Reviewerly/exHarmony huggingface dataset:
A Self-adaptive differential evolutionary extreme learning machine (SaDE-ELM): a novel approach to blast-induced ground vibration prediction

Blast-induced ground vibration is still an adverse impact of blasting in civil and mining engineering projects that need much consideration and attention. This study proposes the use of Self-Adaptive Differential Evolutionary Extreme Learning Machine (SaDE-ELM) for the prediction of ground vibration due to blasting using 210 blasting data points from an open pit mine in Ghana. To ascertain the predictive performance of the proposed SaDE-ELM approach, several artificial intelligence and empirical approaches were developed for comparative purposes. The performances of various developed models were assessed using model performance indicators of mean squared error (MSE), Nash–Sutcliffe Efficiency Index (NSEI) and correlation coefficient (R). Furthermore, the Bayesian Information Criterion (BIC) was applied to select the best performing approach. The obtained prediction results based on the performance indicators showed that the SaDE-ELM outperformed all the competing models as it had the lowest MSE value of 0.01942, respectively. The SaDE-ELM also achieved the highest R and NSEI values of 0.8711 and 0.7537, respectively. The other artificial intelligent approaches had MSE, R and NSEI in the ranges of (0.02166–0.03006), (0.8012–0.8537) and (0.6188–0.7254), respectively. The empirical approaches performed poorly relative to the artificial intelligence approaches by having had MSE, R and NSEI in the ranges of (0.03419–0.06587), (0.7466–0.7833) and (0.1649–0.5665), respectively. The prediction superiority of SaDE-ELM was confirmed when it is achieved the lowest BIC value of − 293.40. Therefore, the proposed SaDE-ELM has demonstrated great potential to be used for on-site prediction, control and management of blast-induced ground vibration to prevent unwanted effects on the environment.
Seamless authentication for online teaching and meeting

The lockdowns and travel restrictions in the current coronavirus pandemic situation has replaced face-to-face teaching and meeting with the online alternatives. Recently, the video conferencing tool Zoom has become extremely popular for its simple-to-use features and low network bandwidth requirement. However, Zoom has serious security and privacy issues. Due to weak authentication mechanisms, unauthorised persons are invading Zoom sessions and creating disturbances (known as Zoom bombing). In this paper, we propose a preliminary work towards a seamless authentication mechanism for Zoom-based teaching and meeting. Our method is based on PRNU (Photo Response Non-Uniformity)-based camera authentication, which can authenticate the camera of a device used in a Zoom meeting without requiring any assistance from the participants (e.g., needing the participant to provide biometric). Results from a small-scale experiment validate the proposed method.
Deep Learning for Sentiment Analysis Based on Customer Reviews

Online reviews became popular as people are taking decisions with the help of them. In this context, the purpose of this project is to develop a deep learning based framework that can be used to classify customer reviews into positive or negative. This process is known as sentiment analysis. It is based on the supervised learning mechanisms where a classifier is built with knowledge of training data and then it is used to classify testing data. A prototype application is built to demonstrate proof of the concept. The success of deep learning highly relies on the availability of large-scale training data. A novel deep learning framework for review sentiment classification which employs prevalently available ratings as weak supervision signals. An algorithm by name Deep Learning based Sentiment Analysis (DLSA) is proposed and implemented to achieve this. A deep learning framework is proposed and implemented. A prototype application is built to demonstrate proof of the concept. The empirical study revealed that the proposed system is better than the state of the art.
Baba is Y’all: Collaborative Mixed-Initiative Level Design

We present a collaborative mixed-initiative system for building levels for the puzzle game "Baba is You". Unlike previous mixed-initiative systems, Baba is Y'all is designed for collaborative asynchronous creation by multiple users over the internet. The system includes several AI-assisted features to help designers, including a level evolver and an automated player for playtesting. The level archives catalogues levels according to which mechanics are implemented and not implemented, allowing the system to ask users to design levels with specific combinations of mechanics. We describe the operation of the system and the results of small-scale informal user test, and discuss future development paths for this system as well as for collaborative mixed-initiative systems in general.
An intelligent strategy map to evaluate improvement projects of auto industry using fuzzy cognitive map and fuzzy slack-based efficiency model

