Reviewerly/exHarmony huggingface dataset:
Trace Element Assessment of Eleyele up Stream Lake, Ibadan, Southwestern Nigeria

This research work was carried out to assess the concentrations of selected trace elements inEleyele upstream lake, Ibadan, South-Western, Nigeria. Nine water samples and eight sediment samples were analyzed for their physiochemical and trace element properties to quantify pollution of the lake by trace elements employing geochemical analytic tools such as ICP-MS, XRD, and XRF. The chemical index of alteration (C.I.A.) shows an average value of 75.21, 89.64, and 82.39, respectively, which shows zero to low affinity or similarities between C.I.A and the trace elements. The Chemical Index of weathering (CIW) value shows that the trace elements sources are related to the basement rock's chemical weathering. Plagioclase Index of Alteration (P.I.A.) shows that there are low to no similarities indicating the source of the selected trace element is not clay. The trace elements analyzed are As, Ca, Cd, Co, Ce, Cu, Ga, K, Li, Mo, Na, Ni, Sb and Zr. As, Ca, K, Na with a constant concentration of 0.5µg/l, 0.02µg/l, 0.1µg/l, and 0.2µg/l, respectively, Cd, Co, Ce, Cu, Ga, Li, Mo, Ni, Sb, and Zr have an average concentration value of 0.04µg/l, 0.67µg/l, 0.05µg/l, 1.01µg/l, 0.06µg/l, 0.72µg/l, 0.14µg/l, 0.79µg/l, 0.05µg/l and 0.03µg/l, respectively. The result analyses show that Cu has the highest concentration in the water and the trace element ranges in abundant from Cu&gt; Ni&gt; Li&gt; As&gt; Co&gt; Mo&gt; Ga&gt; Cd&gt; Sb&gt; Cs&gt; Zr&gt; Ca&gt; Na&gt; K. Constant work is recommended for Eleyele Lake to protect the lake from an increase in the level of contamination.
Correction: Vermeesch, P. Exploratory Analysis of Provenance Data Using R and the Provenance Package. Minerals 2019, 9, 193

There was an error in the original article [...]
IDA: Informed Domain Adaptive Semantic Segmentation

Mixup-based data augmentation has been validated to be a critical stage in the self-training framework for unsupervised domain adaptive semantic segmentation (UDA-SS), which aims to transfer knowledge from a well-annotated (source) domain to an unlabeled (target) domain. Existing self-training methods usually adopt the popular region-based mixup techniques with a random sampling strategy, which unfortunately ignores the dynamic evolution of different semantics across various domains as training proceeds. To improve the UDA-SS performance, we propose an Informed Domain Adaptation (IDA) model, a self-training framework that mixes the data based on class-level segmentation performance, which aims to emphasize small-region semantics during mixup. In our IDA model, the class-level performance is tracked by an expected confidence score (ECS). We then use a dynamic schedule to determine the mixing ratio for data in different domains. Extensive experimental results reveal that our proposed method is able to outperform the state-of-the-art UDA-SS method by a margin of 1.1 mIoU in the adaptation of GTA-V to Cityscapes and of 0.9 mIoU in the adaptation of SYNTHIA to Cityscapes.
Virtual Guidance as a Mid-level Representation for Navigation

In the context of autonomous navigation, effectively conveying abstract navigational cues to agents in dynamic environments poses challenges, particularly when the navigation information is multimodal. To address this issue, the paper introduces a novel technique termed "Virtual Guidance," which is designed to visually represent non-visual instructional signals. These visual cues, rendered as colored paths or spheres, are overlaid onto the agent's camera view, serving as easily comprehensible navigational instructions. We evaluate our proposed method through experiments in both simulated and real-world settings. In the simulated environments, our virtual guidance outperforms baseline hybrid approaches in several metrics, including adherence to planned routes and obstacle avoidance. Furthermore, we extend the concept of virtual guidance to transform text-prompt-based instructions into a visually intuitive format for real-world experiments. Our results validate the adaptability of virtual guidance and its efficacy in enabling policy transfer from simulated scenarios to real-world ones.
Momentum-Based Learning of Nash Equilibria for LISA Pointing Acquisition

This paper addresses the pointing acquisition phase of the Laser Interferometer Space Antenna (LISA) mission as a guidance problem. It is formulated in a cooperative game setup, which solution is a sequence of corrections that can be used as a tracking reference to align all the spacecraft' laser beams simultaneously within the tolerances required for gravitational wave detection. We propose a model-free learning algorithm based on residual-feedback and momentum, for accelerated convergence to stable solutions, i.e. Nash Equilibria. Each spacecraft has 4 degrees of freedom, and the only measured output considered are laser misalignments with the local interferometer sensors. Simulation results demonstrate that the proposed strategy manages to achieve absolute misalignment errors $<1\mu$rad in a timely manner.
Huffman Algorithm Valuation Using Residue Number System

