Reviewerly/exHarmony huggingface dataset:
Intelligent Mobile Edge Computing Integrated with Blockchain Security Analysis for Millimetre-Wave Communication

With the increase in number of devices enabled the Internet of Things (IoT) communication with the centralized cloud computing model. With the implementation of the cloud computing model leads to increased Quality of Service (QoS). The cloud computing model provides the edge computing technologies for the real-time application to achieve reliability and security. Edge computing is considered the extension of the cloud computing technology involved in transfer of the sensitive information in the cloud edge to increase the network security. The real-time data transmission realizes the interaction with the high frequency to derive improved network security. However, with edge computing server security is considered as sensitive privacy information maintenance. The information generated from the IoT devices are separated based on stored edge servers based on the service location. Edge computing data is separated based in edge servers for the guaranteed data integrity for the data loss and storage. Blockchain technologies are subjected to different security problem for the data integrity through integrated blockchain technologies. This paper developed a Voted Blockchain Elliptical Curve Cryptography (VBECC) model for the millimetre wave application. The examination of the blockchain model is evaluated based on the edge computing architecture. The VBECC model develop an architectural model based Blockchain technology with the voting scheme for the millimetre application. The estimated voting scheme computes the edge computing technologies for the estimation of features through ECC model. The VBECC model computes the security model for the data transmission in the edge computing-based millimetre application. The experimental analysis stated that VBECC model uses the data security model ~8% increased performance than the conventional technique.
Retracted: Cognitive Computation in Ideological and Political Classroom Teaching Based on Digital Sensor Technology

[This retracts the article DOI: 10.1155/2022/8723327.].
Convolutional Neural Network Approach and Implementation Sensor Design for Detecting Object

Abstract This research offered sensor design for detecting animal for any applications uses convolutional neural network within JeVois smart camera. The camera is connected to communication processor through serial connection. By using 900 images for training, the sensor has been able to detect the intended animal presence and send the result to end node. The acuracy achieves 95 %, with 90.91% precision, recall 100% and F score of 95.24 %. Even dough the power consumption increases from 2.2 W to 4.08 W, the detection is potentially reduces number of the transmitter images.
Rough Set: Utilizing Machine Learning for the Covid-19 Vaccine

Abstract Rough Set is a machine learning algorithm that analyses and determines important attributes based on an uncertain data set. The purpose of this study is to classify public interest in the Covid-19 vaccine. Vaccination is one of the solutions from the government that is considered the most appropriate to reduce the number of Covid-19 cases. Data collection was taken through a questionnaire distributed to the village community in Air Manik Village, Padang-West Sumatra, randomly as many as 100 respondents. The assessment attributes in this study are Vaccine Understanding (1), Environment (2), Community Education (3), Vaccine Confidence (4), and Cost (5), while the target attribute is the result that contains the community’s interest or not to participate in vaccination. The analysis process is assisted using the Rosetta application. This study resulted in 3 reductions with 58 rules based on 100 respondents. This study concludes that the Rough Set algorithm can be used to classify public interest in the Covid-19 vaccine. Based on this research, it is hoped that it can provide information and input for local governments to be more aggressive in urging and encouraging the public to be vaccinated.
Convolutional Neural Network for object Identification and Detection

Abstract The goal of this study is to use a Convolutional Neural Network to find the optimum architectural model for classifying cloud images. Cirrus Cumulus Stratus Nimbus uses a source dataset that includes 11 cloud classifications and 2545 cloud photos (CCSN). In this study, the best Convolutional Neural Network is retrained almost fast by transferring education from Google’s basic design. Based on the modified Googlenet architecture, the training and testing phases of the classification process are divided into two. The dataset is separated into three sections during the training phase: 70% of the training data, 15% of the validation data, and 15% of the test data. There are two trials to categorize cloud photographs during the test phase, one of which has ten cloud kinds that can be randomly chosen. The precision achieved throughout the training was 44.5%, according to the findings. The results of the two tests are 75%, with an average error of 0.2. In the testing phase, the percentage is 75%.
Coal seam in-situ inorganic analysis based on least angle regression and competitive adaptive reweighted sampling algorithm by XRF–visNIR fusion

