Reviewerly/exHarmony huggingface dataset:
Constructions of Self-Orthogonal Codes From Hulls of BCH Codes and Their Parameters

Self-orthogonal codes are an interesting type of linear codes due to their wide applications in communication and cryptography. It is known that self-orthogonal codes are often used to construct quantum error-correcting codes, which can protect quantum information in quantum computations and quantum communications. Let C be an [n, k] cyclic code over F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> , where F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> is the finite field of order q. The hull of C is defined to be the intersection of the code and its dual. In this paper, we will employ the defining sets of cyclic codes to present two general characterizations of the hulls that have dimension k - 1 or k <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">⊥</sup> - 1, where k <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">⊥</sup> is the dimension of the dual code C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">⊥</sup> . Several sufficient and necessary conditions for primitive and projective BCH codes to have (k - 1)-dimensional (or (k <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">⊥</sup> -1)dimensional) hulls are also developed by presenting lower and upper bounds on their designed distances. Furthermore, several classes of self-orthogonal codes are proposed via the hulls of BCH codes and their parameters are also investigated. The dimensions and minimum distances of some self-orthogonal codes are determined explicitly. In addition, several optimal codes are obtained.
Novel Random k Satisfiability for in Hopfield Neural Network

The k Satisfiability logic representation (kSAT) contains valuable information that can be represented in terms of variables.This paper investigates the use of a particular non-systematic logical rule namely Random k Satisfiability (RANkSAT).RANkSAT contains a series of satisfiable clauses but the structure of the formula is determined randomly by the user.In the present study, RANkSAT representation is successfully implemented in Hopfield Neural Network (HNN) by obtaining the optimal synaptic weights.We focus on the different regimes for k ≤ 2 by taking advantage of the non-redundant logical structure, thus obtaining the final neuron state that minimizes the cost function.We also simulate the performances of RANkSAT logical rule using several performance metrics.The simulated results suggest that the RANkSAT representation can be embedded optimally in HNN and that the proposed method can retrieve the optimal final state.
How Domain Terminology Affects Meeting Summarization Performance

Meetings are essential to modern organizations.Numerous meetings are held and recorded daily, more than can ever be comprehended.A meeting summarization system that identifies salient utterances from the transcripts to automatically generate meeting minutes can help.It empowers users to rapidly search and sift through large meeting collections.To date, the impact of domain terminology on the performance of meeting summarization remains understudied, despite that meetings are rich with domain knowledge.In this paper, we create gold-standard annotations for domain terminology on a sizable meeting corpus; they are known as jargon terms.We then analyze the performance of a meeting summarization system with and without jargon terms.Our findings reveal that domain terminology can have a substantial impact on summarization performance.We publicly release all domain terminology to advance research in meeting summarization.
Twitter Sentiment Analysis of Bangkok Tourism During COVID-19 Pandemic Using Support Vector Machine Algorithm

In the year 2020, SARS-CoV-2, the virus behind the coronavirus disease (COVID-19) pandemic, affected many lives and businesses worldwide. COVID-19, which originated in Wuhan City, China, at the end of December 2019, spread over the entire world in approximately four months. By October 2020, approximately 20 million people were infected and millions had died from this disease. Many health organizations such as the World Health Organization and Centers for Disease Control and Prevention made COVID-19 their primary focus. Many industries, especially, the tourism industry, were affected by the pandemic as many flight and hotel reservations were canceled. Thailand, a country considered one of the world’s most popular tourist destinations, suffered much losses because of this pandemic. Many events and travel bookings were canceled and/or postponed. Many people expressed their views and emotions related to this situation over social media, which is considered a powerful media for spreading news and information. In this research, the views of people who were planning to travel to Bangkok, the capital city and most popular destination in Thailand, were retrieved from Twitter for the dates between April 3 and 30, 2020, the period during which the country underwent nationwide lockdown. Sentiment analysis was performed using the support vector machine algorithm. The results showed 71.03% classification accuracy based on three sentiment classifications: positive, negative, and neutral. This study could thus provide an insight into travelers’ opinions and sentiments related to the tourism business. Based on the significant terms in each sentiment extracted, strengths and weaknesses of each tourism issue could be obtained, which could be used for making recommendations to the related tourism organizations.
Improving the Capability of Real-Time Face Masked Recognition using Cosine Distance