Effective selection of improvement projects is a basic step to enhance an organization’s superiority. If improvement projects are not defined and selected properly, the improvement plan is prone to risks. Conventionally, these projects are created using self-assessment processes based on an intelligent strategy map, which is done in terms of functional perspectives of Balanced Scorecard (BSC). However, due to its static behavior, BSC needs to be reviewed under dynamic conditions and based on strategic goals. In this study, to remove this shortcoming, cause, and effect relationships between goals and performance measurements in organizations’ BSC are investigated. This is accomplished by using the Fuzzy Cognitive Map (FCM) method. Having modification capability over time, this FCM method is suitable for grasping changing strategies of organizations and their competitors. Furthermore, based on the BSC, the effectiveness value of each proposed improvement project on the organization’s goals is calculated considering FCM and a hybrid learning algorithm. Then, improvement projects based on the organization’s goals and resource constraints are prioritized using fuzzy Slack-Based Data Envelopment Analysis (SBDEA). Integrating these methods provides a unique decision support system tool for decision makers. Finally, to evaluate the efficiency and accuracy of the proposed approach, a real case study on an automotive-parts supplier is presented along with its results.
KnitGIST

Automatic knitting machines are robust, digital fabrication devices that enable rapid and reliable production of attractive, functional objects by combining stitches to produce unique physical properties. However, no existing design tools support optimization for desirable physical and aesthetic knitted properties. We present KnitGIST (Generative Instantiation Synthesis Toolkit for knitting), a program synthesis pipeline and library for generating hand- and machine-knitting patterns by intuitively mapping objectives to tactics for texture design. KnitGIST generates a machine-knittable program in a domain-specific programming language.
Applied gamma-ray spectrometry for evaluating tropical soil processes and attributes

Abstract Geophysical methods, such as gamma-ray spectrometry, have great potential to enhance knowledge of the pedosphere (pedogenesis, pedogeochemistry and pedogeomorphology), helping to predict tropical soil attributes. We applied proximal gamma-ray spectrometry to evaluate tropical landscape dynamics, pedogenesis and spatial distribution of radionuclides and selected soil attributes. This study was carried out in southeast Brazil, where 79 soil samples (0–20 cm) along transects were collected to perform physical-chemical analysis coupled with collection of surface gamma-ray spectrometric data, allowing the detection of the radionuclides uranium (U238), thorium (Th232), and potassium (K40). Additionally, we analyzed soils in four toposequences, with varying lithology, relief, and hydrology. The radionuclide concentrations in soils showed a direct relationship with the parent material composition, either rocks or sediments. Weathering degree and the geochemical behavior of each radionuclide determines its permanence or removal in soils. Denudation processes along the toposequences also influence the distribution of radionuclides. On one hand, the radionuclide contents of mature, weathered soils are closely associated with advanced pedogenesis, with a higher clay contents and argic horizon formation, whereas in younger, less weathered soils the parent material exerts a greater influence than pedogenesis. Uranium decreased with altitude, and showed greater mobility compared with thorium. Thorium presented a higher correlation with clay content, and the opposite with sand content. Potassium detection increased with soil cation exchange capacity (CEC) and clay content. Gamma-spectrometry detected significant variations in some segments along the toposequences, undetectable by conventional soil survey techniques. This may indicate changes from one soil class to another or the continuity of a particular soil class, demonstrating the potential of this tool in digital soil mapping, pedogeochemical, pedogeomorphological and pedogenesis processes studies.
Image degradation due to different in-flight aero-optical environments

High speed time-resolved wavefront and imaging measurements were taken synchronously in-flight through both boundary layer and shear layer environments around the Airborne Aero-Optical Laboratory for Beam Control. Instantaneous modulation transfer functions and point spread functions (PSFs), which characterize image degradation, were generated using wavefront data. Instantaneous power-in-bucket ratios were extracted from both the image data and computed from the wavefront data, and the ratios were found to correlate well with each other. The lower power-in-bucket values and related increased blurring that occurred predominantly in the streamwise direction were associated with large-scale, large-amplitude wavefront spatial variations due to large organized vortical structures present in the shear layer. The boundary layer did not create any significant image blurring due to the low level of aero-optical distortions. Finally, spatial autocorrelation functions were extracted from the wavefront data using the stitching method and were used to compute time-averaged PSFs for different aperture diameters.
SVD-CNN: A Convolutional Neural Network Model with Orthogonal Constraints Based on SVD for Context-Aware Citation Recommendation

Context-aware citation recommendation aims to automatically predict suitable citations for a given citation context, which is essentially helpful for researchers when writing scientific papers. In existing neural network-based approaches, overcorrelation in the weight matrix influences semantic similarity, which is a difficult problem to solve. In this paper, we propose a novel context-aware citation recommendation approach that can essentially improve the orthogonality of the weight matrix and explore more accurate citation patterns. We quantitatively show that the various reference patterns in the paper have interactional features that can significantly affect link prediction. We conduct experiments on the CiteSeer datasets. The results show that our model is superior to baseline models in all metrics.
The impact of organizational learning culture, workforce diversity and knowledge management on innovation and organization performance: A structural equation modeling approach

