Reviewerly/exHarmony huggingface dataset:
Chemistry of Marketing: Application of Chemical Thermodynamics Laws to Closed and Isolated Marketing Systems

Over the years, marketing has been influenced by the soft sciences (i.e., economics, psychology, and sociology) and hard sciences (i.e., mathematics and physics). In this study, we discuss the various intersections between chemistry and marketing, and we elaborate on the areas in which chemistry can help develop marketing from both theoretical and practical perspectives. In doing so, we do not intend to assert definitive conclusions or provide substantial scientific evidence but instead to introduce a new interdisciplinary research area that we call the chemistry of marketing and highlight its potential areas of application and some future research directions. To establish the initial scientific bridge between marketing and chemistry, we rely on: 1) metaphors which as figures of speech have long been used in the development of marketing science and 2) the general systems theory which is recognized as a prevailing form of generalization. Moreover, we provide a novel explanation of closed and isolated marketing systems, present initial evidence on their emerging importance in the business world, and offer some suggestions for their potential application to the first and second laws of chemical thermodynamics.
Blockchain-Based Distributed Computing Consistency Verification for IoT Mobile Applications

The maturation of wireless connectivity, blockchain (distributed ledger technologies), and intelligent systems has fostered a comprehensive ecosystem for the Internet of Things (IoT). However, the growing volume of data generated by IoT devices creates substantial pressure on blockchain storage and computation capabilities, impeding the further development of the IoT ecosystem. Decentralizing data storage across multiple chains and utilizing cross-chain technology for data exchange eliminates the need for expensive centralized infrastructure, lowers data transfer costs, and improves accessibility. Hence, the issue of computational and storage pressure in blockchain can be improved. Nonetheless, the data of IoT devices are constantly updating, and ensuring consistency for dynamic data across heterogeneous chains remains a significant challenge. To address the aforementioned challenge, we propose a blockchain-based distributed and lightweight data consistency verification model (BDCA), which leverages a batch verification dynamic Merkle hash tree (BV-MHT) and an advanced gamma multi-signature scheme (AGMS) to enable consistent verification of dynamic data while ensuring secure and private data transmission. The AGMS scheme is reliable and robust based on security analysis while the dependability and consistency of BDCA are verified through inductive reasoning. Experimental results indicate that BDCA outperforms CPVPA and Fortress in communication and computation overhead for data preprocessing and auditing in a similar condition, and the AGMS scheme exhibits superior performance when compared to other widely adopted multi-signature schemes such as Cosi, BLS, and RSA. Furthermore, BDCA provides up to 99% data consistency guarantees, demonstrating its practicality.
Technologies Supporting Screening Oculomotor Problems: Challenges for Virtual Reality

Oculomotor dysfunctions (OMDs) are problems relating to coordination and accuracy of eye movements for processing visual information. Eye-tracking (ET) technologies show great promise in the identification of OMDs. However, current computer technologies for vision screening are specialized devices with limited screen size and the inability to measure depth, while visual field and depth are important information for detecting OMDs. In this experimental study, we examine the possibilities of immersive virtual reality (VR) technologies compared with laptop technologies for increased user experiences, presence, immersiveness, and the use of serious games for identifying OMDs. The results present increased interest in VR-based screening, motivating users to focus better using VR applications free from outside distractions. These limitations currently include lower performance and confidence in results of identifying OMDs with the used HMDs. Using serious games for screening in VR is also estimated to have great potential for developing a more robust vision screening tool, especially for younger children.
Error Analysis and Visibility Classification of Camera-Based Visiometer Using SVM under Nonstandard Conditions

A camera-based visiometer is a promising atmospheric visibility measurement tool because it can meet some specific demands such as the need for visibility monitoring in a strong way, whereas traditional instruments, such as forward scatter-type sensors and transmissometers, can hardly be widely utilized due to their high cost. The camera-based method is used to measure visibility by recording the luminance contrast of the objects in an image. However, lacking standard conditions, they can hardly obtain absolute measurements even with blackbody objects. In this paper, the errors caused by nonstandard conditions in camera-based visiometers with two artificial black bodies are analyzed. The results show that the luminance contrasts of the two blackbodies are highly dependent on the environmental radiance distribution. The nonuniform sky illuminance can cause a large error in the blackbody contrast estimations, leading to substantial visibility measurement errors. A method based on a support vector machine (SVM) is proposed to classify the visibility under nonstandard conditions to ensure the reliability of the camera-based visiometer. A classification accuracy of 96.77% was achieved for the data containing images depicting different illumination conditions (e.g., a clear sky, cloudy sky, and overcast). The results show that the classifier based on the SVM is an effective and reliable method to estimate visibility under complex conditions.
Real Image Deblurring Based on Implicit Degradation Representations and Reblur Estimation

