Reviewerly/exHarmony huggingface dataset:
Research on chat robot based on Seq2seq model

More and more scholars put forward to apply deep learning to chat robots and realize the dialogue processing of chat robots through semantic analysis and emotional dialogue characteristics of deep learning. In actual chat, all kinds of sentences will exist, but there are some uncertainties in the logic of sentences and the part of speech of words, and some questions have different meanings of the same word. In order to carry on the dialogue, the robot must have strong semantic disambiguation ability and remember the answered questions. In this article, an intelligent chat robot semantic matching algorithm based on Seq2seq is proposed, and under certain attribute matching rules, the semantic similarity of a concept at the next level pointed by the attribute is found, and the similarity of each concept is calculated, so that the similarity of all concepts at all levels is weighted and integrated to obtain the semantic similarity between ontologies. The experimental results show that the dialogue generation mechanism designed this time has higher recognition rate than the traditional mechanism, and can correctly identify the dialogue content to make accurate answers, which provides technical support for the development of intelligent chat robots.
Multispectral Image Noise Removal With Adaptive Loss and Multiple Image Priors Model

Multispectral image (MSI) denoising is a crucial pre-processing step for various subsequent image processing tasks, including classification, recognition, and unmixing. This paper proposes a novel image denoising model that integrates both noise modeling and image prior knowledge modeling. Specifically, to account for the complexity and non-uniformity of noise, a non-independent identically distributed mixture of Gaussian model is employed for noise modeling, and a weighted loss function is obtained. The weights used in the loss function are adaptively learned from noisy MSI and employed to adjust the denoising strength of each pixel. Additionally, the model leverages the prior knowledge of the image by utilizing a nonlocal low-rank matrix model that captures the spatial-spectral correlation and nonlocal spatial similarity priors of the image. Moreover, our model adopts the weighted spatial-spectral TV model to encode the local smoothness prior of the image. Both prior models are translated into regularization terms in the denoising model. The efficacy of the proposed method is demonstrated through both simulated and real image experiments.
Research on acoustic parameters of singing voice for singing speech intelligence

In artificial intelligence research, prosodic and pitch features are the focus of singing intelligent speech synthesis. Pitch features are usually studied by extracting fundamental frequencies. In the process of folk song singing, many special vocal skills are used, mainly related to the mode of vocal fold vibration. Using singing voice signals and parameters to study singing pitch and vocal types can better obtain the pitch and prosodic characteristics of singing. This experiment mainly takes Ewenki folk songs as the research object, analyzes singing voice signals and parameters, so as to provide reference for singing voice synthesis. The Ewenki are one of China’s ethnic groups with a small population. The Ewenki folk songs are sung in the Ewenki language, which retains many features of ancient folk songs and singing pronunciation characteristics, especially in the vocal vocalization of the singing voice. The article takes the typical traditional folk song of Ewenki, "Molie Girl", as the object of study. By collecting the singing voice and voice signals of a professional and a non-professional singer, extracted the parameters of fundamental frequency, open quotient and speed quotient. After statistical analysis of the parameters, the overall voice characteristics of the professional and non-professional singers during the singing process and the differences in the voice parameters of the trailing part of the voice were obtained. The analysis revealed both professional and non-professional singers were able to express the artistic characteristics of traditional folk songs, with different vocal styles and singing techniques. The experimental voice acoustic parameters and results can be used as a reference for the synthesis of traditional folk songs and as a method for the teaching of traditional folk songs.
An analysis of the primary and general education system's development in the Chechen Republic for 2001–2021

Subject. This article discusses the role of the educational system in the integration of the Chechen Republic into a single economic space of Russia. Objectives. The article aims to compare the North Caucasian Federal District regions according to various parameters reflecting the level of development of the educational system. Methods. For the study, we used the comparative analysis methods. Results. The article finds that within the period from 2001 through 2021, thanks to the implementation of programmes for the restoration and development of the social sphere of the Chechen Republic, the number of educational institutions in the region increased, and the proportion of those who received a certificate of basic general education in the total number of students increased. Conclusions. The active participation of the Chechen Republic in the implementation of State federal programmes and national projects should be supported. It is necessary to develop a set of measures aimed at improving the teaching profession status.
Grassroots scaling up: Navigating algorithmic scales in a technopolitical landscape

