Reviewerly/exHarmony huggingface dataset:
GEO: Enhancing Combinatorial Optimization with Classical and Quantum Generative Models

Abstract We introduce a new framework that leverages machine learning models known as generative models to solve optimization problems. Our Generator-Enhanced Optimization (GEO) strategy is flexible to adopt any generative model, from quantum to quantum-inspired or classical, such as Generative Adversarial Networks, Variational Autoencoders, or Quantum Circuit Born Machines, to name a few. Here, we focus on a quantum-inspired version of GEO relying on tensor-network Born machines and referred to hereafter as TN-GEO. We present two prominent strategies for using TN-GEO. The first uses data points previously evaluated by any quantum or classical optimizer, and we show how TN-GEO improves the performance of the classical solver as a standalone strategy in hard-to-solve instances. The second strategy uses TN-GEO as a standalone solver, i.e., when no previous observations are available. Here, we show its superior performance when the goal is to find the best minimum given a fixed budget for the number of function calls. This might be ideal in situations where the cost function evaluation can be very expensive. To illustrate our results, we run these benchmarks in the context of the portfolio optimization problem by constructing instances from the S&amp;P 500 and several other financial stock indexes. We show that TN-GEO can propose unseen candidates with lower cost function values than the candidates seen by classical solvers. This is the first demonstration of the generalization capabilities of quantum-inspired generative models that provide real value in the context of an industrial application. We also comprehensively compare state-of-the-art algorithms in a generalized version of the portfolio optimization problem. The results show that TN-GEO is among the best compared to these state-of-the-art algorithms; a re- markable outcome given the solvers used in the comparison have been fine-tuned for decades in this real-world industrial application. We see this as an important step toward a practical advantage with quantum-inspired models and, subsequently, with quantum generative models.
Development of Secured Knowledge Management system using Information Technology Model

Information Technology (IT) has contributed to the development of many areas, including the development of companies and in accordance with the requirements of the free market, so it has become necessary to contribute to the development of the knowledge management system because of its importance in the use and dissemination of knowledge through the IT model that focuses on all the requirements of the knowledge management system. And the factors affecting its development, this model is characterized by meeting the needs of the system and upgradable, as well as being comprehensive and integrated (with other systems). The model is designed in several programming languages ​​such as (C#, PHP and MySQL) and according to business requirements in addition to its ease of use so that everyone can use it (employees, clients, partners), and that the model has the ability to identify obstacles and errors that cause non-development, In terms of communication, it uses all technical resources (interviews, video conferences, chat rooms, etc.) to contribute to enhancing teamwork and communication, enhancing expertise and processing data and relying on human expertise to promote innovation and knowledge acquisition to achieve the real objective of this study which is to develop a knowledge management system.
Needs assessment to promote the digital literacy among students in Thai community colleges

&lt;span lang="EN-US"&gt;The objective of this research was to assess the need to promote digital literacy of the students under the supervision of Tak Community College. The population consisted of 152 students in Tak Community College, Tak Province, Thailand, registered for the second semester of the 2020 academic year. The research instrument was a digital literacy questionnaire, and the data were analyzed using percentage, mean, standard deviation, and the prioritization of the need. The findings revealed that the level of digital literacy skills of the students was moderate. The level of digital literacy that students expected, however, was very high. In concluding remarks, it was found that the highest average of the digital literacy skills of the Tak Community College students was the use of the spreadsheet program (PNIModified=0.30), followed by the use of presentation program (PNIModified=0.27), and the word processor program as the lowest average point of skills (PNIModified=0.23). The students of Tak Community College, therefore, are inevitability required the better digital literacy skills development from the lessons enlightened by the teachers.&lt;/span&gt;
What is the metaverse?

The concept of the metaverse garnered a fresh wave of interest after Facebook rebranded as Meta in 2021, introducing the notion of a virtual-reality-powered future to a global audience. The article considers whether this really is the future of the internet.
The German school system in the COVID-19 pandemic era

This article retrospectively reviews findings to date on the impact of the COVID-19 pandemic in the German school system and discusses selected topics, i.e. general conditions of teaching and learning, didactic design and methodology, and the effects on educational outcomes. Like many other countries, Germany had to close its schools several times and switch to distance or alternate-shift learning, although the measures implemented by the federal states in this respect differed by region. In what follows, we first identify seven phases in the chronological course the pandemic took in Germany across the various ‘Länder’, or federal states. Next, we turn to a discussion of the medium- to long-term development needs of the German school system, based on the empirical findings available to date. Here our focus will be on the following issues: Fostering students’ selfregulated learning, making up for students’ learning losses, with special attention to inequalities exacerbated by the pandemic, and digitalizing the school system. Lastly, we address the future development of concepts for linking synchronous and asynchronous (digital) forms of learning and their integration into classroom instruction.
Large-Field Contextual Feature Learning for Glass Detection

