Reviewerly/exHarmony huggingface dataset:
Treatment of Dairy Wastewater by Electrocoagulation using Iron Filings Electrodes

This study investigated the treatment of dairy wastewater using the electrocoagulation method with iron filings as electrodes. The study dealt with real samples collected from local factory for dairy products in Baghdad. The Response Surface Methodology (RSM) was used to optimize five experimental variables at six levels for each variable, for estimating chemical oxygen demand (COD) removal efficiency. These variables were the distance between electrodes, detention time, dosage of NaCl as electrolyte, initial COD concentration, and current density. RSM was investigated the direct and complex interaction effects between parameters to estimate the optimum values. The respective optimum value was 1 cm for the distance between electrodes, (60 – 120) min for detention time, 250 mg NaCl/L added, C0/6 = 5,775 mg COD/L as initial COD concentration, and 7.884 - 8.077 mA/cm2 as current provided. At the optimum parameter values, the optimum COD removal efﬁciency was 73.4%. Meanwhile, the study also performed removal efficiency for nitrogen (N) and phosphate (P) due to their effects on the aquatic life and systems. The optimum removal efficiency for phosphorus and nitrogen was 98.0% and 80.3%, respectively. Due to its effects on the environment and to comply with local legislations, treating these wastewaters using eco-friendly processes was highly recommended taking in consideration the economic feasibility, flexibility and easiness to operate. In addition, the study proved that the high surface area for iron filings played a crucial role in removing process.
Efficient Sign-Detection-Scheme Using Modular Computation Technique for the Moduli Set {2&amp;lt;sup&amp;gt;n&amp;lt;/sup&amp;gt;-1, 2&amp;lt;sup&amp;gt;n&amp;lt;/sup&amp;gt;, 2&amp;lt;sup&amp;gt;n&amp;lt;/sup&amp;gt;+1, 2&amp;lt;sup&amp;gt;(n+1)&amp;lt;/sup&amp;gt;-1, 2&amp;lt;sup&amp;gt;2n&amp;lt;/sup&amp;gt;-5}

In computer arithmetic, one of the most important things to consider in hardware design is the ability of the system to detect and display numbers with their signs.This when properly managed will reduce errors and ensure hardware reliability.But interestingly, detecting and knowing the sign of a residue number during arithmetic operation is very difficult.Magnitude Comparison, Scaling and Number conversions are some of the other difficult operations in Residue Number System (RNS).Unlike the weighted number system, it is even extremely difficult to determine the sign of a number in an RNS architecture thereby hampering the full implementation RNS in general purpose computing.In this paper, an efficient sign detection algorithm for detecting the sign of a number in an RNS architecture is presented.In formulating the algorithms, X maximum, (X max ) is computed from the Dynamic Range, M=∏ k i=1 (m i ).Modular Computation Technique is employed as a converter to compute X from the residues (r 1 , r 2 , r 3 ) with respect to a given moduli set, say = { 1 , 2 …, n }.X is positive if X-X max <0 otherwise X is negative and the actual value is this case is computed as X-M.The moduli set {2 n -1, 2 n , 2 n +1, 2 (n+1) -1, 2 2n -5} is used for the system design implementation and for numerical illustrations.It is observed that the scheme effectively detects the sign of RNS numbers and theoretical analysis showed that simple hardware resources and low-power modular adders are used in the design.It is also observed that the scheme when implemented practically can help project RNS to be used in general purpose computing.
Multiple Sign Language Identification Using Deep Learning Techniques

The research presents a general overview of sign languages, and a previous survey was conducted on all aspects of sign languages including the tools used to collect sign languages and the best algorithms to achieve the best results.A specialized database is prepared to combine the alphabet signs of the Arabic, American, and British languages, as they are the most important sign languages and the most widespread in the world.Based on different sign languages and deep learning techniques such as LeNet, VGG-16, and CapsNet, which are considered among the best methods for solving sign language problems based on our previous studies.The purpose of the research is to remove the communication gap between the deaf, and normal speaking people who speak one sign language or those who try to communicate from different countries and to identify these languages easily.We applied some of the traditional deep learning techniques such as LeNet, and then we applied VGG-16 using pre-training models and adjusted some layers to suit our problem.Also we applied CapsNet as it is perfectly suitable for solving the problem of sign language deformation, rotation, and scaling.The best results were achieved using VGG-16, as it was trained on a previous database like ImageNet, which contains millions of images.We got an accuracy of 99.69% when training the model of VGG-16, and an accuracy of 99.65% when testing the model.On the other hand, we got lower accuracies in CapsNet and LeNet compared to VGG-16.We got 96.54%, 97.45%, and 94.95% on BSL, ASL, and ArSL respectively while applying LeNet model, while we got 98.4848%, 98.4286%, and 99.5652% on ArSL, ASL, and BSL respectively while applying CapsNet model.Using VGG-16 we got 99.05%, 98.50%, and 99.69% on ArSL, ASL, and BSL respectively.
Academia-Industry Linkages: Alternative Platforms for Engagement Under Nigerian Law