Abstract My theoretical aim in this article is to focus on an examination of processual enactments of scale in light of the technological affordances that are currently at the disposal of a significant majority of humans. I offer the terms algorithmic scales and algorithmic scalar affordances to describe one activist's engagement of practical theories of scale—her ‘algorithmic imagination’ (Bucher 2017)—which led her to design her audience in ways intended to algorithmically scale up, or amplify , her activities on Facebook—to enhance their spread numerically, rapidly, and translocally, making use of the algorithmically constructed communicative possibilities or affordances available to her on the site. (Social media, scale, algorithms, audience design, Facebook, activism, nation, politics)
The k-XORSAT threshold revisited

We provide a simplified proof of the random $k$-XORSAT satisfiability threshold theorem. As an extension we also determine the full rank threshold for sparse random matrices over finite fields with precisely $k$ non-zero entries per row. This complements a result from [Ayre, Coja-Oghlan, Gao, M\"uller: Combinatorica 2020]. The proof combines physics-inspired message passing arguments with a surgical moment computation. \hfill MSc: 60B20, 15B52
Robotics and process automation technologies for healthcare informatics

Healthcare is the backbone of the nation and is a patient-centered industry. The introduction of robots into the health sector will play a vital role. Robots were frequently seen as "advanced technology," and patients may find them indifferent. Nonetheless, robots are starting to appear in a variety of functions in hospitals, i.e., from health monitoring of the patients to the management of the hospitals. Even the management can generate lots of revenues if the robots work perfectly. Robotics&apos; automation can be readily designed to do simple tasks across apps in the same way those humans do. A process with several stages (like monitoring of the patient&apos;s health-related parameters) and operations, such as receiving forms, delivering a reception message, validating the form for compliance, putting the form in a folder, and maintaining a spreadsheet with the identity of the document, the date submitted, and so forth, can indeed be explained through the program. The training of the robots to work efficiently in the healthcare systems faces many issues, as its dysfunction can result in a disaster and the life-threatening to the patients. The chapter discusses the various challenges of the use of robots in the healthcare industry. The challenges are like what training the robots should get to work perfectly with the given environment. How the robots assist in the scarcity of nursing, doctors, and other healthcare providers by supporting the providers with repetitive duties, allowing the practitioner to undertake higher-level functions. How the robots minimize the need for "outsourcing production" management operations in healthcare. These intense observations and literature conclude that the Health Robotics categorization outlines how to offer high-quality patient care, effective medical operations, and a comfortable workplace for both doctors and healthcare staff. The chapter concludes with the case study where the robot ensures to protect the privacy and confidentiality of the patient&apos;s data.
Using Augmented Reality Based Intervention to Teach Science to Students With Learning Disabilities

Augmented reality has the potential to give students a unique experience, including when teaching science subjects. The purpose of this study was to examine the effectiveness of an augmented reality-based intervention in teaching “Solar Systems and Planets” to four students with learning disabilities. The dependent variable was the percentage of the students’ correct answers to the questions about the science unit. The independent variable was the augmented reality-based intervention. A multiple baseline across participants’ designs was used in the study. The results showed a functional relationship between the augmented reality-based intervention and the percentage of correct answers the students gave to the questions. All four students showed significant gains immediately after receiving the intervention and maintained it between one and 5 weeks. Social validity findings also showed that students enjoyed the augmented reality-based intervention and were willing to use it in other subjects.
From skepticism toward celebrities to celebrity culture hate: Mediating role of perceived celebrity deception and perceived dark triad of celebrities

<p xmlns="http://www.ncbi.nlm.nih.gov/JATS1">Though efforts have been made to understand the concept of celebrity hatred, ambiguity still exists about why some people feel intense contempt, antagonism, and fear directed at celebrity culture. This study (N = 1175) aimed to reveal the indirect impact of skepticism toward celebrities on celebrity culture hate by introducing perceived celebrity deception (the perception that celebrities are deceptive) and dark triad traits of celebrities (the perception that celebrities possess dark triad personality characteristics, including Machiavellianism, narcissism, and psychopathy) as potential mediators. The study introduces the Perceived Celebrity Deception Inventory and establishes its psychometric properties. Perceived deception of celebrities and perceived deception of others were relatively high compared to that of one’s immediate family, with social media influencers being seen as the most deceptive category of celebrities. Celebrities were perceived as Machiavellian, narcissists, and psychopaths at the same time, but at dissimilar levels. Skepticism toward celebrities was weakly correlated with celebrity culture hate in the correlation analysis. This relationship was mediated by perceived celebrity deception and perceived dark triad of celebrities in the structural analysis. Results suggest that perceived celebrity deception and dark triad characteristics of celebrities tend to breed celebrity culture hate rooted from skepticism toward celebrities and may provoke “behavioral” component of celebrity culture hate (e.g., celebrity bashing).
The Geochemistry of Carbonate Rocks in Igwe Igarra, Southwestern Nigeria

