Reviewerly/exHarmony huggingface dataset:
Detecting anomalous sensor readings in wireless networks for remote area

In recent years, the use of wireless sensor networks has become increasingly popular in remote areas for various applications, including environmental monitoring and surveillance. However, wireless sensor networks are often vulnerable to anomalous sensor readings, which can be caused by various factors, such as hardware malfunctions, environmental changes, and interference from other sources. Anomalous sensor readings can lead to false alarms, reduced data quality, and decreased network reliability. To address this challenge, this paper presents a study of anomalous sensor reading detection in wireless networks for remote areas. Our approach is based on machine learning algorithms and involves the use of a clustering algorithm to identify normal patterns in the sensor readings, and a classifier to detect readings that deviate from these patterns. We evaluate the performance of our approach using a dataset of real-world sensor readings and compare it to several benchmark methods. The results of our study demonstrate that our approach outperforms the benchmark methods in terms of accuracy and robustness, and that it is capable of effectively detecting anomalous sensor readings. Furthermore, our approach has the advantage of being scalable and easily adaptable to different types of sensor readings and applications. In conclusion, our approach provides a promising solution for the detection of anomalous sensor readings in wireless networks for remote areas. The results of this study have the potential to improve the reliability and performance of wireless sensor networks and support the development of more effective and efficient monitoring systems for remote areas. We hope that this work will inspire further research in the field and contribute to the development of advanced techniques for anomalous sensor reading detection.
Effect of the net radiation proxies on maize and soya evapotranspiration estimation using machine learning methods.

Accurate evapotranspiration (ET) estimation is essential for water management in crops, but it is not an easy task. Empirical ET methodologies require precise net radiation (Rn) measurements to obtain accurate results. Nevertheless, Rn measurements are not easy to obtain from meteorological stations. Thus, this study explored the use of machine learning algorithms with two Rn substitutes, to estimate daily ET: the extraterrestrial solar radiation (Ra) and a modelled Rn (RnM). Support Vector Machine (SVM), Kernel Ridge (KR), Decision Tree (DT), Adaptive Boosting (AB), and Multilayer Perceptron (MLP) were applied to model FLUXNET Rn and ET observations. Adaptive Boosting produced the best field Rn measurements (RnO), yielding a Root Mean Square Error of about 16 % of the mean observed Rn. The resulting Rn (AB RnM) was used to model daily crops ET employing the above-mentioned machine learning methods with RnO, AB RnM, and Ra, in conjunction with meteorological variables and the NDVI index. The evaluated methods were suitable to estimate ET, yielding similar errors to those obtained with RnO, when contrasted with ET observations. These results demonstrate that AB and KR are applicable with rutinary meteorological and satellite data to estimate ET.
Hardware-agnostic Computation for Large-scale Knowledge Graph Embeddings

Knowledge graph embedding research has mainly focused on learning continuous representations of knowledge graphs towards the link prediction problem. Recently developed frameworks can be effectively applied in research related applications. Yet, these frameworks do not fulfill many requirements of real-world applications. As the size of the knowledge graph grows, moving computation from a commodity computer to a cluster of computers in these frameworks becomes more challenging. Finding suitable hyperparameter settings w.r.t. time and computational budgets are left to practitioners. In addition, the continual learning aspect in knowledge graph embedding frameworks is often ignored, although continual learning plays an important role in many real-world (deep) learning-driven applications. Arguably, these limitations explain the lack of publicly available knowledge graph embedding models for large knowledge graphs. We developed a framework based on the frameworks DASK, Pytorch Lightning and Hugging Face to compute embeddings for large-scale knowledge graphs in a hardware-agnostic manner, which is able to address real-world challenges pertaining to the scale of real application. We provide an open-source version of our framework along with a hub of pre-trained models having more than 11.4 B parameters.
Towards Deployable RL -- What's Broken with RL Research and a Potential Fix

Reinforcement learning (RL) has demonstrated great potential, but is currently full of overhyping and pipe dreams. We point to some difficulties with current research which we feel are endemic to the direction taken by the community. To us, the current direction is not likely to lead to "deployable" RL: RL that works in practice and can work in practical situations yet still is economically viable. We also propose a potential fix to some of the difficulties of the field.
Simulation Research of Binocular Machine Vision System

