Reviewerly/exHarmony huggingface dataset:
Targeted Upskilling Framework based on Player Mistake Context in Online Skill Gaming Platforms

Recent proliferation of online platforms for Games of skill have made—an excellent source of recreation and relaxation, and the safest avenues for realising personal worth, respect, and recognition— readily accessible on the tip of a finger. A primary driver towards this enjoyment is the skill of a person in responding to complex game dynamics and states, which eventually impacts the game outcomes. Hence, from both player and platform perspectives, improving the player skills—or conversely, reducing player mistakes—is paramount to improve player experience and engagement. In this talk, we focus on unique personalized and near-real-time up-skilling framework depending on specific mistake contexts identified for the players. This framework leverages a suite of models to determine the correct action in a given game state. For a specific case-study of Rummy—a popular skill game in India—these are CNN-driven deep learning models. However, our framework can be plugged with any other suite, action set, and game state representations (depending on the game) within a broad construct of getting the best reference action set in a game state. Players' actions are then benchmarked w.r.t. the reference actions as adherence measures. A lower adherence essentially indicates deviations from correct actions, i.e., the mistakes. An explainable set of rules are accordingly derived from game features, which sets the contexts for these mistakes, leading to opportunities for targeted up-skilling.
Design and Implementation of Scale Adaptive Kernel Correlation Filtering Algorithm Based on Hls

Moving target tracking is one of the important research subjects in the current field of computer vision, in which the correlation filter algorithms become a hotspot in theory research and application with its excellent comprehensive performance. However, the tracking frame of Kernel Correlation Filter (KCF) algorithms is fixed, and the change of target size will cause tracking drift or even target missing. This design improves it by integrating a scale filter from DSST (Discriminative Scale Space Tracking) algorithm so that it has a better Tracking effect under complex conditions such as target size change, occlusion, deformation and illumination change. At the same time, in order to balance the accuracy and real-time performance of the algorithm, the scale adaptive kernel correlation filtering algorithm is modified in this design, and an optimized design and implementation method based on high-level synthesis (HLS) is proposed to make it more suitable for FPGA.
Application of Image Processing Technology in Target Recognition by UAV

With the continuous development of electronic and information technology, application based scenarios based on image processing and machine vision have been universally seen in all the fields. With the help of image and visual recognition technology, humans have solved many problems. As a key element of national security and crisis management, the capability of military unmanned aerial vehicles (UAVs) in target identification have a critical impact on the performance of their functions. Starting from the requirements on target recognition of military unmanned aerial vehicles (UAVs), this paper conducts discussions and researches on the application of image processing technology in the field, conceiving the application of image processing technology in UAVs field, hoping to provide some foundation for the research and application in related fields.
Spoken Malay Profanity Classification Using Convolutional Neural Network

Foul language exists in films, video-sharing platforms, and social media platforms, which increase the risk of a viewer to be exposed to large number of profane words that have negative personal and social impact. This work proposes a CNN-based spoken Malay foul words recognition to establish the base of spoken foul terms detection for monitoring and censorship purpose. A novel foul speech containing 1512 samples are collected, processed, and annotated. The dataset then has been converted into spectral representation of Mel-spectrogram images to be used as an input to CNN model. This research proposes a lightweight CNN model with only six convolutional layers and small size filters to minimize the computational cost. The proposed model’s performance affirms the viability of the proposed visual-based classification method using CNN by achieving an average Malay foul speech terms classification accuracy of 86.50%, precision of 88.68%, and F-score of 86.83. The class of normal conversational class outperformed the class of foul words due to data imbalance and rarity of foul speech samples compared to normal speech terms.
Imminent Threat with Authentication Methods for AI Data Using Blockchain Security

Since the announcement of Satoshi Nakamoto’s Bitcoin policy document in 2008, blockchain has become one of the most widely discussed techniques for implementing safety storage and processing through decentralized, approved, peer-to-peer networks. This study described peer-reviewed literature, which uses cryptocurrency for cybersecurity purposes and offers a comprehensive overview of the most commonly used application areas of blockchains. This main forward-looking study further illuminates the possible directions for science, education, and practice on blockchain and cyberprotection, such as IoT blockchain security, as well as the need for data analysis of blockchain safe data. Analyses of this data increase the value of the latest machine learning (ML) technologies. There is a logical quantity of data required by ML for correct decisions. Data reliability and sharing in ML are very critical for improving the accuracy of performance. The combination of these two technologies will yield extremely accurate results (ML and BT). In this paper, we present a detailed review of ML adoption to make mobile platforms based on BT more resilient against attacks. Examples of such support systems as support vector machines (SVM) and bagging and deep learning (DL) algorithms can be used to evaluate attacks on a blockchain network, including convolutional neural network (CNN) and long short-term memory (LSTM). Actually, various traditional ML techniques are available. Furthermore, we include the use of both technologies in a variety of smart applications, including UAV, Smart Grid (SG), healthcare, and smart towns and cities. Future technological issues and concerns are also debated. Finally, we discuss the study model with a thesis.
Generalized Template Matching for Semi-structured Text

