Reviewerly/exHarmony huggingface dataset:
Digital Images and Machine Learning Methods for Classification and Soil Total Carbon Determination of South Brazil

The present study aims to investigate the machine learning methods combined with digital images in the classification and total carbon quantification of soils. The present work investigated the RGB (Red, Green, Blue) pattern with Artificial Neural Network (ANN) and Random Forest (RF), classifying Oxisol and Inceptisol collected in South Brazil. Furthermore, we also built ANN, RF, Partial Least Squares (PLS), and Support Vector Machine (SVM) models from digital images to quantify Total Carbon (TC) content in Brazilian soil samples. Preliminary evaluations considered the hypothesis that alternative methods of classification and quantification of soil properties may provide results comparable to traditional methods. The TC content of 91 soil samples, 40 from Oxisol and 51 from Inceptisol, were determined by dry combustion. The ANN and RF were applied in the classification model-building. The quality of the classification was assessed by the percentage of correctly classified instances. The results suggest using digital images and statistics tools as a new methodology to soil classification. The best machine learning method was RF which can be used to classify soil types by the RGB channels colors of the soil images. The use of digital images and RF is an excellent and rapid strategy analysis for TC content determination in Oxisols and Inceptisols. Therefore, the proposed methods represent an advancement in the classification and TC content determination of the most important Brazilian soils.
Prepositional Frame Extraction and Semantic Classification Based on Chinese ChunkBank

The “prepositional frame” is a formal marker that can introduce the predicate argument in the form of “A*B”, where “A” is a front word, mainly a preposition, and “B” is a back word, mainly orientation words, noun words, conjunctions, verbs, and auxiliaries. The combination of front words and back words in Chinese is relatively fixed. In previous studies, scholars paid more attention to the circumstances where single prepositions are used as the marker of certain semantic components. However, prepositional frames in Chinese also function as markers of semantic components and occur at high frequencies in real life usage of language. The object introduced in the frame is often the argument of the statement verb, and both front and back words can be used as the explicit marker for argument elements. Therefore, based on previous studies, we extracted prepositional frames from the Chinese ChunkBank, semantically classified the extracted data according to the semantic role system of Wang Chengwen and opened the sources of the top 200 high-frequency frames for reference, to provide some knowledge support for both prepositional frame recognition and argument recognition in natural language processing, in this paper.
Sensitive Information Protection Model Based on Bayesian Game

A game measurement model considering the attacker's knowledge background is proposed based on the Bayesian game theory aiming at striking a balance between the protection of sensitive information and the quality of service. We quantified the sensitive level of information according to the user's personalized sensitive information protection needs. Based on the probability distribution of sensitive level and attacker's knowledge background type, the strategy combination of service provider and attacker was analyzed, and a game-based sensitive information protection model was constructed. Through the combination of strategies under Bayesian equilibrium, the information entropy was used to measure the leakage of sensitive information. Furthermore, in the paper the influence of the sensitive level of information and the attacker's knowledge background on the strategy of both sides of the game was considered comprehensively. Further on, the leakage of the user's sensitive information was measured. Finally, the feasibility of the model was described by experiments.
Extracting Significant Comorbid Diseases from MeSH Index of PubMed

Text mining is an important research area to be explored in terms of understanding disease associations and have an insight in disease comorbidities. The reason for comorbid occurrence in any patient may be genetic or molecular interference from any other processes. Comorbidity and multimorbidity may be technically different, yet still are inseparable in studies. They have overlapping nature of associations and hence can be integrated for a more rational approach. The association rule generally used to determine comorbidity may also be helpful in novel knowledge prediction or may even serve as an important tool of assessment in surgical cases. Another approach of interest may be to utilize biological vocabulary resources like UMLS/MeSH across a patient health information and analyze the interrelationship between different health conditions. The protocol presented here can be utilized for understanding the disease associations and analyze at an extensive level.
Manipulation Detector: Distinguishing between Real and Manipulated Images

Fake and manipulated images have become prevalent in recent years and have beencredited with deceiving even the most informed. Spreading news using images has become commonplace and manipulated image can be used to spread incorrect information. To prevent this, It is important to be able distinguish between manipulated images and real images. Using advances in deep learning and computer vision, we investigate the task of detecting manipulated images for lossy compression formats such as JPEG using simple approaches that utilize popular CNN architectures. Our findings indicate that just by utilizing Error Level Analysis and a ResNet model, we are able to achieve a 94\% accuracy, 92\% precision, and 93\% recall when detecting manipualtion for images with lossy compression formats.
Mining and the Nature of Gold Deposits

