Reviewerly/exHarmony huggingface dataset:
Secure and Efficient Transmission of Spatial Data using Colored Quick Response (QR) Codes: A Case Study for the EyE Project

We introduce a novel color QR code system for advanced encryption, leveraging spatial data, optimized on AWS. Outperforms conventional systems in speed and cost. Future applications in secure healthcare data storage and quantum color exploration.
Android-Based Courseware as an Educational Technology Innovation for Electrical Circuit Course: An Effectiveness Study

This study investigates the effectiveness of Android-Based Courseware (AC) in teaching electrical circuits to vocational students in higher education. The courseware was developed using Android Studio and was implemented in the Electrical Circuit Course (ECC) at the Industrial Electrical Engineering study program, Universitas Negeri Padang, Indonesia. The AC consists of four primary menus designed to facilitate learning implementation: learning materials, simulations, animations, and interactive quizzes for evaluations. The study utilized a pre-experimental design with a one-group pre-test-post-test design involving a group of students who used the AC in the learning process. The cluster random sampling technique was employed to select the research subjects. This study involved 68 students as research subjects. Data was collected through a written test, utilizing a multiple-choice test as the research instrument. The effectiveness of the AC was evaluated based on the differential analysis of pre-test and post-test scores using the paired-sample t-test. Furthermore, the effect size of the AC on the learning process was determined using Cohen’s d effect size analysis. The results showed a significant difference (alpha significance value is 0.000 and less than 0.05) between the post-test and pre-test scores, with the post-test scores (x̄ = 85.00) being higher than the pre-test scores (x̄ = 60.00). This indicates that the courseware effectively improves the learning process of the ECC. Additionally, the effect size analysis results show that the effect of using the courseware falls within the large category. These findings suggest that the AC can be an effective tool in the learning process of the ECC and can improve student learning outcomes. Specifically, the AC has a significant impact on enhancing students’ cognitive and practical abilities concerning electrical circuit concepts.
SoftGPT: Learn Goal-Oriented Soft Object Manipulation Skills by Generative Pre-Trained Heterogeneous Graph Transformer

Soft object manipulation tasks in domestic scenes pose a significant challenge for existing robotic skill learning techniques due to their complex dynamics and variable shape characteristics. Since learning new manipulation skills from human demonstration is an effective way for robot applications, developing prior knowledge of the representation and dynamics of soft objects is necessary. In this regard, we propose a pretrained soft object manipulation skill learning model, namely SoftGPT, that is trained using large amounts of exploration data, consisting of a three-dimensional heterogeneous graph representation and a GPT-based dynamics model. For each downstream task, a goal-oriented policy agent is trained to predict the subsequent actions, and SoftGPT generates the consequences of these actions. Integrating these two approaches establishes a thinking process in the robot's mind that provides rollout for facilitating policy learning. Our results demonstrate that leveraging prior knowledge through this thinking process can efficiently learn various soft object manipulation skills, with the potential for direct learning from human demonstrations.
[Characteristics of Soil Pollution and Source Analysis of Typical Pollutants in the Petrochemical Site].

Taking the soil of a petrochemical site in Chongqing as the research object, the pollution sources of heavy metals and volatile organic pollutants in the study area were quantitatively analyzed using principal component analysis and a positive definite matrix decomposition model. The research results showed that:① the average values of Cd, Cu, Pb, Hg, and Mn in the soil of the petrochemical site and its surrounding areas were higher than the soil background values in Chongqing, and the content of Co in 6% of sampling points was higher than the risk screening value of the first type of land use for construction land(GB 36600-2018). The pollution level of Hg, Mn, chloroform, ethylbenzene, m/p-xylene, and o-xylene in the soil of the site was more serious than that of the soil around the site. The industrial production activities of the site will cause significant pollution to the soil of the site. ② Eight heavy metals in the soil could be classified into four sources:62.50% Cd, 54.53% Ni, 54.12% Cu, 51.26% Co, 49.34% Pb, and 31.80% Hg were from the mixed sources of industrial emissions and agricultural activities. A total of 47.61% Mn was from the mixed source of natural and raw material production activities; 62.79% Hg, 21.57% As, 19.43% Mn, and 18.42% Cd were from the emission of coal-fired industrial activities; and 78.01% As, 46% Pb, 39.94% Cu, 38.15% Ni, 36.49% Co, 26.12% Mn, and 14.29% Cd were from the original material soil. ③ Seven volatile organic compounds in the soil of the site were mainly classified into three types of sources:86.49% dichloromethane and 90.14% tetrachloroethylene were from human activities in petrochemical sites; 98.88% m/p-xylene, 96.28% o-xylene, 84.62% ethylbenzene, and 76.46% toluene were from petrochemical site pollutant leakage sources; and 80.24% chloroform was from the natural sources.
ADMISSIBILITY OF ELECTRONIC EVIDENCE IN CRIMINAL TRIALS IN NIGERIA AND THE CHALLENGES OF NEW CRIMES

