Reviewerly/exHarmony huggingface dataset:
A Bayesian Network-based Machine-learning Algorithm for Enhancing Performance in Typical Scenarios

Automated machine-learning technology can achieve the automation, efficiency, and intelligence of machine learning. This technology can lower the application threshold of artificial intelligence (AI) and has attracted academic attention. Therefore, taking a typical classification problem as an example, this study proposes a framework of a machine-learning pipeline automation design algorithm combining a Bayesian algorithm and reinforcement learning. Aiming at the CASH problem, the study divides the machine-learning pipeline design problem into two sub-problems. One is to realize a machine-learning pipeline structure search based on reinforcement learning. The other is to realize machine-learning pipeline hyperparameter optimization configurations based on the Bayesian network model. The experimental results showed that when the time budget was four hours, the average balanced accuracy of Auto-PLD(Ed note: You need to define what these are in the abstract.) was 0.003 higher than that of Auto-PLD-random and 0.001 higher than that of Auto-sklearn. The success rate of the Auto-PLD machine-learning pipeline evaluation on various datasets exceeded 92%. Based on the Bayesian model and reinforcement learning, the machine-learning pipeline automation design algorithm framework can also play a good role in practical applications. Moreover, it can promote the development of artificial intelligence technology.
Prophy: An automated reviewer finder to improve the efficiency, diversity and quality of reviews

Peer review is under pressure. Without fair, transparent and efficient peer review we cannot ensure the right proposals get funded and the correct manuscripts get published. In the era of Open Access, which is driving an exponential increase in the number of submitted publications, how we carry out peer review is becoming increasingly important and how we find reviewers is coming under scrutiny. The current methods are slow and produce bias pools of reviewers. As such we need an improved way. At Prophy we have developed a state-of-the-art referee finder that can find experts to review any manuscript from any scientific field in seconds. Then through post-processing filters we can find appropriate candidate referees who are most likely to review a paper, whilst highlighting important conflicts of interest through our complex citation networks. These methods can ensure fair and independent experts who can review interdisciplinary papers from any discipline. These methods are being delivered through APIs and the editorial workflow of editors ensure the right people get access to these tools. Finally, as large-language models improve, so does Prophy and as such we will be looking to drive real innovation in this area in years to come.
Self-supervised monocular depth estimation for high field of view colonoscopy cameras

Optical colonoscopy is the gold standard procedure to detect colorectal cancer, the fourth most common cancer in the United Kingdom. Up to 22%–28% of polyps can be missed during the procedure that is associated with interval cancer. A vision-based autonomous soft endorobot for colonoscopy can drastically improve the accuracy of the procedure by inspecting the colon more systematically with reduced discomfort. A three-dimensional understanding of the environment is essential for robot navigation and can also improve the adenoma detection rate. Monocular depth estimation with deep learning methods has progressed substantially, but collecting ground-truth depth maps remains a challenge as no 3D camera can be fitted to a standard colonoscope. This work addresses this issue by using a self-supervised monocular depth estimation model that directly learns depth from video sequences with view synthesis. In addition, our model accommodates wide field-of-view cameras typically used in colonoscopy and specific challenges such as deformable surfaces, specular lighting, non-Lambertian surfaces, and high occlusion. We performed qualitative analysis on a synthetic data set, a quantitative examination of the colonoscopy training model, and real colonoscopy videos in near real-time.
The Impact of Augmented Reality Applications on the Primary School 2nd Grade Students’ Idioms Learning Levels

This study investigated the impact of augmented reality applications on improving the idioms learning levels of primary school 2nd grade students. The participants of the study were 29 students in the experimental and control groups studying in the second grade of a primary school in Eskişehir. In the experimental group, the idioms were taught with augmented reality applications. The experimental group implementation lasted for two weeks, and ten idioms that were mentioned in the 2nd grade Turkish course book under the theme of "Health, Sports and Play" were examined respectively. Data collection tools such as; "Idioms-Matching Form", "Idioms-Filling the Blank Form", "Idioms-Sentence Forming Form" developed by the researchers were used in the study. In the study, static group pre-test-post-test design, which is one of the pre-experimental designs, was used. The data obtained were analyzed with the statistical package program and it was found that there was a statistically significant difference between the groups in favor of the experimental group. In addition, the responses given to the Idioms-Sentence Forming Form were evaluated by content analysis method in order to demonstrate the change in the students' learning levels of idioms and support the research result. Based on these findings, it can be said that augmented reality applications are effective in improving the idioms learning levels of primary school 2nd grade students. The impact of augmented reality applications on other components of the vocabulary can also be determined.
Recursive least squares method for training and pruning convolutional neural networks