It is a major task to make a gold bar from gold atoms which exist at a few parts per billion (0.0000001%) in the Earth’s crust. This task is a two-stage process involving collaboration between Nature and mining. Nature partitions gold into small volumes of rock that become ore with several parts per million of gold – thousands of times greater than the crustal average. Mining concentrates the few parts per million of gold in that ore further by processing and refining it to produce a gold bar. These upgrades are forms of partitioning of gold between different media. An exception is the formation of specimen nuggets where nature alone achieves the 109 upgrade.
Forecasting of solar radiation using machine learning and deep learning algorithms

This chapter covers four applications: the first application is about one-step ahead forecasting of the daily global horizontal irradiation (GHI), using machine learning methods. The second application is related to one-step ahead forecasting of in-plane solar irradiance using deep learning neural networks. The third application concerns the investigation of deep learning natural networks for multistep ahead forecasting of in-plane solar irradiance. In the last application, emphasis is given to the use of neural networks for one-step ahead forecasting of daily and hourly GHI using meteorological parameters. In all, 16 examples have been described in order to help readers understand how to apply machine learning and deep learning for the forecasting of solar radiation. Four databases have been used to develop different models and all models presented have been written and developed using Python programming language.
Multimodal Pseudo-Labeling Under Various Shooting Conditions: Case Study on RGB and IR Images

In recent years, large-scale datasets with accurate labels have been an extremely important factor in the progress of computer vision. One typical example is object detection in outdoor scenes, where data captured under various conditions such as lighting, weather, and temperature are essential to increase the robustness of object detectors. However, such is time-consuming. In addition, under conditions such as extremely low luminance, it is difficult to assign accurate labels, even manually. When even a small amount of labeled data is available, pseudo-labeling can be used to effectively assign labels to unlabeled data, but if the labeled data is captured under only a single condition (e.g., daytime), it is difficult to perform pseudo-labeling to images under different conditions (e.g., nighttime). In this paper, we propose a pseudo-labeling method under various conditions by using multimodal images. If one of which has a small change in texture depending on the conditions and the other has a large change, we can perform pseudo-labeling and self-training by projecting the outputs mutually. Using videos taken by RGB and IR cameras on a road as a case study, we show the effectiveness of the proposed method in object detection.
Summarization of Crowdsourced Testing Reports

However, while all of these techniques are built on the assumption that duplicate reports are harmful to software maintenance and aim at filtering out this information, Zimmermann et al. and Bettenburg et al. empirically found that duplicate reports are helpful for report comprehension and debugging.
Generating Contextual Weighted Commonsense Knowledge Graphs

There has been a renewed interest in commonsense knowledge and reasoning. To achieve artificial general intelligence, systems must exhibit not only the recognition abilities of humans but also other important aspects of being human, such as commonsense and causality. Recent literature has shown that external commonsense knowledge graphs are beneficial to a variety of systems in multiple ways, including improvements in the commonsense abilities of deep learning models. This paper investigates an auto-generation of weighted commonsense knowledge graphs representing general information, as well as graphs containing contextual information. The method leads to the construction of graphs equipped with frequency based weights associated with nodes and relations. The proposed construction methodology has the advantage of a never-ending learning paradigm. We evaluate the constructed contextual knowledge graphs qualitatively and quantitatively. The commonsense knowledge graphs are inherently explainable and can support commonsense reasoning. We analyze commonsense reasoning approaches using contextual graphs and discuss the results.
Junk-neuron-deletion strategy for hyperparameter optimization of neural networks

