Reviewerly/exHarmony huggingface dataset:
Estimating The Spatial Distribution Of Soil Heavy Metals In Oil Mining Area Using Air Quality Data Based on Hybrid Geostatistical Method

To date, the quantitative estimation about the contribution of air quality on the spatial estimation and mapping of heavy metal by hybrid geostatistical method is scarce. For the end, we collected 116 samples from surface soil of oil mining in the Yellow River Delta (YRD) of China, and analyzed the content of As, Cr, Ni, Pb, and Zn. Furthermore, 40 monitoring stations data of air quality were collected in study area, including CO, NO2, SO2, O3, PM2.5, and PM10. Spatial estimation and mapping of heavy metals in soil were carried out by hybrid geostatistical models, including MLROK, SVMOK, and RFOK. Correlation analysis results showed that the high correlation (r > 0.3, p < 0.01) with five heavy metals were CO, NO2, SO2, and PM2.5. According to the hybrid geostatistical method, RFOK exhibited the highest estimation accuracy (R2) for As (65.76%), Cr (77.85%), Ni (61.47%), Pb (74.64%), and Zn (71.35%) in comparison with other models. And relative R2 of RFOK improved 30%, while MLROK and SVMOK increased over 100% for Zn (RIo = 121.90% and RIo = 121.64%) based on their original R2 of machine learning models. In addition, mapping results by RFOK showed the high concentrations of heavy metals were focused in the central and northeastern (As), northern (Cr), northeastern and northwestern (Ni), central and eastern (Pb), and northern (Zn). Especially, compared with vegetation index and topographic factors, PM2.5 is the highest driving variable for As (18.34%) and Zn (12.91%), and CO is the most important variable for Cr (18.22%), Ni (14.28%). The above results indicated that there is a mechanism of sources-receptor relationship between air quality and soil heavy metals, that is, oil refineries in the study area discharge heavy metal particles into the atmosphere, and then enter the soil through atmospheric deposition and precipitation.
Travel Salesman Problem Algorithm Using Proposed Algorithm Development with K-Smallest City Method

The Traveling Salesman Problem (TSP), classified as an NP-hard problem in combinatorial optimization, requires a lot of time to optimize large-scale data. In this paper, the proposed algorithm is designed to produce the smallest value and the fastest time in the computation process to find the shortest path. The results of the proposed algorithm design will be compared with algorithms that are generally widely used to solve TSP, including Ant Colony Optimization (ACO) and Brute Force, but in addition to comparing the results of the proposed algorithm in terms of minimum value and computational speed developed with the two algorithms, it will also be compared to the OR-Tools algorithm created by Google because to be able to evaluate the level of accuracy on the value generated on the large matrix dimension, it cannot be done on Brute Force because it takes a long time. The results show that the proposed algorithm can provide better results than ACO and shorter computation time than Brute Force even can still compete with Google's OR-Tools algorithm for the data used.
The Effect of Consumer Traits on Their Intention to Use Luxury Virtual-Reality (VR) Products: The Mediating Role of Status Signaling

The increasing integration of Virtual Reality (VR) features into social-networking (SN) environments has provided new opportunities for developing virtual-fashion products. These products are intended to be “worn” either on “real” bodies through Augmented Reality features, or on virtual avatars in gaming environments and the like. These virtual clothes are often worn to show-off status, achievement, and purchasing power in these VR environments. We focused on the emerging category of virtual luxury (VL) products and investigated which consumer segments, based on their personality traits, would be more willing to (partially) switch from physical luxury products to their virtual counterparts in their status-signaling activities through SN posts. We found that consumers with (a) higher self-efficacy regarding SN and VR environment, (b) more positive attitude towards SN and VR environments, (c) higher need-for-status, and (d) higher novelty-seeking motives tend to evaluate status-conveying potential of VL products more positively. Age (negatively) and income (positively) affect such evaluations. Moreover, the perceived status-conveying potential mediates the effect of consumer traits on their intention to switch to VL in their SN posts.
Automatic Classification of University Staff Enquiries in Russian and English

Document or text classification is a typical task in supervised machine learning. In this study we consider a multi-label text classification problem of helpdesk enquiries made by a university staff. To this end, we collect our data and consider the enquiries made in either Russian or in English. The dataset is categorized into eight different labels and underwent a preprocessing stage. A classical Term Frequency-Inverse Document Frequency algorithm is applied to the preprocessed data for feature extraction. For classification and prediction the Support Vector Machine and Multinomial Naive Bayes algorithms were utilized and the findings of experiments were compared. The experimental results show that in both languages, Support Vector Machine algorithm outperforms.
Anomaly Detection in Lexical Definitions via One-Class Classification Techniques

