Reviewerly/exHarmony huggingface dataset:
Asymmetric quantum Griesmer codes detecting a single bit-flip error

The quest to build large-scale quantum computing devices depends on keeping the noise level below a fault-tolerance threshold. In this paper we derive the asymmetric quantum analogue of the Griesmer bound. To benefit from the noise asymmetry in many physical systems, one can decide to only detect a single bit-flip error while maximizing control over the phase-flip errors. We present constructions of such codes via the classical Griesmer codes and obtain infinite families. The optimality of the parameters of the codes in the families is measured against the quantum Griesmer bound. Numerous other codes, which may not be optimal, can also be derived. Choices of their design provide greater flexibility in terms of the resulting quantum parameters. We give examples of good qubit, qutrit, and ququad codes from such a route.
Supplemental Material: Critical metal enrichment in crustal melts: The role of metamorphic mica

&lt;p&gt;Appendix S1 (sample descriptions), Appendix S2 (table with mineral assemblages), Appendix S3 (detailed methods description for LA-ICP-MS analysis), Appendix S4 (LA-ICP-MS spot locations), and Appendix S5 (major- and trace-element data table for samples and secondary standard). &lt;/p&gt;
Applying Evolutionary-based User Characteristic Clustering and Matrix Factorization to Collaborative Filtering for Recommender Systems

&lt;p&gt;In recent years, with the rise of numerous Internet service industries, recommender systems have been widely used as never before. Users can easily obtain the information, products or services they need from the Internet, and businesses can also increase additional revenue through the recommender system. However, in today&amp;rsquo;s recommender system, the data scale is very large, and the sparsity of the scoring data seriously affects the quality of the recommendation. Thus, this study intends to propose a recommendation algorithm based on evolutionary algorithm, which combines user characteristic clustering and matrix factorization. In addition, the exponential ranking selection technology is employed for evolutionary algorithm. The experiment result shows that the proposed algorithm can obtain better result in terms of four indicators, mean square error, precision, recall, and F score for two benchmark datasets.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Design of Fuzzy Logic Controller for a Risley Prism Imaging System

This study presents modelling, control system design, and optimization of an imaging system with a Risley pair. The system model is first created in the computer-aided design software and transferred to the Simulink Simscape Multibody toolbox. In this way, the controller design can be made quickly without the mathematical model of the system. A PD type fuzzy controller is designed to control the Risley prism imaging system. In addition to this controller, it is desired to optimize the controller parameters with genetic algorithm optimization. The responses of the two controllers obtained are given comparatively.
POSTER: Launching a ZIP Bomb on the DICOM-enabled Devices

During the past several years, the healthcare sector has become an interesting target for cybercriminals. The concept of E-health can not be imagined without medical images, which are usually presented, stored and/or transferred by using the DICOM standard. This poster aims to present three different attack scenarios, how one can launch a ZIP bomb against some DICOM-enabled device, which can crash the involved application or the system will run out of memory or out of disk space.
Developing an Augmented Reality System with Real-Time Reflection for Landscape Design Visualization, Using Real-Time Ray Tracing Technique

In landscape design, visualization of a new design on the site with clients can greatly improve communication efficiency and reduce communication costs.The use of augmented reality (AR) allows the projection of design models into the real environment, but the relationship between the models and the physical environment, such as reflections, which are often thoughtfully considered in waterfront landscape design, is difficult to express in existing AR systems.The aim of this study is to accurately render and express the reflections of virtual models in the physical environment in an AR system.Different from traditional rasterized rendering, this study used physically correct ray-tracing algorithms for reflection rendering calculations.Using a smartphone and a computer, we first constructed a basic AR system using a game engine and then performed ray-tracing computations using a shader kernel in the game engine.Finally, we combined the rendering results of reflections with the video stream from a smartphone camera to achieve the reflection effect of a virtual model in a physical environment.Both designers and clients could review the design with a realistic reflection on an actual water surface and discuss design decisions through this system.
Where Do I Go From Here?

Hey, I get it. Switching operating systems is a big deal. There’s a lot to think about. Making such a change has a learning curve, and as a small business owner, you may be wondering where you’ll find the time to learn it all. Nobody likes to have hairpulling tantrums trying to figure out new technology. Perhaps, you’re even seconding guessing yourself, trying to convince your inner geek that the system you’ve been using is good enough, so why take this on.
Academic Article Classification Algorithm Based on Pre-trained Model and Keyword Extraction

