Reviewerly/exHarmony huggingface dataset:
Explainabilty Comparison between Random Forests and Neural Networks—Case Study of Amino Acid Volume Prediction

As explainability seems to be the driver for a wiser adoption of Artificial Intelligence in healthcare and in critical applications, in general, a comprehensive study of this field is far from being completed. On one hand, a final definition and theoretical measurements of explainability have not been assessed, yet, on the other hand, some tools and frameworks for the practical evaluation of this feature are now present. This paper aims to present a concrete experience in using some of these explainability-related techniques in the problem of predicting the size of amino acids in real-world protein structures. In particular, the feature importance calculation embedded in Random Forest (RF) training is compared with the results of the Eli-5 tool applied to the Neural Network (NN) model. Both the predictors are trained on the same dataset, which is extracted from Protein Data Bank (PDB), considering 446 myoglobins structures and process it with several tools to implement a geometrical model and perform analyses on it. The comparison between the two models draws different conclusions about the residues’ geometry and their biological properties.
Graph-Based Semi-Supervised Deep Learning for Indonesian Aspect-Based Sentiment Analysis

Product reviews on the marketplace are interesting to research. Aspect-based sentiment analysis (ABSA) can be used to find in-depth information from a review. In one review, there can be several aspects with a polarity of sentiment. Previous research has developed ABSA, but it still has limitations in detecting aspects and sentiment classification and requires labeled data, but obtaining labeled data is very difficult. This research used a graph-based and semi-supervised approach to improve ABSA. GCN and GRN methods are used to detect aspect and opinion relationships. CNN and RNN methods are used to improve sentiment classification. A semi-supervised model was used to overcome the limitations of labeled data. The dataset used is an Indonesian-language review taken from the marketplace. A small part is labeled manually, and most are labeled automatically. The experiment results for the aspect classification by comparing the GCN and GRN methods obtained the best model using the GRN method with an F1 score = 0.97144. The experiment for sentiment classification by comparing the CNN and RNN methods obtained the best model using the CNN method with an F1 score = 0.94020. Our model can label most unlabeled data automatically and outperforms existing advanced models.
FAR-RIGHT DIGITAL ACTIVISM DURING AND BEYOND THE PANDEMIC

While digital activism has formed part of social movements’ contentious repertoires for at least two decades, online forms of protest have risen to unprecedented importance across the globe in the context of the COVID-19 pandemic. This article studies continuities and shifts in digital activism before, during and beyond the pandemic, drawing from a case study conducted of the Dresden-based far-right social movement organisation PEGIDA. Seeking to shed new light on the role of the internet and social media for sustained far-right mobilisation, I explore long-term trends in PEGIDA’s digital activism since its emergence in 2014. To this aim, I draw from an original patchwork ethnographic dataset generated through participant observation of demonstrations in Dresden and digital ethnography in 2019-21 as well as undertaking a thorough literature review. The empirical analysis indicates three key findings: Firstly, the longitudinal perspective reveals that a social movement actor’s digital practices are not bound to one or a few ideal-types, but highly dynamic over time. Secondly, my interpretive-ethnographic lens emphasises the constitutive dimensions of digital activism. Thirdly, the analytical focus on digital activism during the pandemic adds new insights into the relationship between the online and offline worlds of mobilisation. As a whole, this article underscores some of the key advantages of (patchwork) ethnography in relation to other methods in protest research.
Hiding data hiding

Data hiding (DH) is typically referred to as the art of hiding secret data into a given media for covert communication. In this paper, we make the first step towards hiding “data hiding” (HDH), which disguises DH tools, including a data embedding tool and a data extraction tool, as a deep neural network (DNN) with a normal task. In the method, after training a DNN for style transfer and DH from scratch, while the trained DNN can transfer the style of an image to the target style, it can also hide additional data into a cover image and extract data from a stego image. In other words, the DH tools are hidden by the DNN. Experimental results have shown the feasibility, applicability and superiority.
Assessment of resource potential from mine tailings using geostatistical modeling for compositions: A methodology and application to Katherine Mine site, Arizona, USA