Abstract Convolutional neural networks (CNNs) have shown good performance in many practical applications. However, their high computational and storage requirements make them difficult to deploy on resource-constrained devices. To address this issue, in this paper, we propose a novel iterative structured pruning algorithm for CNNs based on the recursive least squares (RLS) optimization. Our algorithm combines inverse input autocorrelation matrices with weight matrices to evaluate and prune unimportant input channels or nodes in each CNN layer and performs the next pruning operation when the testing loss is tuned down to the last unpruned level. Our algorithm can be used to prune feedforward neural networks (FNNs) as well. The fast convergence speed of the RLS optimization allows our algorithm to prune CNNs and FNNs multiple times in a small number of epochs. We validate its effectiveness in pruning VGG-16 and ResNet-50 on CIFAR-10 and CIFAR-100 and pruning a three-layer FNN on MNIST. Compared with four popular pruning algorithms, our algorithm can adaptively prune CNNs according to the learning task difficulty and can effectively prune CNNs and FNNs with a small or even no reduction in accuracy. In addition, our algorithm can prune the original sample features in the input layer.
Secure semantic optical communication scheme based on the multi-head attention mechanism

In this paper, an artificial-intelligence-based secure semantic optical communication scheme is proposed. The semantic features of the original text information are extracted using Transformer. Compared with other networks, Transformer reduces the complexity of the structure and the associated training cost by using the multi-head attention mechanism. To solve the security problem, the encryption scheme is applied to an orthogonal frequency division multiplexed passive optical network (OFDM-PON). The proposed scheme applies chaotic sequences to produce masking vectors. We encrypt the constellation and frequency, achieving a large key space of 1 × 10 270 . To prove that Transformer can effectively extract the semantic features of text, we have computed the values of ROUGE-1, ROUGE-2, and ROUGE-L, which are 40.9, 18.02, and 37.17, respectively. An encrypted 16 quadrature amplitude modulation (16QAM) OFDM signal transmission over a 2 km seven-core fiber with a data rate of 78.5 Gbits/s was experimentally demonstrated. During the experiments, the bit error rate (BER) was analyzed and the results show that the proposed system improves efficiency and security in an OFDM-PON system.
An Inferential Commonsense-Driven Framework for Predicting Political Bias in News Headlines

Identifying political bias in news headlines holds significant importance as it influences the dissemination and consumption of news stories. However, employing conventional methods to do so poses a formidable challenge, as the short headline text is often complex and lacks sufficient syntactic and semantic context. Existing approaches fail to acknowledge the potential of commonsense reasoning in facilitating text comprehension, although it has been shown to aid numerous downstream applications. To this end, to facilitate comprehension and compensate for the lack of context, we propose leveraging inferential commonsense knowledge to simplify, interpret, and explain events that are not explicitly stated in headlines. Furthermore, to fully utilise its potential and deal with the unnecessary noise it may introduce, we present a method for emphasising significant inferences. Using this knowledge, we introduce a novel framework, IC-BAIT, short for Inferential Commonsense aware BiAs IdenTifier, which is a flexible neural network framework designed to enhance political bias prediction in news headlines. We also present two bias-annotated datasets: MediaBias and GoodNews. Experiments on both datasets demonstrate that IC-BAIT significantly enhances the performance of the baseline models used in the framework. Experiments on the datasets show that IC-BAIT improves the baseline models in terms of accuracy (2.0-10.0%), macro-averaged F1 (2.2-22.2%), Jaccard-score (up to 15.1%), and micro-averaged F1 (up to 18.6%). Our in-depth qualitative analysis reveals the scenarios in which the selected knowledge is beneficial and when it is detrimental. Datasets and scripts are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Swati17293/IC-BAIT</uri> .
FINANCIAL PRODUCTS MARKETING AND ITS IMPLICATIONS ON THE PERFORMANCE OF DEPOSIT MONEY BANKS IN NIGERIA

The study examined the effect of financial products marketing on the Performance of deposit money banks (DMBs) in Nigeria, for the duration of 2008-2022 which is 20years. The secondary data (time series) was gotten from Central Bank of Nigeria (CBN) Statistical Bulletin and Nigeria Deposit Insurance Corporation (NDIC) for the duration under review. The study identified the following measures financial products such as Automated Teller Machine (ATM), Point of Sale (POS), INTB (INTB) and NIBSS Electronic Funds Transfer (NEFT) which was analyzed in relation to the Return on Assets (ROA) (proxy with performance of DMBs in Nigeria). The DP for the preliminary description of the data set, correlation analysis will be use to ascertain the co-movement of the independent variables in relation to the dependent variable while the Multiple Regression analysis were employed with the aid of E-VIEW version 9.0 for the purpose of testing the research hypothesis. The study found that ATM, POS, and INTB significantly affect DMB ROA in Nigeria, while NEFT did not. Finally, financial product marketing has a substantial impact on DMB performance in Nigeria. If DMB management wants to boost ROA, they could focus on ATM service improvements, which will boost customer happiness and patronage.&#x0D; Keywords: Financial Products, Marketing, Internet, Point of Sales and Performance.
Development of signature recognition system using VGG16