During the pandemic, people around the world are expected to wear masks. So far, the ability to recognize the identity of a person who wears a mask is still a challenge. Face recognition is widely used in schools, hospitals, and companies as an attendance system, even as a criminal watchlist examination system. Thus, face recognition implementation is difficult to obtain the identity of people who are wearing masks, and moreover, computer systems might fail to detect the faces. This study used Haar-cascade Face detection and MobileNet while proposing the addition of the cosine distance method. This method compares the middle position of face detection results within the previous frame and the current. The proposed system can generate a person's name and identification number while wearing a mask. The system is designed to utilize multi-threading by comparing the transfer learning methods of VGG16 and Triplet Loss FaceNet for face mask recognition with an accuracy rate of 100% and 82.20%. Real-time implementation speed resulted in 4 FPS and 22 FPS and successfully added cosine distance to generate a person's ID number.
Rate compatible reconciliation for continuous-variable quantum key distribution using Raptor-like LDPC codes

In the practical continuous-variable quantum key distribution (CV-QKD) system, the postprocessing process, particularly the error correction part, significantly impacts the system performance. Multi-edge type low-density parity-check (MET-LDPC) codes are suitable for CV-QKD systems because of their Shannon-limit-approaching performance at a low signal-to-noise ratio (SNR). However, the process of designing a low-rate MET-LDPC code with good performance is extremely complicated. Thus, we introduce Raptor-like LDPC (RL-LDPC) codes into the CV-QKD system, exhibiting both the rate compatible property of the Raptor code and capacity-approaching performance of MET-LDPC codes. Moreover, this technique can significantly reduce the cost of constructing a new matrix. We design the RL-LDPC matrix with a code rate of 0.02 and easily and effectively adjust this rate from 0.016 to 0.034. Simulation results show that we can achieve more than 98% reconciliation efficiency in a range of code rate variation using only one RL-LDPC code that can support high-speed decoding with an SNR less than -16.45 dB. This code allows the system to maintain a high key extraction rate under various SNRs, paving the way for practical applications of CV-QKD systems with different transmission distances.
I like fish, especially dolphins: Addressing Contradictions in Dialogue Modeling

Yixin Nie, Mary Williamson, Mohit Bansal, Douwe Kiela, Jason Weston. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.
Wavelet-Attention CNN for image classification

The feature learning methods based on convolutional neural network (CNN) have successfully produced tremendous achievements in image classification tasks. However, the inherent noise and some other factors may weaken the effectiveness of the convolutional feature statistics. In this paper, we investigate Discrete Wavelet Transform (DWT) in the frequency domain and design a new Wavelet-Attention (WA) block to only implement attention in the high-frequency domain. Based on this, we propose a Wavelet-Attention convolutional neural network (WA-CNN) for image classification. Specifically, WA-CNN decomposes the feature maps into low-frequency and high-frequency components for storing the structures of the basic objects, as well as the detailed information and noise, respectively. Then, the WA block is leveraged to capture the detailed information in the high-frequency domain with different attention factors but reserves the basic object structures in the low-frequency domain. Experimental results on CIFAR-10 and CIFAR-100 datasets show that our proposed WA-CNN achieves significant improvements in classification accuracy compared to other related networks. Specifically, based on MobileNetV2 backbones, WA-CNN achieves 1.26% Top-1 accuracy improvement on the CIFAR-10 benchmark and 1.54% Top-1 accuracy improvement on the CIFAR-100 benchmark.
Robust watermarking algorithm for medical images based on log-polar transform

Abstract In the information age, network security has gradually become a potentially huge problem, especially in the medical field, it is essential to ensure the accuracy and safety of images, and patient information needs to be included with minimal change. Combined log-polar transform (LPT) and discrete cosine transform (DCT), a novel robust watermarking algorithm for medical images, is proposed. It realized the lossless embedding of patient information into medical images. In the process of feature extraction and watermark embedding, the proposed algorithm reflects the characteristics of LPT, scale invariance and rotation invariance, and retains the advantages of DCT's ability to resist conventional attacks and robustness. As it adopts zero-watermarking embedding technology, it solves the defects caused by the traditional watermark embedding technology to modify the original image data and guaranteed the quality of medical images. The good experimental results show the effectiveness of this algorithm.
Progress toward larger molecular simulation on a quantum computer: Simulating a system with up to 28 qubits accelerated by point-group symmetry