Glass is very common in our daily life. Existing computer vision systems neglect it and thus may have severe consequences, e.g., a robot may crash into a glass wall. However, sensing the presence of glass is not straightforward. The key challenge is that arbitrary objects/scenes can appear behind the glass. In this paper, we propose an important problem of detecting glass surfaces from a single RGB image. To address this problem, we construct the first large-scale glass detection dataset (GDD) and propose a novel glass detection network, called GDNet-B, which explores abundant contextual cues in a large field-of-view via a novel large-field contextual feature integration (LCFI) module and integrates both high-level and low-level boundary features with a boundary feature enhancement (BFE) module. Extensive experiments demonstrate that our GDNet-B achieves satisfying glass detection results on the images within and beyond the GDD testing set. We further validate the effectiveness and generalization capability of our proposed GDNet-B by applying it to other vision tasks, including mirror segmentation and salient object detection. Finally, we show the potential applications of glass detection and discuss possible future research directions.
Applications of Blockchain in E-government

Since its inception in 2008, blockchain technology continues to gain prominence and impact. Beyond initial applications in digital currency, token systems, and smart contracts, blockchains have gained use in many varied fields including health care records, education, and food suppy chains. Among the most promising of these applications is e-government. Distributed secure digital ledgers with peer-to-peer architecture implemented on blockchain promise to address scalability, trust, cybersecurity, interoperability, and performance issues that often plague electronic government initiatives. This work is aimed at exploring applications of blockchain in the e-government domain. We provide an overview of the advantages and obstacles of blockchain e-government implementation. Characteristic use cases, including e-voting, finance, and energy markets are explored. By analyzing existing literature, we determine that energy utilities, identity management, transparency, and finance/tax services are major research areas in blockchain-based e-government. General process workflows of primary applications of blockchain are described. This study lays the groundwork for pursuing a common framework capable of integrating these services.
TELUGU ANKELU: A Telugu Spoken Digits Corpora for Mobile Speech Recognition

Automatic Speech Recognition (ASR) deals with recognizing spoken words or sentences through an automated program or an algorithm. ASR has multi-faceted applications in various domains such as Mobile Speech Recognition, IoT, Human-Machine interaction, etc. Researchers have been working on different problems in these domains for more than 5 decades. Mobile speech recognition is a pre-dominant area that supports a wide variety of applications useful for physically challenged, senior citizens and novice mobile users. Though the domain has significant practicality, it faces challenges due to the lack of data in the targeted language. Though it is not major for well-developed languages like English, it is very profound in low-resourced and under-resourced languages. India has 1,391.99 million inhabitants speaking a wide variety of languages with Hindi being the most popular language. There are many native languages spoken in each region of the country. But, research that contributes towards the development of ASR is questionable due to many reasons, and the unavailability of data is one of those. Hence, an open-source speech digit data set has been prepared and will be available through this paper for the Telugu language which is the sixth largely spoken language in the country.
Camera and LiDAR analysis for 3D object detection in foggy weather conditions

Today, the popularity of self-driving cars is growing at an exponential rate and is starting to creep onto the roads of developing countries. For autonomous vehicles to function, one of the essential features that needs to be developed is the ability to perceive their surroundings. To do this, sensors such as cameras, LiDAR, or radar are integrated to collect raw data. The objective of this paper is to evaluate a fusion solution of cameras and LiDARs (4 and 64 beams) for 3D object detection in foggy weather conditions. The data from the two input sensors are fused and an analysis of the contribution of each sensor on its own is then performed. In our analysis, we calculate average precision using the popular KITTI dataset, on which we have applied different intensities of fog (on a dataset we have called Multifog KITTI). The main results observed are as follows. Performances with stereo camera and 4 or 64 beams LiDAR are high (90.15%, 89.26%). Performance of the 4 beams LiDAR alone decreases sharply in foggy weather conditions (13.43%). Performance when using only a camera-based model remains quite high (89.36%). In conclusion, stereo cameras on their own are capable of detecting 3D objects in foggy weather with high accuracy and their performance slightly improves when used in conjunction with LIDAR sensors.
A Proposed Model for Build a Secure Restful API to Connect between Server Side and Mobile Application Using Laravel Framework with Flutter Toolkits

