Reviewerly/exHarmony huggingface dataset:
Automated Physical Distance Estimation and Crowd Monitoring Through Surveillance Video

The contagious Corona Virus (COVID-19) transmission can be reduced by following and maintaining physical distancing (also known as COVID-19 social distance). The World Health Organisation (WHO) recommends preventing COVID-19 from spreading in public areas. On the other hand, people may not be maintaining the required 2-m physical distance as a mandated safety precaution in shopping malls and public places. The spread of the fatal disease may be slowed by an active monitoring system suitable for identifying distances between people and alerting them. This paper introduced a deep learning-based system for automatically detecting physical distance using video from security cameras. The proposed system introduced the TH-YOLOv5 for object detection and classification and Deepsort for tracking the detected people using bounding boxes from the video. TH-YOLOv5 included another prediction head to identify objects of varying sizes. The original prediction heads are then replaced with Transformer Heads (TH) to investigate the prediction capability of the self-attention mechanism. Then, we include the convolutional block attention model (CBAM) to identify attention areas in settings with dense objects. Pairwise L2 vectorized normalization was utilized to generate a three-dimensional feature space for tracking physical distances and the violation index, determining the number of individuals who follow the distance rules. We use the MS COCO and HumanCrowd, CityPersons, and Oxford Town Centre (OTC) data sets for training and testing. Experimental results demonstrate that the proposed system obtained a weighted mAP score of 89.5% and an FPS score of 29; both are computationally comparable.
How to Use the Six-Step Digital Ethnography Framework to Develop Buyer Personas: The Case of Fan Fit

Background One of the key features of digital marketing is customer centricity, which can be applied to the domain of health. This is expressed through the ability to target specific customer segments with relevant content using appropriate channels and having data to track and understand each interaction. In order to do this, marketers create buyer personas based on a wide spectrum of quantitative and qualitative data. Digital ethnography is another established method for studying web-based communities. However, for practitioners, the complexity, rigor, and time associated with ethnographical work are sometimes out of reach. Objective This paper responds to the gaps in the practically focused method of using social media for digital ethnography to develop buyer personas. This paper aims to demonstrate how digital ethnography can be used as a way to create and refine buyer personas. Methods Using a case study of the Fan Fit smartphone app, which aimed to increase physical activity, a digital ethnography was applied to create a better understanding of customers and to create and refine buyer personas. Results We propose two buyer personas, and we develop a 6-step digital ethnography framework designed for the development of buyer personas. Conclusions The key contribution of this work is the proposal of a 6-step digital ethnography framework designed for the development of buyer personas. We highlight that the 6-step digital ethnography could be a robust tool for practitioners and academicians to analyze digital communications for the process of creating and updating data-driven buyer personas to create deeper insights into digital and health marketing efforts.
Understanding and predicting incident mitigation time

Incident management plays a significant role in online service systems. Incidents should be mitigated as soon as possible in order to achieve high service stability. However, available resources tend to be limited, and thus engineers have to schedule their tasks carefully. Time to Mitigate (TTM) refers to the time an incident requires to restore the service availability. Predicting TTM can help better estimate maintenance efforts and provide developers more information when arranging their tasks. Our work aims to predict TTM precisely, which consists of two main steps. First, we perform an empirical study to understand incidents deeply. Then, we design an effective approach for TTM prediction based on the findings from the empirical study. In the empirical study, we used 20 Microsoft online service systems to investigate the duration of each stage in incident management and the relationship between TTM and incident indicators. Then, we propose TTMPred, a deep-learning-based approach for TTM prediction in the continuous triage scenario based on the features identified from our empirical study. In particular, we improve the generality of TTMPred by extending it to predicting the fixing time of traditional software bugs. We investigate the effectiveness of TTMPred on four large-scale online service systems in Microsoft, as well as four widely-used Bugzilla-based projects. The results show that TTMPred performs better than the compared approaches for both incident TTM prediction and bug-fixing time prediction. For example, on average, TTMPred improves the state-of-the-art regression-based approach by 25.66% in terms of MAE (Mean Absolute Error) on the incident data and 42.14% on MAE on the bug data. TTMPred can be extended to the bug scenario, and continuously predict accurate bug-fixing time during the triage process.
Object semantic-guided graph attention feature fusion network for Siamese visual tracking

The similarity matching between the template and the search area plays a key role in Siamese-based trackers. Most Siamese-based trackers adopt correlation operation to perform feature fusion on the template branch and search branch for similarity matching. However, the correlation operation directly uses the template feature to slide the window on the search area feature without distinguishing the discriminant part of the target and the background noise, which blurs the spatial information of the response feature. To address this issue, this work proposes a novel object semantic-guided graph attention feature fusion network that both removes background information and focuses on the discriminative part of the object. The proposed network effectively removes background noise by utilizing an adaptive template instead of the fixed-size template used by the correlation operation. The network also models the contextual semantic relations of the target and uses the resulting semantic relations to guide the feature fusion process in a part-based manner, thereby accurately highlighting the discriminative parts of the target. Therefore, the problem of blurring response feature caused by correlation operation is effectively resolved. Furthermore, we propose an object-aware prediction network to learn object-aware features for classification and regression task, which effectively improves the discriminative ability of the prediction network. Experiments on many challenging benchmarks like OTB-100, LaSOT, TColor-128, GOT-10k and VOT2019, show that our methods achieves excellent performance.
A modified threshold score‐based multilevel thresholding segmentation technique for brain magnetic resonance images using opposition‐based learning hybrid rice optimization algorithm

