Reviewerly/exHarmony huggingface dataset:
LSRGAN: an RFF denoising recognition network based on adversarial autoencoder

The development of mobile communication technology promotes the intercommunication of heterogeneous networks, and the identity authentication of wireless devices is essential to protect communication security of heterogeneous networks. The uniqueness and non-forgery of radio frequency fingerprints (RFFs) originate from the hardware tolerance of wireless devices used by physical layer-based authentication schemes to realize authentication between devices during communication. At this stage, demands for long-distance communication between devices are rising, such as walkie-talkies and drones. In addition, due to the complex and changeable characteristics of the wireless communication environment, noises easily lead to problems of signal feature offset and feature ambiguity, becoming essential factors that affect the accuracy results of device authentication. This paper takes data enhancement as a starting point to study individual identity authentication of wireless devices in the case of low signal to noise ratios (SNRs). Based on adversarial autoencoder (AAE), we propose our low-SNRs signal recognition generative adversarial network (LSRGAN), which reconstructs disturbed and poor-quality wireless signals, and improves recognition performances of signals with low SNRs. Experimental results show that when the SNR is -5dB, the average recognition rate of devices can still reach 95.00%.
Visualization model of relief texture based on DEM

Texture has a wide range of applications in the field of image processing. The application of existing texture research methods in DEM is mostly texture feature extraction and index calculation. However, the formation of landforms is caused by terrain relief. The existing texture analysis methods generally have a deficiency in the reflection on the human visual system. In this paper, the visual boundary lines, visual outline lines, and skeleton lines were used as the DEM relief textures that can visually reflect terrain relief. Then the extraction method is refined in conjunction with digital terrain analysis theory. Finally, a multi-dimensional visualization model of DEM relief texture was built, which achieves a better visual effect and provides new ideas for digital terrain analysis, and also has a wide range of popularization and application value.
The typical factors affecting early warning radar detection range analysis

Situational awareness is one of the main functions of Airborne Early Warning Radar, early warning radar surroundings through active detection awareness, this requires detection distance, wide range of monitoring. Detecting ability affects the decision makers for situational judgment, is of great significance. This paper discusses the influence factors of early warning radar detection performance, the first part of the characteristics of airborne radar internal factors influencing the performances of radar detection. The second part in combination with actual combat environment external factors influencing the performances of radar detection is analyzed. The third part according to various factors put forward ways and means to enhance the capacity of early warning radar to detect. Only fully understand the factors of restricting the detection ability to display the early warning radar performance to the optimal.
ENERGY-RELATED INTERACTION OF THE THING AND THE NAME IN THE PHILOSOPHY OF A.F. LOSEV

The article is devoted to disclosing of relations of “name” and energy” categories in A.F. Losev’s philosophy. The problem of sameness of a name and the thing designated by it was touched upon in theological disputes of the beginning of the 20th century about the Orthodox tradition of “onomatodoxy” study which was adhered to by quite a large part of Hesychast monks in Russian Athos monasteries who confessed that the God Himself is present in a prayfully pronounced God’s name. A.F. Losev, being a representative of the junior generation of onomatodoxes, considered philosophic aspects of onomatodoxy. The essence of the onomatodoxy philosophic sense is revelation, in philosophic categories, of indivisibility of a thing from its name which is inevitable for the human thought. Such a conceptual structure is possible when a name is regarded as energy of the named thing, i.e. emanation of the thing wherein it is perceived by the people around. Energy is a way of presence of a thing outside its contours which are shaped by the form and matter generating the thing. The thing’s name and the name itself create in itself a uniform fact of the thing. Speaking about possibility of the presence of the thing outside itself will be allowed by equation of the thing itself and images wherein the thing is accessible for perceiving thereof by the surrounding world (visual, audial, tactile, olfactory). A name as the energy delivers a thing in outside as a personality, disclosing not the essence of aspect but its hypostasis. The name, being identic to the thing in sense, expresses this sense outside in a matter other in relation to the thing.
Improving Deep Learning-Based Recommendation Attack Detection Using Harris Hawks Optimization

