Reviewerly/exHarmony huggingface dataset:
Hybrid ensemble and soft computing approaches for review spam detection on different spam datasets

Online customer reviews play an important influence in a client's choice to purchase an item and are seen as a significant source of knowledge that can be used to foresee public opinion when making purchase or commodity from an online store. These opinions are frequently manipulated by people who haven't been registered on the websites or who are paid by other organisations to create negative or misleading reviews about an item or brand. Spam reviews are generally fraudulent reviews, and they can have a significant impact on the digital marketplace's behaviour. With the growing need to convert such facts into usable evidence and knowledge, information retrieval and data mining have gotten a lot of attention. The modelling of documents into a clear and accurate set of characteristic single and numerous word phrases, which has an impact on overall actions and achievements, is predicted to be a key problem in natural language processing (NLP) known as feature extraction process. Quite a few kinds of features could be used to perform different tasks through various feature collection methods such as: Text categorization (Bag of words), Linguistic characteristics (LIWC), Genre Identification (POS tagging), Sentimental feature extraction, Term frequencies (TFIDF) and Word2Vec. For traditional classification tasks with smaller datasets, machine learning algorithms have produced the greatest results. Soft computing techniques, on the other hand, when optimised with larger datasets, provide a better solution for real-world scenario problems. This work is organised into several sections, each of which examines a distinct feature selection method, machine ensemble learning, and soft computing approaches in depth. Finally, the hybrid approach for detecting review spam is introduced.
The digital literacy of students belonging to different sectors and studying on multicultural campuses

Most academic campuses in Israel are multicultural, with a diverse composition of students. Using a mixed-method, mostly quantitative approach, this article examines the extent to which students' cultural backgrounds affect their level of digital literacy. The findings show that students in two groups perceived digital learning differently across all seven factors examined: digital information processing and presentation; evaluation of information from the network; data collection and retrieval from the net; digital information management; communication and teamwork; awareness of integrity and ethical standards; and social responsibility. Ultra-Orthodox students perceived the digital environment as a means that enabled them to study, access course sites, and submit assignments. By contrast, students from the general population, with full access to digital environments, perceived access to information and communication technology (ICT) as an end in itself. Accepting cultural differences and different worldviews, will grant students equal opportunities, social mobility, and successful completion of studies.
A probable cheating-free (t, n) threshold secret sharing scheme with enhanced blockchain

A cheating-free (t, n) threshold secret sharing scheme using Blockchain is presented, where Shamir's technique is followed and the Blockchain vulnerabilities due to PoW/PoS (Proof-of-Work/Stake) consensus, are removed. We address the shares' cheating issues and design a Blockchain based SS scheme, in which the participants' shares are recorded secretly in a block and the dealer's identity along with other parameters are made visible through the genesis block. Our scheme uses a short-length Blockchain comprising 2 blocks per secret negotiation session. The main objectives are to improve Shamir's secret sharing scheme with Blockchain such that the cheating activities from dealer and/or participants become infeasible, and to enhance Blockchain technology with a consensus mechanism using Blockchain based SS scheme such that insecurity of PoW/PoS consensus are avoidable. The security and performance analysis of the scheme are provided. Although Blockchain increases some overheads, it makes a complete cheating-free SS scheme.
Best Practices for HPC Workloads on Public Cloud Platforms

HPC (high performance computing) applications come with a variety of requirements for computation, communication, and storage; and many of these requirements can be met with commodity technology available in public clouds. In this article, we report on results for several well-known HPC applications on IBM public cloud, and we describe best practices for running such applications on cloud systems in general. Our results show that public clouds are not only ready for HPC workloads, but they can provide performance comparable to, and in some cases better than, current supercomputers.
Signal-to-Noise Ratio Comparison of Several Filters against Phantom Image

Image denoising methods are important in order to diminish various kinds of noises, which are presented either capturing the image or distorted during image transmission. Signal-to-noise ratio (SNR) is one of the main barriers which avoids the theoretical observations to be accomplished in practice. In this study, we have utilized various kinds of filtering operators against three various noises, which are the signal-to-noise ratio comparison against the phantom image in spatial and frequency domain. In frequency domain, the average filter is used to smooth the image and frequency domain, and Gaussian low-pass filter is applied with empirically determined cutoff frequency. This work has six major parts such as applying average filter, determining the SNR of region of interest, transforming the image in frequency domain by discrete Fourier transform, obtaining the rectangular Gaussian low-pass filter along with a cutoff frequency, multiplying them, and carrying out the inverse Fourier transform. These steps are repeated accordingly until the resulting image SNR is equal to or greater than the spatial domain SNR. In order to achieve the goal of this study, we have analyzed the proposed approach against some of complex phantom images. The performances of these filters are compared against signal-to-noise ratio.
On the Search of Speculations