Abstract In modern clinical diagnostics, magnetic resonance imaging (MRI) is frequently used for brain tumor detection because of its high resolution of soft tissues, which plays a crucial role in the prevention, detection, and treatment planning. Therefore, it is meaningful to obtain high‐quality MR images by automatic thresholding for aiding diagnosis. Most multilevel thresholding techniques are based on histograms. It is susceptible to the limitation of grayscale spatial distribution and is difficult to be used for MR images with variable and complex morphology. In this paper, a novel multilevel thresholding segmentation approach with a non‐histogram using a modified threshold score (MTS) is proposed. An opposition‐based learning hybrid rice optimization (OHRO) algorithm is used to reduce the computational cost of MTS for the purpose of optimizing the threshold search. The strategy of opposition‐based learning expands the space of feasible solutions and avoids the search from stalling. The proposed approach is evaluated through the Harvard Medical School's whole brain atlas dataset. Comparing the results with TS‐OHRO, Tsallis‐OHRO, Kapur‐OHRO, and Masi‐OHRO, MTS‐OHRO achieves better quantitative and qualitative outcomes which demonstrate that the application of MTS‐OHRO to MR images is effective and feasible.
Semi-supervised portrait matting using transformer

Transformer is successfully applied in many tasks but is not sensible to directly embed in the portrait matting. Such an operation can effectively consider global features ignoring local features, which leads to the difficulty of identifying the complete and edge-refined portraits. What is more, the existing supervised architectures are feeble against the unlabeled images. To achieve the effective excavation of both local and global features, we design a semi-supervised network that leverages Transformer to capture the global features. The arduous task of compensating more local features is left to the portrait detailed decoding module (PDDM) that we designed. In addition, to provide the possibility of improving effectiveness when faced with unlabeled images, we design an intelligent pseudo-label generation strategy to embed in our semi-supervised network. This strategy can generate more detailed pseudo-labels than predicted results through redundant foreground filtering and edge adjustment. Compared to existing portrait matting methods, our network successfully achieves performance improvements with a small number of datasets and has the ability to train on unlabeled datasets. The training models and the code will be released at https://github.com/XinyueZhangqdu/SSPMTransformer/.
Privacy-Preserving Federated Learning Using Homomorphic Encryption With Different Encryption Keys

Federated learning (FL) technology has emerged for efficient data collection, data privacy protection, and efficient utilization of computing resources. In FL-based systems, data owners only send intermediate model trained with their local data to a central FL server, and then the central server combines intermediate models to generate globally trained model instead of the central server's collecting raw data from the data owners. However, even if only the trained intermediate model is transmitted to the central server, there are still several instances of privacy infringements using intermediate model information. To prevent data privacy infringement from intermediate data, homomorphic encryption (HE) schemes that enable numerical operations with ciphertexts can be used. The HE schemes have a limitation that data can only be calculated when it is encrypted with the same encryption key. The proposed algorithm improves data security by developing a technique that enables homomorphic operations even if data is encrypted with different encryption keys.
Recent Advances in Privacy Protection Technologies in Blockchain

With the continuous development and wide application of blockchain technology, the privacy leakage problem it faces is becoming more and more prominent, which must be paid great attention. Compared with the traditional centralized architecture, blockchain system does not specifically reply on a trusted central node to process and store data, avoiding the risk of single-point crash and data leakage of centralized servers. However, to achieve consensus among distributed nodes, all transaction records in the blockchain must be disclosed to all nodes, which will significantly increase the risk of privacy leakage. In this paper, we review the literatures published in recent years that discuss privacy issues related to blockchain technology, introduce the methodology and cryptographic measures currently applied, and summarize potential research trends in future.
Handling Missing Data For Sleep Monitoring Systems

Sensor-based sleep monitoring systems can be used to track sleep behavior on a daily basis and provide feedback to their users to promote health and well-being. Such systems can provide data visualizations to enable self-reflection on sleep habits or a sleep coaching service to improve sleep quality. To provide useful feedback, sleep monitoring systems must be able to recognize whether an individual is sleeping or awake. Existing approaches to infer sleep-wake phases, however, typically assume continuous streams of data to be available at inference time. In real-world settings, though, data streams or data samples may be missing, causing severe performance degradation of models trained on complete data streams. In this paper, we investigate the impact of missing data to recognize sleep and wake, and use regression- and interpolation-based imputation strategies to mitigate the errors that might be caused by incomplete data. To evaluate our approach, we use a data set that includes physiological traces - collected using wristbands -, behavioral data - gathered using smartphones - and self-reports from 16 participants over 30 days. Our results show that the presence of missing sensor data degrades the balanced accuracy of the classifier on average by 10–35 percentage points for detecting sleep and wake depending on the missing data rate. The impu-tation strategies explored in this work increase the performance of the classifier by 4–30 percentage points. These results open up new opportunities to improve the robustness of sleep monitoring systems against missing data.
Adaptive risk-aware sharable and individual subspace learning for cancer survival analysis with multi-modality data