The goal of this research is to locate a signature recognition system that can replace individual presence in the process of handwritten signature recognition. It has been discovered that an automatic signature recognition system that does not require a person’s physical presence is necessary throughout corona duration. In this article more accurate signature recognition model employing VGG 16 pre- trained models is proposed. Novel Convolution neural network has exhibited 83% validation accuracy on the GPDS synthetic Signature dataset[1].
Retracted: Competency of Neural Networks for the Numerical Treatment of Nonlinear Host-Vector-Predator Model

[This retracts the article DOI: 10.1155/2021/2536720.].
Privacy and Transparency in an AI-driven World: Does Algorithmic Transparency Fit on Data Privacy Under GDPR?

The quick advancement of AI technology has changed how we relate to our environment. AI is reshaping almost every business nowadays. Data privacy issues have surfaced as AI algorithms are increasingly employed in decision-making processes. Algorithmic transparency has become essential for preserving data privacy in an AI-driven world. Understanding how an algorithm generates decisions and the elements that go into those decisions is referred to as algorithmic transparency. Algorithmic transparency can guarantee that the use of AI technology does not violate people’s rights to privacy by encouraging transparency and accountability.A review of existing AI technologies and their effects on data privacy is given at the outset of this thesis. Then, algorithmic transparency and how it contributes to data privacy are explored under the given topic. The GDPR and the AI Act that regulates data processing and controls the usage of AI technology are examined in the thesis and look at the relationship between privacy and transparency under the GDPR. Besides, it analyses how AI affect privacy and transparency. It questions whether the GDPR can cover AI-related issues and where the AI Act stays with respect to privacy and transparency. To do so, various industrial fields are touched on throughout the thesis, offering a thorough knowledge of the benefits and drawbacks given by the usage of AI technology. The thesis also stresses the importance of limiting transparency considering “Black Box” algorithms, sensitive information, trade secret and other Intellectual Property. Finally, emphasising the legal and regulatory frameworks controlling the use of AI technology, it explicitly explores the impact of algorithmic transparency on data privacy. Thus, this study intends to evaluate how well the GDPR protects privacy and transparency in a world powered by AI and how effective is the transparency requirement under the AI Act.
Layph: Making Change Propagation Constraint in Incremental Graph Processing by Layering Graph

Real-world graphs are constantly evolving, which demands updates of the previous analysis results to accommodate graph changes. By using the memoized previous computation state, incremental graph computation can reduce unnecessary recomputation. However, a small change may propagate over the whole graph and lead to large-scale iterative computations. To address this problem, we propose Layph, a two-layered graph framework. The upper layer is a skeleton of the graph which is much smaller than the original graph, and the lower layer has some disjoint subgraphs. Layph limits costly global iterative computations on the original graph to the small graph skeleton and a few subgraphs updated with the input graph changes. In this way, many vertices and edges are not involved in iterative computations, which significantly reduces the computation overhead and improves the performance of incremental graph processing. Our experimental results show that Layph outperforms current state-of-the-art incremental graph systems by 9.08× on average (up to 36.66×) in response time.
DEVELOPMENT OF JAVANESE SCRIPT LEARNING MEDIA BASED ON UNO STACKO GAME AND PENTA HELIX APPROACH AT SDN 2 SIDOWARNO

&lt;p&gt;The fading of Javanese culture and the literacy crisis are complex problems that have a further impact on the world of education on elementary school students in Javanese script literacy in a multicultural country like Indonesia. Factors that influence the lack of Javanese script literacy are learning media that are less attractive and limited. In realizing Education for Sustainable Development (ESD), one of the efforts to preserve and introduce Javanese script to support Indonesia gold 2045 can use gaming learning media. Empowerment of the Javanese script can be implemented through the role of education staff in learning based on the uno stacko game and the penta helix system. The purpose of this study was to determine the feasibility of learning media based on the uno stacko game and the penta helix approach at SD N 2 Sidowarno and to determine student responses to the feasibility of learning media based on the uno stacko game and the penta helix approach. This research uses the research and development (R&amp;amp;D) method with the Borg &amp;amp; Gall model. Data were obtained from teacher questionnaires and student responses, validation questionnaires from subject matter experts, media experts, and also interviews. The results of the expert assessment were 87.45% for subject matter experts, 85.84% for media experts, and 87.77% for educators, while the student response was 82.43%. From the research results it is known that learning Javanese script using the Uno Stacko game can increase student learning motivation and form effective learning. In addition, based on assessments from material experts, media experts, and teachers as well as responses from students, it can be concluded that the Javanese script learning media based on the uno stacko game and the penta helix approach are suitable for use as learning media. In addition, based on the research results, it can be seen that the existence of a penta helix collaboration system can be an effective effort in supporting Education for Sustainable Development (ESD).&lt;/p&gt;&lt;strong&gt;Keywords: &lt;/strong&gt;Javanese Script, uno stacko game, penta helix
High-Imperceptibility Data Hiding Scheme for JPEG Images Based on Direction Modification