There is no doubt that computers now dominate the world. Little wonder then, the current age has been variously described in like terms as “computer age”, “digital age”, “electronic age”, or “information and communication technology age”. The fact remains however, that computer, in its pre-eminence, has not abrogated statutes. We still live in the age of statutes. By their nature, statutes require updates from time to time, to meet up with societal changes and advancements. The Law of Evidence like every other aspect of law is dynamic. Its dynamism goes hand in hand with evolving nature of modern information and communication technology. This work traces the evolutionary trend of electronic evidence in Nigeria. It acknowledges the law on the subject matter as it was in its near zero tolerance for electronic-generated evidence. It celebrates the eventual arrival of the over delayed enactment of the Evidence Act, 2011 and its bold provisions for admissibility of electronic evidence by the courts. The work identifies the challenges new crimes pose to the bold provisions of the Act accepting electronic evidence as part of the body of laws in force in Nigeria. The article concludes with a bold, revolutionary recommendation for the utility and future of electronic evidence in Nigeria.
Using Active Learning Techniques for Analysis of Photomicroscopy Images

Processing biomedical image data, specifically microscopic data from a blood sample, where it is necessary to identify and classify leukocytes, is a complex issue. Manual processing of such data is time-consuming and can be subjective and prone to human error. Therefore, this work aims to analyze current processing techniques and available tools to simplify and automate this process. The paper includes an overview of the biomedical data used, and describes the analysis, the selection of the relevant data, a proposal for a solution using the general methods and overall automation of the process, an implementation of the solution, and an evaluation of its benefits in further research. Logistic regression was used to predict the leukocyte type, in combination with the active learning approach. The work results in a solution that simplifies and mainly speeds up the processing of microscopic data for leukocyte identification and classification by removing the human error and subjectivity. The proposed solution achieved classification accuracy of 92% in both training and testing dataset (Raabin-WBC) and 84% on vakidation dataset (BCCD), without any changes in the model parameters. This solution may find application in diagnosing various diseases such as blood cancers, infections, autoimmune diseases, or allergies.
Metaverse: Concept, Key Technologies, and Vision

Metaverse is a collective term for all economic and social activities in the space where the physical world, digital world, and consciousness world are interactively integrated and mutually empowered. The metaverse is the advanced stage of digital civilization and the future formation of human society. The basis for developing metaverse is general digital technologies such as high-performance network, high-performance storage, high-performance computing, high-performance security, and artificial intelligence. On the basis of the above, the key to developing the metaverse lies in researching core technologies such as digital life technologies, trusted collaborative network technologies, natural interaction technologies, ubiquitous operating system technologies, technologies and methods for computational experiments, and theories and technologies for crowd intelligence science. We should take typical metaverse application scenarios as entry points, such as the key fields of agriculture, industry, service industry, military, social governance, and other economic and social areas, to break through key metaverse technologies and implement pilot demonstration projects of metaverse. Through the demonstration, we can systematically promote the application of metaverse in economy and society from point to line, and continuously iterate and evolve the metaverse technology to advance the metaverse to a higher stage. This paper systematically analyzes the current development status and future directions of metaverse from the concept, key technology, and vision of metaverse, paving the way for the subsequent research of metaverse.
TKIL: Tangent Kernel Optimization for Class Balanced Incremental Learning

When learning multiple tasks in a sequence, deep neural networks tend to loose accuracy on tasks learned in the past while gaining accuracy on the current task. This phenomenon is called catastrophic forgetting. Memory-based Class Incremental Learning (CIL) methods address this problem by re-learning exemplars retained in the memory from previous tasks. However, due to data imbalances between the training data for the current task and the limited exemplars from previous tasks, existing methods struggle to balance the accuracy across all seen tasks. Here, we propose to address data imbalance and in addition to a generic model to learn a set of task-specific parameters. In particular, we propose a novel methodology of Tangent Kernel for Incremental Learning (TKIL) that seeks an equilibrium between current and previous representations. Specifically, TKIL achieves such equilibrium by tuning different task-specific parameters for different tasks with a new Gradient Tangent Kernel (GTK) loss. Therefore, when representing previous tasks, task-specific models are not impacted by the samples of the current task and are able to retain learned representations. As a result, TKIL equally considers the contribution from all task models. The generalized parameters that TKIL obtains allow it to automatically identify which task is being considered and to adapt to it during inference. Extensive experiments on five CIL benchmark datasets with ten incremental learning settings show that TKIL outperforms existing state-of-the-art methods, e.g., achieving 9.4% boost on CIFAR-100 with 25 incremental stages.
Year 2022 in Medical Natural Language Processing: Availability of Language Models as a Step in the Democratization of NLP in the Biomedical Area

