Reviewerly/exHarmony huggingface dataset:
Evaluating the Pollution Levels and Ecological Risks of Heavy Metals in Sediments of Apapa Port in Lagos, South-western Nigeria

The coastal sediments of Apapa Port in Lagos, Southwestern Nigeria were studied to determine the status of contamination due to the present of heavy metals and its effect on the ecosystem. The sediments samples were subjected to Particle Induce X-ray Emission (PIXE) technique analysis to determine heavy metals present in them. Ten (10) heavy metals were detected with which their concentrations ranged as Ti (2787.0 - 5672.9 mg/kg), Cr (95.4 - 285.9 mg/kg), Mn (124.8 - 526.7 mg/kg), Fe (20263.7 – 49953.7 mg/kg), Cu (8.0 – 13.4 mg/kg), Zn (280.1 – 1246.5 mg/kg), Rb (58.6 – 336.1 mg/kg), Sr (39.3 – 233.5 mg/kg), Zr (89.3 – 190.1 mg/kg), and Pb (13.3 – 31.1 mg/kg). The extent of contamination of the heavy metals was enumerated by comparing their concentrations with the USEPA guidelines, TEL and PEL. Different pollution indices such as enrichment factor (EF), geo-accumulation index (Igeo), contamination factor (CF), modified degree of contamination (mCd), pollution load index (PLI), and potential ecological risk index (PERI) were applied to estimate the level of toxicity imposed on the ecosystem by the detected heavy metals. These showed that the studied sediments were severely enriched with Zn at site 5 and moderately enriched with Cr and Rb at stations 5 and other respectively but the sum of all risk factors for those heavy metals is low. Statistical analysis such as the cluster analysis and Pearson’s correlation coefficient were also employed to determine the relationships of those heavy metals with respect to their origin. These revealed strong positive and negative correlations among the heavy metals and are cluster in terms of similarities as (1) Ti-Fe-Mn (2) Rb-Pb (3) Sr (4) Cu-Zn-Cr-Zr. This studied area requires proper monitoring to avert increase in ecological risk factor due to anthropogenic influences.
Generalized Properties of Generalized Fuzzy Sets GFScom and Its Application

Negative information plays an essential role in knowledge representation and commonsense inference. We further continually develop the theory of a generalized fuzzy set with contradictory, opposite and medium negation (GFScom). The t-norm, s-norm, convexity and concavity properties of GFScom are discussed. By introducing GFScom to Mamdani fuzzy systems, we propose new constructive approaches from any (infinite) input-output data pairs to approximate any continuous function on a compact set to a desired degree of accuracy, and study the first-order and the second-order approximation error bounds for the classes of the constructed fuzzy systems from mathematical viewpoints in details. Furthermore, the new better sufficient conditions for this class of fuzzy systems to be universal approximators are investigated thoroughly. Finally, we illustrate several examples and compare the new results to published error bounds through numerical examples.
Using Digital Stories in Reading Class

Reading achievement and willingness to get involved in reading activities among young learners are affected by their reading motivation and the texts they select for reading. Since there is software that makes stories readable on personal devices, using digital stories in the classroom is one of the strategies to promote reading skills and students' motivation to read. The purposes of this research are to know the implementation of digital stories in teaching reading to young learners and to know the advantages and disadvantages of the use of digital stories in teaching reading to young learners. This research uses a qualitative method. To collect the data, the researcher uses observation and interviews. Meanwhile, to analyze the data, the researcher uses data reduction, data display, and drawing conclusions. The results show that students can use digital stories easily. They click the link given by the teacher and choose the stories they like on the website. Some interesting features can be added to story content while they are reading the text, such as interactive exercises, quizzes, and discussions that are not available in the printed text. The strengths are that digital stories are used as an interesting learning tool, particularly for students who are accustomed to technology, and help young readers enhance their literacy skills and motivation. The weaknesses are the internet connection is sometimes unstable so the students are difficult to access the stories and no quiz menu in the website. It makes the students not challenged to read it at home.
A Systematic Review of Collaboration in Supply Chain 4.0 with Meta-Synthesis Method

A systematic literature review is conducted to analyze and synthesize studies on supply chain collaboration in Industry 4.0 between 2010 and 2023. 152 documents were selected from various databases. The meta-synthesis method categorizes 8 Initiators, 8 Barriers, 7 dimensions, and 4 Outcomes of collaboration. The findings show that collaboration in supply chain 4.0, with the activation of drivers and enablers such as Industry 4.0 technologies, Information and communication technology infrastructure, and with the control of barriers such as Personal benefits and Operational and structural issues can utilize information and communication technologies to highlight sustainable performance and trust throughout the supply chain. The study develops the existing literature and persuades businesses and the scientific community to investigate the power of collaboration in supply chain partner activities. The analytical model in this study focusing on four main sections, can serve as a basis for conducting new research in the development of collaboration.
DEEP LEARNING ALGORITHM FOR CONNECTING SCIENTIFIC RECORDS AND SOCIAL PLATFORM