Data hiding (DH) is an important technology for securely transmitting secret data in networks, and has increasing become a research hotspot throughout the world. However, for Joint photographic experts group (JPEG) images, it is difficult to balance the contradiction among embedded capacity, visual quality and the file size increment in existing data hiding schemes. Thus, to deal with this problem, a high-imperceptibility data hiding for JPEG images is proposed based on direction modification. First, this proposed scheme sorts all of the quantized discrete cosine transform (DCT) block in ascending order according to the number of non-consecutive-zero alternating current (AC) coefficients. Then it selects non-consecutive-zero AC coefficients with absolute values less than or equal to 1 at the same frequency position in two adjacent blocks for pairing. Finally, the 2-bit secret data can be embedded into a coefficient-pair by using the filled reference matrix and the designed direction modification rules. The experiment was conducted on 5 standard test images and 1000 images of BOSSbase dataset, respectively. The experimental results showed that the visual quality of the proposed scheme was improved by 1~4 dB compared with the comparison schemes, and the file size increment was reduced at most to 15% of the comparison schemes.
User Experience Analysis On Application Simulation "Healthy Lifestyle At Home" Based On Virtual Reality

A healthy lifestyle at home is very important to implement; some families do not apply health or clean living rules at home.Nowadays, there are problems that can be explained with technology; one of them is by providing content with a realistic visual concept called Virtual Reality (VR).VR technology is also used in several fields, such as in the field of gaming, health simulation, simulation in the field of education and others.For this reason, the author decided to analyze applications that aim to educate based on virtual reality about the rules of healthy lifestyle at home.The author wants to know the level of user experience of healthy living applications at home.For method of user experience or the experience of this user itself the author decides to use the TCSD (Task Centered System) method.TCSD is a method that is included in Human Computer Interaction (HCI).This method has four stages, namely Identification, User Centered Requirements Analysis, Design through Scenario and System Evaluation.System.And at the final stage of the TCSD method is the walktrough evaluate stage.Evaluate which will be done to evaluate user usability.Evaluation is done with one of the usability methods, namely heuristic evaluation.From the results obtained from research analysis and testing about the healthy living simulation application at home as an application that educates about the importance of healthy living at home.With the method of heuristic calculation method, an average value of 0.81 has been obtained with a severy rating "Cosmetic" which means there are problems but the problem is not too important to fix.And it can be concluded that healthy living simulation application at home can be accepted with low usability problems.usability problems are low.
Development of comic page creator media based on Tumbuhkan, Alami, Namai, Demonstrasikan, Ulangi, Rayakan (TANDUR) learning for elementary school students

The demands of a teacher in this modern era in educating the nation's generation must be able to master IT to support effective learning activities. Data collection techniques used in this study were interviews, preliminary studies, observation, questionnaire collection, and documentation. This development research has gone through the validation stage with the acquisition of an average score of 96.36% from media experts with the criteria of "Very Eligible", material experts of 95% "very feasible", and linguists of 94.54% "very feasible" . In the limited trial the results were obtained, namely, for students at SDN Cikahuripan 01 the percentage of the questionnaire was 88% "very good" and SD Bojong Nangka 02 the percentage of the questionnaire was 90% with the "very good category". So that the comic page creator media based on TANDUR learning can be declared feasible to be tested on students.
Edu-game Application Based-Android Quantity and Unit Concepts Using Game Engine Constructs 2

The purpose of this research is to produce alternative learning media about understanding the concept of unit size as an interactive and fun educational tool. This research uses the Research and Development (R&amp;D) method and the product development model uses the MDLC (Multimedia Development Life Circle)) consisting of six stages, namely concept, design, material collecting, assembly, testing, distribution. The media expert validation test concluded that media expert 1 with an average percentage of 77% included the criteria of "Appropriate", media expert 2 with an average percentage of 80% included the criteria of "Very Eligible", and media expert 3 with an average percentage of 97% included criteria of "Very Eligible". The overall results of the validation of media experts obtained an average percentage of 84% including the "Very Eligible" criteria. Limited trials with 32 respondents resulted in an overall testing average of 97% including the criteria of "Very Eligible" and for achieving student learning outcomes an average of 84.34 was obtained including the criteria above the KKM. The results of this study are the Edugame Application of the Android-Based Concept of Units and Quantities Using Game Engine Construct 2 which is suitable for use.