Reviewerly/exHarmony huggingface dataset:
Digital tools and active leaning in an online university: Improving the academic performance of future teachers

Digital tools are part of any online educational context. When it comes to training future teachers, this point is crucial because they will transfer this technology to their classrooms. Additionally, incorporating these digital resources as part of an active learning proposal will contribute to future teachers' motivation, self-regulation, and autonomy and positively influence their academic performance. This piece of research refers to the evaluation of an educational active learning proposal in which a pre-experimental design without a control group was used in order to study the possible relationship between the use of digital tools and the academic performance of the students. A sample of 908 students participated, distributed in three academic years. Results show that those who used more tools as part of the active learning proposals obtained better final marks, as well as a higher grade in the continuous assessment activities, which they also completed in a higher percentage (p&lt;0.001). It can be concluded that the use of digital tools within an active learning proposal contributes to an improvement in the academic performance of future teachers as well as in the participation and engagement of the students.
Mind2Web: Towards a Generalist Agent for the Web

We introduce Mind2Web, the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents. With over 2,000 open-ended tasks collected from 137 websites spanning 31 domains and crowdsourced action sequences for the tasks, Mind2Web provides three necessary ingredients for building generalist web agents: 1) diverse domains, websites, and tasks, 2) use of real-world websites instead of simulated and simplified ones, and 3) a broad spectrum of user interaction patterns. Based on Mind2Web, we conduct an initial exploration of using large language models (LLMs) for building generalist web agents. While the raw HTML of real-world websites are often too large to be fed to LLMs, we show that first filtering it with a small LM significantly improves the effectiveness and efficiency of LLMs. Our solution demonstrates a decent level of performance, even on websites or entire domains the model has never seen before, but there is still a substantial room to improve towards truly generalizable agents. We open-source our dataset, model implementation, and trained models (https://osu-nlp-group.github.io/Mind2Web) to facilitate further research on building a generalist agent for the web.
DeepSeaNet: Improving Underwater Object Detection using EfficientDet

Marine animals and deep underwater objects are difficult to recognize and monitor for safety of aquatic life. There is an increasing challenge when the water is saline with granular particles and impurities. In such natural adversarial environment, traditional approaches like CNN start to fail and are expensive to compute. This project involves implementing and evaluating various object detection models, including EfficientDet, YOLOv5, YOLOv8, and Detectron2, on an existing annotated underwater dataset, called the Brackish-Dataset. The dataset comprises annotated image sequences of fish, crabs, starfish, and other aquatic animals captured in Limfjorden water with limited visibility. The aim of this research project is to study the efficiency of newer models on the same dataset and contrast them with the previous results based on accuracy and inference time. Firstly, I compare the results of YOLOv3 (31.10% mean Average Precision (mAP)), YOLOv4 (83.72% mAP), YOLOv5 (97.6%), YOLOv8 (98.20%), EfficientDet (98.56% mAP) and Detectron2 (95.20% mAP) on the same dataset. Secondly, I provide a modified BiSkFPN mechanism (BiFPN neck with skip connections) to perform complex feature fusion in adversarial noise which makes modified EfficientDet robust to perturbations. Third, analyzed the effect on accuracy of EfficientDet (98.63% mAP) and YOLOv5 by adversarial learning (98.04% mAP). Last, I provide class activation map based explanations (CAM) for the two models to promote Explainability in black box models. Overall, the results indicate that modified EfficientDet achieved higher accuracy with five-fold cross validation than the other models with 88.54% IoU of feature maps.
Language-led Visual Grounding for Human Computer Interaction

In recent years, with the rapid development of computer vision technology and the popularization of intelligent hardware, as well as people’s increasing demand for intelligent products for human-computer interaction, visual grounding technology can help machines and humans identify and locate objects, thereby promoting human-computer interaction and intelligent manufacturing. At the same time, human-computer interaction is constantly evolving and improving, becoming increasingly intelligent, humane, and efficient. This paper proposes a new VG model and designs a language verification module that uses language information as the main information to increase the model’s interactivity. Additionally, we propose the combination of visual grounding and human-computer interaction, aiming to explore the research status and development trends of visual grounding and human-computer interaction technology, as well as their application in practical scenarios and optimization directions, to provide references and guidance for relevant researchers and promote the development and application of visual grounding and human-computer interaction technology.
[Source Analysis of Soil Heavy Metals in Agricultural Land Around the Mining Area Based on APCS-MLR Receptor Model and Geostatistical Method].