This paper concerns the internal structure of reasoning that, basically consisting in conjecturing and refuting, is too often identified with only deducing, abducing and refuting, that is, with just the deductive search of consequences, hypotheses and refutations. With such identification, it is forgotten that in addition to consequences and hypotheses, there is a third class of conjectures, speculations or proper guesses. Speculations are inferentially non-comparable, or orthogonal, with the premise, and generate creativity. It is presented a very simple formal view for the structure of Commonsense Reasoning, a mathematical model allowing to show the importance of speculations, and specially to start with its systematic computational search.
Binocular measurement method for the continuous casting slab model based on the improved BRISK algorithm

Due to the low accuracy of the traditional image feature matching algorithm in binocular vision measurement, a binocular measurement method for the continuous casting slab model based on the improved binary robust invariant scalable keypoints (BRISK) algorithm is proposed. First, the feature points of the image are detected. After that, local area sampling and sub-area division are carried out with the feature points as the center, sub-areas with low offset values are removed, and the main direction is obtained by using the centroid of the remaining sub-areas. Then, the gray difference threshold is used to replace the traditional gray value intensity comparison to generate descriptors. Finally, the Hamming distance is used to match the feature points, and the three-dimensional coordinates of the matching points are calculated to complete the measurement. Through comparative experiments, the lowest relative error of the improved algorithm in this paper reaches 0.4723%, which meets the requirement of measurement accuracy.
Collaborative Filtering Recommendation of Music MOOC Resources Based on Spark Architecture

With the rapid development of MOOC platforms, MOOC resources have grown substantially, causing the problem of information overload. It is difficult for users to select the courses they need from a large number of MOOC resources. It is necessary to help users select the right music courses and at the same time make the outstanding music courses stand out. Recommendation systems are considered a more efficient way to solve the information overload problem. To improve the accuracy of the recommendation results of music MOOC resources, a mixed collaborative filtering recommendation algorithm based on Spark architecture is proposed. First, the user data and item data are modeled and scored by the collaborative filtering algorithm, then the tree structure of the XGBoost model and the features of regular learning are combined to predict the scores, and then the two algorithms are mixed to solve the optimal objective function to obtain the set of candidate recommendation data. Then, the frog-jumping algorithm is used to train the weighting factors, and the optimal combination of weighting factors is used as the training result of the samples to realize the data analysis of the mixed collaborative filtering recommendation algorithm. The experimental results in the music MOOC resource show that the average absolute error and root mean square error of the proposed method are 0.406 and 1.117, respectively, when the sparsity is 30%, which are lower than those of other existing collaborative filtering recommendation methods, with higher accuracy and execution efficiency.
Motion detection and classification: ultra-fast road user detection

Abstract With the emerge of intelligent and connected transportation systems, driver perception and on-board safety systems could be extended with roadside camera units. Computer vision can be utilised to detect road users, conveying their presence to vehicles that cannot perceive them. However, accurate object detection algorithms are typically computationally heavy, depending on delay-prone cloud computation or expensive local hardware. Similar problems are faced in many intelligent transportation applications, in which road users are detected with a roadside camera. We propose utilising Motion Detection and Classification (MoDeCla) for road user detection. The approach is computationally lightweight and capable of running in real-time on an inexpensive single-board computer. To validate the applicability of MoDeCla in intelligent transportation applications, a detection benchmark was carried out on manually labelled data gathered from surveillance cameras overseeing urban areas in Espoo, Finland. Separate datasets were gathered during winter and summer, enabling comparison of the detectors in significantly different weather conditions. Compared to state-of-the-art object detectors, MoDeCla performed detection an order of magnitude faster, yet achieved similar accuracy. The most impactful deficiency of MoDeCla was errors in bounding box placement. Car headlights and long dark shadows were found especially difficult for the motion detection, which caused incorrect bounding boxes. Future improvements are also required for separately detecting overlapping road users.
Comparison Analysis of Data Augmentation using Bootstrap, GANs and Autoencoder

