams, being used in optimisation and simulation of quantum circuits and the theory of measurement-based quantum computation.In this paper we study phase gadgets for qutrits.We present the flexsymmetric variant of the original qutrit ZX-calculus, which allows for rewriting that is closer in spirit to the original (qubit) ZX-calculus.In this calculus phase gadgets look as you would expect, but there are non-trivial differences in their properties.We devise new qutrit-specific tricks to extend the graphical Fourier theory of qubits, resulting in a translation between the 'additive' phase gadgets and a 'multiplicative' counterpart we dub phase multipliers.This enables us to generalise the qubit notion of multiple-control to qutrits in two ways.The first type is controlling on a single tritstring, while the second type applies the gate a number of times equal to the tritwise multiplication modulo 3 of the control qutrits.We show how both types of control can be implemented for any qutrit Z or X phase gate, ancilla-free, and using only Clifford and phase gates.The first requires a polynomial number of gates and exponentially small phases, while the second requires an exponential number of gates, but constant sized phases.This is interesting, because such a construction is not possible in the qubit setting.As an application of these results we find a construction for emulating arbitrary qubit diagonal unitaries, and specifically find an ancilla-free emulation for the qubit CCZ gate that only requires three single-qutrit non-Clifford gates -provably lower than the four T gates needed for qubits with ancilla.
The Causal Structure of Semantic Ambiguities

Ambiguity is a natural language phenomenon occurring at different levels of syntax, semantics, and pragmatics.It is widely studied; in Psycholinguistics, for instance, we have a variety of competing studies for the human disambiguation processes.These studies are empirical and based on eyetracking measurements.Here we take first steps towards formalizing these processes for semantic ambiguities where we identified the presence of two features: (1) joint plausibility degrees of different possible interpretations, (2) causal structures according to which certain words play a more substantial role in the processes.The novel sheaf-theoretic model of definite causality developed by Gogioso and Pinzani in QPL 2021 offers tools to model and reason about these features.We applied this theory to a dataset of ambiguous phrases extracted from Psycholinguistics literature and their human plausibility judgements collected by us using the Amazon Mechanical Turk engine.We measured the causal fractions of different disambiguation orders within the phrases and discovered two prominent orders: from subject to verb in the subject-verb and from object to verb in the verb object phrases.We also found evidence for delay in the disambiguation of polysemous vs homonymous verbs, again compatible with Psycholinguistic findings.
Q# as a Quantum Algorithmic Language

Q# is a standalone domain-specific programming language from Microsoft for writing and running quantum programs.Like most industrial languages, it was designed without a formal specification, which can naturally lead to ambiguity in its interpretation.We aim to provide a formal language definition for Q#, placing the language on a solid mathematical foundation and enabling further evolution of its design and type system.This paper presents λ Q# , an idealized version of Q# that illustrates how we may view Q# as a quantum ALGOL (algorithmic language).We show the safety properties enforced by λ Q# 's type system and present its equational semantics based on a fully complete algebraic theory by Staton.
Quantum Linear Optics via String Diagrams

We establish a formal bridge between qubit-based and photonic quantum computing.We do this by defining a functor from the ZX calculus to linear optical circuits.In the process we provide a compositional theory of quantum linear optics which allows to reason about events involving multiple photons such as those required to perform linear-optical and fusion-based quantum computing.
Mathematical Model, Stability Analysis and Numerical Simulations for The Spread of Malaria Disease in Yogyakarta City, Indonesia

Indonesia merupakan negara yang sering terjadi penyakit malaria. Oleh karena itu, diperlukan model matematika yang mampu memodelkan distribusi malaria. Tujuan dari penelitian ini adalah untuk membuat model dengan variabel yang memperhatikan suspected, infeksi, dorman dan pulih. Hasil model yang terbentuk kemudian disimulasikan dengan menggunakan software maple 18. Dari hasil simulasi dapat disimpulkan bahwa terjadi penurunan populasi yang terinfeksi dan peningkatan populasi yang pulih dari waktu ke waktu. [Indonesia is a country with frequent malaria cases. Therefore, a mathematical model is needed to model the distribution of malaria. The purpose of this study is to create a model with susceptible, infected, dormant and recovered compartments and to see the results of the simulation performed using maple 18 software. From the simulation results, it can be concluded that there is a decrease in the infected population and an increase in the recovered population over time.]
An Image Based Harassment Detection System Using Emotion Feature Vector Based on MTCNN

The physical or mental harassment is an issue of increasing concern to organizations and institutes of higher learning all over the world. Unsafe work environment leads to harmful effects on the emotional and physical health of employees. Our aim is to model a system which detects harassment at the workplace using images. We have collected a dataset of images from open sources related to harassment activities happening at the workplace. In the proposed model, we used the YOLOv3 for detecting the multiple people in an image. In addition, an emotion recognition module is built using MTCNN to detect emotions on a harassed person’s face. We consider the emotions such as fear, disgust, anger and sadness as the natural sign of presence of harassment in the image and thereby, classifying the image to belong to one of the two categories: harassed or non-harassed based on harassment score.
Secure Electronic Polling Process Utilizing Smart Contracts

