Reviewerly/exHarmony huggingface dataset:
Detection method of coal low calorific value based on machine learning

Coal is a major part of the world’s energy. The low calorific value of coal is the key index to measure the calorific value of coal. Therefore, real-time detection of low calorific value of coal mine plays an indispensable guiding role in the formulation of resource mining plan. To solve the above problems, this paper proposes a DP_WTELM (D: direct orthogonal signal correction; P: principal component analysis; W: whale optimization algorithm) model based on machine learning to detect the low calorific value of coal. The model is improved based on the classical forward propagation network that is extreme learning machine (ELM). The prediction accuracy of the network is improved by using preprocessing, whale optimization algorithm, and increasing the network depth. The experiments show that in contrast to ELM models, the DP_WTELM model has better performance in predicting the low calorific value of coal and can meet the industrial requirements.
GraphWave: A Highly-Parallel Compute-at-Memory Graph Processing Accelerator

The fast, efficient processing of graphs is needed to quickly analyze and understand connected data, from large social network graphs, to edge devices performing timely, local data analytics. But, as graph data tends to exhibit poor locality, designing both high-performance and efficient graph accelerators have been difficult to realize. In this work, GraphWave, we take a different approach compared to previous research and focus on maximizing accelerator parallelism with a compute-at-memory approach, where each vertex is paired with a dedicated functional unit. We also demonstrate that this work can improve performance and efficiency by optimizing the accelerator's interconnect with multi-level multicasting to minimize congestion. Taken together, this work achieves, to the best of our knowledge, a state-of-the-art efficiency of up to 63.94 GTEPS/W with a throughput of 97.80 GTEPS (billion traversed edges per second).
Research on fake reviews detection based on graph neural network

In this paper, we address the characteristics of various associations implied between fake reviewers and fake reviews, and the ability to construct review network graphs with dependency relationships. Firstly, we use graph neural networks to extract association features between reviews, propose a method to improve graph neural networks based on TrustRank and propose a multi-feature detection framework. Then the graph neural network is used to aggregate neighborhood feature output embeddings and connect them with the paragraph vectors extracted by Doc2vec. Finally, the experiments show that the algorithm in this paper can detect more fake reviews and has a better check-all effect.
Mooring loads on Indian Moored Buoy Systems at Extreme Weather

Oceans play a crucial role in regulating the global ecology. As a result, ocean monitoring is critical for understanding and forecasting natural hazards such as cyclonic storms, tsunamis, and other natural disasters. To collect continuous real-time data, several platforms such as moored buoy systems, Argo floats, drifters, and gliders are employed around the world. Moored buoys give simultaneous meteorological, surface, and subsurface measurements. The buoy systems are susceptible to vandalism, bio-fouling, corrosion, and external environmental forces due to cyclones. The design of a moored buoy with a tension recorder to gather the in-situ load caused by wind, wave, and current on the moorings is described in this paper. The numerical analysis was performed with OrcaFlex software, taking into account the moored buoy system’s varied harsh sea environmental parameters. During the Amphan cyclone, the outcome is compared to the measured tension recorder value and a difference of 1% is attained.
Opportunities for quantum computation in computational fluid dynamics

The computational fluid dynamics is a modern study that utilize computational methods such as the direct numerical simulation, and analyze or simulate the interested fluid mechanical models such as air foils and the turbulence model. This paper reports the acquired knowledge of a literature research and introduces the computational fluid dynamics andalgorithms of quantum computation as well as discusses examples of applications of the quantumcomputationalgorithms. Quantum computation or quantum information is an area that utilizes the properties of quanta and trytobuildvarious quantum circuits or even quantum computers. On the one hand, it has an analogy to the classical computation; onthe other hand, it also gains uniqueness and superiority at problem solving. Compared with the classical computingmethods, the quantum algorithms can be quite faster at computing with higher accuracy as well as security. Due tothedemand of improvement with respect to the computational methods in this area and the recent development in quantumcomputation field, there exists an opportunity to closely relate these two areas. By constructing new quantumalgorithms, the computing methods can be expected to be exponentially faster than classical computing methods. Aproposal israised to consider the features of the quanta and try to apply the advantages towards the computational fluid dynamics.
Experiential Learning for Telehealth in Sports Science and Allied Health