Conventional template matching for named entity recognition on book-length text strings is generalized by allowing search phrases to capture distant tokens. Combined with word-type tagging and format variants (alternative name/date formats), a few initial templates (class—search-phrase—extract-phrase triples) can label most of the significant tokens. The program then uses its book-length statistics of tag-label associations to suggest candidate text for further template construction. The method serves as a preprocessor for error-free extraction of semantic relations from text obeying explicit semi-structure constraints. On three sample books of genealogical records, an F-measure of over 0.99 was achieved with less than 3 hours’ user time on each book.
Exploration of Intelligent Grammar Cohesion Based on Deep Learning Technology

In the intelligent age, English writing teaching should not only focus on the transmission of knowledge, but also focus on cultivating students’ [autonomous learning ability and deep learning awareness]. In classroom teaching, teachers can make use of artificial intelligence to develop personalized teaching methods, use the rich teaching resources of artificial intelligence to select unit theme teaching content, use the diversity of artificial intelligence evaluation to carry out continuous evaluation, use the efficiency of artificial intelligence teaching to make hierarchical teaching plan, improve the development technology of artificial intelligence, and boost students’ deep learning.
Paying Firms to Share Cyber Threat Intelligence

Effective cyber defense requires stakeholders to collaborate with each other and share cyber threat intelligence. Sharing such intelligence can improve the community’s cybersecurity posture, preventing others from being hacked or compromised. However, intelligence sharing is still relatively uncommon due in part to the associated costs as well as other legitimate concerns. In this paper, we ask how a central authority could employ monetary incentives to promote intelligence sharing among competitive firms. We propose a novel game-theoretic model of intelligence sharing and derive the minimal incentive payments which ensure that firms profitably share with their competitors. We investigate the value of being able to differentiate incentives among firms (i.e., paying a different amount to each firm), and show formally that the ability to differentiate is the most valuable when the network among firms is highly heterogeneous. Finally, we show that our results are sharp in an important sense: if the authority offers less than the minimal incentive to every firm, this can render no-sharing as the unique Nash equilibrium.
A Study of Explainability Features to Scrutinize Faceted Filtering Results

Faceted search systems enable users to filter results by selecting values along different dimensions or facets. Traditionally, facets have corresponded to properties of information items that are part of the document metadata. Recently, faceted search systems have begun to use machine learning to automatically associate documents with facet-values that are more subjective and abstract. Examples include search systems that support topic-based filtering of research articles, concept-based filtering of medical documents, and tag-based filtering of images. While machine learning can be used to infer facet-values when the collection is too large for manual annotation, machine-learned classifiers make mistakes. In such cases, it is desirable to have a scrutable system that explains why a filtered result is relevant to a facet-value. Such explanations are missing from current systems. In this paper, we investigate how explainability features can help users interpret results filtered using machine-learned facets. We consider two explainability features: (1) showing prediction confidence values and (2) highlighting rationale sentences that played an influential role in predicting a facet-value. We report on a crowdsourced study involving 200 participants. Participants were asked to scrutinize movie plot summaries predicted to satisfy multiple genres and indicate their agreement or disagreement with the system. Participants were exposed to four interface conditions. We found that both explainability features had a positive impact on participants' perceptions and performance. While both features helped, the sentence-highlighting feature played a more instrumental role in enabling participants to reject false positive cases. We discuss implications for designing tools to help users scrutinize automatically assigned facet-values.
Multivariate Extreme Learning Machine Based AutoEncoder for Electricity Consumption Series Clustering

Multivariate electricity consumption series clustering can reflect the trend of power consumption changes in the past time period, which can provide reliable guidance for electricity production. The dimensionality reduction-based method is an effective technology to address this problem, which obtains the low-dimensional features of each variate or all variates for multivariate time series clustering. However, most existing dimensionality reduction-based methods ignore the joint learning of the common representations and the variable-based representations. In this paper, we build a multivariate extreme learning machine based autoencoder model for electricity consumption clustering (MELM-EC), which performs common representation learning and variable-based representation learning simultaneously. MELM-EC maps the common representation and multiple variable-based representations to the original multivariate time series and computes the common output weights within a few iterations. Experimental results on realistic multivariate time series datasets and multivariate electricity consumption series datasets demonstrate the effectiveness of the proposed MELM-EC model.
Efficacy Determination of Various Base Networks in Single Shot Detector for Automatic Mask Localisation in a Post COVID Setup