The fusion of X-ray fluorescence spectroscopy (XRF) and visible near infrared spectroscopy (visNIR) has been widely used in geological exploration. The outer product analysis (OPA) has a good effect in the fusion. The dimension of the spectral matrix obtained by OPA is large, and the Competitive Adaptive Reweighted Sampling (CARS) cannot cover the whole spectrum. As a result, the selected variables by the method are inconsistent each time. In this paper, a new feature variable screening method is proposed, which uses the Least Angle Regression (LAR) to select the high dimensional spectral matrix first, and then uses CARS to complete the secondary selection of the spectral matrix, forming the LAR-CARS algorithm. The purpose is to make the sampling method cover all the spectral data. XRF and visNIR tests were carried out on three cores in two boreholes, and a cross-validation set, validation set and a test set were established by combining the results of wavelength dispersion X-ray fluorescence spectrometer and ITRAX Core scanner in the laboratory. The quantitative model was established with the Extreme Gradient Boosting (XGBoost) and LAR-CARS was compared to these other algorithms (LAR, Successive Projections Algorithm, Monte Carlo uninformative variables elimination and CARS). The results showed that the RMSEP values of the models established by the LAR-CARS for six rock-forming elements (Si, Al, K, Ca, Fe, Ti) were relatively small, and the RPD ranges from 1.424 to 2.514. All these results show that the high-dimensional matrix formed by XRF and visNIR integration combined with LAR-CARS can be used for quantitative analysis of rock forming elements in in-situ coal seam cores, and the analysis results can be used as the basis for judging lithology. The research will provide necessary technical support for digital mine construction.
Analysis Of Sediment Concentration With Flow Speed In The Primary Channel In Saddang

Abstract The decrease in channel capacity can cause sedimentation at the bottom of the channel and the condition of the channel lining is damaged so that seepage / leaks occur and buildings and sluice gates do not function properly. This study aims to analyze the concentration of sediment on the flow velocity. Measurements were carried out in the field to observe field conditions in detail related to sediment velocity and concentration. This research was carried out on the Rappang Main Channel from Upstream to Downstream of the Rappang Main Channel. The distance between upstream to downstream of the Rappang Main Channel is +- 30 Km. The distance between the upstream to the center of the research point is +- 18 Km, while the distance between the middle to the downstream channel is +- 12 Km. Velocity measurement using A.OTT propeller current meter model and sediment collection in the channel using grab sampler and van dorn tools. The results showed the effect of velocity on the value of sediment concentration in the dry season.
Numerical investigation of a segmented-blade ion trap with biasing rods

We report a numerical study of a linear ion trap that has segmented blades and biasing rods. Our system consists of radio frequency (rf) blades, dc blades with ten separate electrodes, and two biasing rods for compensating the ions’ micromotion. After calculating the optical access for the ions, we find rf and dc voltages that result in a stable trapping configuration of $$^{171}$$ Yb $$^{+}$$ ions. We also explore the micromotion compensation with the biasing rods, and calculate the influence of blade misalignment to the trap potential. Our work offers quantitative understanding of the trap architecture, assisting reliable operation of an ion-trap quantum computer.
"Human resource management model of mega sporting events —— the Beijing Winter Olympics as an example"

With the accelerated pace of globalization, the frequency and number of major international sports events are gradually increasing. The human resource management model has also been considered in this context, playing a more important role. In order to discuss this issue in depth, this paper will take the 2022 Beijing Winter Olympic Games as a case study, and discuss the changes and upgrades of HRM model in detail from the perspective of recruitment and management in human resources, from the surface way to the deep content. First of all, in the recruitment, Beijing Winter Olympic Games has a unique way of recruitment, through many media and Internet resources to promote the unprecedented effect. The mass adoption of recruitment on the Internet has attracted talents from all over the world to participate. Secondly, in the management, for the volunteers, due to the epidemic, a longer period of closed management was adopted, and the Beijing Winter Olympic Organizing Committee also focused on the psychological state of the staff and volunteers. In order to ensure people's life and living in a positive and effective way, in order to achieve a more efficient human management. In the end, the author summarizes the changes in the model of human resources management in the Winter Olympics, summarizes the characteristics of large international sporting events, refines the universal model with high feasibility, and summarizes the experience with the actual.
NURO’s future challenge to delivering robots

Business analysis act as an important part of every company. NURO is a newly established business that found an electric-powered delivery vehicle that could drive by itself. The service is based on last-mile delivery of groceries, food, consumer products, and parcels. This article mainly focuses on the prospect development of artificial intelligence which may become a fashion trend in the future. This article uses NURO which is a new technology company, creating a delivery vehicle as an example and analysis it from the aspects of business, strategies, and finance by using the SWOT method. Moreover, there are several goals that we are trying to achieve. For instance, NURO's cars should become more sustainable and safer, making effective delivery happens. As a result, if NURO can reach this altitude, there is no competitor left to compete market with NURO. It would not be limited to delivering parcels like necessities or daily products mainly within the community.
Incorporating Sentiment and Temporal Information for Bitcoin Price Prediction