It takes a long time to build vocabularies and their definitions because they must be approved only by the experts in the meeting of building vocabularies and the definitions are also unstructured. To save time, we applied three techniques of classification to the experiments that are one-class SVMs, isolation forests, and local outlier factors, and also observed how well the method can suggest word definition status via the accuracy. As a result, the local outlier factors obtained the highest accuracy when they used vectors that were produced by USE. They can recognize the boundary of the approved class better and there are several approved clusters and outliers are scattered among them. Also, it is found that the detected status of definitions is both identical and opposite to the reference one. For the patterns of definition writing, the approved definitions are always written in the logical order, and start with wide or general information, then is followed by specific details, examples, and references of English terms or examples. In case of the rejected definitions, they are not always written in the logical order, and their definition patterns are also various - only Thai translation, Thai translation with related entries, parts of speech (POS), Thai translation, related entries, and English term references followed by definitions, etc.
Automatic Domain Extension and Optimization Based on Set-Theory

Autonomous machines promise more flexibility and robustness changes in their environment compared to manually programmed solutions in industrial applications. However, the autonomous planning of actions involves discrete as well as continuous properties, which results in a np-hard planning problem. Especially for multiple machines and long planning horizons the design of domains requires a lot of fine tuning and thus manual effort. Modularization and reuse of existing domain knowledge with formalized models is one solution to this issue. However, the models of different projects tend to be misaligned, in particular when several parties contributed to the project, which deteriorates the performance. In this paper, we present two domain optimization and extension algorithms, which adapt the models to facilitate planning. The first algorithm handles inconsistent units, or even misaligned pieces of sub-information. It automatically generates conversions and allows to call operations with a wider range of input types. The second algorithm aligns models from different sources with varying modeling views. After this reformulation, we can compose models more efficiently to a larger domain. For both optimizations, we rely on the formal set-based models that we also use in our previously presented hierarchical planning algorithm. Our hierarchical approach allows an almost linear scalability with the length of the plan. However, it comes with non-optimality effects due to the imposed intermediate goals that depend on the quality of the model. The optimization algorithms of this paper allow to adapt and extend the model so that valid shortcuts reduce these suboptimalities. We conduct experiments on a task and motion assembly problem, demonstrating scalability for up to 62 parts and plans with over 1000 steps, which either result in discrete state or high-level position changes, with planning times of less than 15 min. Our experiments also include the successful plan execution on a real-world dual arm setup.
Exploring Credibility Scoring Metrics of Perception Systems for Autonomous Driving

Autonomous and semi-autonomous vehicles’ perception algorithms can encounter situations with erroneous object detection, such as misclassification of objects on the road, which can lead to safety violations and potentially fatal consequences. While there has been substantial work in the robustness of object detection algorithms and online metric learning, there is little research on benchmarking scoring metrics to determine any possible indicators of potential misclassification. An emphasis is put on exploring the potential of taking these scoring metrics online in order to allow the AV to make perception-based decisions given real-time constraints. In this work, we explore which, if any, metrics act as online indicators of when perception algorithms and object detectors are failing. Our work provides insight on better design principles and characteristics of online metrics to accurately evaluate the credibility of object detectors. Our approach employs non-adversarial and realistic perturbations to images, on which we evaluate various quantitative metrics. We found that offline metrics can be designed to account for real-world corruptions such as poor weather conditions and that the analysis of such metrics can provide a segue into designing online metrics. This is a clear next step as it can allow for error-free autonomous vehicle perception and safer time-critical and safety-critical decision-making.
Super Resolution of Videos using E-GAN

Video Super-resolution can be defined as the process of improving the details and upscaling the resolution of a given video. In this paper, we present EGAN, an Enhanced-Generative Adversarial Network for the application of video super-resolution. Compared to other Neural Networks, EGAN is easier to train and delivers more consistent performance with reduced artifacts, due to these favorable features it is a viable alternative to be used for super-resolving videos. The proposed neural network is capable of preserving high frequency details and producing perceptually satisfying videos from heavily down sampled input videos. The main difference between EGAN and regular Generative Adversarial Network is the omission of Batch normalization layers, the advantages of which are discussed in brief below. The type of blocks used in the architecture are Residual in Residual Dense Blocks (RRDB). The extensive Mean-opinion-score (MOS) test along with Video Multimethod Assessment Fusion (VMAF) test shows hugely significant gains in terms of human perceptional quality over other state-of-art methods.
A Knowledge Enhanced Chinese GaoKao Reading Comprehension Method