The exact evaluation of the molecular ground state in quantum chemistry requires an exponentially increasing computational cost. Quantum computation is a promising way to overcome the exponential problem using polynomial-time quantum algorithms. A quantum-classical hybrid optimization scheme known as the variational quantum eigensolver is preferred for noisy intermediate-scale quantum devices. However, the circuit depth becomes one of the bottlenecks of its application to large molecules of more than 20 qubits. In this work, we employ point-group symmetry to reduce the number of operators in constructing ansatz so as to achieve a more compact quantum circuit. We illustrate this methodology with a series of molecules ranging from LiH (12 qubits) to ${\mathrm{C}}_{2}{\mathrm{H}}_{4}$ (28 qubits). A significant reduction of up to 82% of the operator numbers is reached on ${\mathrm{C}}_{2}{\mathrm{H}}_{4}$. This also sheds light onto further work in this direction to construct even shallower ansatz with enough expressive power and simulate even larger scale systems.
Towards Representation Alignment and Uniformity in Collaborative Filtering

Collaborative filtering (CF) plays a critical role in the development of recommender systems. Most CF methods utilize an encoder to embed users and items into the same representation space, and the Bayesian personalized ranking (BPR) loss is usually adopted as the objective function to learn informative encoders. Existing studies mainly focus on designing more powerful encoders (e.g., graph neural network) to learn better representations. However, few efforts have been devoted to investigating the desired properties of representations in CF, which is important to understand the rationale of existing CF methods and design new learning objectives. In this paper, we measure the representation quality in CF from the perspective of alignment and uniformity on the hypersphere. We first theoretically reveal the connection between the BPR loss and these two properties. Then, we empirically analyze the learning dynamics of typical CF methods in terms of quantified alignment and uniformity, which shows that better alignment or uniformity both contribute to higher recommendation performance. Based on the analyses results, a learning objective that directly optimizes these two properties is proposed, named DirectAU. We conduct extensive experiments on three public datasets, and the proposed learning framework with a simple matrix factorization model leads to significant performance improvements compared to state-of-the-art CF methods.
Wavelength-shift-free racetrack resonator hybrided with phase change material for photonic in-memory computing

The photonic in-memory computing architecture based on phase change materials (PCMs) is increasingly attracting widespread attention due to its high computational efficiency and low power consumption. However, PCM-based microring resonator photonic computing devices face challenges in terms of resonant wavelength shift (RWS) for large-scale photonic network. Here, we propose a PCM-slot-based 1 × 2 racetrack resonator with free wavelength shift for in-memory computing. The low-loss PCMs such as Sb2Se3 and Sb2S3 are utilized to fill the waveguide slot of the resonator for the low insertion (IL) and high extinction ratio (ER). The Sb2Se3-slot-based racetrack resonator has an IL of 1.3 (0.1) dB and an ER of 35.5 (8.6) dB at the drop (through) port. The corresponding IL of 0.84 (0.27) dB and ER of 18.6 (10.11) dB are obtained for the Sb2S3-slot-based device. The change in optical transmittance of the two devices at the resonant wavelength is more than 80%. No shift of the resonance wavelength can be achieved upon phase change among the multi-level states. Moreover, the device exhibits a high degree of fabrication tolerance. The proposed device demonstrates ultra-low RWS, high transmittance-tuning range, and low IL, which provides a new scheme for realizing an energy-efficient and large-scale in-memory computing network.
Evaluating Computational Assistance for Crisis Response

In this paper we examine the behavior of a human-computer system for crisis response. As one instance of crisis management, we describe the task of responding to spills and fires involving hazardous materials. We then describe Inca, an intelligent assistant for planning and scheduling in this domain, and its relation to human users. We focus on Inca’s strategy of retrieving a case from a case library, seeding the initial schedule, and then helping the user adapt this seed. We also present three hypotheses about the behavior of this mixed-initiative system and some experiments designed to test them. The results suggest that our approach leads to faster response development than user-generated or automatically-generated schedules but without sacrificing solution quality.
Remote Attestation Procedures for Network Security Functions (NSFs) through the I2NSF Security Controller

This document describes the procedures a client can follow to assessthe trust on an external NSF platform and its client-definedconfiguration through the I2NSF Security Controller. The procedure toassess trustworthiness is based on a remote attestation of theplatform and the NSFs running on it performed through a TrustedPlatform Module (TPM) invoked by the Security Controller.
Guest editors’ note: Special issue on clusters, clouds, and data for scientific computing

This special issue gathers selected papers of the Workshop on Clusters, Clouds and Data for Scientific Computing that was held at La Maison des Contes, Dareize-France, on October 3-6, 2016.
A System to Support Teaching Global Software Development

