Reviewerly/exHarmony huggingface dataset:
Faces in scenes attract rapid saccades

During natural vision, the human visual system has to process upcoming eye movements in parallel to currently fixated stimuli. Saccades targeting isolated faces are known to have lower latency and higher velocity, but it is unclear how this generalizes to the natural cycle of saccades and fixations during free-viewing of complex scenes. To which degree can the visual system process high-level features of extrafoveal stimuli when they are embedded in visual clutter and compete with concurrent foveal input? Here, we investigated how free-viewing dynamics vary as a function of an upcoming fixation target while controlling for various low-level factors. We found strong evidence that face- versus inanimate object-directed saccades are preceded by shorter fixations and have higher peak velocity. Interestingly, the boundary conditions for these two effects are dissociated. The effect on fixation duration was limited to face saccades, which were small and followed the trajectory of the preceding one, early in a trial. This is reminiscent of a recently proposed model of perisaccadic retinotopic shifts of attention. The effect on saccadic velocity, however, extended to very large saccades and increased with trial duration. These findings suggest that multiple, independent mechanisms interact to process high-level features of extrafoveal targets and modulate the dynamics of natural vision.
Timing Constraints Due to Real-Time Graph-Traversal Algorithms on Incomplete Cluster States in Photonic Measurement-Based Quantum Computing

Understanding the computational overheads imposed by classical control systems on quantum computing platforms becomes critically important as these quantum machines grow in scale and complexity. In this work, we calculate the overheads imposed by the implementation of real-time graph traversal algorithms needed to find computational paths through incomplete cluster states for the implementation of one-qubit gates; a necessary requirement for a realistic implementation of photonic measurement-based quantum computing. By implementing two different algorithms, a global breadth-first search that searches the entire cluster state and an incremental version that traverses a narrow subsection of the cluster state, we analyze the trade-off between the accuracy of finding viable paths and the speed at which this operation can be performed, which constrains the overall photonic clock cycle of the system. We also outline the broader implications of our results for implementing classical control systems for measurement-based photonic quantum computing.
Finite-temperature quantum condensations in the space of states: A different perspective on quantum annealing

In nature, everything occurs at finite temperature, and quantum phase transitions (QPTs) cannot be an exception. Nevertheless, they are still mainly discussed and formulated at zero temperature. We show that the condensation QPTs recently introduced at zero temperature can naturally be extended to finite temperature just by replacing ground-state energies with corresponding free energies. We illustrate this criterion in the paradigmatic Grover model and in a system of free fermions in a one-dimensional inhomogeneous lattice. In agreement with expected universal features, the two systems show structurally similar phase diagrams. Last, we explain how finite-temperature condensation QPTs can be used to construct quantum annealers having, at finite temperature, output probability exponentially close to 1 in the system size. As examples we consider again the Grover model and the fermionic system, the latter being well within the reach of present heterostructure technology.
DSNet: Dual-stream multi-scale fusion network for low-quality 3D face recognition

3D face recognition (FR) has become increasingly widespread due to the illumination invariance and pose robustness of 3D face data. Most existing 3D FR methods can only achieve excellent performance on complete and smooth faces. However, low-quality 3D FR with missing facial features still suffers from insufficient discriminative feature extraction for visible face regions. In this paper, we propose a dual-stream multi-scale fusion network (DSNet) for low-quality 3D FR. First, in the first stream, we design a new multi-scale local and global feature fusion network, which consists of an enhanced shallow feature extraction module, an enhanced deep feature extraction module, and a layered multi-scale feature correlation fusion module, aiming to obtain more discriminative details and category information of the facial visible region, reducing the interference of similar features and the redundancy of the same features. Second, we also introduced a capsule network as the second stream to enhance the expression of 3D facial spatial position information, thereby further improving the performance of low-quality 3D FR with missing facial features. We conduct extensive experiments on low-quality datasets (Lock3DFace, KinectFaceDB, and IIIT-D) and cross-quality datasets synthesized by Bosphorus. These results show that our proposed DSNet can achieve state-of-the-art recognition performance and exhibit excellent performance on low-quality 3D faces with missing facial features.
{<scp>NCIFD</scp>} — An Internal R Package for a Fisheries Agency

We developed an R package for the Inland Fisheries Division of the North Carolina Wildlife Resources Commission to help team members share R functions and data sets. The {NCIFD} package stores several functions that were developed for agency research, including cleanBIODE(), which cleans query results from a database of standardized fish samples and imports the data into an R session. In addition, {NCIFD} stores a variety of data sets including observations from ongoing research projects, hatchery records, and administrative information. Stored in a package, data sets are instantly available for exploration and analysis in an R session, can share a common naming convention, and retain their metadata. Although developing an R package requires effort, we believe it is a useful tool that other fisheries agencies should encourage interested staff to explore. We share advice to help others get started while avoiding some missteps we encountered.
Smart Home Notifications in Croatian Language: A Transformer-Based Approach