To analyse the content of publications within the medical Natural Language Processing (NLP) domain in 2022.Automatic and manual preselection of publications to be reviewed, and selection of the best NLP papers of the year. Analysis of the important issues.Three best papers have been selected. We also propose an analysis of the content of the NLP publications in 2022, stressing on some of the topics.The main trend in 2022 is certainly related to the availability of large language models, especially those based on Transformers, and to their use by non-NLP researchers. This leads to the democratization of the NLP methods. We also observe the renewal of interest to languages other than English, the continuation of research on information extraction and prediction, the massive use of data from social media, and the consideration of needs and interests of patients.
Optimizing Robotic Task Sequencing and Trajectory Planning on the Basis of Deep Reinforcement Learning

The robot task sequencing problem and trajectory planning problem are two important issues in the robotic optimization domain and are solved sequentially in two separate levels in traditional studies. This paradigm disregards the potential synergistic impact between the two problems, resulting in a local optimum solution. To address this problem, this paper formulates a co-optimization model that integrates the task sequencing problem and trajectory planning problem into a holistic problem, abbreviated as the robot TSTP problem. To solve the TSTP problem, we model the optimization process as a Markov decision process and propose a deep reinforcement learning (DRL)-based method to facilitate problem solving. To validate the proposed approach, multiple test cases are used to verify the feasibility of the TSTP model and the solving capability of the DRL method. The real-world experimental results demonstrate that the DRL method can achieve a 30.54% energy savings compared to the traditional evolution algorithm, and the computational time required by the proposed DRL method is much shorter than those of the evolutionary algorithms. In addition, when adopting the TSTP model, a 18.22% energy reduction can be achieved compared to using the sequential optimization model.
Charting the Financial Landscape: ECU, SDR, BRICS, and Cryptocurrencies - Addressing Challenges and Paving the Path Forward

Currently, the financial landscape is evolving very quickly, new technologies and changes in customer wishes and fulfillment time make currencies take on different forms and functions, each presenting unique challenges and opportunities. This article explores the historical development and contemporary meaning of currencies, ranging from traditional units of account such as the ECU and the SDR to the emerging association of economic power, the BRICS and the disruptive force of cryptocurrencies. The article begins by tracing the historical evolution of these currencies, shedding light on their origins and roles in international finance. It examines the influence of the ECU, SDR and BRICS and their potential in reshaping the global financial order. The rise of cryptocurrencies, their underlying technology (blockchain), and their transformative impact on traditional financial systems are also explored in depth. Common challenges and issues facing these forms of currency are identified, including regulatory complexities, volatility, security concerns, and barriers to adoption. The article examines the integration of traditional coins, simple or composite, into the cryptocurrency ecosystem, offering insights into potential solutions to address these challenges. Regarding the future, in its dynamics, the article offers a forward-looking perspective on the evolving role of these currencies in a globalized economy, highlighting opportunities for adaptation, cooperation, and resettlement of geopolitical and financial grace. The paper concludes with a call to navigate the complexities of the modern financial landscape with flexibility, innovation and attention to socio-economic impact. This article serves as a comprehensive resource for economists, policymakers, investors, companies, and individuals seeking to understand the dynamic interplay of currencies in the ever-changing world of finance.
Resource Optimization for Blockchain-Based Federated Learning in Mobile Edge Computing

With the booming of mobile edge computing (MEC) and blockchain-based federated learning (BCFL), more studies suggest deploying BCFL on edge servers. In this case, edge servers with restricted resources face the dilemma of serving both mobile devices for their offloading tasks and the BCFL system for model training and blockchain consensus without sacrificing the service quality to any side. To address this challenge, this paper proposes a resource allocation scheme for edge servers to provide optimal services at the minimum cost. Specifically, we first analyze the energy consumption of the MEC and BCFL tasks, considering the completion time of each task as the service quality constraint. Then, we model the resource allocation challenge into a multivariate, multi-constraint, and convex optimization problem. While solving the problem in a progressive manner, we design two algorithms based on the alternating direction method of multipliers (ADMM) in both homogeneous and heterogeneous situations, where equal and on-demand resource distribution strategies are respectively adopted. The validity of our proposed algorithms is proved via rigorous theoretical analysis. Moreover, the convergence and efficiency of our proposed resource allocation schemes are evaluated through extensive experiments.
The Blockchain Technology in Transfer Pricing – Artificial Intelligence and Further Synergies