The mandate of Nigerian universities and other institutions of higher learning is to among others, teach and carry on research.The ability of universities to effectively carry on these mandates depend on adequate funding.It is a truism that Universities in Nigeria, both public and private are under funded.The low level funding of public universities in particular, has resulted into low level research outputs.Although there are laws and indeed policies that encourage collaborations between universities, industries and other agencies of government, it does appear that there is low level awareness by researches in leveraging on the existing platforms to bridge the academia-industry gap.This article explores alternative/additional funding options for researches based on collaborative platforms under current laws and policies and concludes that these alternatives/additional funding are not resorted to by researchers largely due to low level awareness of the laws and policies underpinning academia-industry and government collaborations.The inability of universities to take advantage of these platforms has impeded researchers from transforming their research findings into products and services.The article has brought to the fore the need for universities and other institutions of higher learning to explore the legislative and policy framework underpinning collaboration with a view to transforming researches into commercial products and services.The article concludes that universities and indeed other institutions of higher learning engaged in teaching and research do not need to depend wholly on government for funding as is currently the case, rather collaboration with government Ministries, Departments, Agencies (MDAs) and industries is available and are necessary in transforming research outcomes into commercial products and services for the benefit of stakeholders under the applicable intellectual property laws and policies.In so doing, the universities would come closer to bridging the academia -industry gap.
Chinese NER with Softlexion and Residual Gated CNNs

The increment of accuracy and speed on Named Entity Recognition (NER), a key task in natural language processing, can further enhance downstream tasks.The method of residual gated convolution and attention mechanism is proposed to address the problem of insufficient recognition of nested entities and ambiguous entities by convolutional layers in the absence of context.It emphasizes local continuous features fusion to global ones to better obtain contextual semantic information in the stacked convolutional layer.Moreover, the optimized embedding layer with fusing character and lexical information by introducing a dictionary combines with a pre-trained BERT model containing a priori semantic effects, and the decoding layer in an entity-level method to alleviate the problem of nested entities and ambiguous entities in long-sequence text.In order to reduce abundant parameters of Bert model, during the training process, only the residual gated convolutional layer is iterated after fixing Bert layer parameters.After experiments on MSRA corpus, the result of entity recognition task in BERT-softlexion-RGCNN-GP model outperforms other models, with an F1 value of 94.96%, and the training speed is also better than that of the bidirectional LSTM model.Our model not only maintains a more efficient training speed but also recognizes Chinese entities more precisely, which is of practical value for fields required accuracy and speed.
Semi-Decentralized Control of Multi-Robot System for Autonomous Navigation via Multi-Agent Reinforcement Learning

Abstract We consider the navigation problem of multiple mobile robots in a continuous space populated with obstacles. Conventionally, this problem is formulated as multi-robot path planning (MRPP) to generate collision-free paths for all robots. However, MRPP is computationally intractable owing to the involvement of multiple robots in the continuous space resulting in infinite states and actions. Also, the paths from MRPP algorithms need to consider kinematic constraints and dynamics of the robots, further complicating the planning problem. We propose an alternative approach to MRPP for improved run-time efficiency through multi-agent reinforcement learning (MARL) that learns to control the robots directly rather than computing the paths. Our MARL agent receives the information about the environment and generates control inputs to the wheels of the robots. Trained in a simulated environment with three robots and obstacles, each robot gathers the episode trajectories, consisting of observations, actions, rewards through exploring the environment where each robot perceives each other as dynamic obstacles. These trajectories train a homogeneous semi-decentralized policy that incorporates observations of the two nearest robots. The policy can be deployed to more robots, as each only requires the information about nearby robots. Without path computation or kinodynamic constraints, our method efficiently navigates a robot team in an end-to-end manner. Extensive experiments demonstrate its ability to generalize across environments with varying goal locations and obstacle layouts. Also, our method has shown to be capable of navigating more robots without additional training.
Corrigendum for “Research on robotic manipulator fault detection and diagnosis technology based on machine vision in complex environments”