Recommendation attack attempts to bias the recommendation results of collaborative recommender systems by injecting malicious ratings into the rating database. A lot of methods have been proposed for detecting such attacks. Among these works, the deep learning-based detection methods get rid of the dependence on hand-designed features of recommendation attack besides having excellent detection performance. However, most of them optimize the key hyperparameters by manual analysis which relies too much on domain experts and their experience. To address this issue, in this paper we propose an approach based on the Harris Hawks Optimization (HHO) algorithm to improve the deep learning-based detection methods. Being different from the original detection methods which optimize the key hyperparameters manually, the improved deep learning-based detection methods can optimize the key hyperparameters automatically. We first convert the key hyperparameters of discrete type to continuous type according to the uniform distribution theory to expand the application scope of HHO algorithm. Then, we use the detection stability as an early stop condition to reduce the optimization iterations to improve the HHO algorithm. After that, we use the improved HHO algorithm to automatically optimize the key hyperparameters for the deep learning-based detection methods. Finally, we use the optimized key hyperparameters to train the deep learning-based detection methods to generate classifiers for detecting the recommendation attack. The experiments conducted on two benchmark datasets illustrate that the improved deep learning-based detection methods have effective performance.
Coarse trimap expansion based on one‐class classification for image matting

Trimap is required for most image matting algorithms, as it provides partial regions with known opacity. As capturing elaborate trimaps is a time-consuming process, in practice, users prefer to provide coarse trimaps with large unknown regions. However, extant image matting algorithms cannot provide high-quality alpha mattes based on coarse trimaps. Although some matting algorithms include trimap expansion in the pre-processing stage, if this is done by directly comparing the similarity of image features between pixels, errors and omissions can easily occur. To overcome this issue, in this paper, a coarse trimap expansion model based on one-class classification is presented, in which the problem is treated as a process of reclassifying pixels in unknown regions. For this purpose, a coarse trimap expansion method denoted as CTE-OC is proposed, in which the similarity between pixels is reliably determined by measuring semantic features, allowing newly developed one-class classifiers to adequately classify pixels in entire unknown regions. The validity of these strategies is tested experimentally, and the results show that CTE-OC can significantly improve the quality of alpha mattes obtained by extant image matting methods when provided with coarse trimaps.
Printed Texture Guided Color Feature Fusion for Impressionism Style Rendering of Oil Paintings

As a major branch of Non-Photorealistic Rendering (NPR), image stylization mainly uses computer algorithms to render a photo into an artistic painting. Recent work has shown that the ex-traction of style information such as stroke texture and color of the target style image is the key to image stylization. Given its stroke texture and color characteristics, a new stroke rendering method is proposed. By fully considering the tonal characteristics and the representative color of the original oil painting, it can fit the tone of the original oil painting image into a stylized image whilst keeping the artist’s creative effect. The experiments have validated the efficacy of the proposed model in comparison to three state-of-the-arts. This method would be more suitable for the works of pointillism painters with a relatively uniform style, especially for natural scenes, otherwise, the results can be less satisfactory.
Machine Learning with Quantum Matter: An Example Using Lead Zirconate Titanate

Stephen Wolfram (2002) proposed the concept of computational equivalence, which implies that almost any dynamical system can be considered as a computation, including programmable matter and nonlinear materials such as, so called, quantum matter. Memristors are often used in building and evaluating hardware neural networks. Ukil (2011) demonstrated a theoretical relationship between piezoelectrical materials and memristors. We review that work as a necessary background prior to our work on exploring a piezoelectric material for neural network computation. Our method consisted of using a cubic block of unpoled lead zirconate titanate (PZT) ceramic, to which we have attached wires for programming the PZT as a programmable substrate. We then, by means of pulse trains, constructed on-the-fly internal patterns of regions of aligned polarization and unaligned, or disordered regions. These dynamic patterns come about through constructive and destructive interference and may be exploited as a type of reservoir network. Using MNIST data we demonstrate a learning machine.
Tracing Raw Material Sources of Prehistoric Stone Artefacts by Non-Invasive Techniques: The Case of the Early Bronze Age (3rd Mill. BCE) Site of Vathy, Astypalaia, Greece

Recent findings of archaeological research in the Vathy gulf area, Astypalaia Island, indicate its continuous habitation since prehistoric times, most importantly in the transitional period from the Final Neolithic to the Early Bronze Age (late 4th/early 3rd millennium BC). The evaluation of the prehistoric stone artefacts from Vathy using non-invasive analytical methods (Near Infrared Spectroscopy—NIR), in combination with the mineral-petrographic characterization of the main lithological formations of the island, is expected to provide important information about raw material procurement and possible exchange networks. The geological study of the island combined with the analytical methods applied to the archaeological artefacts and the geological samples led to the identification of both local and allogenic materials. The possible locations of raw material sources were established and the origin of allogenic materials was estimated. The stone artefacts made of local geo-materials consist mainly of calcitic sandstone, shale, marl, and limestone/marble, comprising the largest part of the lithological formations of the island, as well as pumice and volcanic rocks of varying chemical composition. By means of a portable microscope and NIR spectroscopy, we were further able to identify allogenic geo-materials including chalcedony, mica schist, bauxite and meta-bauxite, steatite, and paragonite. Based on the mineralogical and petrographic characterization of the stone artefacts, a first attempt is made to evaluate the possible raw material sources and to identify potential intra-island modes of stone exploitation.
On Strictly Positive Fragments of Modal Logics with Confluence