This chapter offers concepts to leverage experiential learning in electronic (e-) and mobile (m-) learning environments with examples from talent development and allied telehealth disciplines. Learning goals need to cater to increasingly complex and ever-changing contexts. Experiential learning can deliver such outcomes in a digital world. E-learning and m-learning cater to the needs of learners who seek dynamic, interactive, multimodal, situated, and personalized learning. How do we incorporate experiential features in e-/m-learning talent development, sport, allied health training, and other workplace contexts? The authors weave the learnings from pilot and action research projects, as well as from real world examples to apply a model of experiential learning for e-/m-learning environments. The model will help learners critically evaluate learning applications in digital environments with tools to design their own experiential learning for e-/m-blended, a/synchronous learning environments.
Symbolism in Digital Space and Transitional Society

Communication is fundamentally a toolbox of symbols used to create meaning in society. Its existence is a necessity spawned by circumstantial and society's contingencies at every turn. It is the reason languages, cues, imageries, and symbols—the building blocks of communication—continue to evolve with the times. The ritual of this dynamism is precipitated by a number of factors like socio-cultural identity crisis, economic and political variables, etc. This transitional socialisation continues to surge in the digital space of multi-platforms and the internet. Thus, this chapter attempts to evaluate the tenders of communication in the digital space, its influence on cultural identity, and the place of Africa in the narrative. The study submits that although the global nature of communication is believed to have overbearing influence on the continent's outlook, Africa being a culturally strong entity can outsource to the rest of the world and into the digital space its numerous rich cues, symbols, and signs to give the cultural identifiers indelibility and relevance on the global map.
Improved NSGA-II algorithm for solving multimodal multi-object path planning problem

This paper proposed an improved NSGA-II algorithm for solving multimodal multi-objective path planning problems. The proposed algorithm can find not only well-converged and well-distributed non-dominated optimal solutions, but also multiple equivalent shortest paths in the decision space. First, non-dominated sorting is adopted to select the wellconverged solutions. Second, the path similarity indicator is designed to maintain the diversity of the shortest paths. Finally, the performance of the proposed algorithm is evaluated on CEC 2021 competition MMOPP test set. The comparison results show the proposed algorithm has a competing performance than chosen state-of-the-art MMOEAs.
Developing and validating an analytic rating scale for a paraphrase task

Paraphrasing is used as a language activity to train students’ comprehensive language use. Although its positive role has been commonly recognized, there is a lack of research on the construct and rating scale for paraphrase tasks. This study developed a five-level analytic rating scale and applied the scale to score one hundred and forty-three examinees’ paraphrase responses. To validate the scale using an argument-based approach, the scores were analyzed by generalizability analysis and many-facets Rasch analysis, and the responses were coded by construct-related linguistic features. The results show that: (1) The analytic scores explained 35.5% of the variance, and the relative Coef_G met the confidence requirements for a rating scale validation study; (2) The task distinguished examinees’ performance into different levels, the rater severity was consistent, and the rating dimensions were relatively independent with significant differences; (3) Construct components are reflected in the examinees’ responses; (4) The rating scale is appropriate for score reporting and decision making, and has a positive effect on teaching and learning. The study will facilitate language teachers’ assessment of students’ performance and provision of effective feedback on paraphrase tasks.
A case-based reasoning system to recommend solutions for source water protection: knowledge acquisition and modelling

“How can drinking water sources be protected, and how can one learn from others who have faced similar problems?” These questions are becoming increasingly relevant in a context where anthropogenic activities threaten natural resources. We describe in this paper a knowledge acquisition and modelling process to support complex decision-making related to source water protection. We conducted online surveys, interviews and applied thematic analysis to gather and structure knowledge from documented and undocumented sources. Our process resulted in structured descriptions of cases that consist of past problems and their implemented solutions. Case design was based on know-how and knowledge needs expressed by water stakeholders. The case base forms the backbone of a knowledge-based recommender system prototype that implements case-based reasoning. It is meant to guide decision-makers in developing solutions based on past experiences. It is a successful application of knowledge management and sharing of lessons learned for decision-making in practice.
A Novel Medical Image De-noising Algorithm for Efficient Diagnosis in Smart Health Environment

Smart healthcare is defined by the technology that leads to better diagnostic tools, better treatment for patients, and devices that improve the quality of life for anyone and everyone. Medical images have significant to facilitate that smart health environment. However, the medical images frequently get noisy in the acquisition process, which engages many different physical mechanisms. Most of the de-noising algorithms conceive the additive white Gaussian noise (AWGN). However, among the popular medical image modalities, several are degraded by some type of non-Gaussian noise, such as Poisson noise. Poisson noise is mainly associated with many imaging modalities like single-photon emission computerized tomography (SPECT), (positron emission tomography) PET, and fluorescent confocal microscopy imaging. Because of the signal-dependent nature of Poisson noise, the various de-noising filters proposed in the literature, including the Non-Local Mean (NL-Mean) filter. In literature, NL-Mean is mostly applied for Gaussian noise extraction and very rarely used for Poisson noise removal. In this work, notable efforts are put to modified NL-Mean filter, and high order NL-Mean Methods are proposed. These novel high order algorithms de-noise images by prominent the signals and noise because it takes the high order odd moment of the medical image. The visual quality of the de-noised medical image (PET) and correlation graph determines that the proposed algorithms outperform the conventional de-noising filter. This study's findings will significantly contribute to the development of a more accurate and robust image analysis model, which is the need of today's modern age of digitization.
A New High-Capacity Audio Watermarking Based on Wavelet Transform using the Golden Ratio and TLBO Algorithm