With the complexity of problems in reality increasing, the sizes of deep learning neural networks, including the number of layers, neurons, and connections, are increasing in an explosive way. Optimizing hyperparameters to improve the prediction performance of neural networks has become an important task. In literatures, the methods of finding optimal parameters, such as sensitivity pruning and grid search, are complicated and cost a large amount of computation time. In this paper, a hyperparameter optimization strategy called junk neuron deletion is proposed. A neuron with small mean weight in the weight matrix can be ignored in the prediction, and is defined subsequently as a junk neuron. This strategy is to obtain a simplified network structure by deleting the junk neurons, to effectively shorten the computation time and improve the prediction accuracy and model the generalization capability. The LSTM model is used to train the time series data generated by Logistic, Henon and Rossler dynamical systems, and the relatively optimal parameter combination is obtained by grid search with a certain step length. The partial weight matrix that can influence the model output is extracted under this parameter combination, and the neurons with smaller mean weights are eliminated with different thresholds. It is found that using the weighted mean value of 0.1 as the threshold, the identification and deletion of junk neurons can significantly improve the prediction efficiency. Increasing the threshold accuracy will gradually fall back to the initial level, but with the same prediction effect, more operating costs will be saved. Further reduction will result in prediction ability lower than the initial level due to lack of fitting. Using this strategy, the prediction performance of LSTM model for several typical chaotic dynamical systems is improved significantly.
Data to Processes, Examples, and Discovery

Determining how gold deposits form is facilitated by the wealth of information available on the Earth’s surface, in underground mines and open pits, through characterisation and experiment in research laboratories, through libraries, and even through mining company annual reports. Designing and collecting the more useful information is one stage, but then it is necessary to decide what to retain and how it should be arranged and prioritised. It is difficult to argue against the complexity and variability within and between deposits, but there is value in occasionally pausing the data collection and reviewing the similarities.
Deep Learning Based Image Forgery Detection Methods

Increasingly advanced image processing technology has made digital image editing easier and easier.With image processing software at one's fingertips, one can easily alter the content of an image, and the altered image is so realistic that it is illegible to the naked eye.These tampered images have posed a serious threat to personal privacy, social order, and national security.Therefore, detecting and locating tampered areas in images has important practical significance, and has become an important research topic in the field of multimedia information security.In recent years, deep learning technology has been widely used in image tampering localization, and the achieved performance has significantly surpassed traditional tampering forensics methods.This paper mainly sorts out the relevant knowledge and latest methods in the field of image tampering detection based on deep learning.According to the two types of tampering detection based on deep learning, the detection tasks of the method are detailed separately, and the problems and future prospects in this field are discussed.It is quite different from the existing work: 1) This paper mainly focuses on the problem of image tampering detection, so it does not elaborate on various forensic methods.2) This paper focuses on the detection method of image tampering based on deep learning.3) This paper is driven by the needs of tampering targets, so it pays more attention to sorting out methods for different tampering detection tasks.
Towards Building a Linked Open Data Cloud to Predict and Regulate Social Relations in the Saudi Society

Background: The trend in producing linked open data to publish high-quality interlinked data has gained widespread traction in recent years. Various sectors are producing linked open data to increase public access and ensure transparency, in addition to a better utilization of government data, namely linked open government data. Problem Definition: As compared to the developed countries, Saudi Arabia lags behind in benefiting from this new era of ubiquitous web of data, despite its publication of government related data in non-linked format. In the context of Saudi open government data, the full potential of multi-category data published by various government agencies at different portals is not being realized as the data are not published in open data format and remain unlinked to other existing datasets. Methodology: To bridge this gap, this study presents a framework to extract and produce semantically enriched data from different data sources in various domains. The framework was used to produce the Saudi linked open government data cloud by interlinking data entities with each other and with external existing open datasets. Results: The effectiveness of our approach is validated by applying it to a socially significant issue, i.e., divorce rate, in Saudi Arabia. By posing smart queries to semantically enriched data, we were able to perform an in-depth analysis of different factors related to increasing divorce rates in Saudi Arabia. Arguably, without using linked open data and related technologies such analysis would not have been possible. Finally, we also present a simulated visual environment for better understanding and communication of such analysis for decision and policy makers.
Probabilistic Cascading Classifier for Energy-Efficient Activity Monitoring in Wearables

Advances in embedded systems have given rise to integrating several small-size health monitoring devices within daily human life. This trend led to an ongoing extension of wearable sensors in a broad range of applications. Wearable technologies, which are firmly connected with the human body, utilize sensors and machine learning to describe individuals’ physical or psychological routines through activity recognition and human movement. Since wearables are used all day long, the power consumption of these systems needs to be reasonably low. Current research considers that such machine learning methods are trained with fixed properties, including sensor sampling rate and statistical features computed from the time series data. However, in reality, wearables require continuous reconfiguration of their computational algorithms due to the personalized nature of human gait and movement. Furthermore, computational algorithms must become energy- and memory-efficient due to these embedded sensors’ limited power and memory. In this paper, we propose a resource-efficient framework for real-time, continuous, and on-node human activity recognition. Typically activity recognition problem is a multi-class classification problem. However, we suggest transforming this problem based on MET (Metabolic Equivalent of Task) into a hierarchical classification model, providing personalized structure for each individual. We discuss the design and construction of this new configurable classification paradigm. Our results demonstrate that the proposed probabilistic cascading system accuracy for different personalized scenarios varies between 94.5% and 96.9% in detecting activities using a limited memory, while power usage of the system is reduced by as high as 17.2% compared to the traditional methods.
Novel Search Strategies for Conducting Systematic Literature Reviews: Comparing a Novel Citation-Based Combined Bfsearch Approach with Text-Based User-Defined Queries and Relevance Prediction Through Active Learning