This paper proposes a binocular vision system. It adopts humanoid design, has four degrees of freedom, and realizes complete synchronization in the horizontal direction at the same time, expounding the application of binocular stereo vision principle and control theory in visual synchronization. The control system uses voltage as an input signal to control the angular velocity of two identical motors in a parallel circuit. Establish the transfer function model of the motor and the four-degree-of-freedom parallel human-like binocular vision mechanism system respectively, and then use SIMULINK to simulate, evaluate the simulation results, analyze the parameters of the curve, and give the optimization path to optimize the response curve.
Heterogeneous Face Recognition By Jointing Fine and Coarse Granularity Level

Heterogeneous Face Recognition (HFR) is a challenging task due to large modality discrepancy as well as insufficient training images in certain modalities. This modality difference affects the performance of the HFR task. Existing approach methods have mostly trained with the sample-based triplet-based loss and identification loss from the fine granularity level, and ignored the coarse granularity level. In this paper, we proposed a dual-granularity quadruplet loss function, which can lead to the model output with a larger inter-class variation and a smaller intra-class variation compared to the triplet loss in a hierarchical fine to coarse granularity manner. The dual-granularity quadruplet loss function can mine the information of both fine granularity and coarse granularity levels to enhance the performance of recognition. Extensive experiments on CUFSF and CASIA NIR-VIS 2.0 show that our approach outperforms other state-of-the-art methods significantly.
Complementary Segmentation of Primary Video Objects with Reversible Flows

Segmenting primary objects in a video is an important yet challenging problem in intelligent video surveillance, as it exhibits various levels of foreground/background ambiguities. To reduce such ambiguities, we propose a novel formulation via exploiting foreground and background context as well as their complementary constraint. Under this formulation, a unified objective function is further defined to encode each cue. For implementation, we design a complementary segmentation network (CSNet) with two separate branches, which can simultaneously encode the foreground and background information along with joint spatial constraints. The CSNet is trained on massive images with manually annotated salient objects in an end-to-end manner. By applying CSNet on each video frame, the spatial foreground and background maps can be initialized. To enforce temporal consistency effectively and efficiently, we divide each frame into superpixels and construct a neighborhood reversible flow that reflects the most reliable temporal correspondences between superpixels in far-away frames. With such a flow, the initialized foregroundness and backgroundness can be propagated along the temporal dimension so that primary video objects gradually pop out and distractors are well suppressed. Extensive experimental results on three video datasets show that the proposed approach achieves impressive performance in comparisons with 22 state-of-the-art models.
Image Stitching Based on Feature Optimization of Bayesian Probability Model

The goal of image stitching is to create a natural mosaic without ghosts, local distortions and artifacts. Towards this end, this paper presents a local feature optimization method based on Bayesian probability model for the image stitching. The spatially-varying algorithm is employed to perfectly align the entire overlapping area of images. A local feature optimization Bayesian probability model is then proposed to remove incorrect matching points in the images aiming to obtain a more accurate local homography estimation for robust alignment. Moreover, the strategy of combining local warping and similarity transformation is applied to reduce the distortion of non-overlapping regions. The salient features of the proposed method are that it is highly compatible with other image transformations and can effectively solve the inevitable ghosting problem of image stitching. Finally, experiments are conducted to demonstrate the effectiveness of the proposed algorithm.
A 4-D HyperChaotic DNA Encryption/Decryption Algorithm for Securing Students Data System

Data security has become a significant issue nowadays with the increase of information capacity and its transmission rate.The most common and widely used techniques in the data security fields is cryptography.Cryptography is the process of concealing and transmitting data in an appropriate format, so that only authorized people can access and process it.The main goal of the cryptographic process is protecting data from being hijacked and altered.This paper proposes an algorithm for encrypting data through the use of Deoxyribo Nucleic Acid (DNA) sequence and four-dimensional hyper chaotic system.Whereby, the hyper chaotic system is applied to generate a binary sequence which is later passed to a permutation function for the key generation of the first level encryption.The proposed encryption algorithm includes several intermediate steps, which are binary-coded form and the generation of arbitrary keys.Experimental results were analyzed by calculating encryption time, key generation time, histogram and correlation coefficient entropy.Furthermore, the proposed text encryption algorithm is implemented on two different students" datasets to improve the security of educational systems.Finally, experimental and comparative studies have shown that, the proposed encryption algorithm reported a uniform encrypted text distribution and correlation coefficient values nearer to "0", which are close to the theoretical optimal value.
Software Resources for Developing Multicriteria Applications: A Review, Part 1: Introduction and types of software resources developed.