We axiomatize strictly positive fragments of modal logics with the confluence axiom. We consider unimodal logics such as K.2, D.2, D4.2 and S4.2 with unimodal confluence ⋄□p→□⋄p as well as the products of modal logics in the set K,D,T,D4,S4, which contain bimodal confluence ⋄1□2p→□2⋄1p. We show that the impact of the unimodal confluence axiom on the axiomatisation of strictly positive fragments is rather weak. In the presence of ⊤→⋄⊤, it simply disappears and does not contribute to the axiomatisation. Without ⊤→⋄⊤ it gives rise to a weaker formula ⋄⊤→⋄⋄⊤. On the other hand, bimodal confluence gives rise to more complicated formulas such as ⋄1p∧⋄2n⊤→⋄1(p∧⋄2n⊤) (which are superfluous in a product if the corresponding factor contains ⊤→⋄⊤). We also show that bimodal confluence cannot be captured by any finite set of strictly positive implications.
Smartphone Sensor-Based Human Motion Characterization with Neural Stochastic Differential Equations and Transformer Model

With many conveniences afforded by advances in smartphone technology, developing advanced data analysis methods for health-related information from smartphone users has become a fast-growing research topic in the healthcare field. Along these lines, this paper addresses smartphone sensor-based characterization of human motions with neural stochastic differential equations (NSDEs) and a Transformer model. NSDEs and modeling via Transformer networks are two of the most prominent deep learning-based modeling approaches, with significant performance yields in many applications. For the problem of modeling dynamical features, stochastic differential equations and deep neural networks are frequently used paradigms in science and engineering, respectively. Combining these two paradigms in one unified framework has drawn significant interest in the deep learning community, and NSDEs are among the leading technologies for combining these efforts. The use of attention has also become a widely adopted strategy in many deep learning applications, and a Transformer is a deep learning model that uses the mechanism of self-attention. This concept of a self-attention based Transformer was originally introduced for tasks of natural language processing (NLP), and due to its excellent performance and versatility, the scope of its applications is rapidly expanding. By utilizing the techniques of neural stochastic differential equations and a Transformer model along with data obtained from smartphone sensors, we present a deep learning method capable of efficiently characterizing human motions. For characterizing human motions, we encode the high-dimensional sequential data from smartphone sensors into latent variables in a low-dimensional latent space. The concept of the latent variable is particularly useful because it can not only carry condensed information concerning motion data, but also learn their low-dimensional representations. More precisely, we use neural stochastic differential equations for modeling transitions of human motion in a latent space, and rely on a Generative Pre-trained Transformer 2 (GPT2)-based Transformer model for approximating the intractable posterior of conditional latent variables. Our experiments show that the proposed method can yield promising results for the problem of characterizing human motion patterns and some related tasks including user identification.
Can You Do That Again? Time Series Consolidation as a Robust Method of Tailoring Gesture Recognition to Individual Users

Robust inter-session modeling of gestures is still an open learning challenge. A sleeve equipped with capacitive strap sensors was used to capture two gesture data sets from a convenience sample of eight subjects. Two pipelines were explored. In FILT a novel two-stage algorithm was introduced which uses an unsupervised learning algorithm to find samples representing gesture transitions and discards them prior to training and validating conventional models. In TSC a confusion matrix was used to automatically consolidate commonly confused class labels, resulting in a set of gestures tailored to an individual subject's abilities. The inter-session testing accuracy using the Time Series Consolidation (TSC) method increased from a baseline inter-session average of 42.47 ± 3.83% to 93.02% ± 4.97% while retaining an average of 5.29 ± 0.46 out of the 11 possible gesture categories. These pipelines used classic machine learning algorithms which require relatively small amounts of data and computational power compared to deep learning solutions. These methods may also offer more flexibility in interface design for users suffering from handicaps limiting their manual dexterity or ability to reliably make gestures, and be possible to implement on edge devices with low computational power.
Sorting Center Value Identification of “Internet + Recycling” Based on Transfer Clustering