A novel combined Backward and Forward search (BFsearch) strategy was developed in this study. Its performance was compared to common user-defined queries and novel relevance prediction through active learning based on textual information using balanced random forest classification. The three search strategies were applied to two typical case studies related to the food science domain: a systematic scoping review and a critical review focussing on trends. Data was collected for both case studies using several online databases. Tools were developed to automatically combine duplicates through unique identifiers and to speed up the categorisation process through automatic text highlighting. The performance of the search strategies was evaluated using precision-recall curves. BFsearch was superior to queries for case study 1, acting complementary in both case studies. More research is needed to further develop the BFsearch strategy. To reach a high level of sensitivity (high recall), active learning proved most efficient with a high precision. Preference was given to relevance prediction based on the abstracts and titles of the papers, as compared to title only. Currently, active learning strategies focus mostly on textual data, so it is advised to study the integration of the BFsearch approach and other citation-based approaches within active learning.
Research on Key Word Information Retrieval Based on Inverted Index

With the advent of the era of big data, data has penetrated into every aspect of social life and become an important production factor in various industries. However, while providing convenience to our life, massive information also brings difficulties to information retrieval, which has problems of low retrieval efficiency and poor retrieval accuracy. Therefore, how to improve the efficiency and accuracy of information retrieval has become a key technical problem to be solved. In this paper, we propose a key word information retrieval scheme based on inverted index, which uses the inverted index segmentation algorithm based on keywords to realize the block retrieval of information, improves the efficiency of information retrieval, and improves the accuracy of retrieval content through reasonable error correction of keywords. The results of evaluation indicate that compared with the mainstream information retrieval methods, the keyword information retrieval scheme based on inverted index can carry out faster and more accurate information retrieval. Experimental results show that the keyword information retrieval scheme with inverted index can further improve the efficiency and accuracy of information retrieval.
Heterogeneous Information Network Representation Learning Incorporating Community Structure

Network embedding usually learns the node representations using their local context information. However, as an important mesoscopic description of network structures, community structures hidden in the networks have been largely ignored. Incorporating community structures into network embedding could provide effective and a wealth of information to overcome the problem of sparse data in local structures, and make the learned node representation more identifiable. Inspired by this, we propose a representation learning model of community-augmented heterogeneous information network embedding (CAHE), which integrates community structure to preserve community structure information of various types of nodes. In detail, we assume that the different types of nodes exist in the different community structures for a heterogeneous information network, and define community embedding as a set of Mixture Gaussian models. Then, we unify the community embedding of all types of nodes to improve the capacity of network representation. A large of experimental results of several kinds of homogeneous and heterogeneous information network representation learning on two public datasets suggest that the proposed CAHE is superior to other several state-of-art methods.
Vulnerability Analysis of MEGA Encryption Mechanism

MEGA is an end-to-end encrypted cloud storage platform controlled by users. Moreover, the communication between MEGA client and server is carried out under the protection of Transport Layer Security (TLS) encryption, it is difficult to intercept the key data packets in the process of MEGA registration, login, file data upload, and download. These characteristics of MEGA have brought great difficulties to its forensics. This paper presents a method to attack MEGA to provide an effective method for MEGA’s forensics. By debugging the open-source code of MEGA and analyzing the security white paper published, this paper first clarifies the encryption mechanism of MEGA, including the detailed process of registration, login, and file encryption, studies the encryption mechanism of MEGA from the perspective of protocol analysis, and finds out the vulnerability of MEGA encryption mechanism. On this basis, a method to attack MEGA is proposed, and the secret data stored in the MEGA server can be accessed or downloaded; Finally, the efficiency of the attack method is analyzed, and some suggestions to resist this attack method are put forward.