The use of blockchain technology and AI is increasingly being discussed to fulfil compliance requirements and automate transfer pricing functions. The development of suitable technical concepts is necessary for this purpose. Even if these concepts are still in their infancy, their use in this area of transfer pricing is becoming noticeable.
Influences of Cloud Microphysics on the Components of Solar Irradiance in the WRF-Solar Model

An accurate forecast of Global Horizontal solar Irradiance (GHI) and Direct Normal Irradiance (DNI) in cloudy conditions remains a major challenge in the solar energy industry. This study focuses on the impact of cloud microphysics on GHI and its partition into DNI and Diffuse Horizontal Irradiance (DHI) using the Weather Research and Forecasting model specifically designed for solar radiation applications (WRF-Solar) and seven microphysical schemes. Three stratocumulus (Sc) and five shallow cumulus (Cu) cases are simulated and evaluated against measurements at the US Department of Energy’s Atmospheric Radiation Measurement (ARM) user facility, Southern Great Plains (SGP) site. Results show that different microphysical schemes lead to spreads in simulated solar irradiance components up to 75% and 350% from their ensemble means in the Cu and Sc cases, respectively. The Cu cases have smaller microphysical sensitivity due to a limited cloud fraction and smaller domain-averaged cloud water mixing ratio compared to Sc cases. Cloud properties also influence the partition of GHI into DNI and DHI, and the model simulates better GHI than DNI and DHI due to a non-physical error compensation between DNI and DHI. The microphysical schemes that produce more accurate liquid water paths and effective radii of cloud droplets have a better overall performance.
Storage Standards and Solutions, Data Storage, Sharing, and Structuring in Digital Health: A Brazilian Case Study

The COVID-19 pandemic has highlighted the necessity for agile health services that enable reliable and secure information exchange, but achieving proper, private, and secure sharing of EMRs remains a challenge due to diverse data formats and fragmented records across multiple data silos, resulting in hindered coordination between healthcare teams, potential medical errors, and delays in patient care. While centralized EMR systems pose privacy risks and data format diversity complicates interoperability, blockchain technology offers a promising solution by providing decentralized storage, ensuring data integrity, enhancing access control, eliminating intermediaries, and increasing efficiency in healthcare. By focusing on a Brazilian case study, this paper explores the significance of EMR standards, security challenges, and blockchain-based approaches to promote interoperability and secure data sharing in the healthcare industry.
Implicit Compositional Generative Network for Length-Variable Co-Speech Gesture Synthesis

Co-speech gesture synthesis is a practical yet challenging task that aims to generate body motion sequences in line with speech audio. Most of the existing methods can only generate the gesture sequence with a fixed number of frames, which does not satisfy the high-quality requirement of the virtual speech video in real-world applications. In this paper, we propose a novel Implicit Compositional Generative Network (ICGN) for length-variable co-speech gesture synthesis. In ICGN, the implicit neural representation is captured and optimized for a whole gesture sequence of arbitrary length with temporal embeddings. Moreover, to enforce the synthesized gestures more realistic and consistent, we compositionally generate the gesture sequence through a well-designed asymmetric two-stream network that effectively captures and utilizes the rich correlations between speech audio and human body motions. In this way, the coarse and fine-grained gestures are synthesized, respectively, according to the corresponding content-aware and emotion-aware audio components. Extensive experiments on four widely-used benchmarks demonstrate that the proposed method renders realistic human gestures and achieves the superior performance against several state-of-the-art methods.
Cryptocurrency Algorithmic Trading with Price Forecasting Analysis using PowerBI

As an alternate form of trade money, cryptocurrencies are now widely accepted and have been integrated into all financial activities. Trading cryptocurrencies is one of the most popular and promising forms of investing. However, the cryptocurrency markets are notorious for their tremendous volatility and price discrepancies over short periods. To ensure accurate and trustworthy predictions, it is necessary to have an automated trading model that includes portfolio management and optimization. Automated algorithmic trading uses computers to carry out trades in accordance with the past and predicted trends following a defined set of rules. These rules include trading instructions based on time, value, quantity, or any other mathematical model of trading. Profits may be achieved through algorithmic trading at inhumanely high speeds and frequencies. In addition to providing profitable trading opportunities, algorithmic trading increases market fluidity and increases trading accuracy by reducing human elements such as emotions and feelings regarding the trade. This paper aims to contribute to the ongoing market revolution by discussing the various aspects of cryptocurrecy trading, its forecasting and actual development of an algorithmic trading bot that will implement client strategies closely accompanied by its own calculations for daily exchanges based on economic conditions and client approaches. It will also contribute to and exchange with ongoing adjustments throughout the day to ensure the best profitability of the clients.