Reviewerly/exHarmony huggingface dataset:
Mining spatial high-average utility co-location patterns from spatial data sets

The spatial co-location pattern refers to a subset of non-empty spatial features whose instances are frequently located together in a spatial neighborhood. Traditional spatial co-location pattern mining is mainly based on the frequency of the pattern, and there is no difference in the importance or value of each spatial feature within the pattern. Although the spatial high utility co-location pattern mining solves this problem, it does not consider the effect of pattern length on the utility. Generally, the utility of the pattern also increases as the length of the pattern increases. Therefore, the evaluation criterion of the high utility co-location mining is unfair to the short patterns. In order to solve this problem, this paper first considers the utility and length of the co-location pattern comprehensively, and proposes a more reasonable High-Average Utility Co-location Pattern (HAUCP). Then, we propose a basic algorithm based on the extended average utility ratio of co-location patterns to mining all HAUCPs, which solves the problem that the average utility ratio of patterns does not satisfy the downward closure property. Next, an improved algorithm based on the local extended average utility ratio is developed which effectively reduces the search space of the basic algorithm and improves the mining efficiency. Finally, the practicability and robustness of the proposed method are verified based on real and synthetic data sets. Experimental results show that the proposed algorithm can effectively and efficiently find the HAUCPs from spatial data sets.
Emotion dataset from Indonesian public opinion

An opinion is a type of judgment or a person's point of view about something. Twitter is a popular social media platform that includes a lot of public opinions and would be a suitable location to mine data in text form. With its vast population and active Twitter user base, Indonesia has the potential to be a source of opinion data mining. An opinion may be processed and result in the form of a person's emotional response towards something, such as whether they like, hate, love, or are happy about it. Upon that basis, a dataset of Indonesian-language tweets conveying public opinion on various topics was formed. The fact that there are only limited publicly available emotions text datasets in the Indonesian language supports our basis in this research to form our emotion dataset. The gathered data was cleaned and normalized in the pre-processing stage to the necessary form for study on the task of classifying emotions in Indonesian. The data collected is annotated with six emotional labels: anger, fear, joy, love, sad, and neutral.
Genetic Algorithm for Artificial Neural Networks in Real-Time Strategy Games

Controlling each member of the soldiers to carry out battle with Non-Playable Characters (NPC) is one of the secrets to winning Real-Time Strategy games. The game could be more complicated and offer a more engaging experience if every NPC acts like humans rather than machines with patterned behavior. Like people during a war, each army member's command requires rapid reflexes and direction to strike or evade attacks. An intelligent opponent based on ANN as NPC can react quickly to their opponents. The accuracy of ANN could be enhanced by weight modifications using a Genetic Algorithm (GA). The crossover and mutation rates significantly impact GA's performance as an ANN setup. This research aims to find the best crossover and mutation rates in GA as a weight adjustment in ANN. Experiments were conducted using an RTS game simulator using 20 scenarios on a maximum of 4000 iterations. The initial setup of each troop is random, with a seven-unit type available. In this research, the troops won because their men were subjected to fewer attacks than the opposing forces. The GA optimal crossover and mutation rates are determined using troop victories as a baseline. According to the findings, the best crossover rate for GA as an ANN weight adjustment is 0.6, whereas the specific mutation rate is 0.09. The crossover rate of 0.6 has the highest average win value and tends to increase every generation. As for the mutation rate of 0.09, it has the highest average win value. Thus, this preliminary study can develop NPC more humanly.
Augmented Reality and Its Presence Online Educations

Throughout the past 10 years, quick advancement of innovation has yielded new manners by which to foster applications for learning. AR which is known as Augmented Reality is as a scholarly medium is transforming into increasingly more open to young understudies in elementary school as well as gifted kids. It's a substitute medium that brings together perspectives from unavoidable registering, significant processing, and social figuring.. This medium offers particular affordances, joining physical and virtual universes, with nonstop and implied client the executives of the motivation behind read and intuitiveness. This document provides a brief overview of the extended Reality and E-Learning innovations. Model applications, significant AR innovations as referenced inside the instructional context. This study presents a logical overview of writing on augmented reality in education, with the most important discovery being the order of current investigation in augmented reality in education and, as a result, the recognizable proof of benefits and limitations of augmented reality for E-Learning. Likewise, latest things, a dream for the long run and open doors for more examination in expanded Reality for educational settings are referenced.
Feature Fusion Network Based on Hybrid Attention for Semantic Segmentation