The mining industry, in most cases, targets a specific valuable commodity that is present in small quantities within large volumes of extracted material. After milling and processing, most of the extracted material and the effluents are stored as waste (tailings) in impoundments, such as dams or waste dumps, or are backfilled into underground mines. In time, tailing materials may become an issue of environmental and health concern due to the hazardous elements, ions, and oxides contained within the waste material. In addition, handling and storage of such waste in dams may pose the risk of dam failure with catastrophic consequences to nature and nearby communities. On the other hand, tailings may offer potential as secondary sources of critical elements (CEs), including rare earth elements (REEs), which may have been overlooked during primary production and processing. Therefore, treating mine tailings as a resource has economic and environmental benefits by reducing the waste from new and historical mine sites through remining. One of the critical steps for taking advantage of these benefits is to spatially quantify the resources and the pollutants, which require the application of adequate data analysis and modeling methods, often to compositional geochemical data. Utilizing adequate methods is especially important for correctly quantifying resource potential, as the quantities will often be at low concentrations. This work presents quantification of resource potential (Au, Ag, Cu, Zn, Pb) and elements of environmental concern (Hg and As) from the tailings of a historic mine site, Katherine Mine, AZ, USA. Data reported by the U.S. Bureau of Mines (USBM) after extensive field campaigns in the 1990s, including sampling from tailing impoundment and surrounding areas for geochemical characterization and geophysical surveys, were used. First, compositional data (CoDa) analysis was employed to explore associations of sampling locations, geochemical parts, and the clustering of samples. Next, sequential Gaussian simulation (SGSIM) was applied to samples that showed a genetic link to tailing material after isometric log-ratio transformation (ilr) and mix/max autocorrelation factor (MAF) transformation for spatial modeling and uncertainty evaluation. Geostatistical results revealed spatial variability of concentrations within the tailing area. Uncertainty evaluation based on realizations indicated that Cu (14.27–20.01 t), Zn (44.23–76.23 t), and Pb (22.56–38.28 t) are the most abundant elements within a 5 %–95 % interval, followed by Ag and Au (~5.3 and 0.18 t, at 50th percentile), respectively. Of the elements of health concern, As was found to be ~4.8 t (50th percentile) in the tailing area. The work also showed that ~0.51 t As, 0.005 t Hg, 0.020 t of Au, and 0.62 t of Ag were carried to Lake Mohave by an ephemeral stream called Katherine Wash, which transects the tailings.
Multi-level Attention-based Domain Disentanglement for BCDR

Cross-domain recommendation aims to exploit heterogeneous information from a data-sufficient domain (source domain) to transfer knowledge to a data-scarce domain (target domain). A majority of existing methods focus on unidirectional transfer that leverages the domain-shared information to facilitate the recommendation of the target domain. Nevertheless, it is more beneficial to improve the recommendation performance of both domains simultaneously via a dual transfer learning schema, which is known as bidirectional cross-domain recommendation (BCDR). Existing BCDR methods have their limitations, since they only perform bidirectional transfer learning based on domain-shared representations while neglecting rich information that is private to each domain. In this article, we argue that users may have domain-biased preferences due to the characteristics of that domain. Namely, the domain-specific preference information also plays a critical role in the recommendation. To effectively leverage the domain-specific information, we propose a M ulti-level A ttention-based D omain D isentanglement framework dubbed MADD for BCDR, which explicitly leverages the attention mechanism to construct personalized preference with both domain-invariant and domain-specific features obtained by disentangling raw user embeddings. Specifically, the domain-invariant feature is exploited by domain-adversarial learning while the domain-specific feature is learned by imposing an orthogonal loss. We then conduct a reconstruction process on disentangled features to ensure semantic-sufficiency. After that, we devise a multi-level attention mechanism for these disentangled features, which determines their contributions to the final personalized user preference embedding by dynamically learning the attention scores of individual features. We train the model in a multi-task learning fashion to benefit both domains. Extensive experiments on real-world datasets demonstrate that our model significantly outperforms state-of-the-art cross-domain recommendation approaches.
Standoff tracking of an unknown object with only distance measurements

In this paper, a guidance algorithm is proposed to solve the problem of standoff tracking control of unknown objects in a GPS-denied environment. Unlike most existing algorithms, the proposed algorithm uses only distance measurements between the unmanned aerial vehicle (UAV) and the object. Algorithm construction is comprised of two steps. The first is to devise a guidance algorithm to drive the UAV to circle around the object assuming that lateral deviation distance measurement and lateral deviation rate measurement are available. The second is to replace the actual measured rate with that estimated by a fixed-time sliding-mode observer. The observer utilizes the lateral deviation distance allowing the UAV to accomplish the tracking mission using only distance measurements. Numerical and hardware-in-the-loop (HIL) simulation results verify the effectiveness of the proposed algorithm, regardless of whether the object is stationary or moving.
The Statistical Parameters of Ivan Franko’s Authorial Style Determined by the Chi-square Test