Most existing image deblurring methods are based on the estimation of blur kernels and end-to-end learning of the mapping relationship between blurred and sharp images. However, since different real-world blurred images typically have completely different blurring patterns, the performance of these methods in real image deblurring tasks is limited without explicitly modeling blurring as degradation representations. In this paper, we propose IDR2ENet, which is the Implicit Degradation Representations and Reblur Estimation Network, for real image deblurring. IDR2ENet consists of a degradation estimation process, a reblurring process, and a deblurring process. The degradation estimation process takes the real blurred image as input and outputs the implicit degradation representations estimated on it, which are used as the inputs of both reblurring and deblurring processes to better estimate the features of the blurred image. The experimental results show that whether compared with traditional or deep-learning-based deblurring algorithms, IDR2ENet achieves stable and efficient deblurring results on real blurred images.
Parametrical T-Gate for Joint Processing of Quantum and Classic Optoelectronic Signals

Unmanned network robotics is a new multidisciplinary field that involves many fields of computer networks, multi-agent systems, control theory, 5G and 6G Internet, computer security, and wireless quantum communications. Efficient conjugation of such technologies needs to design new data verification schemes for robotic agents using the advantages of quantum key distribution lines. For such schemes the joint use of known fuzzy logic parametrical -gates and discrete multiple-valued logic models simplifies the application of quantum quasi-random keys. Namely, the separate regulating parameter in -gates is the most convenient tool to use quantum keys in comparatively simple classical control and verification procedures that do not involve quantum logic gates.
Study on the Comprehensive Optimization of Quantum Radar Stealth Based on the Waverider Warhead

Quantum radar is a novel detection method that combines radar and quantum technologies. It exceeds the detection threshold and poses a threat to conventional stealth targets. This work aims to derive the expression of the quantum radar cross-section of a new complex target. The calculation formula of QRCS was derived after introducing the relative photon parameters and vector dot product. Subsequently, a comprehensive optimization model of quantum stealth and lift–drag ratio based on a genetic algorithm was proposed for the waverider warhead. During the optimization process, we proposed an optimization method with the objective function of the QRCS pioneering design value and achieved better outcomes than the optimization method using the average value in terms of QRCS performance and lift–drag ratio in the important azimuths of the waverider. By changing the design variables of the waverider warhead and using this new optimization method, the QRCS of the waverider in the forward and lateral angles were minimized, remarkably improving the aerodynamic performance of the waverider. Similarly, the optimization results show that the proposed design value optimization method is feasible.
Open Set Vehicle Detection for UAV-Based Images Using an Out-of-Distribution Detector

Vehicle target detection is a key technology for reconnaissance unmanned aerial vehicles (UAVs). However, in order to obtain a larger reconnaissance field of view, this type of UAV generally flies at a higher altitude, resulting in a relatively small proportion of vehicle targets in its imaging images. Moreover, due to the unique nature of the mission, previously unseen vehicle types are prone to appearing in the surveillance area. Additionally, it is challenging for large-scale detectors based on deep learning to achieve real-time performance on UAV computing equipment. To address these problems, we propose a vehicle object detector specifically designed for UAVs in this paper. We have made modifications to the backbone of Faster R-CNN based on the target and scene characteristics. We have improved the positioning accuracy of small-scale imaging targets by adjusting the size and ratio of anchors. Furthermore, we have introduced a postprocessing method for out-of-distribution detection, enabling the designed detector to detect and distinguish untrained vehicle types. Additionally, to tackle the scarcity of reconnaissance images, we have constructed two datasets using modeling and image rendering techniques. We have evaluated our method on these constructed datasets. The proposed method achieves a 96% mean Average Precision at IoU threshold 0.5 (mAP50) on trained objects and a 71% mAP50 on untrained objects. Equivalent flight experiments demonstrate that our model, trained on synthetic data, can achieve satisfactory detection performance and computational efficiency in practical applications.
occams: A Text Summarization Package

Extractive text summarization selects asmall subset of sentences from a document, which gives good “coverage” of a document. When given a set of term weights indicating the importance of the terms, the concept of coverage may be formalized into a combinatorial optimization problem known as the budgeted maximum coverage problem. Extractive methods in this class are known to beamong the best of classic extractive summarization systems. This paper gives a synopsis of thesoftware package occams, which is a multilingual extractive single and multi-document summarization package based on an algorithm giving an optimal approximation to the budgeted maximum coverage problem. The occams package is written in Python and provides an easy-to-use modular interface, allowing it to work in conjunction with popular Python NLP packages, such as nltk, stanza or spacy.
Poland - National regulation on processing data for scientific research purposes and biobanking activities