Text classification, which has extensive application in many fields, assigns a tag to a given piece of text. Academic articles are the most authoritative source of academic information and play an important role in the process of delivering latest academic information. On social media, these academic articles will generate considerable academic news, translated articles, tutorial articles, etc. How to classify these academic articles has become more and more important. In this paper, we employ pre-trained model for academic text classification. Further, to identify terminology in academic papers, we design a convolutional layer to capture local dependencies. We also introduce a max-pooling layer that can get the most important elements in the text. Considering that academic articles are usually long, we propose a fine-tuning technique based on keyword extraction for pre-trained model to obtain global information. We conduct experiments on the Fudan Text Classification Corpus and the SCHOLAT academic news dataset. The experimental results show that the proposed method outperforms the methods commonly used in recent years on both datasets.
Cross-Domain Object Detection Through Image-Category Features Joint Alignment

The object detection task in general assumes that the training and test data come from a same distribution. However, in real situations, the model often does not work as well as expected due to the domain shift problem. In this paper, a domain adaptive model based on image and category features is proposed to solve the cross-domain object detection task. The proposed model for domain adaption is based on the one-stage object detection model RetinaNet. Image-level features are used for adversarial learning so that feature distributions can be aligned. Besides, category-level features for contrastive learning enables foreground features with the same category but from different domains to be mapped more closely. Comprehensive experiments are conducted with existing domain adaptive models on three public available road object detection datasets. The experiments show that the domain shift problem is well solved by our method. Besides, compared with two-stage domain adaptive models, the method in this paper performs better and detects more efficiently.
ON SOME CRYPTOSYSTEMS BASED ON ALGEBRAIC CODES

In 1978 McEliece built the first public key cryptosystem based on error-correcting codes. At the same time, effective attacks on the secret keys of this cryptosystem have not yet been found. The work describes the classical and modernized cryptosystems of McEliece and Niederreiter, also examples of their practical application based on Goppa codes using the Patterson algorithm. Also the algorithms of two-step authentication protocols with zero disclosure based on error-correcting codes are given.
Design of Low Power Versatile Bit-Serial Multiplier in Finite Field GF (2m)

Finite field arithmetic is the most important component in applications like cryptography, computer algebra and error correcting codes. Versatility is an important property the hardware industry lacks and trying to establish as much as possible. To survive in this booming technological word, the new designs should be of an adjustable one, which processes the versatile property. In this we have proposed an efficient VLSI design for versatile bit-serial multiplier in finite fields GF (2m). The versatile multiplier designed here modifications done by reducing the unwanted switching activity removed by clock gating scheme. Our design provides a solution to the power reduction. The introduced multiplier operates over a variety of binary fields up to an order of 2m. The value of m can vary up to 264 bits. Multiplier is designed using Verilog HDL.
Keynote: Multi-User Activity Recognition: State-of-the-art and Research Challenges

Human activity recognition has attracted enormous research interest because it provides precious contextual information in several domains spanning from healthcare to security, safety, and entertainment. So far, robust and consolidated literature focused on the automatic detection of activities performed by single individuals, with a great variety of approaches in terms of sensing modalities, recognition techniques, specific set of recognized activities, and final application objectives. However, much less research attention has been devoted to scenarios in which multiple subjects perform individual or joint activities forming groups to achieve common goals. This problem is often referred as multi-user activity recognition. With the advent of the Internet-of-Things, smart objects are being pervasively spread in the environment and worn on the human body, enabling contextual and distributed recognition of group and multi-user activities. This talk discusses the motivations and advantages of multi-user activity recognition and overviews sensing methods (including those based on Wearable Computing Systems and in particular Collaborative Body Sensor Networks, developed in the context of the SPINE Body-of-Knowledge), recognition approaches, and impact in practical, relevant applications. The multi-user activity recognition approaches are also analyzed from the perspective of situation-aware systems. In particular, an emerging trend analyzed during the talk is the definition of collaborative situation-aware wearable computing systems, i.e., wearable devices able to perceive and understand the situation in order to adapt their behavior and anticipate user's needs. The discussion also outlines important open research challenges and related future directions.
Two-Layer Matrix Factorization and Multi-Layer Perceptron for Online Service Recommendation

Service recommendation is key to improving users’ online experience. The development of the Internet has accelerated the creation of many services, and whether users can obtain good experiences among the massive number of services mainly depends on the quality of service recommendation. It is commonly believed that deep learning has excellent nonlinear fitting ability in capturing the complex interactions between users and items. The advantage in learning intricacy relationships enables deep learning to become an important technology for present service recommendation. Recently, it is noticed that linear models can perform almost as well as the state-of-the-art deep learning models, suggesting that capturing linear relationships between users and items is also very important for recommender systems. Therefore, numerous deep learning systems combined with linear models have been proposed. However, existing models are incapable of considering the size of the embedding. When the embedding dimension is too large, it leads to overfitting and thus influences the model’s ability to capture linear relationships. In this paper, a neural network based on two-layer matrix factorization and multi-layer perceptron—Two-layer Matrix factorization and Multi-layer perceptron Neural Network (TMMNN)—is proposed. To solve the problem of overfitting caused by an oversized embedding dimension, multi-size embedding technology has been integrated into the model. Matrix factorization and the multi-layer perceptron are placed in the upper and lower layers respectively, and they both receive embedding vectors dynamically adjusted for dimensions. In the upper layer, the matrix factorization is responsible for receiving the embedding of users and items, capturing linear relationships, and then yielding the generated new vectors as input to the multi-layer perceptron in the lower layer. Compared to other previously proposed models, the experimental results on the standard datasets MovieLens 20M and MovieLens Latest show that the TMMNN model is evidently better in terms of prediction accuracy.
Coalgebraic Semantics for Nominal Automata