In the published article, the affiliation for the author “Lili Qu” is appearing as “School of Automation, South China University of Technology Guangzhou College, Guangzhou, China” which should be read as “School of Mechatronic Engineering and Automation, Foshan University, Foshan 528231, China.” The authors regret this error.
The Grot Readings as the scientific resource of Samara Universal Scientific Library

The author reviews the All-Russia Scientific Conference “The Seventh Grot Readings” held at Samara Regional Universal Scientific Library on November 17–18, 2022. Researchers of Ac. S. P. Korolev Samara National Research University acted as co-organizers and enthusiastic participants of the conference. The author discusses in brief the history of the Grot Readings and reviews the key topics. Since the first conference in 2008, it has become the significant event for Samara Region. It is to promote historical, local, philological, library and bibliographical studies and to maintain scientific communication within the country. In 2022, researchers and specialists from 22 Russian regions attended the conference. Three Sections of the Seventh Grot Readings are discussed, namely: History of Russia in 19-th–21-st centuries, History and Contemporaneity in Literature, and Book Culture, along with the key topics of the papers presented at each of the sections. The author analyzes the development of Grot Readings as a scientific research site and its potential for Samara Regional Universal Scientific Library and Samara Region, on the whole.
Encryption face area in color images using Chaotic Maps

This paper introduces a region-based selective image encryption technique using a chaoticapproach. The aim is to address the growing need for secure face image transmission and storage in variousapplications where image information requires protection from unauthorized access. Existing imageencryption schemes often rely on generating randomness in the image to hide the content, but they can betime-consuming during the encryption and decryption processes. Therefore, an efficient algorithm is crucialto ensure practicality and effectiveness. To tackle this challenge, the proposed technique focuses onselectively encrypting and reconstructing facial regions in images. By identifying the importance of faceimages in applications where security is critical, the proposed method offers a targeted approach toencryption. This selective encryption allows for improved efficiency in both encryption and decryptionprocesses, reducing computational complexity and enhancing overall performance. The experimentsdemonstrate the successful encryption and reconstruction of face images, highlighting the preservation offacial details and the robustness of the encryption against attacks. These results confirm the suitability ofthe proposed technique for applications that require secure face image transmission and storage.
Development of the ATM Method (Observe, Imitate, and Modify) in Audio Visual Integrated Poetry Reading Skills

This study aims to analyze needs, determine the design and test the validity of teaching materials using the ATM method in audio-visual integrated poetry reading skills in class IV SDN 069 Limbong. The research was developed using the Research and Development (R&amp;D) method and refers to the ADDIE model, which consists of five stages. However, this study was only carried out in up to 3 stages: analysis, design and development. This research was conducted at SDN 069 Limbong, located in Limbong Village, Rongkong District, North Luwu Regency. The instruments used were teacher interviews and student questionnaires. The analysis using teacher interviews shows that the teacher only uses the lecture method in the learning process, so there is no student interest in learning. The results of the student questionnaire show that students prefer teaching materials and media such as videos. The ATM method is structured and designed considering basic competencies and learning objectives. Teaching materials for the integrated audio-visual ATM method were assessed by three experts, including language and material experts, who obtained a percentage of 92.5% with a very valid category, design experts obtained a rate of 95% with a very valid category, and application experts obtained 93.7% with a very valid category.
THE ROLE OF MOODLE IN EDUCATION: A BIBLIOMETRIC REVIEW

Moodle is a trendy digital learning platform in education widely used to create online courses. Moodle's role in education is crucial because it provides many benefits, including facilitating distance learning, increasing student participation, increasing teaching efficiency, enabling easy assessment, enabling personalized teaching, and so on. There have been many studies on Moodle in the form of articles from year to year. Therefore, in this research, a bibliometric study will be carried out on the roles of Moodle in the world of education, mapping research results, looking at research trends, and looking for possible gaps that can still be studied more deeply. The method used in this research is a Systematic Literature Review using bibliometric analysis with the help of VoS Viewer. The study results show that Moodle is widely used to benefit learning in various fields, especially STEM. Moodle is generally used to digitize learning, including, in this case, the assessment and gamification process in learning.
A Study on Systematic Investment Plan

This is an attempt to know how the theories can be applied to practical situations.In the first part of the project report, the company's general information has been collected.Information is gathered through the primary and secondary sources as well.The second part of the report contains the specialized subject study.The project's objective is to work on various marketing strategies to promote the fitness training courses of the company.Goals indicate what a business unit wants to achieve; Strategy is an action plan for achieving the goals.Every business must design a strategy for achieving its goals, consisting of a marketing strategy, a compatible technology strategy, and a sourcing strategy.Marketing strategy is a process that can allow an organization to concentrate its limited resources on the most significant opportunities to increase sales and achieve sustainable competitive advantage.A marketing strategy should focus on the fundamental concept that customer satisfaction is the primary goal
Training shallow ReLU networks on noisy data using hinge loss: when do we overfit and is it benign?