In the healthcare industry, professionals develop big amounts of disorganized data. The complexity of this data and the loss of computational capability lead to delays in the investigation. Nevertheless, with the advent of Deep Learning algorithms and connection to computing power such as Graphic Processor Units (GPUs) and Tensor Processing Units (TPUs), text and image processing has become usable. Deep Learning (DL) data bring about a big outcome in Natural Language Processing (NLP) and computer perception. The main purpose of this study is to build an undivided approach that can relate social platforms, literature, and scientific records to develop an approach to medicinal education for the public and experts.&#x0D; This study focuses on NLP in the healthcare industry and compiles data by Electronic Medical Records (EMR), medical literature, and social platforms. The framework proposed in this study is one for connecting social platforms, medical literature, and Electronic Medical Records scientific records using Deep Learning algorithms. Linking data sources requires defining the relationships between them, and finding concepts in medical texts. The National Library of Medicine (NLM) introduced the Unified Medical Language System (UMLS) and uses this system as the basis for the proposed system. The dynamic nature of a social platform can be recognized and supervised methodologies can be applied under supervision to develop conception. Named entity Recognition (NER) enables the active eradication of data or individuals by the pharmaceutical literature.
Secure Software Development Best Practices

This research aims to explore optimal strategies for fortified software, enhancing the implementation of secure software development practices. Software security involves crafting and designing software that guarantees the integrity, confidentiality, and availability of its code, data, and functionalities. Often, in prioritizing functionality, security takes a back seat when organizations embark on system development. Yet, it's imperative to embed security at every phase of the Software Development Life Cycle (SDLC). Numerous methodologies and models exist for addressing software security, but only a few substantiate creating secure software applications effectively. Despite advancements, software security remains inadequately addressed, posing a challenge to integrating security protocols into the SDLC seamlessly. This review advocates specific security measures to be integrated at each SDLC level, fostering a secure SDLC. Efficient amalgamation of these processes ensures the delivery of secure software systems with minimized resource expenditure. Additionally, it highlights hurdles encountered in employing agile development methodologies for crafting secure software. These challenges stem from assessing agile ideals, principles, and security assurance procedures. These findings underscore the urgency for research facilitating safe software development, addressing barriers inhibiting its adoption. The paper serves as a valuable reference, shedding light on the significance of establishing secure software development processes.
Analyzing Online and Modular Learning Perspectives in the Evolving Educational Landscape

This research abstract presents an assessment of the perceptions of parents and learners towards online and modular learning in the junior and senior high school education setting in Baguio City. The study aimed to investigate the perceptions of both parents and learners regarding various aspects of online and modular learning. The research employed a quantitative data collection method. A structured survey questionnaire was distributed to parents and learners to gather quantitative data, and this included questions related to the time allocated for learning activities, the distribution and retrieval of modules, the effectiveness of learning assessments and activities, the delivery of learning, efficiency of the educational process, accessibility to learning materials, teaching methods, and the overall comfort of the learning experience. The survey utilized a Likert scale to measure responses, allowing for the calculation of mean ratings. In addition to the quantitative data insights were gathered through open-ended questions in the survey, where participants could provide detailed feedback and comments on their experiences with online and modular learning. The findings revealed that both parents and learners generally held positive perceptions of online and modular learning, with parents showing particularly high satisfaction with the time allocation for learning activities, module distribution, and the effectiveness of assessments. Learners, on the other hand, expressed strong satisfaction with the delivery of learning and the efficiency of the educational process. This research demonstrates the positive reception of online and modular learning among parents and learners in Baguio City, highlighting the potential effectiveness of these educational approaches in junior and senior high school settings.
Guiding the Release of Safer E2E Conversational AI through Value Sensitive Design

A. Stevie Bergman, Gavin Abercrombie, Shannon Spruit, Dirk Hovy, Emily Dinan, Y-Lan Boureau, Verena Rieser. Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue. 2022.
Research on Pectoral Muscle Segmentation Algorithm of CT Image Based on Deep Learning

