Reviewerly/exHarmony huggingface dataset:
Spatial mapping of soil properties in Konkan region of India experiencing anthropogenic onslaught

Soils of Indian Konkan region, part of ecologically sensitive Western Ghats have been subjected to anthropogenic activities of late. This has endangered the ecological security through conspicuous losses in topsoil quality. The rationale of the present study was to map the soil properties and create management zones for ensuring food and nutritional security. The study was conducted in South Goa district of the state of Goa located in Konkan region. A total of 258 geo-referenced soil samples were collected and analyzed for pH, EC, SOC, available N, P, K and DTPA extractable micronutrients viz ., Zn, Cu, Fe and Mn. Soil pH was found to be in acidic range. A wide variability existed in SOC content ranging from 0.12–5.85%. EC was mostly neutral with mean value 0.08±0.37 dSm -1 , while available nitrogen (AN), available phosphorus (AP) and available potassium (AK) varied in range from 56.4–621.6 kg ha -1 , 0.5–49.7 kg ha -1 and 31.5–786.2 kg ha -1 with mean values 211.2±76.9, 8.4±8.2 and 202.3±137.6 kg ha -1 , respectively. A wide range was exhibited by cationic DTPA extractable Zn, Cu, Fe and Mn with mean values, 0.22±0.30, 0.44±0.60, 7.78±5.98 and 7.86±5.86 mg kg -1 , respectively. Soil pH exhibited significant positive correlation with EC, AP AK and Zn and negative correlation with Fe and Cu. SOC exhibited significantly correlated with AN, AP, AK, Zn and Fe. Geo-statistical analysis revealed J-Bessel as best fit semivariogram model for pH, AP and AK; Rational Quadratic for EC, SOC, Zn and Mn; Hole effect for AN; Stable for Cu and K-Bessel for Fe for their spatial mapping. Four principal components showed eigenvalues more than one and cumulative variability of 59.38%. Three distinct soil management zones showing significant variation in soil properties were identified and delineated for wider scale management of soils. Precision nutrient management based on spatial variation and their mapping would enable refined agricultural and environmental management practices in the region.
Blockchain-Enhanced Fair and Efficient Energy Trading in Industrial Internet of Things

As the technical support of the industrial Internet of Things, blockchain technology has been widely used in energy trading, data transactions, and Internet of Vehicles. However, most of the existing energy trading models only address the transaction security and transaction privacy issues that arise in the energy trading process, ignoring the fairness of resource allocation and transaction equity in the trading process. In order to tackle those problems, an energy trading scheme called HO-TRAD is proposed in this paper to improve the efficiency of model trading while ensuring the fairness of energy trading. We propose a new trading strategy in the HO-TRAD energy trading scheme that guarantees fairness in the allocation of trading resources by introducing an entity’s active reputation value. Use smart contracts to achieve transparency and ensure fairness in the transaction process. Based on the identity verification foundation of the consortium chain, the scheme enhances the existing PBFT consensus algorithm and improves the efficiency of model transactions. The experimental simulation indicates that the scheme requires less transaction time and has higher transaction fairness and security.
Cross-Lingual Training of Dense Retrievers for Document Retrieval

Dense retrieval has shown great success for passage ranking in English.However, its effectiveness for non-English languages remains unexplored due to limitation in training resources.In this work, we explore different transfer techniques for document ranking from English annotations to non-English languages.Our experiments reveal that zero-shot modelbased transfer using mBERT improves search quality.We find that weakly-supervised target language transfer is competitive compared to generation-based target language transfer, which requires translation models.
Deep Learning-Based X-Ray Baggage Hazardous Object Detection – An FPGA Implementation

This work proposes deep learning neural network-based X-ray image classification. The X-ray baggage scanning machinery plays an essential role in the safeguard of customs, airports, and other systematically very important landmarks and infrastructures. The technology at present of baggage scanning machines is designed on X-ray attenuation. The detection of threatful objects is built on how different objects attenuate the X-ray beams going through them. In this paper, the deep convolutional neural network of YOLO is utilized in classifying baggage images. Real-time performance of the baggage image classification is an essential one for security scanning. There are many computationally intensive operations in the You Only Look Once (YOLO) architecture. The computational intensive operations are implemented in the Field Programmable Gate Array (FPGA) platform to optimize process delays. The critical issues involved in those implementations include data representation, inner products computation and implementation of activation function and resolving these issues will also be a significant task. The FPGA implementation results show that with less resource occupancy, the YOLO implementation provides maximum accuracy of 98.9% in classifying X-ray baggage images and identifying hazardous materials. This result proves that the proposed implementation is best suited for practical system deployments for real-time Baggage scanning.
An Explainable Artificial Intelligence Model for Detecting Xenophobic Tweets