As the core link of the "Internet + Recycling" process, the value identification of the sorting center is a great challenge due to its small and imbalanced data set. This paper utilizes transfer fuzzy c-means to improve the value assessment accuracy of the sorting center by transferring the knowledge of customers clustering. To ensure the transfer effect, an inter-class balanced data selection method is proposed to select a balanced and more qualified subset of the source domain. Furthermore, an improved RFM (Recency, Frequency, and Monetary) model, named GFMR (Gap, Frequency, Monetary, and Repeat), has been presented to attain a more reasonable attribute description for sorting centers and consumers. The application in the field of electronic waste recycling shows the effectiveness and advantages of the proposed method.
On Properties of Empirical and Related Random Processes

A survey is given of some of the results of empirical processes and new propositions proved for modified Kac-type empirical estimators both for identically and non-identically distributed sample data.
On the Stability of Mixtures of Probability Distributions to Perturbations of Mixing Distributions

The problem of stability of mixtures of probability distributions to perturbations of mixing distributions is considered. Estimates of the distance between scale/location mixtures via the distance between the mixing distributions are obtained in terms of the Lévy–Prokhorov metric. Similar problems for scale or location mixtures are considered in terms of the Lévy metric. The results are formulated for normal mixtures. In conclusion, some algorithms are proposed for the construction of analogous estimates for other classes of mixtures.
Information-Theoretic Approach to the Problem of Automatic Image Recognition

The problem of automatic half-tone image recognition from the large database is formulated. Its decision using data on a problem of statistical classification of discrete objects on the basis of the minimum information discrimination principle is proposed. The new probabilistic model of a half-tone image is studied. On the basis of this model, a new image recognition algorithm using the minimum Kullback–Leibler information discrimination criterion is synthesized. It is proved that this criterion is optimal in Bayesian terms for the classification problem of discrete random objects, including digital images. The program and results of experimental research on the face recognition problem are presented. It is shown that the accuracy of image recognition with various light conditions on the basis of the offered criterion is essentially higher than the accuracy of traditional methods.
Digital 3D modeling for preconstruction real-time visualization of home interior design through virtual reality

Purpose The purpose of this paper is to present the investigation on contemporary applications of game design in architectural visualization, urban environmental planning and the results to address the problems of traditional methods specifically in terms of interactivity and visualization. The authors present the prototype that incorporates information modeling and virtual reality (VR) into interactive architectural visualization. Design/methodology/approach The proposed system supports a virtual walkthrough process that allows users to navigate in an architectural space of their imagination and design. The immersion in the design of living space or environment has been made possible through the inclusion of VR through the use of WebVR technology to deploy the design to be experienced. Findings This study investigates and establishes a framework that explores the intricacies of 3 imensional (3D) Architectural Visualization in a real-time engine by designing a visual experience with a better level of user interaction and then deploying it through VR. Originality/value To the best of the authors’ knowledge, this is one of the few experimental frameworks which perfectly integrates VR for visualization in digital home design environments. The prospective applications of this framework are in several fields of construction innovation, architectural design, 3D modeling, virtual and augmented reality.
The need for cybersecurity data and metrics: empirically assessing cyberthreat

Without assessment metrics and data, the cybersecurity community maintains no ability to evaluate the success or scope of operations. Calls for the collection of cybersecurity indicators are empty without strategic guidance on what indicators to collect, for what purpose, and for what method of analysis. This paper reviews the purpose, function and need for cybersecurity data and metrics with an in-depth review of United States metrics guidance offered in the National Defense Authorisation Act (NDAA) and National Institute of Standards and Technology (NIST) publications on metrics. Mission assessment is critical to evaluate the efficacy of ongoing and future cybersecurity efforts; assessments require quantitative metrics that place concrete values on indicators rather than subjective judgments.
Understanding News Text and Images Connection with Context-enriched Multimodal Transformers

The connection between news and the images that illustrate them goes beyond visual concept to natural language matching. Instead, the open-domain and event-reporting nature of news leads to semantically complex texts, in which images are used as a contextualizing element. This connection is often governed by a certain level of indirection, with journalistic criteria also playing an important role. In this paper, we address the complex challenge of connecting images to news text. A context-enriched Multimodal Transformer model is proposed, NewsLXMERT, capable of jointly attending to complementary multimodal news data perspectives. The idea is to create knowledge-rich and diverse multimodal sequences, going beyond the news headline (often lacking the necessary context) and visual objects, to effectively ground images to news pieces.