Biobanks raise great hopes for future research that can help understand many diseases and find ways to treat them. The purpose of the article is to study the legislative acts regulating the activities of biobanks in Poland and to formulate recommendations for improving the legal regulation of biobanking. For the purpose, tasks, object, and subject of research, both general scientific and special methods of scientific knowledge were used in the work. General scientific methods (dialectical; system analysis; formal-logical) are fundamental legal techniques, and means of research. To improve the current legislation of Poland, the following can be proposed. Develop a legal framework that regulates the legality of obtaining samples of human biological materials (both normal and pathologically altered) for submission to the biobank in Poland. Regulate at the legislative level the requirements regarding the organizational and legal form of the creation of a biobank, the legal basis of its functioning, the form of ownership, and financing. Thus, the creation of an effective system of biobanks in Poland with systematic collections of biological samples obtained from people with various diseases, their storage in appropriate conditions, the development of internal documentation and standard operating procedures, taking into account both the ethical and legal components, is of key importance for the formation and development of domestic science, development of innovative medicines, methods of diagnosis and monitoring of diseases.&#x0D;
Polyakov log coarse garnet occurrence in the South Urals

Ontogenic modeling of the origin of coarse garnet of the Polyakov log occurrence (South Urals) in a fne-grained schist sequence allowed the authors to conclude on its metasomatic growth with the replacement of chlorite aggregates. Muscovite, ilmenite, epidote, quartz and apatite remain preserved in the primary schist orientation of individuals. Accessory andesine, cobaltite, pentlandite, pyrrhotite and zircon crystallized together with garnet. The formation of coarse garnet crystals is probably related to fuctuations of supersaturation relative to garnet in areas of schists with abundant chlorite. This allows us to expect the presence of coarse garnet areas in adjacent parts of the schist sequence. Keywords: South Urals, garnet, ontogeny
Homo Electromagneticus II: Paradigms and Paradoxes

Homo Electromagneticus is a musical performance realized in 2022 through a set of original instruments created with open-source and low-cost technologies. Arduino microcontrollers, sensors, brainwaves, MIDI, algorithms, and the interaction with plants’ capacitance provide digital, acoustic and mechatronic sound production forms. Cognizant of the material and energy dump that is the signature of the Anthropocene, the piece thrives on a commitment to the conscientious and responsible production of waste. Thus, in addition to original musical instruments, I also utilized old bottles, kitchen tools, electronics equipment taken from the dump, obsolete musical instruments, human gestures and brain waves as research and performance tools. Homo Electromagneticus reacts to the exclusivity of digital sound production, choosing instead to revitalize and promote acoustic sound production. Thinking along with Zielinski (2010), the piece helps us to consider art “after the media,” meaning that it foregrounds the production of acoustic sound mediated through electromagnetic interfaces. The technological aspect of production is prevalent, but digital and electric sound production does not dominate the performance of the work as a whole. The performance relies on the emergence of a mix compositum in which the distinction between performer and composer becomes blurred. Each performance of the piece requires the players to generate sound anew. Additionally, the very definition of musical “instrument” undergoes a transformation since the performer becomes, in a sense, a necessary instrument in the production of the piece.
Design Education as a Special Pedagogical Area

Introduction. Design education in Russia has been going through a complex, more than a century-long path of reforming and reorganizing educational institutions and the pedagogical process. The rethinking and processing of pedagogical concepts in the fields of education focused on training in-demand specialists takes place in parallel with external global processes on the basis of existing and new scientific knowledge, technology development, as well as ideas about the creative potential of the individual as a whole, and the tasks of the designer as a specialist, in particular, which is acutely relevant in modern realities. The purpose of the article is to study the semantic content of such key terms for pedagogy as the educational field and the education system, as well as the derivation of design education into a separate educational system. Methodology and methods of research. The methodological basis of the research was the following scientific methods: the phenomenological method, through which we identified the main features of the use of certain terms in contradictory meanings, in particular such terms as “educational environment”, “educational field”, “educational industry”, “subject area”; structural and functional method analyzing stable relationships and the relationship between the elements of the concepts considered in the article; the method of comparative analysis, which allowed us to identify common and different in the concepts under study and offer our own vision of the design education system at the present stage. The result of our research is the conclusion that design education, having a special subject area (area of professional activity), can be defined into a separate educational system, meeting the needs of the modern design industry. As a conclusion, we propose to consider design education from the point of view of an independent educational system, which will enable educational institutions to revise the structure and content of design education to increase efficiency in training highly qualified specialists and, as a consequence, to increase the level of economic achievements of the country.
Three‐stream RGB‐D salient object detection network based on cross‐level and cross‐modal dual‐attention fusion