In order to identify the source of heavy metals in the soil around a mining area and provide effective suggestions for the prevention and control of regional soil pollution, 118 topsoil samples (0-20 cm) were collected in the northern part of Wuli Township, Qianjiang District, Chongqing. The heavy metal (Cd, Hg, Pb, As, Cr, Cu, Zn, and Ni) contents in the soil and soil pH were analyzed, and the spatial distribution and sources of heavy metals in the soil were studied using the geostatistical method and APCS-MLR receptor model. The results showed that the content of heavy metals in the soil was significantly higher than the background value in Chongqing; there was obvious surface accumulation; and Hg, Pb, Cd, As, and Zn showed strong variation. The proportions of soil Cd, Hg, Pb, As, and Zn exceeding the risk screening values were 47.11%, 6.61%, 4.96%, 5.79%, and 7.44%, respectively, and the proportions of soil Cd, Hg, Pb, and As exceeding the risk control values were 0.83%, 4.13%, 0.83%, and 0.83%; thus, the problem of excessive heavy metals in the soil was significant. Soil Cd, As, Cr, Cu, and Ni were mainly affected by soil parent materials, and their contribution rates to the total soil elements were 77.65%, 68.55%, 71.98%, 90.83%, and 82.19%, respectively. Soil Hg, Pb, and Zn were mainly affected by the mining of mercury mines and lead-zinc mines, with the contribution rates of 86.59%, 88.06%, and 91.34%, respectively. In addition, agricultural activities also affected soil Cd and As contents. It is recommended to strengthen the safety monitoring of agricultural products and agricultural inputs, plant varieties with a low accumulation of heavy metals, reduce the use of livestock manure, and grow non-edible agricultural products in areas that exceed the control value of heavy metal pollution risk.
Scene Context-aware Graph Convolutional Network for Skeleton-based Action Recognition

Abstract Skeleton-based action recognition methods commonly employ graph neural networks to learn different aspects of skeleton topology information However, these methods often struggle to capture contextual information beyond the skeleton topology. To address this issue, we propose a Scene Context-aware Graph Convolutional Network (SCA-GCN) that leverages potential contextual information in the scene. Specifically, SCA-GCN learns the co-occurrence probabilities of actions in specific scenarios from a common knowledge base and fuses these probabilities into the original skeleton topology decoder, producing more robust results. To demonstrate the effectiveness of SCA-GCN, we conducted extensive experiments on four widely used datasets, i.e.SBU, N-UCLA, NTU RGB+D and NTU RGB+D 120. The experimental results show that SCA-GCN supasses existing methods, and its core idea can be extended to other methods with only some concatenation operations that consume less computational complexity.
Internet of Things for Customer Relationship Management (CRM) Software: Opportunities and Benefits

Customer relationship management software has many advantages for different businesses and economic enterprises. Today, the Internet has become the main tool for information exchange, business, communication, and an integral part of daily life. Internet technologies have witnessed fundamental changes in the way of interaction over a decade. With new technology, objects are also connected to the Internet and with humans; Intelligent systems and data are interacting and exchanging. The Internet of Things will increase the maturity level of customer relationships and will have a profound impact on customer relationship management. This is because the huge data of the Internet of Things contains information about the customer, and textual inputs can greatly increase the capabilities of managing relationships with customers. Therefore, the future of customer relationship management will be provided by cognitive computing, big data analysis, and creating deep knowledge in organizations. In this paper, the vital features and opportunities, and benefits of CRM systems based on the Internet of Things have been examined.
A Comparative Study of Evaluation Metrics for Long-Document Financial Narrative Summarization with Transformers

There are more than 2,000 listed companies on the UK’s London Stock Exchange, divided into 11 sectors who are required to communicate their financial results at least twice in a single financial year. UK annual reports are very lengthy documents with around 80 pages on average. In this study, we aim to benchmark a variety of summarisation methods on a set of different pre-trained transformers with different extraction techniques. In addition, we considered multiple evaluation metrics in order to investigate their differing behaviour and applicability on a dataset from the Financial Narrative Summarisation (FNS 2020) shared task, which is composed of annual reports published by firms listed on the London Stock Exchange and their corresponding summaries. We hypothesise that some evaluation metrics do not reflect true summarisation ability and propose a novel BRUGEscore metric, as the harmonic mean of ROUGE-2 and BERTscore. Finally, we perform a statistical significance test on our results to verify whether they are statistically robust, alongside an adversarial analysis task with three different corruption methods.
Pengenalan Akuntansi Syariah Guna Meningkatkan Kompetensi Siswa/I dan Guru SMK Jurusan Akuntansi