Natural language generation is one of the essential components in achieving spoken interaction between a human user and a computer system. In this paper, we describe the process of generating notifications in Croatian language by employing an approach that relies on sentence transformer models. The solution described here extracts the key information from raw system events available in scope of the IoT platform and uses them to construct user-friendly sentences. The use-case scenario we present in this paper is related to a smart home environment. In this scenario, the generated notifications are used to construct spoken notifications using a speech synthesis component.
Determining Resource Utilization in Cloud Systems: An Analytical Algorithm for IaaS Architecture

Cloud systems have become a key element in today's ICT systems. One solution in this area is to build systems based on IaaS architecture, in which user-dedicated virtual machines are created on available physical devices. When building such systems, it is essential to ensure optimal utilization of individual resources such as RAM, disk, processor, and available bandwidth while limiting their overuse, which can negatively impact the machine's lifespan. Moreover, it is crucial to avoid situations in which some parameters are underutilized while others are highly loaded. Therefore, the authors have developed an analytical algorithm to determine the average utilization of individual component resources of the system during its operation. The proposed analytical algorithm's results were compared with the results obtained through digital simulation of a cloud system based on the IaaS architecture. The proposed algorithm can successfully optimize the system design to ensure appropriate utilization of all its components in the future.
Theory of physical education ecosystem based on SFIC model

Abstract Through comparison and logical analysis, the author expounds the basic concepts and components of the physical education ecosystem are expounded, the SFIC physical education ecosystem model is established, and a student sports performance warning method based on machine learning is proposed. In view of the heterogeneous student behavior data, data preprocessing technology is adopted to preprocess sample data, and data characteristics are extracted based on knowledge points and item types. A sports achievement warning model (K-DNN) based on knowledge points and item types is established, and the students’ sports achievement warning model is designed and implemented. The experimental results show that, compared with DNN algorithm, Adaboost and ridgeregregression, K-DNN algorithm has the highest accuracy in predicting Ac and the lowest mean square error in predicting MS, indicating that K-DNN model has higher accuracy and better motion prediction effect.
Analysis of music teaching technology based on a data mining model

Abstract To solve the problem of low teaching efficiency in the traditional music course online assistant teaching system, the author proposes a music teaching technology analysis method based on a data mining model. The author proposes a music education model based on the RBF algorithm, which will help improve students’ exploration skills and play an important role for teachers. Through learning and teaching, students can become the center of learning and promote their interest and knowledge in music education. Firstly, the research status of intelligent interactive music learning is analyzed, and then the creation of a neuron model and RBF algorithm using intelligent technology, the definition of music learning methods, and the creation of an independent learning platform are discussed. Select an existing manual for comparative testing. The experimental results show that the training performance of the proposed method exceeds 90%. Although the training performance of both systems is improving, the demonstration of the planning process is very high, and there are some opportunities. Conclusion: Intelligent machines play a great role in students’ music learning, providing better learning opportunities, and providing useful aspects for educational research.
Using Multimedia Information Retrieval Technology to Optimize the Strategies of College English Teaching

Abstract In the design of college ELT(English Language Teaching), it is the most important link to create situations conducive to learners’ meaning construction. At present, the starting point of college ELT is no longer simply and mechanically teaching words, sentences, and grammar, but based on language tools, which improves students’ cultural quality, personality, and professional knowledge. Therefore, this paper makes a research on college ELT through multimedia information retrieval technology. The research shows that 38.40% of the students think that the use of multimedia in teaching can stimulate their interest in learning. 14.66% of the students believe that they can effectively stimulate their interest in learning through extracurricular activities in English. Only 7.38% of the students felt that teachers should encourage and help them. Some students think that emphasizing the importance of English learning can stimulate their interest in learning. At present, the English classroom is more scientific, orderly, diversified, and systematic, which provides convenience for the new curriculum reform being implemented. This paper discusses ELT under multimedia information retrieval technology and puts forward predictive strategies for the problems that may arise now and in the future.
Application of decision tree algorithm mining model in career planning goal simulation