BACKGROUND: Innovation is founded as a strong contributor to the formulation of competitive strategy and enhance organizational performance. Different organizations dropped drastically from the competition by not innovating their products and services. PURPOSE: To analyze the relationship of an organizational learning culture, workforce diversity and knowledge management with innovation and organizational performance, this topic is also has become an important topic in the field of management. With a few exceptions, however, the literature has focused on different business and small and medium-size enterprise, but this study intends to bridge this gap by testing the framework in educational institutes. METHODS: A quantitative research method is used to test the conceptual framework. The data was collected from Malaysian, and Thailand private sector universities; a closed-ended questionnaire was administrated to collect the data. Data is analyzed through statistical software SMART-PLS RESULTS: Findings of the study reveal that the exogenous variables knowledge management, organization learning culture, and workforce diversity has a significant and positive relationship with endogenous variable innovation and further innovation has a significant and positive association with organization performance. CONCLUSION: This study reveals that workforce diversity, knowledge management and organizational learning culture play a vital role to bring the innovation which further enhances the organization performance. Due to current pandemic COVID-19, educational institutes required more innovative process of teaching and operation, a learning culture, knowledge management systems and diverse workforce can play a vital role in the educational institute innovation process.
Multi-task gradient descent for multi-task learning

Multi-Task Learning (MTL) aims to simultaneously solve a group of related learning tasks by leveraging the salutary knowledge memes contained in the multiple tasks to improve the generalization performance. Many prevalent approaches focus on designing a sophisticated cost function, which integrates all the learning tasks and explores the task-task relationship in a predefined manner. Different from previous approaches, in this paper, we propose a novel Multi-task Gradient Descent (MGD) framework, which improves the generalization performance of multiple tasks through knowledge transfer. The uniqueness of MGD lies in assuming individual task-specific learning objectives at the start, but with the cost functions implicitly changing during the course of parameter optimization based on task-task relationships. Specifically, MGD optimizes the individual cost function of each task using a reformative gradient descent iteration, where relations to other tasks are facilitated through effectively transferring parameter values (serving as the computational representations of memes) from other tasks. Theoretical analysis shows that the proposed framework is convergent under any appropriate transfer mechanism. Compared with existing MTL approaches, MGD provides a novel easy-to-implement framework for MTL, which can mitigate negative transfer in the learning procedure by asymmetric transfer. The proposed MGD has been compared with both classical and state-of-the-art approaches on multiple MTL datasets. The competitive experimental results validate the effectiveness of the proposed algorithm.
Chatbot: A Deep Neural Network Based Human to Machine Conversation Model

A conversational agent (chatbot) is computer software capable of communicating with humans using natural language processing. The crucial part of building any chatbot is the development of conversation. Despite many developments in Natural Language Processing (NLP) and Artificial Intelligence (AI), creating a good chatbot model remains a significant challenge in this field even today. A conversational bot can be used for countless errands. In general, they need to understand the user's intent and deliver appropriate replies. This is a software program of a conversational interface that allows a user to converse in the same manner one would address a human. Hence, these are used in almost every customer communication platform, like social networks. At present, there are two basic models used in developing a chatbot. Generative based models and Retrieval based models. The recent advancements in deep learning and artificial intelligence, such as the end-to-end trainable neural networks have rapidly replaced earlier methods based on hand-written instructions and patterns or statistical methods. This paper proposes a new method of creating a chatbot using a deep neural learning method. In this method, a neural network with multiple layers is built to learn and process the data.
Counting Down Thunder

The Lightning Network is a scaling solution for Bitcoin that promises to enable rapid and private payment processing. In Lightning, multi-hop payments are secured by utilizing Hashed Time-Locked Contracts (HTLCs) and encrypted on the network layer by an onion routing scheme to avoid information leakage to intermediate nodes. In this work, we however show that the privacy guarantees of the Lightning Network may be subverted by an on-path adversary conducting timing attacks on the HTLC state negotiation messages. To this end, we provide estimators that enable an adversary to reduce the anonymity set and infer the likeliest payment endpoints. We developed a proof-of-concept measurement node that shows the feasibility of attaining time differences and evaluate the adversarial success in model-based network simulations. We find that controlling a small number of malicious nodes is sufficient to observe a large share of all payments, emphasizing the relevance of the on-path adversary model. Moreover, we show that adversaries of different magnitudes could employ timing-based attacks to deanonymize payment endpoints with high precision and recall.
Blockchain based Land Registry with Delegated Proof of Stake (DPoS) Consensus in Bangladesh