Today, technology has become a basic necessity for every individual in society. There were times when the internet was used only to send digital mail. We never imagined that technology would be at the tip of our fingers someday, with no long queues and no waiting times. The solution is just a few clicks away on your smartphone. The COVID-19 pandemic outbreak pushed many services online, and the need for an online voting mechanism became the need of the hour. Blockchain innovation is an approach to putting away information that makes it troublesome or difficult to change, switch, or delude the system. It eliminates the need for third-party approval by making the transaction between two parties. The existing electronic voting (e-voting) system failed to build trust among voters due to several security vulnerabilities. We offer a different model that addresses the shortcomings of both the electronic and traditional voting systems with the use of blockchain technology. It will make the voting mechanism more secure and impenetrable to tampering. In this article, we provide an improved environment for unbiased voting. The Ethereum platform is utilized along with Ganache and Metamask to set up a local blockchain network and transaction records.
A Novel Nodesets-Based Frequent Itemset Mining Algorithm for Big Data using MapReduce

Due to the rapid growth of data from different sources in organizations, the traditional tools and techniques that cannot handle such huge data are known as big data which is in a scalable fashion. Similarly, many existing frequent itemset mining algorithms have good performance but scalability problems as they cannot exploit parallel processing power available locally or in cloud infrastructure. Since big data and cloud ecosystem overcomes the barriers or limitations in computing resources, it is a natural choice to use distributed programming paradigms such as Map Reduce. In this paper, we propose a novel algorithm known as A Nodesets-based Fast and Scalable Frequent Itemset Mining (FSFIM) to extract frequent itemsets from Big Data. Here, Pre-Order Coding (POC) tree is used to represent data and improve speed in processing. Nodeset is the underlying data structure that is efficient in discovering frequent itemsets. FSFIM is found to be faster and more scalable in mining frequent itemsets. When compared with its predecessors such as Node-lists and N-lists, the Nodesets save half of the memory as they need only either pre-order or post-order coding. Cloudera's Distribution of Hadoop (CDH), a MapReduce framework, is used for empirical study. A prototype application is built to evaluate the performance of the FSFIM. Experimental results revealed that FSFIM outperforms existing algorithms such as Mahout PFP, Mlib PFP, and Big FIM. FSFIM is more scalable and found to be an ideal candidate for real-time applications that mine frequent itemsets from Big Data.
RLaMs-Dehazing: Optimized Depth Map Improvement Single Colour Image Dehazing

Visibility Degradation is a classical problem owing to the presence of Atmospheric Particulate Matter (APM). There are different image dehazing algorithms. Any one method cannot be relied upon as each haze condition is unique. An innovative algorithm has been proposed inverting the image formation atmospheric scattering model [2, 32]. The model has been improvised by one key factor. This is Regularized Lagrangian multiplier (RLaM) based Depth Map (DM) refinement. The algorithm has low time complexity which intrigues real-time efficient applications. Different state-of-the-art visibility algorithms have been studied and their subjective and objective performance evaluations have been evaluated. Extensive investigation shows remarkable improvement with the proposed algorithm. This method is equally applicable to different atmospheric conditions. Time complexity experimented with execution time and Big (O) for real-time effectiveness. Extensive experiment results show the potential of the proposed algorithm independent of the influence of atmospheric conditions and capturing devices adaptive to computer vision applications. Time complexity and quality output trade-off achieved with the removal of ringing artifacts efficiently.
JS01-2 Toward efficient operation of expert panels

The implementation of cancer genome profiling test in Japan requires the holding of an expert panel, which is a molecular tumor board composed of multidisciplinary specialists that reviews the recommended treatment and germline findings based on the test results. Since the number of hospitals that can hold an expert panel is limited to 45 hospitals of two types: designated 'core' hospitals and 'hub' hospitals for cancer genome medicine, the spread of cancer genome medicine increases the burden on these hospitals.
A Completed Multiply Threshold Encoding Pattern for Texture Classification

The binary pattern family has drawn wide attention for texture representation due to its promising performance and simple operation. However, most binary pattern methods focus on local neighborhoods but ignore center pixels. Even if some studies introduce the center based sub-pattern to provide complementary information, extant center based sub-patterns are much weaker than other local neighborhood based sub-patterns. This severe unbalance limits the classification performance of fusion features significantly. To alleviate this problem, this paper designs a multiply threshold center pattern (MTCP) to provide a more discriminative and complementary local texture representation with a compact form. First, a multiply thresholds encoding strategy is designed to encode the center pixel that generates three 1-bit binary patterns. Second, it adopts a compact multi-pattern encoding strategy to combine them into the 3-bit MTCP. Furthermore, this paper proposes a completed multiply threshold encoding pattern by fusing the MTCP, local sign pattern, and local magnitude pattern. Comprehensive experimental evaluations on three popular texture classification benchmarks confirm that the completed multiply threshold encoding pattern achieves superior texture classification performance.
An Efficient and Secure Lightweight Certificateless Hybrid Signcryption Scheme