Nigeria has an abundance of valuable solid minerals and rocks which can generate revenue for the government if they are fully exploited.Carbonate rock is one of such rocks that occur prominently in Igwe Igarra area of Edo State.Five (5) carbonate rock samples (Four marble and One Calcsilicate) were subjected to geochemical analyses.A petrographic study reveals that marble and calcsilicate contain calcite, quartz, microcline, plagioclase, biotite and opaque minerals.Geochemical data shows that carbonate rocks have variable geochemical characteristics: Igwe marble is highly Calcitic (96% -98%) and very low in dolomite Mg(CO 3 ).Calcsilicate rock is low in CaO (27.5%) and higher in marble.The Igwe marble is pure and contains fewer impurities compared to calcsilicate rock which makes it unideal for usage.Based on these characteristics the marble from Igwe is suitable as raw materials for the productions of glass, papers, beet sugar, aggregates, lubricants and fillers.
A Fuzzy Theory Based Attitude Control for Takeoff of Quadrotor

Unmanned Aerial Vehicles (UAVs) are utilized in various fields such as aerial shots, transportation, spraying chemicals in agriculture and surveys of plant growth in forestry. It is necessary to consider the ground effect when operating UAVs/AAVs. The lift coefficient changes and significantly affects the controllability when operating sharp turns within the range of ground effect. So, it is essential to attend to safety-based takeoff and landing as the risk of accidents. In this paper, we propose a fuzzy control for the attitude control of a quadrotor based takeoff.
Proposal of a Music Auditioning Application Using Music Compact Disk Jacket as Augmented Reality Marker

In this study, we implemented a music auditioning application that makes use of the compact disk (CD) jacket as an augmented reality (AR) marker. This application includes a music auditioning function as well as a related information presentation function. With the music auditioning function, users can easily listen to the music CDs they want to listen to on their smartphones. Furthermore, the related information presentation function recognizes the image of the CD jacket using AR technology and displays various information about the artist superimposed on the real space. We evaluated the effectiveness of the music auditioning function and related information presentation function for 30 subjects to evaluate this system. As a result, we were able to confirm the high effectiveness of the two functions.
An Aircraft Assembly System Based on Improved YOLOv5

Aircraft assembly is challenging to ensure accuracy and feasibility due to its numerous components and stringent quality requirements. The assembly assistant system with computer vision has advanced rapidly. However, there is a lack of the dataset required for object detection because of the confidentiality of aircraft components, resulting in a degraded performance of the assistant system. In this paper, we proposed a mixed aircraft component dataset (MACD), including real photos and synthetic images. We adopted Squeeze and Excitation (SE)-YOLOv5 by introducing the SE-Layer into CSPDrkNet53 to improve object detection accuracy. In addition, we defined the price-performance ratio (PPR) as a measure of dataset quality. We also developed an augmented reality assembly assistant system that offers simple and convenient assembly assistance and can improve assembly efficiency and quality.
A High-Randomness and High-Stability Electronic Quantum Random Number Generator without Post Processing

Random numbers are one of the key foundations of cryptography. This work implements a discrete quantum random number generator (QRNG) based on the tunneling effect of electrons in an avalanche photo diode. Without any post-processing and conditioning, this QRNG can output raw sequences at a rate of 100 Mbps. Remarkably, the statistical min-entropy of the 8,000,000 bits sequence reaches 0.9944 bits/bit, and the min-entropy validated by NIST SP 800-90B reaches 0.9872 bits/bit. This metric is currently the highest value we have investigated for QRNG raw sequences. Moreover, this QRNG can continuously and stably output raw sequences with high randomness over extended periods. The system produced a continuous output of 1,174 Gbits raw sequence for a duration of 11,744 s, with every 8 Mbits forming a unit to obtain a statistical min-entropy distribution with an average value of 0.9892 bits/bit. The statistical min-entropy of all data (1,174 Gbits) achieves the value of 0.9951 bits/bit. This QRNG can produce high-quality raw sequences with good randomness and stability. It has the potential to meet the high demand in cryptography for random numbers with high quality.
Research on the construction technology of cardiovascular disease Q&amp;A system based on knowledge graph

Cardiovascular disease is a serious threat to human life and health, with high prevalence, high disability and high mortality. Cardiovascular disease Q&A system is more targeted and reliable than search engines, and users can get more concise and clear information. As a structured data source of the Q&A system, the knowledge graph enhances the flexibility and accuracy of the system. In this paper, knowledge extraction from authoritative medical websites and Baidu Baike using web spiders, knowledge fusion with reference to medical textbooks on cardiovascular diseases, and knowledge storage using Neo4j graph database were used to build the knowledge graph of cardiovascular diseases. On this basis, the Q&A system was built based on template matching and vector parsing methods, using word2vec model, TF-IDF algorithm, Naive Bayes classifier and support vector machine classifier from the perspective of doctors and patients respectively. The experiments showed that the Q&A system can effectively answer most of the questions on cardiovascular diseases.
Research and application of talent training evaluation model based on deep learning

