Reviewerly/exHarmony huggingface dataset:
Application of the fine-grained soil prospecting method in a loess-covered area of central China

Loess-covered landscapes occupy a large area of the globe and have substantially high potential for mineral exploration; however, as mineral information is obscured by loess cover, suitable prospecting techniques need to be applied. The fine-grained soil prospecting method—a deep-penetrating geochemical technology that can be used to identify concealed deposits—has been proven effective in some cases. In this study, we applied this method for mineral exploration in the loess-covered Luoning Basin in Central China. Our results revealed strong geochemical anomalies (e.g., Au, Ag, Pb, Zn, and Cu) over related and concealed ore deposits. Furthermore, a regional geochemical survey in the Luoning Basin revealed the spatial distribution and suites of elements that were consistent with the mineralization type. Moreover, higher geochemical anomalies of Au, Ag, Pb, and Zn were spatially related to an Au ore deposit and Ag–Pb–Zn polymetallic deposit, respectively. The results of a detailed geochemical survey in the Zhonghedi Ag–Pb–Zn polymetallic deposit further revealed that the distribution of surface element anomalies was associated with tectonic fractures throughout the exploration region, which are closely related to the distribution of orebodies. These results imply that the fine-grained soil prospecting method is suitable for anomaly delineation during detailed geochemical surveys as well as regional-scale surveys. Based on our results, we suggest that geochemical anomalies in the loess-covered surface in our study area are presumably the result of geogas dispersion processes; we provide a schematic model explaining how ore-forming and associated materials are brought to the surface by geogas flowing upwards along faults and fractures and, ultimately, accumulating in fine-grained loess soils. Overall, our research demonstrates that fine-grained soil prospecting can effectively aid the search for concealed mineral deposits in loess-covered landscapes.
Hough transform based recognition of space curves

Given a set of points representing a curve embedded in the Euclidean space and a family of space curves, we face the problem of finding the curve of the family that best fits these points. For our purposes, we apply the Hough transform (HT), which allows the recognition of a template curve from a set of points and its exact representation. Our method directly applies to digital models of 3D objects and combines the HT based recognition of curves expressed in both implicit and parametric form in a single flow. Furthermore, we extend the number of space curves that can be recognised, by including also those curves whose profile P can be approximated as the intersection of a quadric surface and a cylinder generated by a plane curve.
Morphometric Analysis of Scoria Cones to Define the ‘Volcano-Type’ of the Campo de Calatrava Volcanic Region (Central Spain)

The Campo de Calatrava Volcanic Region is the largest volcanic field in the Iberian Peninsula and presents a complex volcanic history, with more than 360 monogenetic basaltic volcanoes developed in effusive, Strombolian, and hydromagmatic eruptions. The large number of scoria cones, compared to the other existing types of volcanic morphologies, indicates that these landforms represent the most common eruptive events that occurred during Calatrava’s geological past. In this work, a morphometric analysis of the scoria cones was carried out, based on statistical analysis of the main morphological parameters of these volcanoes (height, cone width, crater width, crater depth, slope, area, etc.). The results were used to identify the most frequent scoria cone by means of statistical analysis of its main morphological features. To do this, a methodology based on statistical correlations of the morphological and morphometric parameters that best define the morphology of these volcanoes was applied. The number of cones and their distribution correspond to platform volcanic fields. The most frequent identified monogenetic volcano corresponds to a scoria cone developed in Strombolian dynamics with lava flows, with mean dimensions of 36.54 m height, 0.008113 km3 volume and an area of 0.454 km2.
Design and Implementation of Intelligent Teaching System Based on Artificial Intelligence and Computer Technology

The purpose of this paper is to investigate and research the intelligent teaching system based on artificial intelligence (AI) and computer technology, which can provide a better education platform for the majority of education and teaching workers and also help college students improve their learning efficiency. This paper first introduces AI, system modules, computer technology, and some algorithms for establishing intelligent teaching systems, specifically introducing student modules, Bayesian algorithms, and so on, and then introduces intelligent teaching based on AI and computer technology. The experimental results show that the intelligent teaching system based on AI and computer technology has improved the teaching efficiency and management ability of the school, and the help of students has also played a great role. The intelligent teaching system not only helps students improve their learning efficiency by 9.8% but also is favored by 56.8% of teachers and students.
The Effects of Using Online Applications to Teach Vocabulary to English Learners of HUFI in Ho Chi Minh City

