Reviewerly/exHarmony huggingface dataset:
COMPUTING PLATFORM FOR INTEGRATING WIRELESS HETEROGENEOUS NETWORKS INTO INDUSTRIAL INTERNET OF THINGS SYSTEMS

Wireless data transmission interfaces have advantages that ensure their wide and almost inevitable application in a number of tasks of creating modern automation systems, cyber-physical systems, Industrial Internet of Things, and sensor networks. However, there is a certain methodological gap between radiophysicists who are engaged in the design of wireless networks, and IT specialists who need to implement them into various computing systems. The gap is caused by the insufficiently high quality of documentation, software stacks and tools, especially in terms of conceptual and microarchitectural descriptions. The situation is aggravated by the high complexity, closeness and poor compatibility of hardware and software components of different standards provided by vendors. This situation does not always allows to find and use optimal solutions that fully utilize the capabilities of existing network technologies, forcing us to limit ourselves only to standard configurations and application scenarios imposed by vendors. The article reasons the possibility and prospects of building a heterogeneous wireless communications platform that allows using several wireless communication technologies in one system at once and flexibly configuring their parameters. This will expand the range of solved applied tasks in the field of the Industrial Internet of Things. The principles of designing and implementing the proposed platform are considered using the original methodology of high-level design of custom computing systems based on cross-level design entities called kernels and microarchitectural patterns. The results of prototyping the main components of a wireless heterogeneous network platform are presented.
Silver, cobalt and nickel mineralogy and geochemistry of shale ore in the sediment-hosted stratiform Nowa Sól copper–silver deposit, SW Poland

The Nowa Sól deposit is a part of the newly discovered Northern Copper Belt and is situated some 30 km NW of the Lubin–Sieroszowice mining district (the so-called New Copper District) in SW Poland. The ore horizon spans across the upper part of the Lower Permian (Rotliegend) terrestrial redbeds and the lower part of the Upper Permian (Zechstein) marine rocks. It comprises three lithotypes: sandstone, shale and carbonate. The high-grade shale ore has polymetallic characteristics and is a crucial host for by-product metals such as Ag, Co and Ni (studied in this paper), but also Mo, V and Re. The results of bulk-rock and electron microprobe as well as mineralogical (optical and scanning electron microscope) data of the mineralized, organic-rich shale ore from the Nowa Sól deposit are presented. This thin stratigraphic horizon, ranging from 0.06 to 0.59 m, shows notable concentrations of critical metals, including on average 15.9 wt% Cu, 715 grams per tonne (g t -1 ) Ag, 318 g t -1 Co and 345 g t -1 Ni. It constitutes less than 10% of the total ore mass, but accounts for 36% of the Ag, 40% of the Ni and 42% of the Co found within the deposit. The ore sulfides in the mineralized shale in the Nowa Sól deposit include chalcocite, djurleite and bornite, accompanied by digenite, covellite, tennantite, galena, sphalerite and pyrite. The Ag content within the Cu sulfides exhibits a linear decrease: chalcocite &gt; djurleite &gt; bornite. Three primary Ag minerals are identified within the shale ore, namely native Ag, Ag amalgam and stromeyerite. Two types of Ag amalgam are observed: Hg-rich and Hg-poor. Cobaltite and gersdorffite represent the primary Co and Ni minerals, occurring as micrometre-sized inclusions within chalcocite and djurleite. Textural observations suggest that the Ag, Co and Ni mineralization postdates the major phase of Cu sulfide precipitation. It is shown that in the Nowa Sól deposit, the Kupferschiefer horizon has acted as a geochemical barrier for the above-mentioned metals during protracted time, from early syndepositional to late epigenetic stage of basin evolution. Supplementary material: The supplementary material contains the results of the electron microprobe analyses and the specific experimental conditions employed for these analyses – available at https://doi.org/10.6084/m9.figshare.c.6873631
3D MODELING OF THE ROSIA POIENI ORE DEPOSIT USING THE LEAPFROG GEO PROGRAM

As part of the GoldenEye project with European co-financing type H2020, the Academic Leapfrog Geo licensed program was purchased, with which a 3D modeling of the deposit body was made based on open source data, geological maps of the Romanian Geological Institute, non- secret data of CUPRUMIN company and drone flights. The ore has not been modeled in 3D up to now, this work presents for the first time the first consolidated modeling that highlights the size of the ore deposit body. These modelings will allow the taking of technical decisions regarding: the mode of exploitation ( open-pit /underground, redefining the depth of the exploitation limit, the location and development of waste dumps, the geometry of the open pit, the location of underground mining works and preparation works for underground exploitation, data from structural geology). The 1:1 scale geometry of the open pit was traced through topographical measurements from the ground, drone and satellite surveys using Copernicus images. These data were correlated to check the information flow and the efficiency of the internal reporting of the project team in close collaboration with the CUPRUMIN beneficiary team. Three large monitoring campaigns were carried out in the period August 2021-May 2022-September 2022, on the current geometric model of the open pit, the drillings of thousands of meters were positioned with the initial coordinates and lengths, the main faults, the state of stability/instability of the slopes, the identification of four major categories of rock types.
Chosen-ciphertext secure code-based threshold public key encryptions with short ciphertext