The Huffman algorithm is a widely used method for lossless data compression, which assigns variable-length codes to characters based on their frequency of occurrence in the input data. However, the traditional implementation of Huffman coding using binary arithmetic can be computationally intensive, particularly for large data sets. In recent years, the Residue Number System (RNS) has emerged as a promising alternative to binary arithmetic for certain types of computations, due to its potential for parallel processing and reduced hardware complexity. This paper evaluates the use of RNS as a basis for implementing the Huffman algorithm, comparing its performance with the traditional binary approach. The results demonstrate that RNS-based Huffman coding can achieve comparable or superior compression ratios, while reducing the computational requirements and potentially enabling faster compression and decompression. The study also highlights the importance of choosing appropriate RNS moduli and operands to optimize performance. Overall, the evaluation suggests that RNS can be a viable and efficient alternative to binary arithmetic for implementing the Huffman algorithm, particularly in applications with high computational demands or limited hardware resources. However, further research is needed to explore the potential benefits and limitations of RNS in other areas of data compression and signal processing.
Robustness, Evaluation and Adaptation of Machine Learning Models in the Wild

Our goal is to improve reliability of Machine Learning (ML) systems deployed in the wild. ML models perform exceedingly well when test examples are similar to train examples. However, real-world applications are required to perform on any distribution of test examples. Current ML systems can fail silently on test examples with distribution shifts. In order to improve reliability of ML models due to covariate or domain shift, we propose algorithms that enable models to: (a) generalize to a larger family of test distributions, (b) evaluate accuracy under distribution shifts, (c) adapt to a target distribution. We study causes of impaired robustness to domain shifts and present algorithms for training domain robust models. A key source of model brittleness is due to domain overfitting, which our new training algorithms suppress and instead encourage domain-general hypotheses. While we improve robustness over standard training methods for certain problem settings, performance of ML systems can still vary drastically with domain shifts. It is crucial for developers and stakeholders to understand model vulnerabilities and operational ranges of input, which could be assessed on the fly during the deployment, albeit at a great cost. Instead, we advocate for proactively estimating accuracy surfaces over any combination of prespecified and interpretable domain shifts for performance forecasting. We present a label-efficient estimation to address estimation over a combinatorial space of domain shifts. Further, when a model's performance on a target domain is found to be poor, traditional approaches adapt the model using the target domain's resources. Standard adaptation methods assume access to sufficient labeled resources, which may be impractical for deployed models. We initiate a study of lightweight adaptation techniques with only unlabeled data resources with a focus on language applications.
A Captcha-Based Graphical Password with Strong Password Space and Usability Study

stract: Security for authentication is required to give a superlative secure users’ personal information. This paper presents a model of the Graphical password scheme under the impact of security and ease of use for user authentication. We integrate the concept of recognition with re-called and cued- recall based schemes to offer superior security compared to existing schemes. Click Symbols (CS) Alphabet combine into one entity: Alphanumeric (A) and Visual (V) symbols (CS-AV) is Captcha-based password scheme, we integrate it with recall- based n×n grid points, where a user can draw the shape or pattern by the intersection of the grid points as a way to enter a graphical password. Next scheme, the combination of CS-AV with grid cells allows very large password space (2.4 × 104 bits of entropy) and provides reasonable usability results by determining an empirical study of memorable password space. Proposed schemes support most applicable platform for input devices and promising strong resistance to shoulder surfing attacks on a mobile device which can be occurred during unlocking (pattern) the smartphone
Addressing Bias in News with Advanced Machine Learning Techniques

The pervasive nature of bias in news is a complex problem that has proven difficult to address. Traditional methods for identifying and mitigating bias have relied on human editors and fact-checkers, which are time-consuming and subject to their own biases. In contrast, machine learning algorithms can process vast amounts of data quickly and objectively, making them a promising solution for tackling this issue. Our proposed framework leverages several advanced machine learning techniques to detect and correct bias in news articles. Firstly, we preprocess the news articles using natural language processing techniques, such as tokenization and lemmatization, to transform the raw text into a format that can be fed into a neural network. We then train the network using a combination of supervised and unsupervised learning methods, incorporating attention mechanisms to improve the model’s ability to identify relevant features in the text. Additionally, we use adversarial training to enhance the model’s robustness and ability to handle complex, realworld scenarios. To evaluate the effectiveness of our framework, we conduct extensive experiments on a large-scale dataset of news articles. Our results show that our approach outperforms traditional methods in detecting and correcting bias, achieving significant improvements in accuracy and precision. Furthermore, our framework can be customized to target specific types of bias, such as political or racial bias, by adjusting the training data and neural network architecture. In conclusion, our proposed framework demonstrates the potential of advanced machine learning techniques to address bias in news and promote more objective reporting. Future research in this area could explore the application of our framework to other domains, such as social media and online forums, where bias is also prevalent. Additionally, further investigation is needed to ensure that the use of machine learning for bias detection and correction is transparent, ethical, and equitable.
Gaze tracking with machine learning in interface design