Chronic obstructive pulmonary disease (COPD) is a common respiratory disease, which seriously endangers human health and is also one of the important causes of death. The death rate of COPD in China is the highest in the world, and the problem of under-diagnosis of the disease is very serious. The gold standard for the diagnosis of COPD is lung function examination, and clinical studies have shown that CT and other imaging methods can be included in the auxiliary diagnosis of COPD. CT images can be used to assess pectoral muscle area, which is associated with COPD severity. Patients with lower pectoral muscle area often have more severe expiratory airflow obstruction and other problems. Therefore, the key of the research is to accurately segment the pectoral muscle in CT images. The medical image segmentation method based on deep learning can dig out more abundant internal information of data, so it has gradually become the preferred method in the aspect of medical image segmentation. In this paper, a pectoral muscle segmentation algorithm based on U-Net and its variant U-Net++ is proposed, which is of great significance for evaluating the severity of disease in patients with COPD. The network is composed of symmetrical encoders and decoders, which can effectively learn from very little labelled data by using appropriate data enhancement methods, and is therefore very suitable for medical image segmentation. The experimental results on the data set provided by Jiangsu Province Hospital show that the average Dice coefficient of the proposed algorithm is more than 94% and the average accuracy rate is 91%. The algorithm can accurately segment the pectoral muscle in CT images and has good segmentation performance.
Can Foreign Aid and Remittances Reduce Poverty in Afghanistan An Application of Cointegration and Vector Error Correction Model

International remittances and foreign aid flowing into developing countries are attracting increasing attention as of their rising volume and their effect on receiver countries. The basic objective of the present paper is to determine the long-run and short-run associations among poverty, foreign aid, and remittances. It is hypothesized that foreign aid and remittances are the possible variables affecting poverty. The foreign aid, remittances, and poverty comparison are tested both for the short-run and long-run. Johansen and Juselius's (JJ) cointegration and Vector Error Correction Model (VECM) was used for establishing short-run and long-run relationships between poverty and its determinants. Secondary data was collected for poverty, foreign aid, and remittance from the year 2002 to 2018. The study found a significant long-term and short-term relationship between poverty and its determinants such as foreign remittance, foreign aid, 7and per capita income. Our results are consistent with the findings of Hossain (2013), Connors (2012), Goff (2010), Banga and Sahu (2010), Asra et.al. (2005), and Jongwanich (2007) who found foreign aid and remittances do not help to poverty eradication. Keywords: Foreign Aid, Remittances, Poverty Eradication, Johansen Cointegration, Vector Error Correction Model (VECM), Afghanistan. JEL Codes: F35, F50, F24, F22, I32, O1
Investigating the Representation of Open Domain Dialogue Context for Transformer Models

Vishakh Padmakumar, Behnam Hedayatnia, Di Jin, Patrick Lange, Seokhwan Kim, Nanyun Peng, Yang Liu, Dilek Hakkani-Tur. Proceedings of the 24th Meeting of the Special Interest Group on Discourse and Dialogue. 2023.
Applying Item Response Theory to Task-oriented Dialogue Systems for Accurately Determining User’s Task Success Ability

While task-oriented dialogue systems have improved, not all users can fully accomplish their tasks. Users with limited knowledge about the system may experience dialogue breakdowns or fail to achieve their tasks because they do not know how to interact with the system. For addressing this issue, it would be desirable to construct a system that can estimate the user’s task success ability and adapt to that ability. In this study, we propose a method that estimates this ability by applying item response theory (IRT), commonly used in education for estimating examinee abilities, to task-oriented dialogue systems. Through experiments predicting the probability of a correct answer to each slot by using the estimated task success ability, we found that the proposed method significantly outperformed baselines.
Educational Pattern Guided Self-knowledge Distillation for Siamese Visual Tracking

Existing Siamese-based trackers divide visual tracking into two stages, i.e., feature extraction (backbone subnetwork), and prediction (head subnetwork). However, they mainly implement task-level supervision (classification and regression), barely considering the feature-level supervision in the knowledge learning process, which could result in deficient knowledge interaction among the features of the tracker’s targets and background interference during the online tracking process. To solve the issues, this paper proposes an educational pattern-guided self-knowledge distillation methodology by guiding Siamese-based trackers to learn feature knowledge by themselves, which can serve as a generic training protocol to improve any Siamese-based tracker. Our key insight is to utilize two educational self-distillation patterns, i.e., focal self-distillation and discriminative self-distillation, to educate the tracker to possess self-learning ability. The focal self-distillation pattern educates the tracking network to focus on valuable pixels and channels by decoupling the spatial learning and channel learning of target features. The discriminative self-distillation pattern aims at maximizing the discrimination between foreground and background features, ensuring that the trackers are unaffected by background pixels. As one of the first attempts to introduce self-knowledge distillation into the visual tracking field, our method is effective and efficient and has a strong generalization ability, which might be instructive for other research. Codes and data are publicly available.
PMFNet: A Progressive Multichannel Fusion Network for Multimodal Sentiment Analysis