Xenophobia is a social and political behavior that has been present in our societies since the beginning of humanity. The feeling of hatred, fear, or resentment is present before people from different communities from ours. With the rise of social networks like Twitter, hate speeches were swift because of the pseudo feeling of anonymity that these platforms provide. Sometimes this violent behavior on social networks that begins as threats or insults to third parties breaks the Internet barriers to become an act of real physical violence. Hence, this proposal aims to correctly classify xenophobic posts on social networks, specifically on Twitter. In addition, we collected a xenophobic tweets database from which we also extracted new features by using a Natural Language Processing (NLP) approach. Then, we provide an Explainable Artificial Intelligence (XAI) model, allowing us to understand better why a post is considered xenophobic. Consequently, we provide a set of contrast patterns describing xenophobic tweets, which could help decision-makers prevent acts of violence caused by xenophobic posts on Twitter. Finally, our interpretable results based on our new feature representation approach jointly with a contrast pattern-based classifier obtain similar classification results than other feature representations jointly with prominent machine learning classifiers, which are not easy to understand by an expert in the application area.
Improved Resolution and Cost Performance of Low-Cost MEMS Seismic Sensor through Parallel Acquisition

In earthquake monitoring, an important aspect of the operational effect of earthquake intensity rapid reporting and earthquake early warning networks depends on the density and performance of the deployed seismic sensors. To improve the resolution of seismic sensors as much as possible while keeping costs low, in this article the use of multiple low-cost and low-resolution digital MEMS accelerometers is proposed to increase the resolution through the correlation average method. In addition, a cost-effective MEMS seismic sensor is developed. With ARM and Linux embedded computer technology, this instrument can cyclically store the continuous collected data on a built-in large-capacity SD card for approximately 12 months. With its real-time seismic data processing algorithm, this instrument is able to automatically identify seismic events and calculate ground motion parameters. Moreover, the instrument is easy to install in a variety of ground or building conditions. The results show that the RMS noise of the instrument is reduced from 0.096 cm/s2 with a single MEMS accelerometer to 0.034 cm/s2 in a bandwidth of 0.1-20 Hz by using the correlation average method of eight low-cost MEMS accelerometers. The dynamic range reaches more than 90 dB, the amplitude-frequency response of its input and output within -3 dB is DC -80 Hz, and the linearity is better than 0.47%. In the records from our instrument, earthquakes with magnitudes between M2.2 and M5.1 and distances from the epicenter shorter than 200 km have a relatively high SNR, and are more visible than they were prior to the joint averaging.
Video encryption/compression using compressive coded rotating mirror camera

Compressive coded rotating mirror (CCRM) camera is a novel high-speed imaging system that operates under amplitude optical encoding and frame sweeping modalities in a passive imaging mode that is capable of reconstructing 1400 frames from a single shot image acquisition and achieves the highest compression ratio of 368 compared to the other compressive sensing (CS) based single-shot imaging modalities. The integrated optical encoding and compression adds a strong layer of encryption on the observed data and facilitates the integration of the CCRM camera with the imaging applications that require highly efficient data encryption and compression due to capturing highly sensitive data or limited transmission and storage capacities. CCRM uses amplitude encoding that significantly extends the key space where the probability of having the exact encoder pattern is estimated as [Formula: see text], hence drastically reducing the possibility of data recovery in a brute force manner. Data reconstruction is achieved under CS based algorithms where the obtained amplitude-based pattern from optical encoder operates as the key in the recovery process. Reconstruction on the experimental as well as the synthetic data at various compression ratios demonstrate that the estimated key with less than 95[Formula: see text] matching elements were unable to recover the data where the achieved averaged structural similarity (SSIM) of 0.25 before 95[Formula: see text] encoder similarity and 0.85 SSIM at 100[Formula: see text] encoder similarity demonstrates the high-sensitivity of the proposed optical encryption technique.
Prediction of Short-Term Stock Price Trend Based on Multiview RBF Neural Network