In the deep learning based real-time image semantic segmentation task, there are high requirements for the inference speed of the network. Due to the small amounts of parameters of the lightweight backbones, the calculation speed is often faster, which meets the requirements of real-time tasks. However, the ability of the lightweight networks to extract features is relatively weak, resulting in much worse segmentation accuracy than the large model. Therefore, how to make full use of the lightweight networks to extract more image information to achieve better segmentation performance has become a key problem. Here, we propose an efficient feature fusion network based on attention mechanism. First, the widely used MobileNetV2 is selected as the lightweight backbone network, and then spatial attention and channel attention are calculated for both high-resolution low-level features and low-resolution high-level features, thus the final feature map got a global receptive field. Besides, through the multi-levels supervised learning for each stage of the backbone, the multi-stage auxiliary loss function enables the network to be trained more effectively. Finally, on the cityscapes dataset, the our proposed network reached 74.12% mIoU, and the inference speed remained at 110 fps.
Comparative Analysis of AlexNet, Resnet-50, and Inception-V3 Models on Masked Face Recognition

Since the outbreak of the coronavirus pandemic in December 2019, there has been increased interest in developing better facial recognition systems. This stems from the need to protect everyone from the spread of the virus. However, the measures taken to prevent the spread of the virus pose a challenge to security and surveillance systems as existing systems are unable to match faces with masks more efficiently. For this study, a custom dataset was generated due to the unavailability of a large face dataset for masked face recognition, and the existing datasets focused on Caucasians (white race faces) while Aethiopians (black race faces) were neglected. In this study, a comparative analysis was conducted between the AlexNet, ResNet-50, and Inception-V3 models to recognize faces with surgical masks, fabric masks, and N95 masks. The results of the study showed that the CNN models achieve excellent recognition accuracy for masked and unmasked faces. Analysis of the models' performance showed that the AlexNet model achieved 95.7%, ResNet-50 achieved 97.5%, and Inception-V3 also achieved 95.5%. From the study, ResNet-50 performed better than Inception-V3 and AlexNet models in recognizing masked faces.
Chunking Algorithms

As the first and key stage in the data deduplication workflow, chunking phase plays the role of dividing the data stream into chunks for deduplication. In this chapter, we discuss the problems facing the chunking phase and the solutions to address the problems. The rest of this chapter is organized as follows: Section 3.1 presents the state-of-the-art chunk algorithms for traffic redundancy elimination and storage systems. Section 3.2 describes the design and implementations of our proposed AE chunking algorithm for traffic redundancy elimination system. Section 3.3 describes another proposed chunking algorithm, called FastCDC, for storage system.
Designing Virtual Environments for Smoking Cessation: A Preliminary Investigation

The recent pandemic of COVID-19 is placing smokers at a high risk of death as a result of the combination of smoking and COVID-19. This signals a need to address this problem among dual users (cigarette and vape users) and provides them with successful tools to quit tobacco. This pilot project aims to test a novel tool, a Virtual Reality and Motivational Interviewing combined approach that will assist dual users to quit tobacco products. The investigators wanted to pilot test the equipment and scenario for user-friendliness and interface. For the first phase of the pilot, we developed four Virtual Reality scenarios that contain different triggers for smoking, such as noise, stress, and cigarettes. We used Oculus Quest 2 for the hardware because the equipment does not require towers or connections to computers, operates utilizing WIFI, and is mobile. To develop the Software, we used the “Unity3D” game engine. A total of 21 participants tested the equipment and scenarios. The participants ranged between ages 18–71 with various gaming and virtual reality experience. The majority of the participants felt immersed in the Virtual reality environment. Some participants had some challenges with the equipment and the Software and provided valuable feedback to enhance the scenarios. The virtual reality environment promises to be a novel tool to assist tobacco users, mainly dual users, in quitting tobacco.
A Self Learning Yoga Monitoring System Based on Pose Estimation

Modern life is stressful - long working hours, poor diets, inactivity, and increasing social isolation in the digital age have all contributed to rising rates of anxiety and depression. The COVID-19 pandemic escalated this situation with a series of quarantines. Without physical interaction, learning a new skill can be frustrating and stressful, especially a skill like Yoga that requires balance and physical coordination. On the one hand, many people cannot afford a personal trainer, but on the other, books and video tutorials do not offer personalized feedback. These limitations make learning Yoga on our own an overwhelming task. We propose a minimal investment model that will help people learn and practice correct Yoga forms from the comfort of their homes in an easy, stress-free manner - just by using their camera. A deep learning model is proposed based on PoseNet which gives an accuracy of 96.77% on the test dataset. We also conducted a survey to measure the satisfaction, efficiency and effectiveness of our system. Overall, 81.8% of the participants felt that our system helped them learn and perform the exercises better and 52.2% of them rated it as being “very good”.
Towards Explainable Summary of Crowdsourced Reviews Through Text Mining