Teaching students how to work in globally distributed groups is being done throughout the World.As such, a myriad of different tools have been created to help students work in teams and collaborate.However, there is still the need for effective tools that will enable multi-universities to effectively schedule those distributed group projects.This paper presents a Web-based tool that was developed in the context of a globally distributed software engineering course.The Web-based software enables instructors from multi-universities to view instructors' information and schedule projects throughout the year.The authors of the paper discuss the challenges in creating multi-university projects, as well as present the main features of the software and an example of how it is being used to create globally distributed software projects.
REAL TIME HAND GESTURE RECOGNITION BASED IMAGE VIEWER

2 Abstract: The study related to human machine interaction has been done intensively to improve the ways human communication with the machine. The means of communicating with computers at the moment are limited to keyboards, mice, light pen, trackball, keypads, etc. These devices have grown to be familiar but inherently limit the speed and naturalness with which we interact with the computer. This paper proposes to use image processing and gesture recognition to simulate an environment where performing technical activities will become more like real- world. The system proposes a real time human hand gesture recognition based system which interacts with the machine and processes the image. The classification of the gesture is based on the direction and gesture of the movement of the human hand. The motion of the hand will be captured and then the keyboard press actions are processed. Depending on this the image will move accordingly left, right, zoom in and zoom out.
Cpas: On The Structure And Usability Of A Course Planning And Audit System

We present CPAS, a system that allows students and their advisors to track progress in an academic program.Our design is able to perform a semi-automatic audit of degree requirements; the only human-approved components are ones that require discretion.The underlying mechanism is based on robust propositional logic with extensions that make the entry, interpretation, and audit of complex degree requirements straightforward.The system also includes support for human-approved requirements due to the natural discretion that must be applied when evaluating certain requirements, such as multiple courses with a "cohesive theme".Additionally, we demonstrate an implementation, which focuses on providing a clear, straightforward interface for all users while maintaining the appropriate level of security and privacy.To the best of our knowledge, CPAS is the first degree audit system that was designed with a web-based, graphical interface from the start.CPAS indicates in simple terms how a student is progressing in the degree and allows students to explore other academic programs offered at their university.We also address several key software engineering research questions, such as the storage of complex major requirements and the design of lucid user interfaces for a variety of users.Considering the number of academic institutions and the variety of academic programs offered, quantifying and collecting correct expressions for major requirements in our system is a nontrivial problem.We explore the possibility of using a collaborative social network, with appropriate security and quality controls, for this purpose.We show how CPAS was used to enter the major requirements of complete academic programs and present a visualization functionality that illustrates such programs.CPAS is a fundamental contribution to education research since it provides a way for academic programs to be mapped out in a generalized ontology.Thus, it allows students to maximally utilize the academic resources of their university, and it allows faculty members and departments to plan and represent programs and to advise students effectively.
Online Crime Reporting System

Here we want to create an online crime reporting system software which is well accessible to the general public, the department of local government and there-fore the body department. The traditional public in Asian country is afraid to lodge a grievance as a result of their full of a false worry regarding the department of local government. A web grievance registering system can allay the fears of the general public and can conjointly facilitate within the public serving to the department of local government in catching criminals. This system has been taken as a protective and safety based application for both public and government interest.
Mpss: A System For Mobile And Vocational Education And Training

Mobile devices are always available, are very popular and can be used for different learning functionalities, ranging from communication and collaboration purposes to providing access to contents, both informational and instructional.Performance-centered approach has been proven to be more effective than the traditional lecture-practice-test (expository inductive) in training higher order skills, for preparing learners for self-learning, improving, adapting for changing jobs.The mPSS project applies performance-centered approach in mobile learning management system for educational and training purposes.Students receive a set of learning resources, consisting in description of adaptive scenarios performance centered assessment methods and criteria for evaluation and experts' advices.Evaluation plan/strategy and the measurement instruments are aimed at measuring the effect of the project on knowledge, skills and attitudes of students and trainees.We present the design and implementation of this project for several courses that will be used by different students of various matters in several European countries.
PERBANDINGAN METODE CBR, NN-5 DAN NAÏVE BAYES UNTUK KLASIFIKASI INFEKSI NOSOKOMIAL

Classification method is a method that is widely used for the completion of the diagnosis of disease, particularly in humans. One type of disease that is dangerous is potentially emerging nosocomial infection in patients postoperatively. Approach classification method based upon the similarity of character data, there is also a reference to the emergence of statistical data. Some methods of classification needs to be tested in order to obtain the appropriate reference method for this case. The purpose of this study was to compare the performance of three methods of classification that Case Based Reasoning method (CBR), Neirest Neighbor-5 (NN-5) and Naive Bayes for diagnosis of nosocomial infection. Test results showed that the method of CBR and NN-5 has a very good degree of accuracy than Naive Bayes, Naive Bayes but has a faster processing time