Stock price prediction is important in both financial and commercial domains, and using neural networks to forecast stock prices has been a topic of ongoing research and development. Traditional prediction models are often based on a single type of data and do not account for the interplay of many variables. This study covers a radial basis neural network modeling technique with multiview collaborative learning capabilities for incorporating the impacts of numerous elements into the prediction model. This research offers a multiview RBF neural network prediction model based on the classic RBF network by integrating a collaborative learning item with multiview learning capabilities (MV-RBF). MV-RBF can make full use of both the internal information provided by the correlation between each view and the distinct characteristics of each view to form independent sample information. By using two separate stock qualities as input feature information for trials, this study proves the viability of the multiview RBF neural network prediction model on a real data set.
Experimental Comparison and Survey of Twelve Time Series Anomaly Detection Algorithms

The existence of an anomaly detection method that is optimal for all domains is a myth. Thus, there exists a plethora of anomaly detection methods which increases every year for a wide variety of domains. But a strength can also be a weakness; given this massive library of methods, how can one select the best method for their application? Current literature is focused on creating new anomaly detection methods or large frameworks for experimenting with multiple methods at the same time. However, and especially as the literature continues to expand, an extensive evaluation of every anomaly detection method is simply not feasible. To reduce this evaluation burden, we present guidelines to intelligently choose the optimal anomaly detection methods based on the characteristics the time series displays such as seasonality, trend, level change concept drift, and missing time steps. We provide a comprehensive experimental validation and survey of twelve anomaly detection methods over different time series characteristics to form guidelines based on several metrics: the AUC (Area Under the Curve), windowed F-score, and Numenta Anomaly Benchmark (NAB) scoring model. Applying our methodologies can save time and effort by surfacing the most promising anomaly detection methods instead of experimenting extensively with a rapidly expanding library of anomaly detection methods, especially in an online setting.
Sentiment Analysis of Product Reviews as A Customer Recommendation Using the Naive Bayes Classifier Algorithm

In an e-commerce Shopee, the process of selling and buying continues to run every day, and the comments given by consumers will increase more and more. Comments given by consumers will be the reference/review of a product that has been purchased by consumers. Consumers freely provide a review containing positive comments and negative comments in the Comments field listed on the Shopee e-commerce website. With the above problems, researchers will do a research with the method of sentiment analysis to distinguish classes in product review comments that include positive comment class or negative comment class using a combination of K-means and naive Bayes classifier. K-means used to determine the grouping of classes; naive Bayes classifier used to get the value of accuracy. The results obtained based on clustering K-means include getting 116 negative comments on product reviews and 37 negative comments product reviews. Accuracy results obtained from product review comment data of 77.12%. Thus, the accuracy value using K-means and naive Bayes classifier without manual data get a higher accuracy value is compared using K-means, Naive Bayes classifier, and manual data get results lower accuracy of 56.86%. From the results above the most comments is a negative comment of 116 data review comments product, from the results of the study can be concluded that one of the products of Spatuafa named high heels women know the Ribbon Ikat FX18 the condition of the product is not good enough due to the high negative comments compared to positive comments
Twisted Edwards Elliptic Curves for Zero-Knowledge Circuits

Circuit-based zero-knowledge proofs have arose as a solution to the implementation of privacy in blockchain applications, and to current scalability problems that blockchains suffer from. The most efficient circuit-based zero-knowledge proofs use a pairing-friendly elliptic curve to generate and validate proofs. In particular, the circuits are built connecting wires that carry elements from a large prime field, whose order is determined by the number of elements of the pairing-friendly elliptic curve. In this context, it is important to generate an inner curve using this field, because it allows to create circuits that can verify public-key cryptography primitives, such as digital signatures and encryption schemes. To this purpose, in this article, we present a deterministic algorithm for generating twisted Edwards elliptic curves defined over a given prime field. We also provide an algorithm for checking the resilience of this type of curve against most common security attacks. Additionally, we use our algorithms to generate Baby Jubjub, a curve that can be used to implement elliptic-curve cryptography in circuits that can be validated in the Ethereum blockchain.
Improving the Creative Thinking Ability of Junior High School Students Through GeoGebra Assisted Learning Community in Mathematics

This study examines and analyzes the differences between GeoGebra-assisted community learning and conventional learning in improving students' creative thinking skills in junior high schools. The research method uses quantitative, designed with an experimental pattern of nonequivalent control groups. Samples taken not randomly from two different classes amounted to 79 people. One class gets community learning with the help of GeoGebra, and the other class gets conventional learning. Data obtained by using the test. The test was conducted twice, namely pretest and posttest. Normalized N-Gain is done to measure the increase in creative thinking skills. Data analysis used an independent sample t-test. Based on the results of the analysis, it was found that there was a significant increase in students' creative thinking skills. Learning community is student-centered learning in groups (Student-Centered Learning). The research findings consider it important to implement GeoGebra-assisted learning communities according to the characteristics and development of the student learning environment.
AI Model for Predicting Legal Judgments to Improve Accuracy and Explainability of Online Privacy Invasion Cases