In order to improve predictive accuracy for insufficient observations, data augmentation is a well-known and commonly useful technique to increase more samples by generating new data which can avoid data collection problems. This paper presents comparison analysis of three data augmentation methods using Bootstrap method, Generative Adversarial Networks (GANs) and Autoencoder for increasing a number of samples. The proposal is applied on 8 datasets with binary classification from repository data websites. The research is mainly evaluated by generating new additional data using data augmentation. Secondly, combining generated samples and original data. Finally, validating performance on four classifier models. The experimental result showed that the proposed approach of increasing samples by Autoencoder and GANs achieved better predictive performance than the original data. Conversely, increasing samples by Bootstrap method provided lowest predictive performance.
Intelligent Space-Based Future Convenience Store for Customer Interaction Service with New Experience, Safety, and Flexibility

In the Future Convenience Store Challenge (FCSC) of the World Robot Summit (WRS), the Customer Interaction Task has been conducted from 2017 to 2020 to propose new services and store designs that can be achieved using robot technology to serve customers in convenience stores in the near future. In the Customer Interaction Task of WRS 2020 FCSC, held in Aichi, Japan, we present a convenience store introducing intelligent space, with new experiences, safety, and flexibility as key concepts. We propose a product recommendation that reflects the owner's intentions, an assistive robot that provides customers desired assistance, and a monitoring service with safety and relief.
Integrated Design of Financial Self-Service Terminal Based on Artificial Intelligence Voice Interaction

Integrated design of financial self-service terminal based on artificial intelligence voice interaction with the rapid development of science and technology, artificial intelligence technology is deepening in the field of intelligence and automation. The financial industry is the lifeblood of a country's economy, with great growth potential and high growth rate. The integrated design of intelligent financial self-service terminal has become an important topic in the field of rapid development of social economy and science and technology. Therefore, this paper designs the integration of financial self-service terminal based on artificial intelligence voice interaction. First, this paper introduces the meaning and composition of financial self-service terminal integration, then studies the voice interaction principle based on artificial intelligence technology, and designs the integrated structure of financial self-service terminal with voice interaction. After that, this paper makes a series of tests on voice interaction technology, user experience, and the performance of financial self-service terminal. Finally, the test results of voice interaction are as follows: the delay estimation results of voice interaction of the terminal are relatively accurate, and the error points are basically within five sampling points, which indicate that the delay estimation algorithm is practical. The endpoint detection method based on CO complexity can effectively overcome the impact of noise environment on speech endpoint detection system and is suitable for the requirements of robust speech recognition system. Considering that the actual application scenario of voice positioning can judge the speaker's position and turn to the speaker's direction during human-computer interaction, the azimuth error is acceptable within a few degrees to meet the application requirements. The direction angle error is acceptable within a few degrees to meet the application requirements. The accuracy of the improved algorithm is improved in intercepting effective speech signals. The terminal has short running time and delay time, small memory, and central processing unit (CPU) occupation and can meet the needs of users. The speech recognition accuracy of the financial self-service terminal basically reaches more than 80%, which can basically meet the daily needs.
A deformation model of pulsating brain tissue for neurosurgery simulation

For neurological simulation, an accurate deformation model of brain tissue is of key importance for faithful visual feedback. Existing models, however, do not take into account intracranial pulsation, which degrades significantly the realism of visual feedback.In this paper, a finite element model incorporating intracranial pressure is proposed for simulating brain tissue deformation with pulsation. An implicit Euler method is developed to calculate the deformation of brain tissue. A circuit model of intracranial pressure dynamics is established based on cerebral blood and cerebrospinal fluid circulations. The intracranial pulsation of pressure is introduced into the deformation model, so that the simulated brain tissues pulsate with a rhythm in accord with the changes of intracranial pressure, which resembles real-life neurosurgery.The experimental implementation of the proposed deformation model and the calculation method shows that it provides realistic simulation of brain tissue pulsation and real-time performance is achieved on an ordinary computer for certain procedures of neurosurgery.
Delineation of sedimentary bauxite deposits in Shaanxi Province using the gravity and transient electromagnetic methods

In this study, a proposed method integrating gravity and transient electromagnetic (TEM) surveys was used for bauxite prospection in Shaanxi Province, China. Although the gravity method enables the identification of interfaces related to mineralization, it is unsuitable for determining the location and burial depth of such an interface. Conversely, the TEM method can accurately detect the burial depth and location of an interface, and thus, its integration with the gravity method can enable the determination of burial depth and shape for interfaces related to the mineralization of bauxite ore bodies. To verify the effectiveness of the integrated approach, gravity and TEM surveys were first conducted in an area in Puxian County, Shaanxi Province, China, which hosts known bauxite deposits. Next, Ordovician limestones associated with depressions were delineated using high-precision gravity survey, and interfaces likely to be related to mineralization were identified. These potential regions were verified using TEM data, and their locations were evaluated. Bauxite ore was identified on the interfaces between limestone and the basal surface of overlying strata, and this was validated using drilling results. This integrated approach was then extended to an unexplored area in Heyang County, Shaanxi Province, and could be applicable to other sedimentary bauxite deposits in the North China Craton.
Answer Set Planning: A Survey