Chinese GaoKao Reading Comprehension is a chal-lenging NLP task. It requires strong logical reasoning ability to capture deep semantic relations between the questions and answers. However, most traditional models cannot learn sufficient inference ability, because of the scarcity of Chinese GaoKao reading comprehension data. Intuitively, there are two methods to improve the reading comprehension ability for Chinese GaoKao reading comprehension task. 1). Increase the scale of data. 2). Introduce additional related knowledge. In this paper, we propose a novel method based on adversarial training and knowledge distillation, which can be trained in other knowledge-rich datasets and transferred to the Chinese GaoKao reading comprehension task. Extensive experiments show that our proposed model performs better than the state-of-the-art baselines. The code and the relevant dataset will be publicly avaible.
A Study on Smart Agriculture Utilizing IoT using Block Chain in Smart Contract

All sectors have been updated with new technologies, but India's agriculture, especially Tamil Nadu, is still lagging behind. Third-party interventions are so high that reduced food chain traceability, increased costs, and security issues affect smallholder’s farmer and provide consumers with sufficient knowledge to buy products at low prices. Use of blockchain technology in smart contracts combined with the Internet of Things. This paper analyses literature studies that focus primarily on food chain traceability and explores aspects of cost analysis, time consumption, and security. Therefore, these challenges are blockchain technology in smart contracts, which is being monitored by smallholder’s farmer and consumers are also benefiting from it.
EVALUATION OF RESEARCH ACTIVITIES OF HIGHER EDUCATION INSTITUTIONS ACADEMIC STAFF AS PART OF THE SYSTEM OF EDUCATION QUALITY ASSURANCE

In modern society, the issue of quality assurance by higher education institutions is of particular importance. One of the aspects of the internal quality assurance system is to monitor the level of professional competence of teachers, ensuring openness, transparency of evaluation indicators. In their turn, indicators of research activities of teachers, such as citations in scientometric databases Scopus, Web of Science, Google Scholar directly affect the ranking of higher education institutions in international and Ukrainian rankings. Therefore, the issue of formation and implementation of systems for the analysis of professional activity of teachers, in particular research, which allow monitoring and evaluation of key performance indicators of research activities of academic staff, is particularly relevant. The paper analyzes the experience of Ukrainian and foreign universities in implementing systems for presenting the professional activities of teachers, analyzing the research activities of academic staff and building ratings of professional and research activities. Modern higher education institutions use various technologies to present the professional activities of teachers in the public domain - Wiki technology, published pages on official websites and developed information systems. The main indicators used in rating the effectiveness of research activities are citation indicators in scientometric databases Scopus, Web of Science and Google Scholar - citation value, number of documents, h-index, i10-index, therefore, for high-quality presentation of research results in rankings it is important to ensure the visibility of scientific publications - the use of Open Journal Systems, placement of publications in open institutional repositories, electronic libraries, updating profiles of scientists in scientometric and bibliometric databases to update citation indices, etc.
High Speed VLSI Architecture Design Using FFT for 5G Communications

A high speed FFT processor is designed supporting 16- to 4096-point FFTs and 12- to 2400-point DFTs for 5G, WLAN. The processor is designed for high speed applications and source code is written in Verilog. Synthesis and simulation is done in Xilinx ISE 14.7. The power dissipation is minimized (20.3 mW) and delay is 9.539 ns and further extension is done using CORDIC processor delay is 7.55 ns. In this paper, high speed VLSI Architecture designed using FFT for 5G Communications. The proposed results are compared with the existed work.
Career Aura – Smart Resume and Employment Recommender

Recruitment and Job seeking are two major factors that are directly proportional to each other. Due to the competitive nature of the present world, the process of acquiring the best resource effectively and efficiently has become a challenging aspect for the companies. As a result, modern job portals have become increasingly popular to address the challenges identified in the early recruitment and job search process. The purpose of this research is to introduce an optimal solution to address the ineffective areas identified in the job and recruitment domain which can further enhance the recruitment and job seeking decisions by utilizing deep learning and sentiment analytic approach along with descriptive analysis. The proposed system recommends the relevant job opportunities by omitting the irrelevant job advertisements for job hunters who are interested in the IT job domain while they input their resume to the system and additionally, they can improve their career decisions by adhering to the prediction schemes. Moreover, the system facilitates recruiters to headhunt top talents efficiently once they input job requirements to the system and candidate suggestions are not only made depending on their resume information but also analyzing their LinkedIn endorsements.
CFCR: A Convolution and Fusion Model for Cross-platform Recommendation