In the last years, some Software Resources for Developing Multicriteria Applications (SRDMA) have been developed. The purpose of this review is to determine the state of the art on SRDMA to obtain a catalog of what has been developed which include libraries, frameworks, webservices and others. For this, it was performed a web search of research published in the last 10 years. We have published this review in three different parts and presented the on three different congresses of Mol2Net series according to the topic. The publications and software found were classified into 3 different categories. Part 1: types of software resources developed (NIECXSM), Part 2: multicriteria methods implemented (USEDAT-08) and operating systems supported (CATCHTOHIT-03). To analyze them we carry out a Contextual and a Formal Concept Analysis. Findings suggest that the type of software resources most developed was frameworks and the most used multicriteria methods were AHP and TOPSIS. The most supported operating systems were Windows, Linux and Mac. Thus, we consider that future research should contemplate the development of more generic SRDMA that implement a greater variety of multicriteria methods than those used up to now, which are available to developers through flexible licenses that allow them to be easily integrated into new application developments.
Adaptive Social Acceleration Constant Based Particle Swarm Optimization

In this paper, an attempt has been made to develop an Adaptive Social Acceleration Constant based PSO (ASACPSO).ASACPSO converge faster in comparison to basic PSO.The best value has been selected based on the minimum number of kounts required to minimize the function.Adaptive Social Acceleration Constant based PSO (ASACPSO) has been developed using the best value of adaptive social acceleration constant.The Adaptive Social Acceleration Constant has been searched using three formulations which led to the development of three algorithms-ALDPSO, AELDPSO-I and AELDPSO-II.All three were implemented on Rosenbrock function to get the best value of adaptive social acceleration constant.Similarly it has been implemented on seven mathematical benchmark functions and its performance has been compared to Basic Particle Swarm Optimization (BPSO).ASACPSO was observed to converge faster and give better accuracy.Results show that Kounts required for convergence of mathematical function is lesser for ASACPSO in comparison to basic PSO.ASACPSO reduces the computational time to optimize the function.
Augmented Reality and its Science

Massive innovation enhancements all through the world have ignited wild rivalry among organizations, with each endeavoring to draw in clients utilizing an alternate methodology.Augmented Reality is a relatively new approach (AR).AR is a relatively latest technology which can provide better possibilities than other inventions to match.Conduct of genuine items under specific circumstances, which is especially pertinent to this work.Unfortunately, developing realistic 3D material is difficult in and of itself and necessitates a lot of human labor.Data analysis requires the use of scientific visualization.Augmented Reality (AR), on the other hand, is a relatively new trend that includes the overlay of computer visuals on the actual environment.In this review, we utilize both of these tools to analyze logical information.As a result, we may use graphical representations derived from numerical statistics to enrich reality.A simulated system can likewise be put in an authentic climate to get further knowledge into a peculiarity.essential.This paper reviews research work done in Augmented Reality including the history and technologies that enable augmented reality like the devices and various means of merging real world with virtual world like tracking types.The review gives detailed explanation of various applications where AR is currently being used and discusses future significance of AR where it will be necessary for AR-technology to co-exist with mankind.AR technology is constantly improving and upgrading makes it exponentially more valuable for businesses to adopt AR tech for better profitability.
Development E-commerce Information System of Agriculture in Samarinda

Samarinda village is a village that is predominantly working as a farmer and has a wide range of agricultural products, in addition to the abundance of agricultural products there is a problem of marketing of agricultural products that do not have access to sell their agricultural products.Authors conducted research in order to increase sales and expand marketing in the Village Samarinda through sales system-based Business to the Business and method development using the Research and Development.The results obtained in the form of a web site that can be accessed to serve online sales transaction so that it can increase sales in the village Samarinda.
ANALISA SENTIMEN REVIEW PRODUK HANDPHONE PADA SITUS AMAZON MENGGUNAKAN PENDEKATAN LEXICON BERDASARKAN SENTIWORDNET

Sentiment analysis is part of opinion mining, is the process of understanding, extracting and processing textual data automatically to obtain information. It is done to find out the attitude of a speaker or writer later to be classified into positive or negative sentiment groups. In this study, the determination of the word value in the document is determined using Lexicon Sentiwordnet as a benchmark for word value, the review document will go through several pre-processing stages including Delete URLs, Remove Punctuation, Casefolding, Delete Stopwords, POS Tagging, Sentence tokens, and tokens. word so that the review document data are structured. Furthermore, the Sentiwordnet Interpretation calculation is carried out to determine the value of a term or word in the document to determine whether the word is a positive or negative word based on its category in the POS Tagging process. Then do the calculation of the term score summation, calculate the value of the sentence, calculate the text score, and the results of the rating system that will generate a rating to determine the quality of a mobile phone product based on user reviews.
Impact Analysis of a Virtual Stadium: Measuring Sport in the Metaverse