Since there are growing concerns regarding online privacy, firms may have the risk of being involved in various privacy infringement cases resulting in legal causations. If firms are aware of consequences from possible cases of invasion of online privacy, they can more actively prevent future online privacy infringements. Thus, this study attempts to predict the probability of judgment types caused by various invasions within US judicial cases that are related to online privacy invasions. Since legal judgment results are significantly influenced by societal factors and technological development, this study tries to identify a model that can accurately predict legal judgment with explainability. To archive the study objective, it compares the prediction performance by applying five types of classification algorithms (LDA, NNET, CART, SVM, and random forest) of machine learning. We also examined the relationship between privacy infringement factors and adjudications by applying network text analysis. The results indicate that firms could have a high possibility of both civil and criminal law responsibilities if they distributed malware or spyware, intentionally or non-intentionally, to collect unauthorized data. It addresses the needs of reflecting both quantitative and qualitative approach for establishing automatic legal systems for improving its accuracy based on the socio-technical perspective.
Solving Stance Detection on Tweets as Multi-Domain and Multi-Task Text Classification

Stance detection on tweets aims at classifying the attitude of tweets towards given targets.Existing work leverage attention-based models to learn target-aware stance representations.While those methods achieve substantial success, most of them usually train a model for each target separately despite the scarcity of annotated data for each target.To alleviate limitation of annotated data, some methods turn to external linguistic resources, additional sentiment annotations or target-aware data augmentation techniques for better detection results.We argue that the sharedness of stance-related features across targets in the existing stance detection dataset is not fully exploited.However, directly training on mixed examples of all targets may confuse the model in learning best features for each target.To this end, we borrow the idea from transfer learning and multi-task learning, and formulate stance detection on tweets as a multi-domain multitask classification problem.We apply the target adversarial learning to capture stance-related features shared by all targets and target descriptors for learning stance-informative features correlating to specific targets.Experimental results on the benchmark SemEval 2016 dataset demonstrate the effectiveness of our model, which outperforms BERT model by over 2% on macro average F1 and achieves superior performance than many recent methods utilizing external resources.We further provide detailed analyses to illustrate the superiority of fully utilizing features shared by different targets.
Human Action Recognition Method Based on Multi-Attention Mechanism and Spatiotemporal Graph Convolution Networks

<p indent="0mm">Human action recognition has become a challenging task in computer vision because it is difficult to combine spatiotemporal information. A multi-attention spatiotemporal graph convolution network is proposed. The core idea is to construct a connected graph according to the time series information and natural connection of human skeleton, and use the spatiotemporal graph convolution network with multi-attention mechanism to automatically learn spatial and temporal features and optimize the connected graph to realize prediction. Graph attention module is introduced, the topological structure of the graph constructed by the model will be optimized with the process of network training after initialization, then the topological structure which is more suitable for expressing human actions will be obtained. In addition, the channel attention module is added to make the network pay more attention to the important channel information, so as to extract the features of describing actions more effectively. A large number of experiments are carried out on the recognized large datasets: NTU-RGDB and Kinectics, which show that the method has higher recognition accuracy.
Device Authentication Codes based on RF Fingerprinting using Deep Learning

In this paper, we propose Device Authentication Code (DAC), a novel method for authenticating IoT devices with wireless interface, by exploiting their radio frequency (RF) signatures.The proposed DAC is based on RF fingerprinting, an information-theoretic method, feature learning, and the discriminatory power of deep learning.Specifically, an autoencoder is used to automatically extract features from the RF traces and the reconstruction error is used as the DAC, and this DAC is unique to each individual device.Then Kolmogorov-Smirnov (K-S) test is used to match the distribution of the reconstruction error generated by the receiver and the DAC in the received message, and the result will determine whether the device of interest is an intruder.We validate this concept on two experimentally collected RF traces from six ZigBee devices and five universal software defined radio peripheral devices, respectively.The traces span a range of Signal-to-Noise Ratio by varying locations, mobility of the devices, channel interference, and noise to ensure robustness of the model.Experimental results demonstrate that DAC is able to prevent device impersonation by extracting salient features that are unique to each wireless device of interest and can be used to identify radio frequency devices.Furthermore, the proposed method does not need the RF traces of the intruder during model training to be able to identify devices not seen during training, which makes it practical.