Abstract Threshold public-key encryption (threshold PKE) has various useful applications. A lot of threshold PKE schemes are proposed based on RSA, Diffie–Hellman and lattice, but to the best of our knowledge, code-based threshold PKEs have not been proposed. In this paper, we provide three IND-CCA secure code-based threshold PKE schemes. The first scheme is the concrete instantiation of Dodis–Katz conversion (Dodis and Katz, TCC’05) that converts an IND-CCA secure PKE into an IND-CCA secure threshold PKE using parallel encryption and a signature scheme. This approach provides non-interactive threshold decryption, but ciphertexts are large (about 16 kilobytes for 128-bit security) due to long code-based signatures even in the state-of-the-art one. The second scheme is a new parallel encryption-based construction without signature schemes. Unlike the Dodis–Katz conversion, our parallel encryption converts an OW-CPA secure PKE into an OW-CPA secure threshold PKE. To enhance security, we use Cong et al.’s conversion (Cong et al., ASIACRYPT’21). Thanks to eliminating signatures, its ciphertext is 512 bytes, which is only 3% of the first scheme. The decryption process needs an MPC for computing hash functions, but decryption of OW-CPA secure PKE can be done locally. The third scheme is an MPC-based threshold PKE scheme from code-based assumption. We take the same approach Cong et al. took to construct efficient lattice-based threshold PKEs. We build an MPC for the decryption algorithm of OW-CPA secure Classic McEliece PKE. This scheme has the shortest ciphertext among the three schemes at just 192 bytes. Compared to the regular CCA secure Classic McEliece PKE, the additional ciphertext length is only 100 bytes. The cons are heavy distributed computation in the decryption process.
SAFETY AND SECURITY PLANNING AND DISASTER MANAGEMENT IN CASE OF AN EARTHQUAKE IN BULGARIA

Security planning and disaster management are critical areas of focus in Bulgaria, given the country's vulnerability to various natural and man-made disasters, including earthquakes. Bulgaria has established an emergency management system for disaster preparedness, response, and recovery. This system includes various government agencies, non-governmental organizations, and volunteers who work together to ensure an effective response to disasters. The Bulgarian government and the science community conduct risk assessments to identify potential earthquake hazard zones and to assess the risks they pose to people and infrastructure. Bulgaria actively participates in international cooperation efforts related to disaster management as those of the European Union's Civil Protection Mechanism and the United Nations Office for Disaster Risk Reduction. Several projects have been implemented funded by the European Commission as the SHARE project, which aimed to provide a community-based seismic hazard model for the Euro-Mediterranean region, and DACEA and ESNET projects aimed to develop a seismic hazard and risk assessment for the Balkan region, and mechanisms for early warning, public awareness, and quick response in case of a disaster. The present paper gives an overview of the country's focus on risk assessment, disaster preparedness, response, recovery efforts, and international cooperation, underscoring its commitment to disaster management. The topic is particularly relevant now because of the severe consequences of Turkey's recent strong earthquakes. We present findings from various international cooperation efforts in Bulgaria related to earthquake disaster management and results from research related to the assessment of seismic hazards and risk in particular regions in the country.
Ultrasonic Sensors in Companion Robots: Navigational Challenges and Opportunities

This paper investigates the integral role of ultrasonic sensors in developing multi-sensor companion robots. It focuses on environment mapping, landmark-based localization, and path reconstruction. Despite the significant advancements in the field, the research identifies notable gaps that hinder the full potential of ultrasonic sensors. Addressing these gaps could revolutionize companion robots’ functionality, reliability, and efficiency, particularly in real-world applications such as healthcare, elderly care, and home assistance. The research emphasizes the importance of sensor fusion techniques and the potential improvements in environment mapping and landmark-based localization, which could significantly enhance the navigation capabilities of companion robots. Furthermore, advancements in path reconstruction could transform how companion robots retrace their steps, increasing their operational efficiency. The findings of this research contribute significantly to the field of robotics and pave the way for future studies, highlighting the transformative potential of advanced environment mapping techniques, sensor fusion, and path reconstruction in propelling advancements in robotic navigation.
HMARNET—A Hierarchical Multi‐Attention Residual Network for Gleason scoring of prostate cancer