Digital watermarking is one of the best solutions for copyright infringement, copying, data verification, and illegal distribution of digital media. Recently, the protection of digital audio signals has received much attention as one of the fascinating topics for researchers and scholars. In this paper, we presented a new high-capacity, clear, and robust audio signaling scheme based on the DWT conversion synergy and golden ratio advantages using the TLBO algorithm. We used the TLBO algorithm to determine the effective frame length and embedded range, and the golden ratio to determine the appropriate embedded locations for each frame. First, the main audio signal was broken down into several sub-bands using a DWT in a specific frequency range. Since the human auditory system is not sensitive to changes in high-frequency bands, to increase the clarity and capacity of these sub-bands to embed bits we used the watermark signal. Moreover, to increase the resistance to common attacks, we framed the high-frequency bandwidth and then used the average of the frames as a key value. Our main idea was to embed an 8-bit signal simultaneously in the host signal. Experimental results showed that the proposed method is free from significant noticeable distortion (SNR about 29.68dB) and increases the resistance to common signal processing attacks such as high pass filter, echo, resampling, MPEG (MP3), etc.
IMPLEMENTASI ALGORITMA FUZZY C-MEANS DAN FUZZY POSSIBILISTICS C-MEANS UNTUK KLASTERISASI DATA TWEETS PADA AKUN TWITTER TOKOPEDIA

Social media has become the most popular media, which can be accessed by young to old age. Twitter became one of the effective media and the familiar one used by the public, thus making the company make Twitter one of the promotional tools, one of which is Tokopedia. The research aims to group tweets uploaded by @tokopedia Twitter accounts based on the type of tweets content that gets a lot of retweets and likes by followers of @tokopedia. Application of text mining to cluster tweets on the @tokopedia Twitter account using Fuzzy C-Means and Fuzzy Possibilistic C-Means algorithms that viewed the accuracy comparison of both methods used the Modified Partition Coefficient (MPC) cluster validity. The clustering process was carried out five times by the number of clusters ranging from 3 to 7 clusters. The results of the study showed the Fuzzy C-Means method is a better method compared to the Fuzzy Possibilistic C-Means method in clustering data tweets, with the number of clusters formed is 4. The content type formed is related to promo, discount, cashback, prize quizzes, and event promotions organized by Tokopedia. Content with the highest average number of retweets and likes is about automotive deals, sports tools, and merchandise offerings. So, that PT Tokopedia can use this content type as a tool for advertising on Twitter because it gets more likes by followers of @tokopedia.Keywords: Data Tweets, Clustering, Fuzzy C-Means, Fuzzy Possibilistics C-Means, Modified Partition Coefficient.
Predicting Potential Threats and Megarisks in Information Technology Development

This paper considers the methodological aspects of forecasting the emergence of threats and risks relating to the development of information technologies on the basis of a multicriterion analysis of human interaction with the information environment, as well as problems of information security of a person and society, taking into account the influence of factors such as the globalization of the world economy and the formation of a new technological structure. Informatization, convergence of computer, telecommunication technologies, and multimedia provide a fundamentally new level of civilizational development, increasingly influencing the life of a person and society. From the point of view of a systemic approach, the application of scientific metrics and multidimensional analysis methods, the causal relationships of the emergence of the most dangerous and significant threats and risks of the implementation of information technologies for the economy and society as a whole are investigated. Some conclusions, priorities, and the most urgent tasks of developing an interdisciplinary problem of forecasting the assessment and minimizing risks of information technology development are formulated.
The Performance Research of the Data Augmentation Method for Image Classification