This paper provides a coalgebraic approach to the language semantics of two types of non-deterministic automata over nominal sets: non-deterministic orbit-finite automata (NOFAs) and regular nominal non-deterministic automata (RNNAs), which were introduced in previous work. While NOFAs are a straightforward nominal version of non-deterministic automata, RNNAs feature ordinary as well as name binding transitions. Correspondingly, words accepted by RNNAs are strings formed by ordinary letters and name binding letters. Bar languages are sets of such words modulo $$\alpha $$ -equivalence, and to every state of an RNNA one associates its accepted bar language. We show that the semantics of NOFAs and RNNAs, respectively, arise both as an instance of the Kleisli-style coalgebraic trace semantics as well as an instance of the coalgebraic language semantics obtained via generalized determinization. On the way we revisit coalgebraic trace semantics in general and give a new compact proof for the main result in that theory stating that an initial algebra for a functor yields the terminal coalgebra for the Kleisli extension of the functor. Our proof requires fewer assumptions on the functor than all previous ones.
Overview: Identifying strategic opportunities for Albania

Since its independence, Albania has achieved significant success across various social, economic and institutional dimensions. It became a democratic society with a functional open market economy. Productivity growth across all sectors has improved employment, incomes and standards of living. To address its development constraints and embrace a durable, sustainable and inclusive growth path, Albania needs a long-term vision. This chapter takes a holistic view of Albania’s development performance across a range of outcomes, spanning the breadth of the Sustainable Development Goals. It then outlines key strategic opportunities to help Albania improve the quality of life for all citizens, strengthen education and skills, find new sources of growth, build local government capabilities, broaden the revenue base, address air pollution, and respect diversity.
Supplemental Material: Ancient structural inheritance explains gold deposit clustering in northern Perú

&lt;p&gt;Tables S1 and S2, methods, and Figures S1–S12. &lt;/p&gt;
Perancangan Sistem Informasi Kasir Dengan Barcode Berbasis Web Dan Framework Codeigniter

In the development of information technology business is what the world needs without exception for Indonesia. In all fields, IT has used IT to process the ease of business activities. Of course, IT is applied in the company to create wider market opportunities so that the company products get closer to consumers. The purpose of the research on the Information System for drug promotion media is to make it easy for the public / Android users to find out some classifications of drugs, to promote drugs to the public that are available in Kimia Farma Pharmacy which is based on Android.
A Study on the effects of Childcare Teachers' Calling and Resilience on Psychological Burn-out

본 연구는 보육교사의 소명의식, 회복탄력성, 심리적소진 간 어떠한 관계가 있는지 살펴보고, 소명의식과 회복탄력성이 심리적소진에 미치는 영향을 알아보는데 목적이 있다. 이를 위해 J 지역의 보육교사 218명을 대상으로 설문조사를 실시하였으며, 연구대상의 특성과 각 변인 간 관계파악을 위해 기술통계, 상관관계분석, 중다회귀분석을 실시하였다. 연구결과를 살펴보면, 첫째, 보육교사의 소명의식은 심리적소진과 부적 상관관계가 나타났고, 회복탄력성 또한 심리적소진과 부적 상관관계가 나타났다. 둘째, 보육교사의 소명의식과 회복탄력성은 심리적소진에 유의한 부적영향을 미치는 것으로 나타났으며, 회복탄력성의 하위요인인 대인관계능력, 긍정성은 부적영향을 미치는 것으로 나타났다. 본 연구결과는 보육교사의 심리적소진에 영향을 미치는 소명의식과 회복탄력성을 증진시켜 심리적소진을 감소를 위한 개인적 차원의 노력과 사회적 차원의 지원을 모색하는데 있어 기초자료로 활용되어질 수 있을 것이다.The purpose of this study is to investigate the effect of childcare teachers' calling and resilience on psychological burn-out. For this purpose, a survey was conducted of 218 childcare teachers in the area of J. Pearson's correlation analysis and multiple regression analyses were performed by using the SPSS WIN 27.0. The results are as follows. First, childcare teachers' calling was negatively correlated with psychological burn-out, and resilience was also negatively correlated with psychological burn-out. Second, childcare teachers' calling and resilience were found to have a significant negative effect on psychological burn-out, and interpersonal ability and positivity, which are sub-factors of resilience, were found to have a negative effect. The results of this study can be used as basic data to improve calling and resilience that affect psychological burn-out of childcare teachers, and to seek individual efforts and social support to reduce psychological burn-out.
Autoethnography on on-line class operation of Gyeonggi Dream University by physical education career educator