Abstract Manual delineation of prostate cancer (PCa) from whole slide images (WSIs) demands requires pathologists with adequate domain knowledge. This process is generally strenuous and may be subjected to poor inter‐pathologist reproducibility. Accurate Gleason scoring is an important step in the computer‐aided diagnosis of PCa. This work proposes a novel lightweight convolutional neural networks (CNN) to extract significant hierarchical features from the histopathology images. It learns meticulous attention‐guided feature representations through the convolutional layers for precise scoring of Gleason grades. The Hierarchical Multi‐Attention Residual (HMAR) block extracts attention‐guided features and fuses the resulting feature maps from multiple levels with a reduced number of trainable parameters. We have also proposed a new lightweight channel‐attention module, enhanced channel attention (ECA) to extract inter‐channel features from different receptive fields. With the presence of different attention mechanisms, the network learns to focus on more significant features rather than unnecessary ones. Additionally, mixed FocalOHEM loss is proposed to optimize the CNN and efficiently minimize the error. While the focal loss helps to address the class imbalance present in the TCGA‐PRAD dataset, the online hard example mining (OHEM) loss focuses on optimizing hard negative samples. It achieved an accuracy of 89.19% with a Kappa score of 86% on the TCGA‐PRAD dataset.
Implementing Blockchain Technology for Accreditation and Degree Verification

In the evolving academic landscape, the integrity of educational certificates is paramount. These documents serve as formal attestations of one's educational attainment, however, the increasing prevalence of document fraud undermines their credibility. To combat this, we introduce a robust, web-based certificate validation system Universiti Teknologi Malaysia's Blockchain-Based Accreditation and Verification System (UTM-BADVES). Built upon the transparent and immutable infrastructure of Blockchain technology, complemented with a diverse array of advanced technologies, UTM-BADVES offers a secure, efficient, and intuitive solution for real-time validation of academic credentials. The system focuses on data privacy, enabling transcript verification, selective data dissemination, and efficient credential revocation. By doing so, it significantly reduces the potential for academic credential fraud. Consequently, UTM-BADVES safeguards the integrity of educational qualifications, while simultaneously providing immense benefits for educational institutions, employers, and society as a whole. This paper delves into the design and application of UTM-BADVES, elucidating the crucial role it plays in maintaining the sanctity of academic certifications in the digital age.
Real-Time Simulation for Controlling the Mobility of an Unmanned Ground Vehicle Based on Robot Operating System

Robot Operating System (ROS) is a framework platform that runs on UNIX operating systems and enables more efficient operation and administration of different kinds of robots. The Jaguar 4 $$\,\times \,$$ 4 Wheel Mobile Robot by Dr. Robot Inc. is one example of an unmanned ground vehicle (UGV) that can be controlled using ROS. Dr. Robot Inc. has released a Windows-based software development kit (SDK) and open-source code to allow developers to develop this kind of robot. However, to manipulate this robot’s control and monitoring by ROS, a set of Linux command lines should be executed to launch the robot without the ability to use Dr. Robot’s SDK. Therefore, in order to benefit from the provided open-source code with ROS packages with Python and to avoid the need for running multiple Linux command lines, this paper presents a Python-based SDK and a real-time simulation for controlling and monitoring the movement of the Jaguar robot. The methodology technique that is considered in this paper is to analyze the open-source code to access all the robot’s sensor data and nodes using the proposed SDK, then simulate the hardware (the Jaguar Mobile Robot) with ROS Visualization (RViz) 3D software in real-time. The practical experiment is carried out by using a keyboard and a gamepad as controllers to achieve mobility control. The results show the robot’s position and orientation visualized by RViz, as well as its turn angle (theta), linear and angular velocities in a 2D space x-y plot. Using the RViz visualization tool and with the aid of the proposed SDK, it is straightforward to access and monitor all topics and nodes in real-time. Consequently, algorithms such as obstacle avoidance and object tracking could be readily tested and implemented in future works.
A Preliminary Study on the Possibility of Scene Captioning Model Integration as an Improvement in Assisted Navigation for Visually Impaired Users

This research introduces a new approach to augment image captioning for visually impaired individuals by integrating depth data with RGB images. An overview of existing assistive tools and technologies indicates their limited adoption due to high costs and various constraints. The proposed model, designed to tackle these challenges, has the potential to enhance scene comprehension and navigation for the visually impaired. The model, which includes stages from data collection to its integration with assistive tools, uses a unique neural network architecture to process both image types, merge their outputs, and generate more detailed and practical descriptions of the environment. However, specific challenges exist, such as securing an appropriate RGB-D image dataset and creating an efficient neural network. Ongoing research efforts are vital to refine the model and evaluate its real-world applicability.
WebGIS Visualization of Infectious Disease Clustering with a Hybrid Sequential Approach