Recent years have witnessed the rapid development of bitcoin as the first digital currency. Considering the advantages of bitcoin for both individuals and society, the price prediction of bitcoin is a hot topic. However, there remain two main challenges to be addressed in this task. Firstly, because bitcoin is vulnerable to the attitudes of the investors, incorporating the sentiment semantics from the social media into the prediction is challenging. Secondly, it is intractable to predict extreme volatility of the bitcoin price. To tackle the above challenges, this paper proposes to incorporate sentiment and temporal information simultaneously. For the first challenge, this paper employs external unsupervised corpus to conduct the domain-specific post-pretraining on the off-the-shelf language model. And the sentiment analysis on the tweets is done to obtain the scores. For the second one, Long Short Term Memory (LSTM) network is leveraged to joint model the temporal price data and the sentiment scores, thus deriving the final predictions. Experiments on the real data show that compared with the single-layer LSTM model, the model in this paper works better, which provides help for investors to specify trading strategies and also provides implications for government agencies that are developing digital currencies.
Comparison on the Implementations of ERPs with Different Versions

As a typical type of Management Information System (MIS), Enterprise Resource Planning (ERP) systems are mature on assisting in managerial operation with businesses. With the development of digital technologies and wide acceptance on MIS, Cloud ERP has received extensive attractions than Traditional ERP. This paper aims to analyze and evaluate the comparison between Cloud ERP with Traditional ERP. Both Traditional ERP and Cloud ERP have their competitive advantages since Cloud ERP is not mature in all dimensions. Traditional ERP is more stable, safer and cheaper, whereas Cloud ERP is more efficient and more flexible. Therefore, small or middle-size companies are suitable to use Traditional ERP, and large enterprises with rapid growth and changes are more suitable for Cloud ERP. Suitability and adaptability are the main factors that should be considered when the enterprise chooses its ERP system. The results of this paper provide general suggestions on the directions of selecting ERP systems for enterprises of different sizes.
Media freedoms and civic space in the digital age for transparency, accountability and citizen participation

This chapter provides an overview of the status of press freedom and civic space in a digitalised world, including relevant legal frameworks. It discusses harassment and attacks targeting journalists and makes suggestions on building the necessary enabling environment for reliable, fact-based journalism. It considers the protection of online civic space for citizens and related challenges such as hate speech and mis- and disinformation. It concludes with an analysis of the importance of personal data protection for civic space and safeguarding civic freedoms in the context of increased use of artificial intelligence (AI).
Attentional-Biased Stochastic Gradient Descent

In this paper, we present a simple yet effective provable method (named ABSGD) for addressing the data imbalance or label noise problem in deep learning. Our method is a simple modification to momentum SGD where we assign an individual importance weight to each sample in the mini-batch. The individual-level weight of sampled data is systematically proportional to the exponential of a scaled loss value of the data, where the scaling factor is interpreted as the regularization parameter in the framework of distributionally robust optimization (DRO). Depending on whether the scaling factor is positive or negative, ABSGD is guaranteed to converge to a stationary point of an information-regularized min-max or min-min DRO problem, respectively. Compared with existing class-level weighting schemes, our method can capture the diversity between individual examples within each class. Compared with existing individual-level weighting methods using meta-learning that require three backward propagations for computing mini-batch stochastic gradients, our method is more efficient with only one backward propagation at each iteration as in standard deep learning methods. ABSGD is flexible enough to combine with other robust losses without any additional cost. Our empirical studies on several benchmark datasets demonstrate the effectiveness of the proposed method.\footnote{Code is available at:\url{https://github.com/qiqi-helloworld/ABSGD/}}
Quantum advantage for combinatorial optimization problems, Simplified

We observe that fault-tolerant quantum computers have an optimal advantage over classical computers in approximating solutions to many NP optimization problems. This observation however gives nothing in practice.
Improving Uncertainty Quantification of Variance Networks by Tree-Structured Learning

To improve the uncertainty quantification of variance networks, we propose a novel tree-structured local neural network model that partitions the feature space into multiple regions based on uncertainty heterogeneity. A tree is built upon giving the training data, whose leaf nodes represent different regions where region-specific neural networks are trained to predict both the mean and the variance for quantifying uncertainty. The proposed Uncertainty-Splitting Neural Regression Tree (USNRT) employs novel splitting criteria. At each node, a neural network is trained on the full data first, and a statistical test for the residuals is conducted to find the best split, corresponding to the two sub-regions with the most significant uncertainty heterogeneity between them. USNRT is computationally friendly because very few leaf nodes are sufficient and pruning is unnecessary. Furthermore, an ensemble version can be easily constructed to estimate the total uncertainty including the aleatory and epistemic. On extensive UCI datasets, USNRT or its ensemble shows superior performance compared to some recent popular methods for quantifying uncertainty with variances. Through comprehensive visualization and analysis, we uncover how USNRT works and show its merits, revealing that uncertainty heterogeneity does exist in many datasets and can be learned by USNRT.
STRUDEL: Structured Dialogue Summarization for Dialogue Comprehension