As sports teams enter the metaverse through launching virtual versions of their home stadiums, there is a pressing need to reimagine the stadium experience and how virtual stadiums differ from their physical counterparts. This review establishes a foundation for crucial conversations that can shape future research and managerial practice amidst an emerging trend in the sport industry.
On the care and feeding of virtual assistants: Automating conversation review with AI

Abstract With the rise of intelligent virtual assistants (IVAs), there is a necessary rise in human effort to identify conversations containing misunderstood user inputs. These conversations uncover error in natural language understanding and help prioritize improvements to the IVA. As human analysis is time consuming and expensive, prioritizing the conversations where misunderstanding has likely occurred reduces costs and speeds IVA improvement. In addition, less conversations reviewed by humans mean less user data are exposed, increasing privacy. We describe Trace AI, a scalable system for automated conversation review based on the detection of conversational features that can identify potential mis‐communications. Trace AI provides IVA designers with suggested actions to correct understanding errors, prioritizes areas of language model repair, and can automate the review of conversations. We discuss the system design and report its performance at identifying errors in IVA understanding compared to that of human reviewers. Trace AI has been commercially deployed for over 4 years and is responsible for significant savings in human annotation costs as well as accelerating the refinement cycle of deployed enterprise IVAs.
Motion Pattern Based Anomalous Pedestrian Activity Detection

In this paper, an efficient technique for anomalous pedestrian activity detection in the academic institution is proposed.At the pixel and block levels, the proposed method elicits motion components that accurately represent pedestrian action, velocity, and direction, as well as along a frame.We also adopted these motion features to detect anomalous actions.The detection of anomalous behavior in academic environments is not available at the moment.Similarly, the existing method produces a high number of false positives.An anomaly detection dataset and a newly designed proposed student behavior database were used to validate the proposed framework.A significant improvement in anomalous activity recognition has been demonstrated in experimental results.Based on motion features, the proposed method reduces false positives by 3% and increases true positives by 5%.A discussion of future research directions concludes the paper.
A Comparative Analysis of Lossless Compression Algorithms on Uniformly Quantized Audio Signals

This paper analyses the performance of various lossless compression algorithms employed on uniformly quantized audio signals.The purpose of this study is to enlighten a new way of audio signal compression using lossless compression algorithms.The audio signal is first transformed into text by employing uniform quantization with different step sizes.This text is then compressed using lossless compression algorithms which include Run length encoding (RLE), Huffman coding, Arithmetic coding and Lempel-Ziv-Welch (LZW) coding.The performance of various lossless compression algorithms is analyzed based on mainly four parameters, viz., compression ratio, signal-tonoise ratio (SNR), compression time and decompression time.The analysis of the aforementioned parameters has been carried out after uniformly quantizing the audio files using different step sizes.The study exhibits that the LZW coding can be a potential alternative to the MP3 lossy audio compression algorithm to compress audio signals effectively.
Divisors computing minimal log discrepancies on lc surfaces

Let $(X\ni x,B)$ be an lc surface germ. If $X\ni x$ is klt, we show that there exists a divisor computing the minimal log discrepancy of $(X\ni x,B)$ that is a Koll\'ar component of $X\ni x$. If $B\not=0$ or $X\ni x$ is not Du Val, we show that any divisor computing the minimal log discrepancy of $(X\ni x,B)$ is a potential lc place of $X\ni x$.
The Time-Varying Impact of Fed’s Rate Hikes on Yield and Volatility of Bitcoin

Covid-19 has had a significant impact on financial markets around the world, and various countries have adopted their methods to combat the impact of Covid-19 on equity markets.And because of Covid-19, the market share of the cryptocurrency market is increasing rapidly.This article focuses on how exchange rate changes caused by the Fed's interest rate hike affected the cryptocurrency market after the epidemic.The article uses ARMA-GARCH and VAR models to analyze the future change of the cryptocurrency market after 2022 and how an increase in interest rate affects cryptocurrency market volatility.Furthermore, the model predictions have not been found that the interest rate increase in early 2022 has produced volatility in the cryptocurrency market.Compared to traditional equity markets or real estate markets.Cryptocurrencies are not responsive to government intervention.