Abstract The effective integration of RGB and depth map features to improve the performance of RGB‐D salient object detection (SOD) has garnered significant research interest. The existing dual‐stream models can be used for high‐level feature fusion or unidirectionally transferring depth features to RGB features; however, they are unable to fully exploit the differences in modality. Furthermore, owing to the influence of image background information, the generated salient object is affected by background swallow. Herein, a three‐stream RGB‐D SOD method based on cross‐layer and cross‐modal dual‐attention (CMDA) fusion is proposed. In the encoding stage, the CMDA fusion module is used to fuse RGB and depth features layer by layer. Through this module, merged interactive features may be used to extract the richer features of salient objects, realize the commonality and complementarity of fusion features, and achieve effective cross‐modal fusion. In addition, for the decoding stage, a cross‐level feature fusion module that introduces global context features into the up‐sampling process, reduces the impact of salient objects being swallowed by the background, and helps to accurately detect salient areas is proposed. Three different branch features are used for simultaneous end‐to‐end training. The experimental results demonstrate that the proposed method outperforms other methods in terms of multiple evaluation metrics on four datasets. Furthermore, the authors visualize the precision–recall curve, F‐measure curve, and saliency map, which indicate that the detection effect of the proposed method is superior to those of other methods. During the testing stage, our model ran at 14 frames per second (FPS).
-Stable convergence of heavy-/light-tailed infinitely wide neural networks

Abstract We consider infinitely wide multi-layer perceptrons (MLPs) which are limits of standard deep feed-forward neural networks. We assume that, for each layer, the weights of an MLP are initialized with independent and identically distributed (i.i.d.) samples from either a light-tailed (finite-variance) or a heavy-tailed distribution in the domain of attraction of a symmetric $\alpha$ -stable distribution, where $\alpha\in(0,2]$ may depend on the layer. For the bias terms of the layer, we assume i.i.d. initializations with a symmetric $\alpha$ -stable distribution having the same $\alpha$ parameter as that layer. Non-stable heavy-tailed weight distributions are important since they have been empirically seen to emerge in trained deep neural nets such as the ResNet and VGG series, and proven to naturally arise via stochastic gradient descent. The introduction of heavy-tailed weights broadens the class of priors in Bayesian neural networks. In this work we extend a recent result of Favaro, Fortini, and Peluchetti (2020) to show that the vector of pre-activation values at all nodes of a given hidden layer converges in the limit, under a suitable scaling, to a vector of i.i.d. random variables with symmetric $\alpha$ -stable distributions, $\alpha\in(0,2]$ .
Interaction Information Guided Prototype Representation Rectification for Few-Shot Relation Extraction

Few-shot relation extraction aims to identify and extract semantic relations between entity pairs using only a small number of annotated instances. Many recently proposed prototype-based methods have shown excellent performance. However, existing prototype-based methods ignore the hidden inter-instance interaction information between the support and query sets, leading to unreliable prototypes. In addition, the current optimization of the prototypical network only relies on cross-entropy loss. It is only concerned with the accuracy of the predicted probability for the correct label, ignoring the differences of other non-correct labels, which cannot account for relation discretization in semantic space. This paper proposes an attentional network of interaction information to obtain a more reliable relation prototype. Firstly, an inter-instance interaction information attention module is designed to mitigate prototype unreliability through interaction information between the support set and query set instances, utilizing category information hidden in the query set. Secondly, the similarity scalar, which is defined by the mixed features of the prototype and the relation and is added to the focal loss to improve the attention of hard examples. We conducted extensive experiments on two standard datasets and demonstrated the validity of our proposed model.
Multiclass Laplacian support vector machine with functional analysis of variance decomposition

In classification problems, acquiring a sufficient amount of labeled samples sometimes proves expensive and time-consuming, while unlabeled samples are relatively easier to obtain. The Laplacian Support Vector Machine (LapSVM) is one of the successful methods that learn better classification functions by incorporating unlabeled samples. However, since LapSVM was originally designed for binary classification, it can not be applied directly to multiclass classification problems commonly encountered in practice. Thus we derive an extension of LapSVM to multiclass classification problems using an appropriate multiclass formulation. Another problem with LapSVM is that irrelevant variables easily degrade classification performance. The irrelevant variables can increase the variance of predicted values and make the model difficult to interpret. Therefore, this paper also proposes the multiclass LapSVM with functional analysis of variance decomposition to identify relevant variables. Through comprehensive simulations and real-world datasets, we demonstrate the efficiency and improved classification performance of the proposed methods.