This paper provides an overview of the implementation of community service carried out at SMK Bina Harapan Ciseeng. This community service is carried out in order to improve the competence of vocational students and teachers, especially the accounting department by providing an introduction to sharia accounting for students of SMK Bina Harapan Ciseeng. This Paper is a description Paper captured a series of preparatory activities and during the community service agenda. This activity was carried out targeting students and teachers at SMK Bina Harapan Ciseeng and the implementation is divided into several sessions. The summary of this community service is that it is a need to improve competence regarding sharia accounting among these students so that students' interest in sharia accounting can be accomodated and competencies related to industrial needs can be channeled through competent graduates in their fields.
The Forefront of Cyberattack Countermeasures Focusing on Traces of Attacks

Cyberattacks have become more capable of infiltrating corporate networks with malware by skillfully deceiving the target, and it is becoming increasingly difficult to prevent infections before they occur.Regarding web servers that are open to the public, the frequency of attacks increases and alerts occur more frequently as attack techniques become well known.It is thus becoming difficult to determine which attack to respond to.In this article, the forefront of cyberattack countermeasures focusing on traces left by attacks is discussed to address this issue.
Analysis of Information Geometry for Optimization and Inference Applications

A mathematical framework called "information geometry" investigates the geometrical attributes and features of probability distributions and statistical models. In a variety of domains, including as machine learning, optimisation, and inference, it offers a potent toolkit for analysing and optimising complicated systems. Information geometry and its uses in optimisation and inference are examined in this work.First, we give a general overview of information geometry's foundational ideas and principles, including topics like the Fisher information metre, divergence measures, and exponential families. We go over how to quantify the geometric links between probability distributions and derive practical geometric structures using these ideas.The use of information geometry in optimisation issues is what we investigate next. We show how the Fisher information metric can direct effective search strategies and convergence analysis in optimisation algorithms by utilising its geometric characteristics. We go over the benefits of applying information geometry to a variety of optimisation tasks, including parameter estimation, model choice, and neural network training. We also look into how information geometry affects statistical inference. We emphasise how the development of effective and reliable inference algorithms is made possible by the geometric structures of exponential families. We go over the use of divergence measures to quantify the differences between distributions, making tasks like model comparison and hypothesis testing easier.We also review current developments in information geometry, especially its application to probabilistic programming and deep learning. We go over how information geometry can improve deep neural networks' capacity for generalisation, interpretation, and uncertainty estimation.In this study, information geometry and its uses in optimisation and inference are thoroughly studied. Information geometry provides useful insights and methods for resolving challenging issues in a variety of fields by taking advantage of the geometric aspects of probability distributions.
Enhancing the Security of Cloud using Fault Resolving Script (FRS)

Abstract: Cloud computing has made individual users lives and the work of businesses much easierby providing data storage services at very low costs. Individual users can store and access their data via a shared cloud storage service from any location at any time. A cloud attack is a cyber attack that targets cloud-based service platforms such as computing, storage, or hosted application ina Platform as a Service (PaaS) or Softwareas a Service (SaaS) model. A majority of breaches and cyber attacks in cloudinfrastructure are the result of human error and misconfiguration vulnerabilities customer-centric tools are crucial to mitigating these threats. Yet existing cloud security models are largely unable to address these issues. We propose Fault ResolvingScript (FRS) techniques to address the challenges. FRS applies the principle of chaos engineering to cloud security.
Scrapy-Based Incremental Housing Rental Information Crawling System Design

A web-controlled incremental crawling system is designed for incremental crawling of property rental information on websites because of the need for massive data sets to train the housing rental system model, and to solve the problem of always using site-wide crawling and multiple database accesses for crawling websites based on the Scrapy framework. In order to achieve incremental crawling, a download middleware is added to the Scrapy framework, the system loads the seed page, the visited URLs and their hash lists and the control page list when the crawler starts, obtains the URLs of the sub-level pages and enters them into the database, then crawls the sub-level pages in bulk and parses the property information in the sub-level pages. The data is cleaned by verifying the data format, completing missing items, removing duplicate data and detecting abnormal data to get the eligible property data.
Face recognition based physical layer security system for next-generation wireless communication