With the emergence of various online platforms, associating different platforms is playing an increasingly important role in many applications. Cross-platform recommendation aims to improve recommendation accuracy through associating information from different platforms. Existing methods do not fully exploit high-order nonlinear connectivity information in cross-domain recommendation scenario and suffer from domain-incompatibility problem. In this paper, we propose an end-to-end convolution and fusion model for cross-platform recommendation (CFCR). The proposed CFCR model utilizes Graph Convolution Networks (GCN) to extract user and item features on graphs from different platforms, and fuses cross-platform information by Multimodal AutoEncoder (MAE) with common latent user features. Therefore, the high-order connectivity information is preserved to the most extent and domain-invariant user representations are automatically obtained. The domain-incompatible information is spontaneously discarded to avoid messing up the cross-platform association. Extensive experiments for the proposed CFCR model on real-world dataset demonstrate its advantages over existing cross-platform recommendation methods in terms of various evaluation metrics.
The Calculation of Verb Similarity Based on Ternary Morpheme Collocation

Currently, there are about three issues in calculating the similarity of Chinese vocabulary: one is that the calculation of vocabulary similarity generally only focuses on the semantic similarity of words, and the grammatical similarity of words is not paid enough attention; the second is that there is no relevant research to calculate similarity for a part of speech; third, the collocation relationship between words is not fully used. By targeting these three questions, this article locates the verb research object and uses the collocation information between words to calculate the grammatical and semantic similarity of the verb. Firstly, based on the dictionary of verb synonyms, using dependent data to construct the ternary collocation relationship between the verb and its subject-object core morphemes, and embed the ternary collocation information into the vector representation of the verb, finally using the cosine formula to calculate the grammatical and semantic similarity of the verb. According to the different results, the synonyms of a verb are divided into synonyms with the same grammar and semantics, synonyms with similar grammar and semantics, and synonyms with similar semantics. By tagging these three types of labels on the synonyms of verbs, more grammatical and semantic information is provided for clause-level retelling.
Complex-valued Channel-weighted Aggregated Residual Networks for Image Recognition

Convolutional neural networks (CNN) are more and more used in image recognition. Recent studies have shown that complex-valued neural networks have richer representational capability than real-valued neural networks. However, the existing complex-valued CNN still has the following problems: (1) Most of the existing models are two-branch structures; (2) The model does not consider the importance of each complex feature map. In this paper, split-transform-merge and feature recalibration scheme in complex field are proposed, and applied to complex-valued neural networks to form Complex-valued Channel-weighted Aggregated Residual Networks (cSE-ResNeXt). This paper performs the experiments on CIFAR-10 and CIFAR-100 datasets. The experimental results show that the proposed algorithm is superior to both two complex-valued nextworks (ResNeXt and ResNet) and three real-valued nextworks (SE-ResNeXt, ResNeXt and ResNet).
Document Classification by Order of Context, Concept and Semantic Relations: OCCSR

The contemporary study in text or document mining is focusing on syntactic components and the semantic environment. In order to accomplish this, and with the motivation gained from our previous research contributions, we investigated a mining model to classify documents based on the Order of Context, Concept, and Semantic Relations (OCCSR). This proposed model categorises documents into three levels: context, concept, and semantic. The document context is defined by the meta-data in the document, the concept is defined by the order of features, and semantic relations are assessed by correlating the activities observed in the documents. The experimental results show that the OCCSR has high classification accuracy,&nbsp; scalable, and&nbsp; robust. The study findings lead us to the conclusion that context similarity, in addition to concept and semantic similarity, is more important in achieving classification accuracy in supervised learning. The OCCSR is evaluated using a confusion matrix and discriminator metrics. The model developed here is extremely useful, particularly for assessing the relationship of documents published in social communities such as electronic journals, publishers, and blogs.
A COMPREHENSIVE ANALYSIS ON THE CRIME PATTERN IMPLEMENTATION AND DEVELOPMENT

Crime is a significant issue, where our management has given the full focus. Efficient analysis of various remote detecting methods is refined for crime examination. This exploration work proposes the use of short detecting systems to concentrate on Crime with the assistance of research devices. This paper extends a sign of the devices and procedures executed in the Crime with effective innovations dependent on remote detecting. The advances in doing the device have great temptation in the current changing crime circumstance. They can be used as a predominant device by law authorisation for crime investigation, recognition and prevention.
Comparison of Nature-Inspired Approaches for Path Planning Problem of Mobile Robots in MATLAB

Robot path planning is a significant research topic that comprises massive mathematical computations for generating an optimal shortest collision-free path from starting position to goal position within minimum processing time. Robotics is an interdisciplinary field that is directly related to automation. Automation requires planning and organization of workspace for the autonomous working of robots and machines. Recently, researchers’ curiosity has increased to implement nature-inspired evolutionary (NIE) approaches for path planning and obstacle avoidance (PPOA) problem. NIE approaches allow robots to learn from elements of nature and perform better compared to conventional and heuristic PPOA approaches. This work draws a comparison between the IWO and ABC algorithm for the same set of environments and initial conditions for the PPOA problem. The results show that the IWO algorithm has a faster convergence rate and high precision than the ABC algorithm.