The novelty of the research is the method of a combination of an analysis of the absolute and relative frequencies of occurrence of letters of Ukrainian and the chi-square test for identification of Ivan Franko’s authorial style in the literary works ”Na loni pryrody’ and “Malyy Myron”. According to the results, typical of Ivan Franko’s authorial style are the letters of Ukrainian, the values of absolute frequencies of occurrence of which differ in less than 10 units (б, з, т, у, ф, х, ч, ц, ш, щ, ю). The same percentage has been obtained for the relative frequencies of occurrence of the following letters: б, в, г, д, ж, з, к, у, ф, ч, ц, ш, ь, ю, я. The applied statistical method – the chi-square test has shown that the differences between the literary works ”Na loni pryrody’ and “Malyy Myron” are not statistically significant and consequently, the works are written by the same author. The results have proved that the chi-square test is efficient for solving the authorship attribution task on the level of letters. The test validity is 95%. The used software is Python and R. The practical application of the research is identification of the authorial style on the level of letters.
The Advance of Deep Learning and Attention Mechanism

Due to the significant increment of computing power in recent years, research related to machine learning developed and changed dramatically. A new learning model named Transformer, based on an attention mechanism, was introduced by the research team at Google a few years ago. It not only has incredibly high performance but also united two major research fields of machine learning, computer vision, and natural language processing for the first time. However, behind its remarkable success in implementation, there are still potential issues that underlie it. This work is focused on the attention mechanism, for its dominance in the area predominantly affects how machine learning progresses in the future. This paper reviews the development of attention, and its functions, along with many recent models. Besides, this paper will discuss issues related to attention mechanisms and their future directions.
GMDH-Based Computer Technology for Information Support of Decisions in the Foundry Production Process

A technology based on GMDH has been developed, which consists of a set of software tools for solving the problems of modeling processes occurring during the manufacture of iron castings. Such a system increases the efficiency of supporting the decisions of the caster in the process of manufacturing castings. The software package uses database of (i) standard metals and alloys data, prepared in accordance with DSTU and ISO, (ii) a database of accumulated cooling curves (reference curves) describing the cooling process of castings of alloys with certain chemical composition and physical and mechanical properties, (iii) and a database of cooling curves under the influence of various modes, as well as a number of modules for solving problems in the process of forming a casting.
Metaverse: Challenges for Extended Reality and Holographic-Type Communication in the Next Decade

The metaverse is an iteration of the Internet with immersive and hyperreal 3D digital virtual worlds. It can recreate the physical world using technologies such as digital twins, avatars, and holograms. Besides creating digital models of physical environments, the metaverse allows the existence of virtual information, objects, human beings, and environments that do not exist in the physical world. The mixture of real and virtual content in the metaverse can significantly improve users’ capabilities for work, entertainment, education, healthcare, and manufacturing [1] . Development of the metaverse includes three major areas: (1) metaverse content/service design, (2) metaverse core and edge network design, and (3) fully connected networks for metaverse devices in the physical world.
Design of Spectrum Analyzer Android-based Instructional Media for Vocational High School Student

This research aims to produce spectrum analyzer instructional media that will be implemented in Radio Transmission Operations and Maintenance subjects. Research development adopts the ADDIE model according to William W. Lee & Diana L. Owens, which includes: analysis, design, development and implementation, and evaluation. In the development and implementation steps, there are activities in the form of expert validation of instructional media based on Android applications. Theory experts, media experts, and users were necessary for the evaluation. Furthermore, the spectrum analyzer instructional media was tested on learning activity in Telecommunication Transmission Engineering Skill Competency at SMK Telkom Jakarta. The gain from the range of questionnaire values is converted to determine the eligibility category. The results showed that teaching and learning activities in operating and maintaining radio transmission subjects require instructional media for practical activity learning in the form of software on an Android-based smartphone consisting of theory, quiz, and job sheets; and a manual guide of media. The research results show that the value of the spectrum analyzer instructional media quality, in general, is 3.46, which is interpreted in the excellent category. Because the Android-based spectrum analyzer instructional media obtained an excellent feasibility value, this media reliable to be implemented in classroom learning.
MedSeq: Semantic Segmentation for Medical Image Sequences

Medical image segmentation plays a critical role in computer-aided diagnosis, while the diversity and complexity of medical images make it difficult to segment precisely. In practice, medical images of specific modalities (e.g. Magnetic Resonance Imaging, Colonoscopy and Ultrasonography) are collected as sequences independently for every patient. However, 1) there exists few works exploiting sequence information among successive frames, neglecting inter-frame relationships that are useful to locate target objects; 2) the performance of medical image segmentation is limited to the low contrast or blurry boundary of medical images, and intra-frame dependencies are not fully explored. Thus in this paper, we propose MedSeq for segmenting objects of interest in medical image sequences. Following the “locate-then-refine” paradigm, we locate target regions by modeling cross-frame relationships and then perform refinement on coarse masks. More specifically, we design a Cross-frame Attention module to learn correlations among frames, taking advantages of their similar appearances. For refinement, we propose a novel Boundary-aware Transformer to improve the segmentation of boundary patches. Extensive experiments are conducted on benchmark datasets of Cardiac Segmentation and Video Polyp Segmentation. Our method achieves superior performance over the state-of-the-art methods.
An Overview of the Present and Future of User Authentication