With the ever broad availability of technology, organizations and merchants are now able to collect large amounts of reviews online. Each review usually consists of an interval datum, such as a one to five ranking, along with a text comment. While an aggregated numeric scale commonly serves as an overall rating; a human user usually needs to read text manually to comprehend it. The aim of this work is to generate an explainable summary computationally through mining all text comments with natural language processing (NLP). In this initial work, we are able to derive an overall numeric scale of the reviews through sentiment analysis. We further combine methods of text summarization together with document clustering to obtain an explainable text summary that is easy for human users to comprehend. As an experiment, our approach has produced an explainable summary from a review dataset publicly available from Amazon.
Knowledge Engineering and Ontology for Crime Investigation

Building upon the possibilities of technologies like ontology engineering, knowledge representational models, and semantic reasoning, our work presented in this paper, which has been performed within the collaborative research project PREVISION (Prediction and Visual Intelligence for Security Information), co-funded by the European Commission within Horizon 2020 programme, is going to support Law Enforcement Agencies (LEAs) in their critical need to exploit all available resources, and handling the large amount of diversified media modalities to effectively carry out criminal investigation. A series of tools have been developed within PREVISION which provide LEAs with the capabilities of analyzing and exploiting multiple massive data streams coming from social networks, the open web, the Darknet, traffic and financial data sources, etc. and to semantically integrate these into dynamic knowledge graphs that capture the structure, interrelations and trends of terrorist groups and individuals and Organized Crime Groups (OCG). The paper at hand focuses on the developed ontology, the tool for Semantic Reasoning and the knowledge base and knowledge visualization.
Deep Learning-Based Emotion Classification of Hindi Text from Social Media

Emotion analysis is a widely researched topic in the domain of natural language processing (NLP) due to its numerous applications in our daily lives as well as in the industries, especially the e-commerce industry. Emotions such as happiness, sadness, rage, fear, surprise, and disgust can be extracted from a document, allowing us to determine the viewpoint of an individual on a given subject. Previously, social media outlets could only support English, but thanks to Internet advances many Websites, blogs, and other platforms now support a variety of regional languages. This enabled us to investigate emotion detection in Indian languages. In this work, Hindi comments are gathered from social media platforms like YouTube, Facebook, and Twitter. These comments are then manually labeled with happy, sad, and anger. Machine learning (ML) and deep learning models are trained using this corpus, and metrics are measured to determine the accuracy of the model and also to test the quality of the dataset collected. According to the results of the experiments, the FastText embedding model with support vector machine (SVM) classifier performed better, with an accuracy of 64% and an F1-score of 62.91. While the pre-trained BERT multilingual base model achieved the highest accuracy of 76%.
Applications of Deep Learning Approaches in Speech Recognition: A Survey

Automated speech recognition (ASR) appeared to be a driving force for a variety of machine learning (ML) techniques, include to ubiquitously utilized discriminative learning, Bayesian learning, hidden Markov model, adaptive learning, and structured sequence learning. Although machine learning utilize ASR as a large scale, it can reasonable application to thoroughly test viability for a given procedure and to motivate unused issues emerging from intrinsically consecutive and discourse energetic nature. Also, although ASR is accessible commercially for a few applications used in this research through the limitation and research gaps that the researcher try to access high accuracy of these systems. The advance technology from new ML techniques appears incredible guarantee to progress the literature review in ASR innovation. This study gives reader with a diagram of present-day ML methods as used within the relevant and current as significant for ASR future systems and research. The study goal is to promote advanced cross-pollination between ML and ASR communities more than has hither to occurred.
Retrofitting Structural Graph Embeddings with Node Attribute Information

Representation learning for graphs has attracted increasing attention in recent years. In this paper, we define and study a new problem of learning attributed graph embeddings. Our setting considers how to update existing node representations from structural graph embedding methods when some additional node attributes are given. To this end, we propose Graph Embedding RetroFitting (GERF), a method that delivers a compound node embedding that follows both the graph structure and attribute space similarity. Unlike other attributed graph embedding methods, GERF is a novel representation learning method that does not require recalculation of the embedding from scratch but rather uses existing ones and retrofits the embedding according to neighborhoods defined by the graph structure and the node attributes space. Moreover, our approach keeps the same embedding space all the time and allows comparing the positions of embedding vectors and quantifying the impact of attributes on the representation update. Our GERF method updates embedding vectors by optimizing the invariance loss, graph neighbor loss, and attribute the neighbor loss to obtain high-quality embeddings. Experiments on WikiCS, Amazon-CS, Amazon-Photo, and Coauthor-CS datasets demonstrate that our proposed algorithm receives similar results compared to other state-of-the-art attributed graph embedding models despite working in retrofitting manner.
UP-MLE: Efficient and Practical Updatable Block-Level Message-Locked Encryption Scheme Based on Update Properties