This research aims at analyzing the outcome and effectiveness of applying several mobile apps or websites to teach and learn vocabulary in a particular university in Ho Chi Minh City (HCMC). Under the era of technological bloom and advances in mobile applications, teaching and learning English is certainly facilitated by these tools; as a result, many teachers use some apps, such as Kahoot, Quizlet, Quizizz, and so forth. In order to engage their students to learn and review lexical resources. This paper discusses how deeply and effectively online apps influence the way Vietnamese teachers teach lexicon to students and how they should be used in further context to maximize the efficacy of teaching new words. In this article, the author uses semi-structured interviews with six different interviewees who are currently lecturers at HUFI university in HCM city. This embraces questions about frequency, efficiency, self-judgment, and their ideas about this issue. Results indicate that vocabulary learning apps have a strikingly profound and meaningful impact on teaching and learning, facilitating students' learning process and enhancing vocabulary retention. The study also provides recommendations for the use of mobile applications in teaching vocabulary.
Design of Performance Evaluation Algorithm for Diversified Talent Training in Modern Universities Considering Innovative Thinking

The global economic trends and the winds of technological change have elevated the status of integration between industry and education for innovation and entrepreneurship. Technologies and the Internet have significantly changed all aspects of our lives. College education has improved as a result of the enormous benefits of the Internet and big data. Reforms and development have still a lot of application space in the training and practice of educational technology talents of a college education. To improve the performance evaluation capabilities of educational technology experts in Chinese universities through education and “Internet +” has become a challenge that has to be addressed. This article is under such a formal background to have a detailed study about the current college education in China. In this study, an analysis is presented for the performance evaluation of the education technology talent training in modern universities based on big data technology. A fuzzy evaluation algorithm based on composite elements is proposed to evaluate the multielement and multilevel college education system. Experimental results illustrate the feasibility and effectiveness of the proposed algorithm. The proposed method has the potential to solve the complicated performance evaluation problem in colleges.
Inter-Attribute awareness for pedestrian attribute recognition

The task of pedestrian attribute recognition (PAR) is to distinguish a series of person semantic attributes. Generally, existing methods adopt multi-label classification algorithms to tackle the PAR task by utilizing multiple attribute labels. Despite remarkable progress, this kind of method normally ignores relations between different attributes. In order to be aware of relations between attributes, we propose an inter-attribute aware network via vector-neuron capsule for PAR (IAA-Caps). Our IAA-Caps method replaces traditional one-dimensional scalar neurons with two-dimensional vector-neuron capsules by embedding them in IAA-Caps. Specifically, during IAA-Caps training, one dimension in capsules is used to recognize different attributes, and the other dimension is used to strengthen the relations of different attributes. Through considering inter-attribute relations, compared with previous methods that use a heavyweight backbone (e.g., ResNet50 or BN-Inception), a more lightweight backbone (i.e., OSNet) can be adopted in our proposed IAA-Caps to achieve better performance. Experiments are conducted on several PAR benchmark datasets, including PETA, PA-100K, RAPv1, and RAPv2, demonstrating the effectiveness of the proposed IAA-Caps. In addition, experiments also show that the proposed method can improve the performance of PAR on different backbones, showing its generalization ability.
Identifying out of distribution samples for skin cancer and malaria images

Deep neural networks have shown promising results in disease detection and classification using medical image data. However, they still suffer from the challenges of handling real-world scenarios especially reliably detecting out-of-distribution (OoD) samples. We propose an approach to robustly classify OoD samples in skin and malaria images without the need to access labeled OoD samples during training. Specifically, we use metric learning along with logistic regression to force the deep networks to learn much rich class representative features. To guide the learning process against the OoD examples, we generate in distribution's similar-looking examples by either removing class-specific salient regions in the image or permuting image parts and distancing them away from in-distribution (ID) samples. During inference time, the K-reciprocal nearest neighbor is employed to detect out-of-distribution samples. For skin cancer OoD detection, we employ two standard benchmark skin cancer ISIC datasets as ID, and six different datasets with varying difficulty levels were taken as out of distribution. For malaria OoD detection, we use the BBBC041 malaria dataset as ID and five different challenging datasets as out of distribution. We achieve state-of-the-art results, improving 5% and 4% in TNR@ TPR95% over the previous state-of-the-art OoD detection methods for skin cancer and malaria images while preserving the ID classification ability.
Joint Entity and Relation Extraction Network with Enhanced Explicit and Implicit Semantic Information