Abstract To help college students choose their first career type, the author proposes an application method of a decision tree algorithm mining model in constructing a career planning goal model. The author uses OLAP technology to study and analyze the internal factors such as gender, personality, temperament, and interest of college students, as well as the career type that college students choose for the first time, excavate the potential law between the career type that college students choose for the first time and the internal factors of students, and apply it to the choice of college students’ first career type. The actual results show that the overall accuracy of the mining model is quite high when 50% of the data is used to obtain 50% of the target. The prediction accuracy of the decision tree model in 50% of the data is 47.14%. When the amount of data is 100%, the prediction accuracy of the model reaches 94.29%. The prediction accuracy of the actual decision tree model in 50% of the data is 47.14%. When the amount of data reaches 100%, the prediction accuracy of the model is 68.57%.
Philosophical Aspects of the Problem of "Artificial Man" in Fiction

The problem of the creation of artificial man and the creation of artificial intelligence are issues that have now become not just potential, but also actual scientific tasks. The original genetic kinship of philosophy and literature as forms of human culture and meaning formation made it possible to comprehend the most important problems in works rich in ideological content and beautiful in form. The subject of the research is the philosophical aspects of the problem of the creation of artificial man in the classic works of fantasy literature of the XIX-XX centuries. This goal is achieved by consistent consideration and comparison of philosophical and anthropological ideas that can be isolated from the works of Johann Wolfgang Goethe, Mary Shelley, Herbert George Wells and M.A. Bulgakov. Hermeneutical method as interpretation and reconstruction of meanings, comparative historical analysis, philosophical comparative studies are used as the methodological basis of this historical and philosophical research. The novelty of the research lies in the historical and philosophical reconstruction of the problem of creating an artificial person, posed for the first time on the pages of the world art classics. The very idea of creating an artificial man was a continuation of the God-fighting aspirations of the Renaissance and the embodiment of the ardent desire to become not only on a par with the Creator, but also to surpass Him. When creating the image of the homunculus, Goethe also had in mind the contrivance, artificiality and fruitlessness of many enlightenment ideas, because the enlighteners questioned the very existence of God, putting a scientist-creator in His place. Goethe's idea of the futility and danger of experimenting with human nature was later developed by Mary Shelley, H. G. Wells and Mikhail Bulgakov. Wagner's homunculus and the creation of Dr. Frankenstein are the closest to the idea of man that prevailed in the philosophy of Modern times and educational pedagogy. Powerful notes of philosophical foresight of many plot moves of the coming history of mankind sound in the novel by Wells (the creation of Dr. Moreau) and the story of Bulgakov (Sharikov).
Categories of hero and character in drama in the light of historical poetics (in the non-classical period of the epoch of modality)

The purpose of the proposed article is to consider the key trends occurring with the categories of the hero and character of the dramatic text in the third global period of historical poetics – the era of poetic modality (in its non–classical period), since it is at this stage of the literary process that the most attention is paid to the subjective sphere of the work. The author of the work, among other things, notes: transformations of images of actors caused by both actual social realities and crises of their identity; changes in terms of communicative interaction and speech of heroes and characters; transformation of dramatic conflict from external (event) to internal (personal), etc. The key methods used in the work include comparative typological analysis, descriptive and hermeneutic. The novelty of this scientific research lies in the systematization of existing ideas concerning this issue, as well as in the breadth of the material concerned (from European and domestic dramaturgy of the turn of the XIX and XX centuries, plays of symbolists and absurdists to the domestic "new drama" of the XX – XXI centuries). A special contribution to the development of the poetics of dramaturgy is the theoretical justification and practical application of the term "image-silhouette" proposed by the author (by analogy with the concepts of "image-type", "image-character" and "image-personality" proposed in the concept of historical poetics by S. N. Broitman) demonstrated in the article.
Evaluation of True Random Number Sequences Generated by Utilizing Timing Jitters in Superconducting Integrated Circuits

Abstract A Hardware Random Number Generator (HRNG), which generates true random number sequences, is studied. In superconducting Single Flux Quantum (SFQ) circuitry, HRNGs that utilized timing jitters in trigger signals have been demonstrated. In this paper, we propose an HRNG with two oscillators to enhance stability against variation of oscillator frequencies. We investigate the characteristics of random numbers generated with the proposed HRNG not only by numerical simulation but also by experiments using Nb test circuits. In the numerical simulation, the probability of “1” in random number sequences has a local minimum of “1” output probability at conditions where f clk (the clock oscillator frequency) and f gate (the gate oscillator frequency) are approximately equal ( f clk ≈ f gate ) for f clk ≤ 60 GHz. It is also found that pass rates for the FIPS140-2 random number tests are little affected by variations of f clk at f gate = 69.2 GHz. The random number generation rate for f clk = 60 GHz and f gate = 43 GHz reaches 3.98 GHz. In experimental measurements, random numbers of 1 Mbits generated by a test circuit pass the SP800-22 random number tests at f clk = 48.79 GHz and f gate = 19.81 GHz.
Process of Machine Learning Methods