The COVID-19 pandemic is one of the rarest events of global crises where a viral pathogen infiltrates every part of the world, leaving every country face an inevitable threat of having to lock down major cities and economic hubs and put firm restrictions on citizens thus slowing down the economy. The risk of removal of lockdowns is the emergence of new waves of a pandemic causing a surge in new cases. These facts necessitate the containment of the virus when the lockdowns end. Wearing masks in crowded places can help restrict the spread of the virus through minuscule droplets in the air. Through the automatic detection, enumeration, and localisation of masks from closed-circuit television footage, it is possible to keep violations of post-COVID regulations in check. In this paper, we leverage the Single-Shot Detection (SSD) framework through different base convolutional neural networks (CNNs) namely VGG16, VGG19, ResNet50, DenseNet121, MobileNetV2, and Xception to compare performance metrics attained by the different variations of the SSD and determine the efficacies for the best base network model for automatic mask detection in a post COVID world. We find that Xception performs best among all the other models in terms of mean average precision.
New Methods for Confusion Detection in Course Forums: Student, Teacher, and Machine

This article provides computational and rule-based approaches for detecting confusion that is expressed in students&#x0027; comments in couse forums. To obtain reliable, ground truth data about which posts exhibit student confusion, we designed a decision tree that facilitates the manual labeling of forum posts by experts. However, manual labeling is costly in time and resources, which limits the amount of data that can be generated using this process. Our strategy for overcoming these limitations was to generate rules for detecting confusion based on student input via hashtags, which reflect the student's affective states. We show that the resulting rules closely align with the ground truth judgement of experts. We next applied these rules to datasets of students&#x0027; forum posts in a large-scale biology course, thereby automatically generating thousands of labeled instances of &#x201C;confused posts.&#x201D; Finally, the resulting dataset was used to train a machine learning model for detecting whether students&#x0027; posts exhibit confusion in the absence of hashtags. In this task, the pretrained language model based on bidirectional encoder representation from transformers (BERT) was able to outperform traditional machine learning models for classifying confusion in posts. This model was also able to generalize and detect student confusion across different offerings of the same course. Ultimately, the use of pretrained language models of this type will provide teachers with better technologies for detecting and alleviating confusion in online discussion forums by leveraging the combined input of teachers and students.
Performance Measurement of Multiple Supervised Learning Algorithms for Gender Identification from Bengali Names

Gender recognition of names is today regarded as one of the most significant problems in data extraction. Machine learning and NLP are used to solve this. Recognition of the gender of a name relies heavily on various kinds of NLP applications such as Question addressing systems, Text summarization etc. Throughout this article, we will investigate the Bengali name entity and attempt to extract and recognize the Person's name entity to determine if the name is male or female. Most people's names have a sexual orientation refinement. However, determining genders from Bengali names with greater accuracy might be difficult. We will demonstrate an artificial intelligence-based characterization technique that can accurately determine sexual orientations from Bengali names. We'll use a variety of algorithms to figure out which computations get the best results. We attained an accuracy of 84 percent by using Naive Bayes.
ContourDiff: Revealing Differential Trends in Spatiotemporal Data

Changes in spatiotemporal data may often go unnoticed due to their inherent noise and low variability (e.g., geological processes over years). Commonly used approaches such as side-by-side contour plots and spaghetti plots do not provide a clear idea about the temporal changes in such data. We propose ContourDiff, a vector-based visualization over contour plots to visualize the trends of change across spatial regions and temporal domain. Our approach first aggregates for each location, its value differences from the neighboring points over the temporal domain, and then creates a vector field representing the prominent changes. Finally, it overlays the vectors along the contour paths, revealing differential trends that the contour lines experienced over time. We evaluated our visualization using real-life datasets, consisting of millions of data points, where the visualizations were generated in less than a minute in a single-threaded execution. Our experimental results reveal that ContourDiff can reliably visualize the differential trends, and provide a new way to explore the change pattern in spatiotemporal data.
The involvement of persons with disabilities in disaster risk management