The problem of human-computer interaction can be seen as two powerful information(human and computer) trying to communicate with each other through a narrowbandwidth and highly constrained interface. To cope with it, we are looking for faster, more natural andmore natural and more convenient means of exchanging information between users and computers. On the user side, it islimited by the nature of human communication organs and capabilities; the computer side is limited by theonly by the input/output devices and interaction techniques we can devise. Presenttechnology is stronger in the computer-user direction than the user-computer, which is whytoday's user-computer dialogues are rather one-way, with bandwidth from the computer to theuser is much greater than that from user to computer. Using eye movements as a link between userscomputer can help overcome this imbalance. This chapter describes the relevant characteristics of the human eye, eye-tracking technology, how to design interaction techniques that incorporate eye movements into the user-computer dialogue in a convenient andand the relationship between eye-movement interfaces and virtual environments.
Virtualized Resource Provisioning for Hybrid Cloud Computing Services Using Optimal Data Analytics

Abstract Resource Provisioning is the important method to offer cloud as services to the user. Nowadays cloud computing has multiple access and application levels. We need a hybrid and virtualized method to provide efficient resource provisioning method. In existing resources can be offered based on user request or availability. But current scenario we need any time access privilege based services offering systems. Also data analytics is another factor to analyse the data and make effective decision making approach. We propose an efficient virtualized dynamic resource provisioning method for cloud application and virtualized optimizer for topological cloud management. We prepare optimal data analytics method to predict the accuracy and performance of cloud access and resources. In this paper automated machine learning technique is applied to measure the temporal features and queuing results. In this case loads are distributed to server and optimize the cloud using service level agreement. Our research method can provide the result of time service analysis, accuracy index, performance factors and optimality value. Also our method can test over provisioning and energy consumption. We use CloudSim for simulating cloud environment and TensorFlow for testing accuracy using Deep belief network optimization.
Teaching English Methods Used in Sekolah Luar Biasa (School for Students with Special Needs) Negeri 2 Singaraja

The choice of teaching method will be largely determined by several factors, one of which is the characteristics of the student. Different characteristics of students require educators to always apply variations in teaching methods. Likewise when teaching students with special needs. Students with special needs can study in public schools (inclusion classes) and some attend special classes, for example in Extraordinary Schools (SLB). This study aims to determine 1) the teaching methods used to teach English for junior and senior high school students at SLB Negeri 2 Singaraja; 2) problems encountered by teachers in teaching English to SP and SMA students at SLB Negeri 2 Singaraja. Data collection was carried out by means of observation, interviews and literature studies (RPP). The data were qualitatively analyzed using the Miles and Huberman model. The output of this research is not only in the form of articles and research reports but also in the form of donations of ideas or ideas related to teaching methods that can be used in teaching English to students with special needs at the junior and senior high school levels. The results showed that 1) the teaching method most often used by English teachers at both the junior and senior high school levels at Sekolah Luar Biasa Negeri 2 Singaraja is a direct method, especially the drilling method; 2) The challenges that teachers often encounter in teaching English are limited focus and memory of students on the subject matter, limitations in receiving and disclosing information and planned materials often not in accordance with student needs when implementing learning.
Low Power FPGA Implementation of an Efficient AES- SBOX Realization for Health Care Applications

Abstract In present digital world, the data transmitted over network has become worldwide. The information compiled, processed and communicated in Bits and bytes of digital format over communication channels. For secure communication an advanced encryption Standard (AES) cryptographic algorithm is employed to avoid the risk of secret information tampering. This Research article predominantly focuses on low power and low area hardware architecture designed using Field Programmable Gate array. The novel architecture is derived by employing composite field arithmetic for substitute byte and inverse substitute bytes operation. By this approach the unwanted delay occurs during the Look up table (LUT) method has been averted. Optimized Positive polarity reed Muller (OPPRM) architecture is introduced in order to minimize the hardware utilization, eventually subpipeling technique has brought in, to enhance the speed of the architecture. The Performance metrics of OPPRM-AES is evaluated with respect to Bonded IPADS, Slice, LUTS, Number of slices, Static as well as Dynamic Power. The performance of a proposed architecture is compared with AES-PPRM, AES-CFA and AES-ECG.The architecture is implemented onto Zynq ZC702 Evaluation Board and the simulation is performed through the vivado 2019.1 software.
Classification of Large Social Twitter Network Data Using R