To collect full-labeled data is a challenge problem for learning classifiers. Nowadays, the general tendency of developing a model is becoming larger to be able to obtain more potential capacity to effectively predict unknown instances. However, imbalanced datasets still are not able to meet the needs for training a robustness classifier. A convincing guidance to extract invariance features from images is training in augmented input datasets. However, selecting a proper way to generate synthetic samples from a larger quality of feasible augmentation methods is still a big challenge. In the paper, we use three types of datasets and investigate the merits and demerits of five image transformation methods—color manipulate methods (color and contrast) and traditional affine transformation (shift, rotation, and flip). We found a common experiment result that plausible color transformation methods perform worse against traditional affine transformations in solving the overfitting problem and improve the classification accuracy.
An ASIP for Neural Network Inference on Embedded Devices with 99% PE Utilization and 100% Memory Hidden under Low Silicon Cost

The computation efficiency and flexibility of the accelerator hinder deep neural network (DNN) implementation in embedded applications. Although there are many publications on deep neural network (DNN) processors, there is still much room for deep optimization to further improve results. Multiple dimensions must be simultaneously considered when designing a DNN processor to reach the performance limit of the architecture, including architecture decision, flexibility, energy efficiency, and silicon cost minimization. Flexibility is defined as the ability to support as many multiple networks as possible and to easily adjust the scale. For energy efficiency, there are huge opportunities for power efficiency optimization, which involves access minimization and memory latency minimization based on on-chip memory minimization. Therefore, this work focused on low-power and low-latency data access with minimized silicon cost. This research was implemented based on an ASIP (application specific instruction set processor) in which an ISA was based on the caffe2 inference operator and the hardware design was based on a single instruction multiple data (SIMD) architecture. The scalability and system performance of our SoC extension scheme were demonstrated. The VLIW was used to execute multiple instructions in parallel. All costs for data access time were thus eliminated for the convolution layer. Finally, the processor was synthesized based on TSMC 65 nm technology with a 200 MHz clock, and the Soc extension scheme was analyzed in an experimental model. Our design was tested on several typical neural networks, achieving 196 GOPS at 200 MHz and 241 GOPS/W on the VGG16Net and AlexNet.
Game Theory for Cooperation in Multi-Access Edge Computing

Cooperative strategies amongst network players can improve network performance and spectrum utilization in future networking environments. Game Theory is very suitable for these emerging scenarios, since it models high-complex interactions among distributed decision makers. It also finds the more convenient management policies for the diverse players (e.g., content providers, cloud providers, edge providers, brokers, network providers, or users). These management policies optimize the performance of the overall network infrastructure with a fair utilization of their resources. This chapter discusses relevant theoretical models that enable cooperation amongst the players in distinct ways through, namely, pricing or reputation. In addition, the authors highlight open problems, such as the lack of proper models for dynamic and incomplete information scenarios. These upcoming scenarios are associated to computing and storage at the network edge, as well as, the deployment of large-scale IoT systems. The chapter finalizes by discussing a business model for future networks.
Mammographic mass recognition using feature reuse and channel attention mechanism

Abstract Mass recognition is one of the important steps in early diagnosis for breast cancer. However, one of the major issues in mass recognition is low sensitivity. In this study, a novel convolutional neural network using feature reuse and channel attention mechanism is proposed and evaluated for mass recognition in mammograms. To improve representational power of network, our model takes advantage of two advanced techniques: one is feature reuse for exploiting the potential of the network through directing connections, and the other is the channel attention mechanism for modelling the interdependencies between channels by integrating the channel‐wise information. The performance of our model is evaluated on DDSM and the values of the evaluation are 92.05%, 91.25%, 91.25%, 92.71%, 0.91 and 0.96 in terms of accuracy, precision, sensitivity, specificity, F1_score and AUC, respectively. The experimental results demonstrate the effectiveness of the proposed model to mass recognition task in mammograms.
Luminance calibration of a full sky HDR imaging system using sky scanner measurements

A full sky High Dynamic Range imaging system, based on a Single-Lens Reflex camera with a fisheye lens, has been constructed and calibrated with a sky scanner luminance meter. The method considers the geometrical, spectral, timing and orientation issues between instruments. The calibration data sets, having nearly simultaneous measurements under stable sky conditions, were obtained from approximately one month of data using selection variables based in the experimental design. For luminance estimation we use the standard CIEY RGB combination and a Spectrally Matched Luminance (SML) predictor, matching the spectral response of the instruments. With 738 calibration points having luminances up to 23.6kcd/m2, covering 98.5% of the sky luminance range, CIEY is linearly correlated with sky scanner measurements with a coefficient of determination R2=0.9927 and a Root Mean Squared Error (RMSE) of 7.7%. SML gives better results, with R2=0.9973 and RMSE=5.3%. With 253 calibration points with luminances up to 12.9kcd/m2, comprising 94.1% of the sky luminance range, both predictors clearly improve, with R2=0.9964 and RMSE=4.1% in case of CIEY and R2=0.9982 and RMSE=2.9% in case of SML.