Any business these days need a mobile application connected with central database, like E-commerce, E-Learning, Restaurant applications …etc., and in order to create a professional application -connected with backend, Restful API is needed, therefore, choose the best languages, tools and frameworks to build a mobile application in the client side, and Database, API and Dashboard in the backend is crucial. this paper proposed a model to create a Cross-platform mobile application using Flutter Framework with some packages like http to connect with API, (flutter_secure_storage) to store a token and (GetX) for state management and MVC Architectural style, beside using Laravel framework with MySQL database to build an Restful API and the dashboard in the backend, and for make a system for Rules and permissions, Spatie package has been used with Laravel, as well as use Sanctum for Authentication and Authorization system to protect the APIs with JWT, the result is build a full stack application (frontend and Backend) called (My Services), which is a kind of Ecommerce application for services only in less than one month.
The Use of Technology in Teaching and Learning (Case Study in Two State Schools in Kupang, Indonesia during the COVID-19 Pandemic)

This article is an exposition and analysis based on a case study of two public schools in Kupang city. Data were obtained through observations and questionnaires distributed to teachers who teach at the schools. The results show that learning continues even though the pandemic of COVID-19 forces learning to take place virtually. Teachers have no difficulty in delivering teaching materials to their students because of the availability of technology along with its various applications and features. In this situation, teachers find new challenges in managing learning. However, there are various obstacles that contribute to the effectiveness of learning. These obstacles come from students and technical infrastructure which is also a problem for teachers. The findings in this study were analyzed from five aspects, namely pedagogical, the mastery of technology, human resource readiness, economic, and social aspects. All these aspects are interrelated, therefore intervention from all stakeholders is needed to realize learning in an effective virtual classroom environment.
Design of Intelligent Nursing System Based on Artificial Intelligence

As the number of the elderly population and the population dependency ratio increase year by year, the issue of old-age care has become the focus. However, due to the shortage of carers and the large-scale and expensive auxiliary equipment for the elderly, it is difficult to give thoughtful care to all the elderly. In view of the above background, this paper designs a set of intelligent nursing system for the elderly based on artificial intelligence (AI) algorithms, which mainly includes sensor terminals and AI processing algorithms. Among them, the sensor terminal mainly includes two parts: video monitoring and human biological signal monitoring. For video surveillance signals, this paper uses scene event detection algorithm to detect abnormal events, so as to automatically perceive possible unexpected situations. For human biological signals, such as heart rate, blood pressure, and pulse, the abnormal detection is carried out through data analysis and comparison with the normal index range. So, the possible problems in the physical state of the elderly can be judged in time. Through more comprehensive state monitoring and reliable algorithm processing, the system can effectively solve many hidden dangers in the current elderly care and provide a feasible solution for smart nursing.
Improving the quality of welding training with the help of mixed reality along with the cost reduction and enhancing safety

Welding is widely used in all industries, and its demand is drastically increasing. Today, all sectors of engineering need quality welders to meet their standards. Welders need years of experience and knowledge to meet those standards. They require lots of training, equipment, tools, and safety standards to master the welding skill. The cost of training is very expensive as they will be practicing every day with different materials. The purpose of this project is to train them in a new merged-up environment of the virtual and real world. As a result, this method would reduce training costs and enhance the safety of the users. This method is a medium that helps users to have a better understanding of welding operations and gives them the confidence to perform proficiently in reality along with the elimination of risk, liability, and injury.
Multihoming Big Data Network Using Blockchain-Based Query Optimization Scheme

In this paper, we have proposed the intimate environment of multiblockchain optimization algorithm using big data inquiry to mend the effectiveness of association query handling among numerous multihoming blockchains by implementing the big data system. This technique adds semantic evidences to the old-styled multiblockchain prototype and constructs a semantic model of multiblockchain that delivers a foundation for linking queries among the various blockchain multihoming system in big data. On the base of this model, distributed databases have index arrangement, and a linking index arrangement is proposed among the numerous blockchains, with several attributes linked to these blockchains employed to improve the efficacy of linking calculation. Besides, the communication cost is <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>d</mi> </math> for data communication. On this foundation, a multichain linking enquiry algorithm based on optimization is anticipated to progress the productivity of multiblockchain connection queries. To conclude, two genuine big data community sets of data are used to conduct experiments on. The associating index arrangement among blockchains is unchanging and is equated with the old-styled direct linking inquiry operation. The multiblockchain linking query method of optimization shortens the probe processing procedure. It acquires the query outcomes directly by retrieving the linking index, sinking the local calculating ability, system overhead, and illuminating query efficacy.
MMFL-net: multi-scale and multi-granularity feature learning for cross-domain fashion retrieval