Land Registry documents are legally binding documents provided by the government to owners of land as proof of their ownership. In developing countries such as Bangladesh, it is essential to combat the challenges which a traditional Land Registry system faces. To keep a digital ledger of information about land assets in a transparent and secured manner, Blockchain technology can be used to overcome the hurdle. However, any Blockchain based system needs to be integrated in a well thought out manner. We propose a novel approach on enhancing the Delegated Proof of Stake consensus to provide a private ledger based system for transacting land assets which can be easily integrated into the existing traditional Land Registry system for smooth operation.
Face Gender Recognition based on Face Recognition Feature Vectors

Automatic facial gender recognition is a widely used task in the field of computer vision, which is very easy for a human, but very challenging for computers. In this paper, a face gender classification algorithm based on face recognition feature vectors is proposed. Firstly, face detection and preprocessing are performed on the input images, and the faces are adjusted to a unified format. Secondly, the face recognition model is used to extract feature vectors as the representation of the face in the feature space. Finally, machine learning methods are used to classify the extracted feature vector. Meanwhile, this study uses t-distributed Stochastic Neighbor Embedding (T-SNE) to visualize the face recognition feature vectors to verify the effectiveness of the face recognition feature vectors on the issue of gender classification. The proposed method has achieved a recognition rate of 99.2% and 98.7% on the FEI dataset and the SCIEN dataset, respectively. Besides, it also achieves a recognition rate of 97.4% on the Asian star face dataset, outperforming existing methods, which shows that the proposed method is helpful for the research of facial gender.
A high payload data hiding scheme based on dual tree complex wavelet transform

In this paper, an image steganography method is proposed to overcome the limitations associated with the most existing image steganography methods based on either spatial domain or transform domain. Although these methods try to increase the embedding capacity, the produced quality of the stego-images is still low and the embedding capacity is still limited by the size of cover image. To address this problem, an image steganography method based on dual tree complex wavelet transform (DT-CWT) is presented to increase the capacity and to keep the quality at its highest possible value. Also considering human visual system which is less sensitive to the edges, more secret bits can be hidden in the coefficients associated with edge pixels. Experimental results show the superiority of the proposed method in terms of capacity and image fidelity compared to the existing steganographic methods. Moreover, the proposed method is robust against universal analysis.
Efficient Bug Triage For Industrial Environments

Bug triage is an important task for software maintenance, especially in the industrial environment, where timely bug fixing is critical for customer experience. This process is usually done manually and often takes significant time. In this paper, we propose a machine-learning-based solution to address the problem efficiently. We argue that in the industrial environment, it is more suitable to assign bugs to software components (then to responsible developers) than to developers directly. Because developers can change their roles in industry, they may not oversee the same software module as before. We also demonstrate experimentally that assigning bugs to components rather than developers leads to much higher accuracy. Our solution is based on text-projection features extracted from bug descriptions. We use a Deep Neural Network to train the classification model. The proposed solution achieves state-of-the-art performance based on extensive experiments using multiple data sets. Moreover, our solution is computationally efficient and runs in near real-time.
Semi-supervised Training of Optical Flow Convolutional Neural Networks in Ultrasound Elastography

Convolutional Neural Networks (CNN) have been found to have great potential in optical flow problems thanks to an abundance of data available for training a deep network. The displacement estimation step in UltraSound Elastography (USE) can be viewed as an optical flow problem. Despite the high performance of CNNs in optical flow, they have been rarely used for USE due to unique challenges that both input and output of USE networks impose. Ultrasound data has much higher high-frequency content compared to natural images. The outputs are also drastically different, where displacement values in USE are often smooth without sharp motions or discontinuities. The general trend is currently to use pre-trained networks and fine-tune them on a small simulation ultrasound database. However, realistic ultrasound simulation is computationally expensive. Also, the simulation techniques do not model complex motions, nonlinear and frequency-dependent acoustics, and many sources of artifact in ultrasound imaging. Herein, we propose an unsupervised fine-tuning technique which enables us to employ a large unlabeled dataset for fine-tuning of a CNN optical flow network. We show that the proposed unsupervised fine-tuning method substantially improves the performance of the network and reduces the artifacts generated by networks trained on computer vision databases.
Advanced User Interaction for Web-based Collaborative Virtual Reality

Despite their support in contemporary specifications for web-based virtual, augmented and mixed reality, such as WebXR, user interaction is limited in several ways in corresponding web applications. This paper introduces software components providing solution for two of these limitations: a lack of six degrees of freedom motion capturing for smartphone-based VR headsets and no hand gesture support for MS HoloLens. The components have been developed for LIRKIS Global - Collaborative Virtual Environments, but can be used for any web applications utilizing the A-Frame framework. The paper describes both components and evaluates their performance.