With the limited resources of the Internet of Things, security and efficiency have become a challenge. The hybrid signcryption scheme is a proper method to ensure data security and integrity. Recently, through continuous and in-depth research, the signcryption scheme has made many achievements, but there are still some problems. Most proposals use bilinear mapping, which reduces computational efficiency. It brings a heavy burden to the resource-constrained Internet of Things. And many constructions cannot meet the claimed security, causing the leakage of private messages. Therefore, we propose a lightweight certificateless hybrid signcryption (CLHS) scheme with better performance and higher security to solve the above problems. First, we have reduced storage pressure and improved computational efficiency by using certificateless public key cryptography and elliptic curves instead of bilinear maps to construct a signcryption scheme, and the construction is lightweight. At the same time, to securely transmit messages of different lengths, we employ a hybrid signcryption scheme. Through formal security proof, efficiency, and performance analysis, our proposal has the security requirements of confidentiality and unforgeability and has better computational efficiency and security performance. Finally, we propose a secure data transmission protocol based on our CLHS scheme in Smart Grid, which inherits the advantages of high computational efficiency and better security of the underlying CLHS scheme.
Multilingual Speech Identification Framework (MSIF) A Novel Approach in Language Identification

Multilingual language detection is the process of automatically identifying the language(s) present in a given speech corpus that may contain multiple languages. Several approaches have been proposed for multilingual speech corpus detection, including statistical methods, machine learning algorithms, and deep learning models. These models have difficulty determining specific language, especially when dealing with biased towards certain accents, dialects, or languages, and reduce the accuracy of the model. Hence a novel framework named “Multilingual Speech Identification Framework (MSIF)” is developed to solve this problem by data augmentation and increase the accuracy of language identification. There is a limited amount of datasets available for languages except English makes it difficult to train the Indian regional language. So the proposed framework uses a novel Superintendence Neuvised Network, which combines GAN and CNN for data augmentation and transfer learning for feature extraction. The existing multilingual models have been implemented to identify the languages but these models were not able to detect dialect variations because these model does not utilize the attention mechanism. For this reason, the proposed model uses a novel Duel Atenuative memory network, which integrates a Generalized self-attention mechanism with bi-LSTM to understand dialect variations thereby providing better language detection in the Indian regional language.
A STUDY ON EFFECTIVE SAFETY MEASURE TOWORDS EMPLOYEE MOTIVATION AT ASHIRWAD PIPES LIMITED, ATTIBELE

Employee motivation is a critical factor in achieving organizational success, productivity, and employee satisfaction. Ensuring a safe and secure working environment plays a pivotal role in boosting employee motivation. This abstract highlights the importance of implementing effective safety measures as a means to enhance employee motivation. It discusses the relationship between safety and motivation, detailing how safety measures can create a sense of well-being, trust, and job satisfaction among employees. The abstract also emphasizes the role of leadership and communication in fostering a culture of safety, which, in turn, positively influences employee motivation. By examining the connection between safety and motivation, organizations can develop strategies to create a workplace where employees are not only safe but also highly motivated and engaged, ultimately contributing to their overall success.
Web-Based Antam Gold Savings and Pawn Calculation Application

The demand for a user-friendly website that enables the withdrawal of funds from gold savings accounts at Pegadaian has experienced substantial growth amidst the ongoing epidemic. Due to its inherent value and stability, gold assumes particular significance in times of economic uncertainty, allowing individuals to liquidate their savings when necessary. The old approach entailed the manual input of customer information through Excel, which consequently required face-to-face interactions for payment processing. Nevertheless, a significant shift has transpired through the integration of an internet-based platform, facilitating the provision of personal data by clients and thereby establishing virtual profiles to conduct online transactions. Implementing this transformation has resulted in a more streamlined process, enhancing efficiency and improving accessibility for clients.&#x0D; The research methodology employed for this project is descriptive, encompassing direct field observations. Furthermore, the system employs the Object-Oriented Analysis and Design (OOAD) methodology to analyze and design the system. The main objective is to develop a system that streamlines the procedures of gold pawning and online savings for consumers. The data input process utilizes the PHP programming language, whereby the data is safely stored within a MySQL database. The integration of these elements guarantees a system that is both secure and efficient, effectively addressing the requirements of both customers and administrators.
KUALITAS PRODUK, DESAIN PRODUK DAN KETEPATAN WAKTU TERHADAP KEPUASAN PELANGGAN BATIK PRINTING THOYIB SURAKARTA