Instance-level image retrieval in fashion industry is a challenging issue owing to its increasing importance in real-scenario visual fashion search. Cross-domain fashion retrieval aims to match the unconstrained customer images as queries for photographs provided by retailers; however, it is a difficult task due to a wide range of consumer-to-shop (C2S) domain discrepancies and also considering that clothing image is vulnerable to various non-rigid deformations. To this end, we propose a novel multi-scale and multi-granularity feature learning network (MMFL-net), which can jointly learn global-local aggregation feature representations of clothing images in a unified framework, aiming to train a cross-domain model for C2S fashion visual similarity. First, a new semantic-spatial feature fusion part is designed to bridge the semantic-spatial gap by applying top-down and bottom-up bidirectional multi-scale feature fusion. Next, a multi-branch deep network architecture is introduced to capture global salient, part-informed, and local detailed information, and extracting robust and discrimination feature embedding by integrating the similarity learning of coarse-to-fine embedding with the multiple granularities. Finally, the improved trihard loss, center loss, and multi-task classification loss are adopted for our MMFL-net, which can jointly optimize intra-class and inter-class distance and thus explicitly improve intra-class compactness and inter-class discriminability between its visual representations for feature learning. Furthermore, our proposed model also combines the multi-task attribute recognition and classification module with multi-label semantic attributes and product ID labels. Experimental results demonstrate that our proposed MMFL-net achieves significant improvement over the state-of-the-art methods on the two datasets, DeepFashion-C2S and Street2Shop. Specifically, our approach exceeds the current best method by a large margin of +4.2% and + 11.4% for mAP and Acc@1, respectively, on the most challenging dataset DeepFashion-C2S.
Quantum message-passing algorithm for optimal and efficient decoding

Recently, Renes proposed a quantum algorithm called belief propagation with quantum messages (BPQM) for decoding classical data encoded using a binary linear code with tree Tanner graph that is transmitted over a pure-state CQ channel \cite{renes_2017}, i.e., a channel with classical input and pure-state quantum output. The algorithm presents a genuine quantum counterpart to decoding based on the classical belief propagation algorithm, which has found wide success in classical coding theory when used in conjunction with LDPC or Turbo codes. More recently Rengaswamy <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>e</mml:mi><mml:mi>t</mml:mi></mml:math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>a</mml:mi><mml:mi>l</mml:mi><mml:mo>.</mml:mo></mml:math> \cite{rengaswamy_2020} observed that BPQM implements the optimal decoder on a small example code, in that it implements the optimal measurement that distinguishes the quantum output states for the set of input codewords with highest achievable probability. Here we significantly expand the understanding, formalism, and applicability of the BPQM algorithm with the following contributions. First, we prove analytically that BPQM realizes optimal decoding for any binary linear code with tree Tanner graph. We also provide the first formal description of the BPQM algorithm in full detail and without any ambiguity. In so doing, we identify a key flaw overlooked in the original algorithm and subsequent works which implies quantum circuit realizations will be exponentially large in the code dimension. Although BPQM passes quantum messages, other information required by the algorithm is processed globally. We remedy this problem by formulating a truly message-passing algorithm which approximates BPQM and has quantum circuit complexity <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi class="MJX-tex-caligraphic" mathvariant="script">O</mml:mi></mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:mtext>poly&amp;#xA0;</mml:mtext><mml:mi>n</mml:mi><mml:mo>,</mml:mo><mml:mtext>polylog&amp;#xA0;</mml:mtext><mml:mfrac><mml:mn>1</mml:mn><mml:mi>&amp;#x03F5;</mml:mi></mml:mfrac><mml:mo stretchy="false">)</mml:mo></mml:math>, where <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi></mml:math> is the code length and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>&amp;#x03F5;</mml:mi></mml:math> is the approximation error. Finally, we also propose a novel method for extending BPQM to factor graphs containing cycles by making use of approximate cloning. We show some promising numerical results that indicate that BPQM on factor graphs with cycles can significantly outperform the best possible classical decoder.