Abstractive dialogue summarization has long been viewed as an important standalone task in natural language processing, but no previous work has explored the possibility of whether abstractive dialogue summarization can also be used as a means to boost an NLP system's performance on other important dialogue comprehension tasks. In this paper, we propose a novel type of dialogue summarization task - STRUctured DiaLoguE Summarization - that can help pre-trained language models to better understand dialogues and improve their performance on important dialogue comprehension tasks. We further collect human annotations of STRUDEL summaries over 400 dialogues and introduce a new STRUDEL dialogue comprehension modeling framework that integrates STRUDEL into a graph-neural-network-based dialogue reasoning module over transformer encoder language models to improve their dialogue comprehension abilities. In our empirical experiments on two important downstream dialogue comprehension tasks - dialogue question answering and dialogue response prediction - we show that our STRUDEL dialogue comprehension model can significantly improve the dialogue comprehension performance of transformer encoder language models.
Out-of-Distribution Detection with Reconstruction Error and Typicality-based Penalty

The task of out-of-distribution (OOD) detection is vital to realize safe and reliable operation for real-world applications. After the failure of likelihood-based detection in high dimensions had been shown, approaches based on the \emph{typical set} have been attracting attention; however, they still have not achieved satisfactory performance. Beginning by presenting the failure case of the typicality-based approach, we propose a new reconstruction error-based approach that employs normalizing flow (NF). We further introduce a typicality-based penalty, and by incorporating it into the reconstruction error in NF, we propose a new OOD detection method, penalized reconstruction error (PRE). Because the PRE detects test inputs that lie off the in-distribution manifold, it effectively detects adversarial examples as well as OOD examples. We show the effectiveness of our method through the evaluation using natural image datasets, CIFAR-10, TinyImageNet, and ILSVRC2012.
COST EFFECTIVE MECHANISM TO TREAT WASTE WATER

Cost effective mechanism is the treatment of waste water to use treated water further for irrigation purpose and also to increase ground water level. That means, to reduce the concentration of TDS, BOD, COD, DO of waste water. Waste water includes plastic, sludge, and solid waste which directly and indirectly affect human health, underground water, and agriculture. In primary treatment, Screens are used for separating the solid waste and sludge flowing with waste water. We can use this separated sludge as a fertilizer for agriculture purpose. Purification of the waste water is done by aeration and sedimentation processes by providing porous bandhara, solid bandhara, &amp; porous gravel filter. In further process a horizontal constructed wetlands are used. Constructed wetlands are shallow extensively vegetated water bodies that use enhanced sedimentation; fine filtration and pollutant absorb process to remove pollutants from waste water. This technology is using a natural way to effectively treating the waste water where no external energy is required as well as having low maintenance cost. This method is beneficial in draught areas to increase ground water level.
ICT ADOPTION AND EFFECTS ON TEACHING AND LEARNING IN POST COVID-19, NIGERIA

With the Covid-19 pandemic and its attendant devastating effects that have hampered every aspect of human existence; it becomes pertinent on the government, stakeholders, and the academia to invigorate possible measures to mitigate the impact of the pandemic as it relates to teaching and learning; considering curtailing measures of the pandemic that restricted human contacts and social gatherings; hence the adoption of ICT by schools for teaching and learning. This is what necessitated this study, to ascertain the effect of ICT adoption in Nigerian schools in the post Covid-19 era. The study explored the purposive sampling technique of data collection. A wellstructured questionnaire was distributed to 250 students and 50 lecturers, selected from 5 chosen private universities in South-East Nigeria. The reliability of the instrument was determined using the Cronbach’s Alpha coefficient. The study applied the mean, standard deviation, and the Pearson’s Correlation for data Analysis. The result revealed that ICT adoption has a significant positive effect on effective teaching and learning through the provision of various ICT tools in the post Covid-19 era in Nigeria; significant positive effect in mitigating the problems of Students-Teacher ratio (mostly in overcrowded classes), and also a significant positive effect in influencing teacher subjectmatter mastery and lesson preparedness. In furtherance, the result revealed that ICT adoption in schools has no significant effect on effective classroom management (through internal learning distractors’ mitigation), and also no significant effect on education affordability (through cost implication reduction). The study concluded that ICT adoption in schools, on the average, will influence effective teaching and learning in the post Covid-19 Nigeria to a high extent, and recommended among others the need for curriculum review by the Nigerian Government for ICT inclusion, to achieve effective teaching and learning in schools in the post Covid-19 Nigeria.