We study benign overfitting in two-layer ReLU networks trained using gradient descent and hinge loss on noisy data for binary classification. In particular, we consider linearly separable data for which a relatively small proportion of labels are corrupted or flipped. We identify conditions on the margin of the clean data that give rise to three distinct training outcomes: benign overfitting, in which zero loss is achieved and with high probability test data is classified correctly; overfitting, in which zero loss is achieved but test data is misclassified with probability lower bounded by a constant; and non-overfitting, in which clean points, but not corrupt points, achieve zero loss and again with high probability test data is classified correctly. Our analysis provides a fine-grained description of the dynamics of neurons throughout training and reveals two distinct phases: in the first phase clean points achieve close to zero loss, in the second phase clean points oscillate on the boundary of zero loss while corrupt points either converge towards zero loss or are eventually zeroed by the network. We prove these results using a combinatorial approach that involves bounding the number of clean versus corrupt updates across these phases of training.
Towards Better Certified Segmentation via Diffusion Models

The robustness of image segmentation has been an important research topic in the past few years as segmentation models have reached production-level accuracy. However, like classification models, segmentation models can be vulnerable to adversarial perturbations, which hinders their use in critical-decision systems like healthcare or autonomous driving. Recently, randomized smoothing has been proposed to certify segmentation predictions by adding Gaussian noise to the input to obtain theoretical guarantees. However, this method exhibits a trade-off between the amount of added noise and the level of certification achieved. In this paper, we address the problem of certifying segmentation prediction using a combination of randomized smoothing and diffusion models. Our experiments show that combining randomized smoothing and diffusion models significantly improves certified robustness, with results indicating a mean improvement of 21 points in accuracy compared to previous state-of-the-art methods on Pascal-Context and Cityscapes public datasets. Our method is independent of the selected segmentation model and does not need any additional specialized training procedure.
LARGE LANGUAGE MODELS FOR CIPHERS

This study investigates whether transformer models like ChatGPT (GPT4, MAR2023) can generalize beyond their training data by examining their performance on the novel Cipher Dataset, which scrambles token order. The dataset consists of 654 test cases, and the analysis focuses on 51 text examples and 13 algorithmic choices. Results show that the models perform well on low-difficulty ciphers like Caesar and can unscramble tokens in 77% of the cipher examples. Despite their reliance on training data, the model's ability to generalize outside of token order is surprising, especially when leveraging large-scale models with hundreds of billions of weights and a comprehensive text corpus with few examples. The original contributions of the work focus on presenting a cipher challenge dataset and then scoring historically significant ciphers for large language models to descramble. The real challenge for these generational models lies in executing the complex algorithmic steps on new cipher inputs, potentially as a novel reasoning challenge that relies less on knowledge acquisition and more on trial-and-error or out-ofbounds responses.
PROCEDURAL GENERATION IN 2D METROIDVANIA GAME WITH ANSWER SET PROGRAMMING AND PERLIN NOISE

Designing metroidvania games often poses a unique challenge to video game developers, namely the difficulty of consistently preventing soft-locking, which hinders or blocks the player’s ability to traverse through levels effectively [1]. As a result, many turn to hand-making all levels to ensure the level’s traversability, but in the process often forsaking the ability to rely on procedural generation to lessen the time and burden on human game developers [2]. On the other hand, when developers rely on popular ways of procedural generation such as using perlin noise, they find themselves unable to control those procedural algorithms to guarantee certain characteristics of the outputs such as traversability [3]. Our paper aims to present a procedural solution that can also effectively guarantee the traversability of the generated level. Our method uses Answer Set Programming (ASP) to verify generation based on restrictions we place, guaranteeing the outcome to be what we want [4]. The generation of a level is divided into rooms, which are first mapped out in a graph to ensure traversability from a starting room to an ending boss area. The rooms’ geometry is then generated accordingly to create the full level. Using perlin noise, we were also able to create a demonstration of how traversability works in another form of procedural generation, and compare it with our methodology to identify strengths and weaknesses. To demonstrate our method, we applied our solution as well as the perlin noise algorithm to a 2D metroidvania game made in the Unity game engine and conducted quantitative tests on the ASP method to assess how well our method works as a level generator [5].