This study aims to determine whether there is a significant effect between the variables of product quality, product design and timeliness on ccustomer satisfaction on customer satisfaction of Batik printing Thoyib Surakarta. The methodsinsthis,research,uses quantitative,methods..Theppopulationiin this.study,wereccustomers of,batik printing thoyib surakarta, the sample used in the.study,was,100.respondents..The sampling technique used was,probability sampling with,a,random samplingpapproach. The author uses random sampling because it provides equal opportunities for each member of the population to be selected as a sample for this study.. The,dataatypesuuseddinnthislstudy. are.primary.and.secondaryddataaincluding observation, documentation, questionnaires, interviews and data collection technique usingliterature reviews. Theedataaanalysissmethods.innthissstudyyare.classical,hypothesis.testing,multiple,regression,analysis andltheecoefficienttoffdetermination.. The results oftthis,studylconcluded that the fluctuating producttqualitylhas a.positive and,significanttpositive impact on,customerssatisfacction a the results SPSS calculations are tcount &gt; ttable (3,541 &lt; 1,985) and;sig.of.0,001.is 'smaller,thann 0,005. Theeproductt.design variable,was declared to.have,a positivee and significant.effect,on,customer satisfaction because.the,result,of SPSSccalculationsoobtainedttcount &gt; ttable (3,440 &gt; 1,985) and sig of 0,001.is smaller than00,005. While thettimeliness variablehhas,appositive;andlsignificant effecton customer satisfaction because,the SPSSccalculation results’obtained,tcount &gt; ttable (2,711 &gt; 1,985)]and sig off00,008 is smaller thanp0,005. Keywords: Customer,Satisfaction, Product Quality, Product Design, Timeliness.
Web Based Intrusion Detection System for SQLIA

SQL Injection Attack (SQLIA) refers to an injection attack wherein an attacker can execute malicious SQL statements that control a web application’s database server. By leveraging SQL Injection vulnerability, given the right circumstances, an attacker can use it to bypass a web application’s authentication and authorization mechanisms and retrieve the contents of an entire database. SQL Injection can also be used to add, modify and delete records in a database, affecting data integrity. The main idea of our work is to allow developers the freedom to write and execute code without having to worry about these attacks. In this paper we propose a Web Based Intrusion Detection System for SQLIA to extract a SQL query connecting to database from a PHP file. The structure of the query under observation will be converted to XML file and compared against the legitimate queries stored in the XML file using association rule mining thus minimizing attacks. WEBIDS is expected to reduce the time and manual effort as it only focuses on fragments that are vulnerable for attacks. Key Words: XML Rule Mining, PHP, SQL injection,
Ancilla-Assisted Protection of Information: Application to Atom-Cavity Systems

One of the major obstacles faced by quantum-enabled technology is the environmental noise that causes decoherence in the quantum system, thereby destroying much of its quantum aspects and introducing errors while the system undergoes quantum operations and processing. A number of techniques have been invented to mitigate the environmental effects, and many of these techniques are specific to the environment and the quantum tasks at hand. Here, we propose a protocol that makes arbitrary environments effectively noise-free or transparent using an ancilla, which, in particular, is well suited to protect information stored in atoms. The ancilla, which is the photons, is allowed to undergo restricted but a wide class of noisy operations. The protocol transfers the information of the system onto the decoherence-free subspace and later retrieves it back to the system. Consequently, it enables full protection of quantum information and entanglement in the atomic system from decoherence. We propose experimental schemes to implement this protocol on atomic systems in an optical cavity.
MonoProb: Self-Supervised Monocular Depth Estimation with Interpretable Uncertainty

Self-supervised monocular depth estimation methods aim to be used in critical applications such as autonomous vehicles for environment analysis. To circumvent the potential imperfections of these approaches, a quantification of the prediction confidence is crucial to guide decision-making systems that rely on depth estimation. In this paper, we propose MonoProb, a new unsupervised monocular depth estimation method that returns an interpretable uncertainty, which means that the uncertainty reflects the expected error of the network in its depth predictions. We rethink the stereo or the structure-from-motion paradigms used to train unsupervised monocular depth models as a probabilistic problem. Within a single forward pass inference, this model provides a depth prediction and a measure of its confidence, without increasing the inference time. We then improve the performance on depth and uncertainty with a novel self-distillation loss for which a student is supervised by a pseudo ground truth that is a probability distribution on depth output by a teacher. To quantify the performance of our models we design new metrics that, unlike traditional ones, measure the absolute performance of uncertainty predictions. Our experiments highlight enhancements achieved by our method on standard depth and uncertainty metrics as well as on our tailored metrics. https://github.com/CEA-LIST/MonoProb