The core of multimodal sentiment analysis is to find effective encoding and fusion methods to make accurate predictions. However, previous works ignore the problems caused by the sampling heterogeneity of modalities, and visual-audio fusion does not filter out noise and redundancy in a progressive manner. On the other hand, current deep learning approaches for multimodal fusion rely on single-channel fusion (horizontal position/vertical space channel), and models of the human brain highlight the importance of multichannel fusion. In this paper, inspired by the perceptual mechanisms of the human brain in neuroscience, to overcome the above problems, we propose a novel framework named Progressive Multichannel Fusion Network (PMFNet) to meet the different processing needs of each modality and provide interaction and integration between modalities at different encoded representation densities, enabling them to be better encoded in a progressive manner and fused over multiple channels. Extensive experiments conducted on public datasets demonstrate that our method gains superior or comparable results to the state-of-the-art models.
Revocable Cryptography from Learning with Errors

Quantum cryptography leverages unique properties of quantum information in order to construct cryptographic primitives that are oftentimes impossible classically. In this work, we build on the no-cloning principle of quantum mechanics and design cryptographic schemes with key revocation capabilities. We consider schemes where secret keys are represented as quantum states with the guarantee that, once the secret key is successfully revoked from a user, they no longer have the ability to perform the same functionality as before. We define and construct several fundamental cryptographic primitives with key-revocation capabilities, namely pseudorandom functions, secret-key and public-key encryption, and even fully homomorphic encryption, assuming the quantum sub-exponential hardness of the learning with errors problem. Central to all our constructions is our method of making the Dual-Regev encryption (Gentry, Peikert and Vaikuntanathan, STOC 2008) scheme revocable.
A Survey: Cryptography Techniques for Communication System

Abstract: Sensitive data is being used more and more in online communication these days. Therefore, internet consumers' top concern is data security. The best course of action is to utilize a cryptography technique that encrypts data, translates it over the internet, and then decrypts it back to the original data. The process of securely transmitting data is the focus of the field of cryptography. The intention is to prevent eavesdroppers from comprehending a message while enabling the intended recipients to receive it correctly. A collection of methods known as cryptography are used to jumble or hide data so that only a person skilled in data restoration may access it in its original format. Cryptography offers modern computer systems a robust and costeffective foundation for maintaining data secrecy and confirming data indignity. While our traditional cryptography techniques, like RSA signature and AES encryption, function well on computers with respectable amounts of RAM and computing capacity, they are not well suited to the realm of embedded systems and sensor networks. As a result, techniques for lightweight cryptography are put forth to address numerous issues with traditional cryptography. This work develops a new hybrid method of plaintext encryption with the goal of adding to the body of knowledge in the field of classical cryptography. For an additional degree of protection, the cryptosystem employs three distinct numerical and alphabetical keys in cipher. Super Cipher is the name given for the new proposed cipher.
Texture Classification of Paddy Soil using Convolutional Neural Network

This research aims to classify the types of paddy soil based on its image, which consists of sand, clay and clay sand. The data is divided into 3 parts, training data of 1277 images and data testing and validation of 160 images with 224x224 pixel. Some of the transfer learning models used in this trial are Resnet152, VGG16, Xception, MobilenetV2 and DenseNet201, with 10 epochs and a batch size of 32. The results show that the accuracy of the Resnet152 model has 63.75% at best epoch 5, the VGG16 model 90.62% at epoch 9, the Xception model 91.25% at epoch 10, the MobilenetV2 model 86.87% at epoch 7 and Densenet201 model 93.12% at epoch 7. The model with the highest accuracy, Densenet201, was then tested with 100 epochs resulting in 95% accuracy. This research shows that the Densenet201 model is the best model for classifying paddy soil types.
Dual Encoding++ : Optimization of Text-Video Retrieval via Fine-tuning and Pruning

Video-text retrieval involves searching for related videos based on text queries and vice versa. While video-text retrieval models effectively retrieve information, they can be slow to train and computationally expensive. Our study applies optimization and compression techniques to an existing Dual Encoding model to address these issues. In the fine-tuning phase, various learning strategies are applied to enhance the model performance. Moreover, Architecture adjustments and network pruning are used to reduce complexity. The results of the experiments show improved performance with fewer parameters. The contributions of this study include a lightweight model with enhanced performance achieved through optimization and compression techniques. These findings suggest directions for future research on model enhancements in video-text retrieval. Re-implemented code can be found at https://github.com/dongYoun2/dual-encoding-system.
Digital Visual Development of Youth is an Important Task of Modern Education

The subject matter of the article is the substantiation of the need for digital visual development of students receiving qualifications related to the development of digital content. The authors analyze a wide range of educational programs, according to which domestic institutions of higher education provide appropriate training for students. Attention is focused on the need to exchange experience between Ukrainian universities regarding the quality of educational programs, as well as the usefulness of adopting educational practices from abroad. The work describes the methods and tools that should be used in design education, including an application that is being prepared for experimental use in the training of future computer science teachers.