The purpose of this study is to explore the difficulties and improvement process of teachers who operate non-face-to-face physical education classes at Gyeonggi Dream University. The participant in this study is 'I', the instructor. I used the autoethnography methodology to discover educational meaning in the class.. For the research, self-recovery diary and reflective journal collected. Data analysis were used Spradley(1979)'s area and classification analysis method. As a result of the study, first, students did not turn on the camera during class, showed passive behavior, focused on something else, participated in the class for student record, and were not interested in the class. Second, the class was changed to lead students to participate in the class. It was ducted to select a learning topic by reflecting students' opinions, to communicate freely using text and voice, to present interesting learning activities using the Internet, and to increase students' competence. Third, students opened their closed minds and participated in classes to acquire knowledge of physical education and learn about their careers. Accordingly, the necessity of strengthening the physical career education system and practical ability of Gyeonggi Dream University, enhancing the non-face-to-face education competency of instructors, and research on physical education was proposed.
An Empirical Analysis of Ergonomic Gaming Peripherals Improving Gaming Performance

The gaming industry is a huge industry that is based on creativity and the use of media as well as the latest technology. According to the Entertainment Software Association (ESA) in 2021 more than 227 million Americans playing video games, and 75% of USA households have at least one person who plays games and has a gaming device on their home. The facts mentioned before makes this industry very profitable to enter. In hardware aspect, some company innovates to make a specific device which is used special for gaming needs. This gaming device main selling points is high specification and ergonomics factor for improving gaming performance. Despite the successful sale of this product, many people still doubt the effectiveness of gaming peripheral products to improve gaming performance and whether the higher specification of gaming peripheral can truly improve player performance during gameplay. The Study is based on the effectiveness of peripheral on human perception sensor that can be used in the implementation of ergonomic science / physical engineering or HCI (Human Computer Interaction), namely vision, hearing, and touch. In that case, with qualitative research method (direct observation, interview &amp; simulation) this study found as the result, that was true the gaming peripherals are able to improve the performance of the user, but not for all types of users. The competitive gamer who has high gameplay hours can benefit the most and use maximum potential performance of gaming peripherals.
A Framework for Chatbots in Medical Pre-Diagnosis

Conversational AI systems or bots are a software framework designed to simulate human conversations. They interact with users, understand their needs and preferences, and recommend the next-best action with minimal human intervention. The core function of chatbots is to give the best response to any query that it receives. It shall answer sender’s questions, providing the most relevant information, asking follow-up questions, and making the conversation as realistic as possible. For accomplishing the ”realism” goal, these bots need to understand the intentions, sentiment, or underlying emotion of the sender’s message, and determine the most appropriate response. The bots may use metadata, such as speaker identity, preferences, or emotional state to provide relevant, grammatically correct response. Sometimes, sentiment analysis allows the Chatbot to ’understand’ the user’s mood by analyzing verbal and sentence structuring clues. Bots are common in HR, IT help desk, hospitality, and self-service applications with various levels of sophistication. Currently, these chatbots are a little rigid and underperform in-person counselling which are required in medical pre-diagnosis. However, one of the areas ripe with opportunities is in Healthcare Dialog systems. They have significant impact in democratizing the latest medical interventions globally with scale. In this paper, we will identify key functional requirements for a practical bot, particularly for pre-diagnosis based on the symptom extraction, extract the most appropriate and suggest a framework for achieving some of the functionality with algorithms.
Secure Digital Voting System Based on Blockchain Technology - A Survey

Online voting is a trend that is gaining momentum in modern society. It has great potential to decrease organizational costs and increase voter turnout. It eliminates the need to print ballot papers or open polling stations— voters can vote from wherever there is an Internet connection. Despite these benefits, online voting solutions are viewed with a great deal of caution because they introduce new threats. A single vulnerability can lead to large- scale manipulations of votes. Electronic voting systems must be legitimate, accurate, safe, and convenient when used for elections. Nonetheless, adoption may be limited by potential problems associated with electronic voting systems. Blockchain technology came into the ground to overcome these issues and offers decentralized nodes for electronic voting and is used to produce electronic voting systems mainly because of their end-to-end verification advantages. On the other hand, the most often mentioned issues in blockchain applications are privacy protection and transaction speed. For a sustainable blockchain- based electronic voting system, the security of remote participation must be viable, and for scalability, transaction speed must be addressed. Due to these concerns, it was determined that the existing frameworks need to be improved to be utilized in voting system.