Cybersecurity is an ever-evolving discipline that aims to protect every aspect of an information system, including its users, from digital threats, adversaries and attacks. When it comes to the overall security of an account or a system as a whole, the combination of people and passwords have always been considered the weakest link in the chain since poorly chosen weak, leaked, reused and easy-to-remember passwords still continue to pose an insurmountable threat to the security of innumerable accounts and systems. Yet, much to the dismay of cybersecurity specialists and researchers from all over the world, password-based authentication still remains as one of the most dominant ways of verifying a user's identity, thus making our password-protected accounts, systems and devices a highly lucrative target for cybercriminals. This paper aims to highlight the strengths and weaknesses of passwords in comparison with various other techniques such as multi-factor and adaptive risk-based authentication schemes that have been adopted over the years to augment password-based authentication systems as well as discuss the recent advent of the FIDO2 authentication standard that aims to bid adieu to passwords in favor of making biometric and possession-based authentication the new norm by making them more easily accessible to developers and users alike while ensuring an optimum level of security and privacy at all times.
An Upscaling–Downscaling Optimal Seamline Detection Algorithm for Very Large Remote Sensing Image Mosaicking

For the mosaicking of multiple remote sensing images, obtaining the optimal stitching line in the overlapping region is a key step in creating a seamless mosaic image. However, for very large remote sensing images, the computation of finding seamlines involves a huge amount of image pixels. To handle this issue, we propose a stepwise strategy to obtain pixel-level optimal stitching lines for large remote sensing images via an upscaling–downscaling image sampling procedure. First, the resolution of the image is reduced and the graph cut algorithm is applied to find an energy-optimal seamline in the reduced image. Then, a stripe along the preliminary seamline is identified from the overlap area to remove the other inefficient nodes. Finally, the graph cut algorithm is applied nested within the identified stripe to seek the pixel-level optimal seamline of the original image. Compared to the existing algorithms, the proposed method produces fewer spectral differences between stitching lines and less-crossed features in the experiments. For a wide range of remote sensing images involving large data, the new method uses less than 10 percent of the time needed by the SLIC+ graph cut method.
THREATGET: Towards Automated Attack Tree Analysis for Automotive Cybersecurity

The automotive domain is moving away from simple isolated vehicles to interconnected networks of heterogeneous systems forming a complex transportation infrastructure. The additional means of communication result in increased attack surfaces which can be exploited by physical as well as remote attackers if not secured thoroughly. Thus, the automotive sector is exposed to new cyber risk factors. Consequently, joint approaches targeting securing vehicles and infrastructure by identifying and mitigating potential threats for the automotive domain have been developed in several research projects. This paper builds on developments originating from these projects and correlated standards and regulations. Moreover, the extension of an existing threat modeling tool—THREATGET—with a novel automated approach toward attack propagation will be introduced. Therefore, we will conduct an analysis of a real-world example from the automotive domain. Furthermore, we will identify and analyze potential threats and discuss their accumulation to automatically generate an attack tree.
An SSD-MobileNet Acceleration Strategy for FPGAs Based on Network Compression and Subgraph Fusion

Over the last decade, various deep neural network models have achieved great success in image recognition and classification tasks. The vast majority of high-performing deep neural network models have a huge number of parameters and often require sacrificing performance and accuracy when they are deployed on mobile devices with limited area and power consumption. To address this problem, we present an SSD-MobileNet-v1 acceleration method based on network compression and subgraph fusion for Field-Programmable Gate Arrays (FPGAs). Firstly, a regularized pruning algorithm based on sensitivity analysis and Filter Pruning via Geometric Median (FPGM) was proposed. Secondly, the Quantize Aware Training (QAT)-based network full quantization algorithm was designed. Finally, a strategy for computing subgraph fusion is proposed for FPGAs to achieve continuous scheduling of Programmable Logic (PL) operators. The experimental results show that using the proposed acceleration strategy can reduce the number of model parameters by a factor of 11 and increase the inference speed on the FPGA platform by a factor of 9–10. The acceleration algorithm is applicable to various mobile edge devices and can be applied to the real-time monitoring of forest fires to improve the intelligence of forest fire detection.