Persons with disabilities tend to be neglected in disaster risk management. This study aims to assess the preparedness of persons with disabilities in experiencing disasters and their involvement in disaster risk management using a combination of qualitative and quantitative methods. Data and information to measure the preparedness and involvement of persons with disabilities were collected using a questionnaire that was probed by interviews and observations. The study involved 120 respondents consisting of 30 respondents with disabilities for each location. The results showed that persons with disabilities were still not prepared to face disasters. Researchers recommend the active involvement of persons with disabilities in disaster risk management at all phases and a service unit for persons with disabilities in disaster management (ULDPB) in each district/city. Those affirm the commitment of the ULDPB in providing services for them. It is also suggested to build facilities and infrastructure for mitigation and self-rescue that are friendly to persons with disabilities. Therefore, a disaster risk management model involving persons with disabilities needs to be prepared.
Multi-scale Iterative Residuals for Fast and Scalable Stereo Matching

Despite the remarkable progress of deep learning in stereo matching, there exists a gap in accuracy between real-time models and slower state-of-the-art models which are suitable for practical applications. This paper presents an iterative multi-scale coarse-to-fine refinement (iCFR) framework to bridge this gap by allowing it to adopt any stereo matching network to make it fast, more efficient and scalable while keeping comparable accuracy. To reduce the computational cost of matching, we use multi-scale warped features to estimate disparity residuals and push the disparity search range in the cost volume to a minimum limit. Finally, we apply a refinement network to recover the loss of precision which is inherent in multi-scale approaches. We test our iCFR framework by adopting the matching networks from state-of-the art GANet and AANet. The result is 49 × faster inference time compared to GANet-deep and 4 × less memory consumption, with comparable error. Our best performing network, which we call FRSNet is scalable even up to an input resolution of 6K on a GTX 1080Ti, with inference time still below one second and comparable accuracy to AANet+. It out-performs all real-time stereo methods and achieves competitive accuracy on the KITTI benchmark.
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:mi>∊</mml:mi></mml:math>-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si2.svg"><mml:mi>k</mml:mi></mml:math> anonymization and adversarial training of graph neural networks for privacy preservation in social networks

With the explosive growth of social networks, privacy preservation as a social good has been one common concern. Graph neural networks (GNNs) have been utilized by social network service providers to improve business service. However, traditional anonymization techniques of social networks cannot satisfy the desired privacy preservation of node attribute and graph structure and introduce information disturbance from the anonymization, leading to the performance degradation of GNNs in social network analysis. To protect sensitive user data and persist GNNs’ performance in social network analysis, we propose a two-stage privacy-preserving method of graph neural networks in the social network domain. During the first stage, we design a novel ∊-k anonymization method that can achieve ∊-local differential privacy (∊-LDP) and k-degree anonymity by incorporating the classical LDP and k-degree anonymization (k-DA) while retaining as much network community information as possible. At the second stage, we develop an adversarial training mechanism for GNNs to resist the disturbance from ∊-k anonymization and retain as much task performance as possible on anonymous social network data. Comprehensive experiments on several real-world social network datasets demonstrate the effectiveness of the proposed method for privacy-preserving node classification, link prediction, and graph clustering in social networks. The proposed method represents an interesting and important combination of classical anonymous technologies and recent GNNs and can preserve user privacy while providing business service.
Enhanced Deep Hierarchical Long Short-Term Memory and Bidirectional Long Short-Term Memory for Tamil Emotional Speech Recognition using Data Augmentation and Spatial Features

Neural networks have become increasingly popular for language modelling and within these large and deep models, overfitting, and gradient remains an important problem that heavily influences the model performance. As long short-term memory (LSTM) and bidirectional long short-term memory (BILSTM) individually solve long-term dependencies in sequential data, the combination of both LSTM and BILSTM in hierarchical gives added reliability to minimise the gradient, overfitting, and long learning issues. Hence, this paper presents four different architectures such as the Enhanced Deep Hierarchal LSTM &amp; BILSTM (EDHLB), EDHBL, EDHLL &amp; EDHBB has been developed. The experimental evaluation of a deep hierarchical network with spatial and temporal features selects good results for four different models. The average accuracy of EDHLB is 92.12%, EDHBL is 93.13, EDHLL is 94.14% &amp; EDHBB is 93.19% and the accuracy level obtained for the basic models such as the LSTM, which is 74% and BILSTM, which is 77%. By evaluating all the models, EDHBL performs better than other models, with an average efficiency of 94.14% and a good accuracy rate of 95.7%. Moreover, the accuracy for the collected Tamil emotional dataset, such as happiness, fear, anger, sadness, and neutral emotions indicates 100% accuracy in a cross-fold matrix. Emotions such as disgust show around 80% efficiency. Lastly, boredom shows 75% accuracy. Moreover, the training time and evaluation time utilised by EDHBL is less when compared with the other models. Therefore, the experimental analysis shows EDHBL as superior to the other models on the collected Tamil emotional dataset. When compared with the basic models, it has attained 20% more efficiency.