The main purpose of the joint entity and relation extraction is to extract entities from unstructured texts and extract the relation between labeled entities at the same time. At present, most existing joint entity and relation extraction networks ignore the utilization of explicit semantic information and explore implicit semantic information insufficiently. In this paper, we propose Joint Entity and Relation Extraction Network with Enhanced Explicit and Implicit Semantic Information (EINET). First, on the premise of using the pre-trained model, we introduce explicit semantics from Semantic Role Labeling (SRL), which contains rich semantic features about the entity types and relation of entities. Then, to enhance the implicit semantic information and extract richer features of the entity and local context, we adopt different Bi-directional Long Short-Term Memory (Bi-LSTM) networks to encode entities and local contexts, respectively. In addition, we propose to integrate global semantic information and local context length representation in relation extraction to further improve the model performance. Our model achieves competitive results on three publicly available datasets. Compared with the baseline model on Conll04, EINET obtains improvements by 2.37% in F1 for named entity recognition and 3.43% in F1 for relation extraction.
Experimental Investigation and Comparison of the Net Energy Yield Using Control-Based Solar Tracking Systems

As the world trend is going towards renewable energy, solar photovoltaic (PV) energy shines the most due to the low generation cost especially by using the latest PV cell technologies and materials, although the conventional silicon cell has an efficiency in the range of 15-16%. A PV tracking system (PVTS) could be a considerable method to increase electrical PV efficiency. In this study, the performance enhancement of the daily output power and energy has been experimentally investigated using single- and dual-axis tracking mechanisms for flat-plate conventional PV panels under the insolation conditions of the Eastern province of Saudi Arabia (SA). In the current study, the active PVTS has been designed and implemented with PID controllers and controlled in real-time with an embedded system. The fixed-tilt (FTPV), single-axis solar tracking system (SAST), and dual-axis solar tracking system (DAST) were tested simultaneously under clear-sky conditions. The PV electrical energy balance has revealed that the energy consumed by the electrical controllers of the SAST and DAST is 3.4% and 3.9%, respectively. In addition, the energy losses due to the actuators of the SAST and DAST are 7.8% and 13.0%, respectively, where they contributed mainly to the energy losses. However, the PV energy production by the DAST was high enough to compensate for the higher actuating mechanism losing rate compared to the SAST and performed incremental net energy output of around 8.64%. On other hand, the DAST and SAST were more efficient than the FTPV recording higher net energy increase by 28.98% and 18.73%, respectively.
Depth Image Denoising Algorithm Based on Fractional Calculus

Depth images are often accompanied by unavoidable and unpredictable noise. Depth image denoising algorithms mainly attempt to fill hole data and optimise edges. In this paper, we study in detail the problem of effectively filtering the data of depth images under noise interference. The classical filtering algorithm tends to blur edge and texture information, whereas the fractional integral operator can retain more edge and texture information. In this paper, the Grünwald–Letnikov-type fractional integral denoising operator is introduced into the depth image denoising process, and the convolution template of this operator is studied and improved upon to build a fractional integral denoising model and algorithm for depth image denoising. Depth images from the Redwood dataset were used to add noise, and the mask constructed by the fractional integral denoising operator was used to denoise the images by convolution. The experimental results show that the fractional integration order with the best denoising effect was −0.4 ≤ ν ≤ −0.3 and that the peak signal-to-noise ratio was improved by +3 to +6 dB. Under the same environment, median filter denoising had −15 to −30 dB distortion. The filtered depth image was converted to a point cloud image, from which the denoising effect was subjectively evaluated. Overall, the results prove that the fractional integral denoising operator can effectively handle noise in depth images while preserving their edge and texture information and thus has an excellent denoising effect.
Analytics Methods to Understand Information Retrieval Effectiveness—A Survey

Information retrieval aims to retrieve the documents that answer users’ queries. A typical search process consists of different phases for which a variety of components have been defined in the literature; each one having a set of hyper-parameters to tune. Different studies focused on how and how much the components and their hyper-parameters affect the system performance in terms of effectiveness, others on the query factor. The aim of these studies is to better understand information retrieval system effectiveness. This paper reviews the literature of this domain. It depicts how data analytics has been used in IR to gain a better understanding of system effectiveness. This review concludes that we lack a full understanding of system effectiveness related to the context which the system is in, though it has been possible to adapt the query processing to some contexts successfully. This review also concludes that, even if it is possible to distinguish effective from non-effective systems for a query set, neither the system component analysis nor the query features analysis were successful in explaining when and why a particular system fails on a particular query.
Text Mining Approach to Improve Mobile Role Playing Games Using Users’ Reviews