Deduplication is widely used to improve space efficiency in cloud storage. The Updatable block-level Message-Locked Encryption (UMLE) has been proposed to achieve efficient block-level updates for a deduplication system. However, the update design of the current UMLE instantiation adopts a static structure, which does not fit in with real update scenarios. This paper analyzes the File System and Storage Lab (FSL) Homes datasets that are widely used in deduplication research and reveals two interesting properties: i) Updated blocks are more likely to be updated again; ii) Updated blocks are always clustered in files. Based on these properties, we propose and implement an efficient and practical UMLE scheme. Experiments on real-world datasets show that our update algorithm is 24.85% more efficient than its foremost counterpart, increasing the space overhead by $$\le $$ 0.39%.
A Low-cost Raspberry Pi-based Control System for Upper Limb Prosthesis

recent years, robots have been introduced in most factories. However, manual work still continues to be done in some places where giant robots cannot be installed. In particular, traditional Japanese crafts are done by hand, and people that engage in such crafts are called craftsmen. Generally, such artisans need years of training and cannot become experts right away. One of the problems these artisans face is the lack of successors. To address this challenge, this paper proposes a raspberry pi hardware based control method for a prosthetic hand using hand gestures from camera sensor, which will allow a prosthetic hand to learn the hand movements of the craftsmen and perform the crafts. The advantage of this is that there is no need for training, which usually takes years. To control the prosthetic hand, hand gestures are captured from a camera sensor, converted to HSV and binarized, and then classified into one of five gestures using a CNN implemented on the raspberry pi hardware. The recognized gesture is then relayed to the prosthetic hand to mimic the classified gesture. A dataset containing 2000 captured images of each gesture was created to evaluate the performance, and these gestures clearly define the closing and opening of the fingers. Using a 32×32 hand gesture image dataset captured from camera, we validated the trained CNN first in software for hand recognition without using Raspberry Pi, and achieved an accuracy of 99.63%, and then implemented on the raspberry pi, and performed real-time evaluation by recognizing five hand gestures captured from the camera sensor in real-time. Out of the hand gestures, four were correctly recognized. We presented the design of a low-cost prosthetic hand based on raspberry pi hardware, and evaluated its real-time hand gesture recognition. The evaluation result show that the proposed system is able to correctly recognize four hand gestures.
CoInMPro: Confidential Inference and Model Protection Using Secure Multi-Party Computation

In the twenty-first century, machine learning has revolutionized insight generation by using historical data across domains like health care, finance, and pharma. The effectiveness of machine learning solutions depends largely on the collaboration between data owners, model owners, and ML clients, without privacy concerns. The existing privacy-preserving solutions lack efficient and confidential ML inference. This paper addresses this inefficiency by presenting the Confidential Inference and Model Protection, also known as the CoInMPro, to solve the privacy issue faced by model owners and ML clients. The CoInMPro technique is suggested with an aim to boost the privacy of model parameters and client input during ML inference, without affecting the accuracy and by paying a marginal performance cost. Secure multi-party computation (SMPC) techniques were used to calculate inference results confidentially after sharing client input and model parameters privately from different model owners. The technique was implemented in Python language using the open-source SyMPC library to support the SMPC function. The Boston Housing Dataset was used, and the experiments were run on Azure data science VM using Ubuntu OS. The result suggests CoInMPro’s effectiveness in addressing privacy concerns of model owners and inference clients, with no sizable impact on accuracy and trade-off. A linear impact on performance was noted with an increase of secure nodes in the SMPC cluster.
MEMTD: Encrypted Malware Traffic Detection Using Multimodal Deep Learning

Malware that generates encrypted traffic presents a great threat to Internet security. The existing state-of-the-art malware traffic detection techniques based on deep learning (DL) ignore the heterogeneity of encrypted traffic, resulting in their inability to further improve detection performance. This paper applies multimodal DL to detect encrypted malware traffic, proposing a multimodal encrypted malware traffic detection (MEMTD) approach. MEMTD extracts features from three types of modal data—the transport layer security (TLS) handshake payload bytes (encryption behavior modal data), packet length sequence (spatial modal data), and packet arrival-time interval sequence (time modal data) of encrypted traffic. Moreover, an intermediate fusion mechanism is adopted in the MEMTD approach to mine the dependencies among modalities and fuse the discriminative traffic features, improving detection performance. The experimental results on datasets containing 8 malware families and normal traffic show that the MEMTD approach achieves 0.9996 macro-F1 and outperforms other single-modal DL detection methods.