The application of deep learning technology in target detection algorithm significantly improves the performance of the algorithm. Based on the traditional target detection algorithm, the task of target detection is summarized, including evaluation index, open data set, algorithm framework and the defects of traditional algorithm. Therefore, taking the needs of object detection as the fulcrum, the training goal of research travel talents is clarified in this paper. There are two classification criteria: whether there is an explicit regional suggestion and whether a prior anchor frame is defined. The existing target detection algorithms are classified, and the evolutionary route of each algorithm is reviewed, and the mechanism, advantages, limitations and application scenarios of each method are summarized. The performance of representative target detection algorithms in open data sets is compared and analyzed.
Research on resource scheduling optimization technology for power cloud edge collaboration

Multi-station integrated power grid system has a large number of cloud and edge data centers. It needs to solve the problem of collaborative use of cloud, edge and terminal resources, realize the rapid migration of computing tasks in the case of failure, and achieve the consistency of primary and standby resources in the cloud and edge. It needs to solve the problem of streaming processing in high concurrent state. This paper studies the resource scheduling optimization technology adapted to the power cloud edge collaboration, innovatively proposes a multi-data center resource optimization and upgrading method based on the graph data structure, adapts to the multi-center resource optimization and upgrading scenario, uses the RDF resource description framework, TLGM data model to build the multi-data center resource database, uses the global scheduler, the edge scheduler to process the calculation request, uses the data linkage state data model, scheduling rules The probability calculation matrix converts the resource consistency and resource utilization into graph query, and uses the original graph retransmission, subgraph merging technology and efficient load balancing to realize graph query, so as to realize the optimization and upgrading of resources in multiple data centers. This paper innovatively proposes a stream data processing method that is suitable for the cloud edge collaborative multi- data center scenario. It is suitable for the cloud edge collaborative multi-data center stream data processing and analysis scenario. The stream business control and orchestration center construct a serial-parallel collaborative flow analysis process based on the pipeline processing model and parallel processing model. The flow control center control terminal, edge data center, and cloud data center implement flow analysis and scheduling according to the business priority, give full play to the advantages of cloud edge collaborative multi-data center distributed computing to achieve rapid processing and analysis of streaming data.
The Cambridge handbook of working memory and languageBy John W.Schwieter and Zhisheng (Edward)Wen (Eds.), Cambridge: Cambridge University Press, 2022. Hardcover <scp>US</scp> $155.00. <scp>ISBN</scp>: 9781108845342

The author declares that there is no conflict of interest. The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.
A data mining approach to malicious websites detection

Cyber Security plays a significant role in internet era nowadays, since information is exchanged all the time. During this period, the relationship between daily economic behavior and the network are gradually strengthened. Although network technology presents a trend of continuous development, there is still a long way to go before the technology becomes mature. In network applications, fraudulent websites commonly steal the private information of users. The implementation of data mining technologies to develop a deep learning model for identifying dangerous websites is a vital measure for network security.
Application of BERT-based local optimization algorithm in intelligent question answering system design

With the rapid growth of artificial intelligence, especially deep learning, Natural Language Processing (NLP) is playing an increasingly important role in human learning, work and life. The huge amount of information makes it difficult for people to get valuable information from it. How to quickly screen this valuable information is the key to the intelligent question answering system. In order to obtain knowledge information that is more in line with users' expectations, NLP technology has been researched and developed, and related technical products have been successfully integrated into people's lives, among which intelligent question answering system can better meet people's demand for accurate information. This article explains some limitations and defects of current semantic matching technology, puts forward a local optimization algorithm based on Bert, and applies it to the design of intelligent question answering system. The simulation results show that this algorithm is more accurate for text feature recognition, which is 19.85% higher than the contrast algorithm. The system interacts with users through the visual interface, and automatically replies to the questions raised by users, thus achieving the purpose of practical application.
A case study of carbon monoxide content based on differential equations

Differential equation plays an important role in engineering technology, military, economy, medicine, biology, ecology and other fields. This paper uses differential equation to solve practical problems about carbon monoxide content. Through the analysis, modeling and solving of the case, not only the analytical solution of the equation is obtained, but also the numerical solution of the equation is given by using MATLAB program.