The genre characteristics of RPG (role-playing game), which have high expectations for high sales and continuous profit generation, have created a phenomenon that has attracted many developers to become interested in the RPG market. However, recently, RPG development has been mass-produced that includes similar content, sensational advertisements, low quality, and excessive billing induction, which have negatively affected the game market and users’ gameplay experiences. In this regard, we conduct a big data analysis on the users’ reviews to find out what will be needed to have a positive gaming experience for mobile RPGs. User reviews are collected in the Google Play Store and analyzed to find ways to improve mobile RPG gameplay. In order to extract meaningful information from the collected big data, we use text mining techniques of topic modeling with an LDA algorithm to visualize the data. We propose a text mining approach as a practical method for better design of mobile RPGs since it analyze users’ reviews and objectively grasps their opinions based on their gameplay. The results can be used to develop the mobile RPGs that can be played continuously by.
Surfacing Racial Stereotypes through Identity Portrayal

Content warning: this paper discusses and contains content that may be offensive or upsetting.
Providing Item-side Individual Fairness for Deep Recommender Systems

Recent advent of deep learning techniques have reinforced the development of new recommender systems. Although these systems have been demonstrated as efficient and effective, the issue of item popularity bias in these recommender systems has raised serious concerns. While most of the existing works focus on group fairness at item side, individual fairness at item side is left largely unexplored. To address this issue, in this paper, first, we define a new notion of individual fairness from the perspective of items, namely (α, β)-fairness, to deal with item popularity bias in recommendations. In particular, (α, β)-fairness requires that similar items should receive similar coverage in the recommendations, where α and β control item similarity and coverage similarity respectively, and both item and coverage similarity metrics are defined as task specific for deep recommender systems. Next, we design two bias mitigation methods, namely embedding-based re-ranking (ER) and greedy substitution (GS), for deep recommender systems. ER is an in-processing mitigation method that equips (α, β)-fairness as a constraint to the objective function of the recommendation algorithm, while GS is a post-processing approach that accepts the biased recommendations as the input, and substitutes high-coverage items with low-coverage ones in the recommendations to satisfy (α, β)-fairness. We evaluate the performance of both mitigation algorithms on two real-world datasets and a set of state-of-the-art deep recommender systems. Our results demonstrate that both ER and GS outperform the existing minimum-coverage (MC) mitigation solutions [Koutsopoulos and Halkidi 2018; Patro et al. 2020] in terms of both fairness and accuracy of recommendations. Furthermore, ER delivers the best trade-off between fairness and recommendation accuracy among a set of alternative mitigation methods, including GS, the hybrid of ER and GS, and the existing MC solutions.
Blockchain Privacy and Scalability in a Decentralized Validated Energy Trading Context with Hyperledger Fabric

Lower renewable energy generator prices are leading people to install solar panels to reduce their electricity bills or, in some cases, even sell the surplus generated energy to the grid and earn credits from the grid operator. Generally, they are limited to trading the energy they generate with the grid company, which has a dominant role in price determination. Decentralized energy markets might increase both market competitiveness and incentive to further people's adoption of renewable energy, reducing security vulnerabilities and improving resiliency. Blockchain is a widely studied technology to provide decentralization for energy markets in this context. Scalability, privacy, market design, and user security are some of the open research topics. This work analyzes the literature related to blockchain and energy markets, proposes a model, implements it, performs experiments, and analyzes network scalability and data generation. The model, implemented with Hyperledger Fabric, enables validated clean energy trading with anonymized buyers to prevent consumption pattern exposure. The maximum transaction throughput was achieved with 5000 sensors, 5000 buyers, and 5000 sellers. The data generation rate by network and the baseline deployment costs were also analyzed to judge the network viability. Furthermore, this work provides empirical results on a topic that the literature lacks.
Automated hate speech detection and span extraction in underground hacking and extremist forums

Abstract Hate speech is any kind of communication that attacks a person or a group based on their characteristics, such as gender, religion and race. Due to the availability of online platforms where people can express their (hateful) opinions, the amount of hate speech is steadily increasing that often leads to offline hate crimes. This paper focuses on understanding and detecting hate speech in underground hacking and extremist forums where cybercriminals and extremists, respectively, communicate with each other, and some of them are associated with criminal activity. Moreover, due to the lengthy posts, it would be beneficial to identify the specific span of text containing hateful content in order to assist site moderators with the removal of hate speech. This paper describes a hate speech dataset composed of posts extracted from HackForums, an online hacking forum, and Stormfront and Incels.co, two extremist forums. We combined our dataset with a Twitter hate speech dataset to train a multi-platform classifier. Our evaluation shows that a classifier trained on multiple sources of data does not always improve the performance compared to a mono-platform classifier. Finally, this is the first work on extracting hate speech spans from longer texts. The paper fine-tunes BERT (Bidirectional Encoder Representations from Transformers) and adopts two approaches – span prediction and sequence labelling. Both approaches successfully extract hateful spans and achieve an F1-score of at least 69%.