The field of machine learning (ML) has grown to be a prominent subject within developed businesses while aiming to implement data-driven techniques to better day-to-day business activities. The reader is introduced to the most popular learning models in this chapter. Although unsupervised learning, reinforcement learning, and semi-supervised learning are incredibly important, the authors won't go into further detail about them here. This section will go into great length about supervised learning environments. The authors propose the following as a summary of the contributions to this chapter: Emphasizing some of the earlier works of literature that tackled these problems and discussing their limitations. In order to do so, the authors propose to review the regression family (i.e., simple regression and multiple linear regression) and decision tree family (i.e., CART, ID3, C4.5, chi-squared automatic interaction detection (CHAID), bagging and boosting). Examining the function and promise of ML techniques to resolve dilemmas and present potential implementation strategies.
Blockchain and Machine Learning Framework for Financial Performance in Pharmaceutical Supply Chains

Financial performance assesses how well a company can manage resources from its primary line of business and create income. Financial performance is critical for the healthcare sector, especially pharmaceutical supply chains. Medicine shortage is the main problem in healthcare management. Medicine shortages negatively affect patient care and financial performance. This chapter explains how blockchain technology and machine learning framework can contribute to pharmaceutical supply chains regarding financial performance. The proposed framework contributes to business analytics tools by presenting a hybrid machine learning method to improve financial performance.
SCANet: Real-Time Face Parsing Using Spatial and Channel Attention

This paper presents a real-time face parsing method that is efficient and robust to small facial components. The proposed approach utilizes two separate attention networks, namely the Spatial and Channel Attention Networks (SCANet), to integrate local features with global dependencies and focus on the most critical contextual features. Specifically, the Spatial attention module (SAM) captures the spatial relationships between different facial features, while the Channel attention module (CAM) identifies important features within each channel of the feature map, such as skin texture or eye color. Moreover, an edge detection branch, which helps differentiate edge and non-edge pixels, is added to improve segmentation precision along edges. To address class imbalance issues, which arise from limited data on accessories such as necklaces and earrings, we utilize a weighted cross-entropy loss function that assigns higher weights to rare classes. The proposed method outperforms state-of-the-art methods on the CelebAMask-HQ dataset, especially in small facial classes like necklaces and earrings. Additionally, the model is designed to operate in real-time, making it a promising solution for various face recognition and analysis applications.
ANALISIS TERHADAP KEBUTUHAN DESAIN PEMBELAJARAN MATEMATIKA BERBASIS PERMAINAN DI SEKOLAH MENENGAH PERTAMA

This study aims to analyze the needs of mathematics learning designs for educators and students at the junior high school (SMP) level. The research method used was descriptive qualitative with the research subjects being junior high school mathematics educators and seventh grade students. The research instruments used were interview sheets and questionnaire sheets. The results of the study show that there are still many students who are constrained and have difficulty understanding online mathematics learning. Then educators also still lack variety in interesting learning media for students, so they don't get bored and are passionate about learning mathematics. The conclusion from this study is that the design of mathematics learning at the junior high school level is still in the less category because there are still many students who have difficulty learning and understanding mathematics and there are still many educators who do not yet have an attractive learning design and increase the enthusiasm for learning for students, so a design is needed. game-based learning. The benefits of this research can be used as a preliminary study to conduct further research on junior high school mathematics learning design.
The application of video text generation technology in assessing the effectiveness of teaching ethnic traditional sports

Abstract Ethnic traditional sports are forms of physical activity unique to one or more ethnic groups. Effective evaluation of the teaching effectiveness of ethnic traditional sports helps to promote the development of ethnic sports and the transmission of ethnic traditional sports. Currently, the evaluation of ethnic traditional sports is mainly a scoring system, which evaluates students’ performance in ethnic traditional sports through teachers’ scoring, and this evaluation method is difficult to assess the teaching effectiveness of ethnic traditional sports accurately. In this paper, based on the video text generation technology, the 3D residual attention network model is constructed by introducing the attention mechanism into the 3D residual module in the video feature extraction through the encoding-decoding video text description framework and improving the traditional deep residual network for evaluating the teaching effect of ethnic sports. After experimental validation, it is shown that the 3D residual attention network model can more accurately describe the evaluation of ethnic traditional sports teaching effectiveness using natural language.