The development of social networks has altered computer science research. Now, a vast amount of data is available via Twitter, Facebook, emails, and IoT. (Internet of Things). So, storing and analyzing these data has become very difficult for academics. Conventional frameworks have been ineffective in processing massive amounts of data. R is an open-source programming language designed for large-scale data analysis with higher accuracy.&#x0D; Additionally, it offers the chance to implement the R programming language. This essay examines the application of R to classify sizable social network data. The Naive Bayes method is used to categorize massive amounts of Twitter data. The experiment has demonstrated that a sizable portion of data may be adequately classified with positive outcomes utilizing the R framework.
Weakly Supervised Realtime Dynamic Background Subtraction

Background subtraction is a fundamental task in computer vision with numerous real-world applications, ranging from object tracking to video surveillance. Dynamic backgrounds poses a significant challenge here. Supervised deep learning-based techniques are currently considered state-of-the-art for this task. However, these methods require pixel-wise ground-truth labels, which can be time-consuming and expensive. In this work, we propose a weakly supervised framework that can perform background subtraction without requiring per-pixel ground-truth labels. Our framework is trained on a moving object-free sequence of images and comprises two networks. The first network is an autoencoder that generates background images and prepares dynamic background images for training the second network. The dynamic background images are obtained by thresholding the background-subtracted images. The second network is a U-Net that uses the same object-free video for training and the dynamic background images as pixel-wise ground-truth labels. During the test phase, the input images are processed by the autoencoder and U-Net, which generate background and dynamic background images, respectively. The dynamic background image helps remove dynamic motion from the background-subtracted image, enabling us to obtain a foreground image that is free of dynamic artifacts. To demonstrate the effectiveness of our method, we conducted experiments on selected categories of the CDnet 2014 dataset and the I2R dataset. Our method outperformed all top-ranked unsupervised methods. We also achieved better results than one of the two existing weakly supervised methods, and our performance was similar to the other. Our proposed method is online, real-time, efficient, and requires minimal frame-level annotation, making it suitable for a wide range of real-world applications.
A Study On Detection Of Counterfeit Money By Image Processing

This system, known as the Currency Recognition System, is utilized all over the globe for the purpose of detecting currency fraud. One can find applications for this framework in financial frameworks, cash-monitoring devices, cash-trade frameworks, and so on. In today's technologically sophisticated world, this framework is crucial, and it ought to be simple to obtain. In this essay, we suggest a method for automatically identifying paper money using a programed we've created. Simple, reliable, and effective describe the method that was used. The methodology is divided into several sections, such as those for preparing the images, extracting the characteristics, and comparing the prepared and test pictures. The output includes the currency's denomination, whether it is a genuine note or a fake, and a conversion of the currency into the currency of another country. User-friendly and well-designed mobile software was produced. A challenging dataset with various constraints was used to determine the performance of the proposed system.
Pengaruh Pencampuran Etilen Glikol dan Propilen Glikol Terhadap Sifat Optik Kawat Nano Perak yang Disintesis Menggunakan Metode Polyol

Research has been carried out aimed for looking effect of mixing Ethylene Glycol (EG) and propylene glycol (PG) on optical characteristics of silver nano wire (AgNWs) which synthesized using the polyol method. Synthesis was carried out for ± 5 hours on temperature of 130 oC inside erlenmeyer flask. The material used are silver (Ag) which is obtained from AgNO3, mixed solution of EG/PG with a concentration ratio of 100:0%; 75:25%; 50:50%; 25:75%; and 0:100%, FeCl3.6H2O, and capping agent in the form of Polyvynil Phyrolidone (PVP). The optical characteristics of AgNWs were observed through UV-Vis, FTIR, and length distribution through optical microscopy. The results of UV-Vis analysis showed four absorption peak at a wavelength of around 340, 350, 360 and at a wavelength of 390 nm as well as a change in the absorbance value. While the optical microscope test results also show that the length of the AgNWs increased on sample EG/PG 75:25% and 50:50%. Based on these results, it can be concluded that the mixing of EG and PG can affect the optical characteristics of AgNWs.
Impact of Cloud Services on Performance of Information Management of Small Enterprises. A Review of the Literature

Purpose: The aim of the study is to examine the impact of cloud technology on performance of information Management in Small Enterprises.&#x0D; Methodology: This study adopted a desktop literature review. It utilized content analysis of the reviewed literature related to cloud computing in in small enterprises.&#x0D; Findings: The study revealed that the adoption of cloud computing contributes to improved performance of SME’s. The small enterprises have been able to serve their customers better and have reduced operational costs and increased profits. Cloud computing also helps organizations and SME’S to offer decentralized services and give flexibility to staff as they can operate wherever they are as long as they have internet connection.&#x0D; Unique Contribution to Theory, Practice and Policy: The study recommended that issues associated with connectivity and speed of access should be addressed by government bodies concerned with internet service provision regulation. The service providers should ensure that the systems are user friendly easy to use to enable better adoption access across the rest of the SME’s so that the effect of cloud service can be realized.