Infectious diseases are diseases that can be transmitted through various media. Based on the results of research that has been conducted at several Health Center in Gowa Regency, it is found that from 2020 to 2022, Health Center of Bontomarannu is one of the Health Center in Gowa Regency that handles many cases of infectious diseases, namely 212 cases of tuberculosis, 224 cases of dengue fever, and 427 cases of typhoid fever. This makes it difficult for the Health Center to classify what factors cause the increase in these diseases every day for one reason, namely the limitations of medical personnel to manually detect the indicators that cause the emergence of these diseases through medical record data. Based on data obtained through questionnaires given to patients of the Bontomarannu Health Center, there are several variables associated with infectious diseases. Therefore, this study aims to visualize the results of clustering infectious disease data (tuberculosis, dengue fever, and typhoid) per village in Gowa Regency in the form of WebGIS. The method used is K-means clustering which will be optimized using the Particle Swarm Optimization (PSO) algorithm to obtain better results. Moreover in the WebGIS visualization section, the frontend will be made using NextJS, the backend using Flask Py-thon, and for DBMS using SQLAlchemy. This WebGIS visualization will display cluster information for each village in Gowa Regency. Through quantitative analysis and clustering, the study aims to visualize the data and identify patterns and trends associated with infectious diseases in Gowa Regency, ultimately aiding in better decision-making and resource allocation for disease prevention and control.
Similarity Discriminating Algorithm for Scientific Research Projects

An enormous challenge for project management is to identify similar research projects accurately and efficiently among numerous proposals. To address this challenge, this paper proposes an algorithm to calculate the similarity between research projects using an improved generating method for fused word order sentence vectors based on USIF (unsupervised random walk sentence embeddings). The experimental results show that the proposed algorithm is about 15.8% more accurate than the existing approaches. The authors also propose a pre-checking algorithm by introducing a complex research cooperation graph to enhance query efficiency. The results show the pre-checking method reduces the query time cost by 96% on average.
Explainable Activity Recognition in Videos using Deep Learning and Tractable Probabilistic Models

We consider the following video activity recognition (VAR) task: given a video, infer the set of activities being performed in the video and assign each frame to an activity. Although VAR can be solved accurately using existing deep learning techniques, deep networks are neither interpretable nor explainable and as a result their use is problematic in high stakes decision-making applications (in healthcare, experimental Biology, aviation, law, etc.). In such applications, failure may lead to disastrous consequences and therefore it is necessary that the user is able to either understand the inner workings of the model or probe it to understand its reasoning patterns for a given decision. We address these limitations of deep networks by proposing a new approach that feeds the output of a deep model into a tractable, interpretable probabilistic model called a dynamic conditional cutset network that is defined over the explanatory and output variables and then performing joint inference over the combined model. The two key benefits of using cutset networks are: (a) they explicitly model the relationship between the output and explanatory variables and as a result, the combined model is likely to be more accurate than the vanilla deep model and (b) they can answer reasoning queries in polynomial time and as a result, they can derive meaningful explanations by efficiently answering explanation queries. We demonstrate the efficacy of our approach on two datasets, Textually Annotated Cooking Scenes (TACoS), and wet lab, using conventional evaluation measures such as the Jaccard Index and Hamming Loss, as well as a human-subjects study.
Hate Speech Detection in Limited Data Contexts using Synthetic Data Generation

A growing body of work has focused on text classification methods for detecting the increasing amount of hate speech posted online. This progress has been limited to only a select number of highly-resourced languages causing detection systems to either under-perform or not exist in limited data contexts. This is majorly caused by a lack of training data which is expensive to collect and curate in these settings. In this work, we propose a data augmentation approach that addresses the problem of lack of data for online hate speech detection in limited data contexts using synthetic data generation techniques. Given a handful of hate speech examples in a high-resource language such as English, we present three methods to synthesize new examples of hate speech data in a target language that retains the hate sentiment in the original examples but transfers the hate targets. We apply our approach to generate training data for hate speech classification tasks in Hindi and Vietnamese. Our findings show that a model trained on synthetic data performs comparably to, and in some cases outperforms, a model trained only on the samples available in the target domain. This method can be adopted to bootstrap hate speech detection models from scratch in limited data contexts. As the growth of social media within these contexts continues to outstrip response efforts, this work furthers our capacities for detection, understanding, and response to hate speech. Disclaimer: This work contains terms that are offensive and hateful. These, however, cannot be avoided due to the nature of the work.