Abstract Answer Set Planning refers to the use of Answer Set Programming (ASP) to compute plans , that is, solutions to planning problems, that transform a given state of the world to another state. The development of efficient and scalable answer set solvers has provided a significant boost to the development of ASP-based planning systems. This paper surveys the progress made during the last two and a half decades in the area of answer set planning, from its foundations to its use in challenging planning domains. The survey explores the advantages and disadvantages of answer set planning. It also discusses typical applications of answer set planning and presents a set of challenges for future research.
Web Page Classification Algorithm Based on Deep Learning

Transmit and process information to establish a learning mechanism and realize the processing of image data and sound data. However, the current research on Web page classification algorithm (WPCA) based on deep learning (DL) is not in-depth. Therefore, the main research of this article is the research of WPCA based on DL. This article first uses the keyword weight calculation method to reduce the impact of a small number of high-frequency words in the web page document on the weight calculation and reduces the value of the low-frequency word weights so that the WPCA is more accurate in the calculation process; second, the use of Chinese web pages: the classification method calculates the similarity between the text to be classified and all the class templates and then determines the category of all texts according to the similarity and certain classification rules; finally, in order to improve the learning rate of DL, consider using adaptive parameters. The optimization algorithm automatically adjusts the size of the learning rate, making the research of WPCA based on DL more efficient. After comparing the DL-based WPCA with the traditional algorithm, the data shows that in terms of time expenditure, the DL WPCA is 354 s, the traditional algorithm is 2436 s; in terms of memory overhead, the DL WPCA is 6.35 s, the traditional algorithm is 186.25 s. The experimental results show that WPCA based on DL are faster and more efficient than traditional algorithms and consume less system memory.
Antifascist argumentation in Giacomo Matteotti’s 1924 speech to the Italian Parliament

Abstract Decades after the Second World War, fascism is still rife in Europe. After acknowledging a certain difficulty on the part of contemporary antifascist arguers to affirm their tenets and discredit those of their opponents, this investigation looks back at the past to examine an inspiring antifascist speech. The speech that socialist leader Giacomo Matteotti delivered to the Italian Parliament in 1924 is analysed though the methodological lens of Pragma-dialectics, focusing on the notions of strategic manoeuvring, argumentative structure and argumentative style. The results show that Matteotti’s argumentation against the fascists hinged on facts and rules as premises of argumentation. The speaker supported his arguments with factual evidence and organised them in a multiple argumentation structure. In addition, he “dressed up” his speech, apparently characterised by a detached argumentative style, with traits of a more engaged style, whose combination has turned Matteotti’s invective against the fascist regime into an argumentative masterpiece.
Face Mask Detection using MobileNetV2

To stem the COVID19 pandemic, great attention is needed to mitigate public health and the global economy which are negatively impacting. To overcome this, a technique is required to urge people put on the face mask. To contribute to the health of communities, this article aims to design a very precise and real-time analysis that can efficiently detect non-mask faces in public and thus, enforcing to wear mask. According to the World Health organization, the most effective way to fight the transmission of the corona virus is to wear medical masks. The detection of face mask in is done with the machine learning by using the series of stages involved through classification of images: MobileNetV2. The steps and stages used for developing the model square measure grouping the information, and pre-processing the data to remove noisy data, splitting the data, testing the model for the accuracy, and implementation of the model. The engineered model will sight those that square measure sporting a mask associated not sporting it at an accuracy of 95.85 percent.
AI Crowd Control Detection System Implemented on FPGA Hardware Development Platform

This paper proposes a detection application for crowd control. Detecting objects is mainly to identify pedestrians at close range. The deep learning network uses Yolo-like. And change Yolo-like[1] into a network architecture that only recognizes pedestrians at close distances. The hardware uses Convolution and Detection Layer IP hardware accelerators. And realize the function of Maxpooling and Shortcut on FPGA platform. The FPGA platform is Xilinx Zynq UltraScale+ MPSoC ZCU102 Evaluation Kit. Finally, the FPGA platform uses HDMI to display the recognition results that only detect pedestrians at close distances. Achieve the detection effect of crowd control.