Security system in every sector all over the world is a most demandable and crucial issue to protect the fake user especially in wireless communication system. The most common feature in current wireless communication system is only transmitted users’ information; consequently, it is difficult to identify the actual sender to attain the desired security. Face recognition is one of the most popular security systems in biometric authentication, which can detect intruders to restricted or high-security areas in wireless communication sector, and help in minimizing the face user. In this paper, we proposed a system that is the senders will transmit message information with their image to confirm the original senders. A face-recognition algorithm will be implemented at the receiver end. When a senders’ transmitted information enters to the receiver section, the image of sender will be separated from original message signal and sent to the face-recognition algorithm to be analysed and compared with an existing database of trusted user. An alarm goes off if the user is not recognized.
Post-quantum cryptographic key distribution for autonomous systems operating in contested areas

The deployment of post-quantum cryptographic algorithms currently under standardization by NIST have the potential to mitigate quantum computer based attacks, which present a whole new range of cybersecurity challenges. We recognize post-quantum cryptography (PQC) key distribution, across autonomous systems operating in contested areas, as a particularly difficult problem to solve. Verifying the origin and integrity of mission instructions is of extreme importance, especially in the context of autonomous vehicles, missiles, and drones; the lack of which can enable opponents to leverage attacks effecting catastrophic damage to life, property, and strategy. This work proposes a protocol for controlled key distribution through the use of PQC algorithms, strengthened by fingerprints of embedded devices. With this framework, we can assure the origin and correctness of digital information sent to autonomous systems, and enforce that only authorized members can send instructions to these devices. This protocol replaces the deterministic, pseudo-random seed in these PQC protocols with a fingerprint derived from the physical disorder of the embedded device to enhance security and maintain the integrity of the system. We demonstrate how this system can be used to send instructions, verify the origin of the instructions upon receipt, and certify the integrity of the instruction packet received. We also discuss situations in which an attacker attempts to falsify information and how to detect that malicious action. We measure the quality and performance of this prototype system by measuring the latency and bit error rates. We demonstrate this prototype system using the CRYSTALS-DILITHIUM digital signature algorithm (DSA).
“Enter with Green Code Only”

Abstract This article uses the app Health Code, a smartphone-based application for contact tracing and risk assessment that serves as a COVID-19 health passport, as an example to explore how “biometric citizenship,” a new mode of preemptive social regulation, functions and malfunctions in contemporary China. First, through a review of China's transition from socialism to neoliberalism, the authors trace how the tenet of citizenship shifts from biological/biopolitical to biometric as the party-state's agenda changes along the process. Second, drawing on media coverage and comments from social media, we show how Health Code consolidates the biometric paradigm of citizenship and turns the relatively stabilized temporal basis of biological/biopolitical citizenship into fragmented temporalities to tighten social control. This preemptive control system improves the state's acumen for self-revamping and self-preservation and enhances its capacities to harness people's re/productivities. It also disrupts the habituated way of living and further marginalizes the groups with diminishing re/productive potentialities.
The Critical Value of Telepathology in the COVID-19 Era

Telepathology, which includes the use of telecommunication links, helps enable transmission of digital pathology images for primary diagnosis, quality assurance, education, research, or second opinion diagnoses.This review covers all aspects of telepathology implementation, including the selection of platforms, budgets and regulations, validation, implementation, education, quality monitoring, and the potential to improve practice. Considering the long-term trends, the lessons of the COVID-19 pandemic, and the potential for future pandemics or other disasters, the validation and implementation of telepathology remains a reasonable choice for laboratories looking to improve their practice.Though barriers to implementation exist, there are potential benefits, such as the wide spectrum of uses like frozen section, telecytology, primary diagnosis, and second opinions. Telepathology represents an innovation that may transform the future of pathology practice.
The Role of Parents in Guiding Children in Online Learning in the RT 2/ RW4 Environment of Malanu Village

This study aims to see how the role of parents in terms of learning assistance during Covid 19. The method used is field research (FieldReseach), that is, if this research departs directly into the field to make direct observations about the role of parents towards children in online learning that is carried out from the student's home or environment. The research location is in RT 2/RW 4, Malanu Village, Sorong City. The data collection techniques used were questionnaires and interviews with parents of students. The results of this study indicate that 6 parents have tried to be able to take the time to be able to help and accompany their children's learning activities directly. While the other 4 parents have tried, even though they have not been able to maximize it. The several inhibiting factors are 1) external factors and, 2) internal factors.