Biomedical multi-modality data (also named multi-omics data) refer to data that span different types and derive from multiple sources in clinical practices (e.g. gene sequences, proteomics and histopathological images), which can provide comprehensive perspectives for cancers and generally improve the performance of survival models. However, the performance improvement of multi-modality survival models may be hindered by two key issues as follows: (1) how to learn and fuse modality-sharable and modality-individual representations from multi-modality data; (2) how to explore the potential risk-aware characteristics in each risk subgroup, which is beneficial to risk stratification and prognosis evaluation. Additionally, learning-based survival models generally refer to numerous hyper-parameters, which requires time-consuming parameter setting and might result in a suboptimal solution. In this paper, we propose an adaptive risk-aware sharable and individual subspace learning method for cancer survival analysis. The proposed method jointly learns sharable and individual subspaces from multi-modality data, whereas two auxiliary terms (i.e. intra-modality complementarity and inter-modality incoherence) are developed to preserve the complementary and distinctive properties of each modality. Moreover, it equips with a grouping co-expression constraint for obtaining risk-aware representation and preserving local consistency. Furthermore, an adaptive-weighted strategy is employed to efficiently estimate crucial parameters during the training stage. Experimental results on three public datasets demonstrate the superiority of our proposed model.
The Waste Separation Game to Promote Computational Thinking Through Mixed Reality Technology

The primary issue of garbage is its direct impact on the environment and quality of life. Several types of trash have piled up in Thailand. Many individuals don’t use the correct way of waste management because they have lacked knowledge and understanding of how to sort rubbish since childhood. To increase the possibility to fully utilize the knowledge of waste separation correctly, the researcher aims to promote computational thinking through the learning of a game called “Trashman”. This game simulates various objects for situations of sorting garbage into the appropriate sorting bin. Trashman has 3 levels in the game. Each level increases the number of bins and the amount of garbage, starting from level 1-3 which refers to easy, moderate, and difficult levels, respectively. The level indicates the error of separating the waste if putting it into a wrong bin type. The educating about different types of waste separation through the game utilizes Mixed Reality (MR) technology of the Magic Leap One device. The developer’s expectation for an alternative learning medium in the form of a game is to allow players to apply their knowledge by practicing computational skills by sorting garbage correctly. The game evaluates and summarizes scores of each level at the game’s end. This will evaluate player’s performance of CT through their ability separating trash. Moreover, the Sustainable Development Goals can be applied for lifelong learning to follow-up and assessment in furtherance of correct trash sorting.
Survey on unsupervised learning methods for optical flow estimation

Optical flow is an important component in many computer vision applications. Thanks to deep learning, there have been great improvements in optical flow estimation in the past several years. But all of the top performing models are trained using a supervised method, on synthetic data sets. As the creation of accurately labeled optical flow data sets from real world images is incredibly difficult, many researchers have turned to developing unsupervised approaches. In this paper we conduct a survey of some of the most recent papers in unsupervised learning of optical flow, and present some of the key elements that are universally utilized. In addition, we did a results comparison, and found that the best performing unsupervised models are UnDAF-RAFT for the MPI-Sintel benchmark, and UpFlow on the KITTI benchmark. But both models still have considerably worse results when compared to supervised methods.
Effect of Organizational Learning on Improving Hard Skills, Soft Skills, and Innovation on Performance

Introduction/Main Objectives: This study sought to quantify how hard skills, soft skills, organization learning, and innovation capability all affected performance. Background Problems: The abilities required to advance in the workplace are changing due to the expanding workplace. Every university graduate is expected to perform exceedingly well, possess high levels of competence, and be adept at their jobs. Educational institutions play a crucial role in developing competent graduates with hard skills, soft skills, and self-efficacy. Novelty: This study evaluates the similarities and differences between the previous research and the research conducted because the earlier research did not integrate the three variables in one research title. Research Methods: This study employs a qualitative literature study. Secondary data is gathered from research findings from multiple references that are pertinent to the study's subject and goals. Finding/Results: The findings demonstrated that organizational learning, hard and soft skills, and innovation capability all had a direct, positive, and significant impact on performance. Additionally, among other factors, soft skills have the most significant an impact on performance. Conclusion: Every educational institution must continue to focus on developing hard and soft skills through training to improve employee performance.
Quantum Circuit Simulator based on FPGA

The quantum computer is emerging technology since it can resolve complex tasks such as factorization or search problem faster than classical computers. However, simulation of the quantum computer is difficult because computational load increases exponentially as the number of qubits used in the circuit is increased. This paper proposes a hardware architecture for quantum circuit simulation. To implement simulator smaller, our architecture operates based on a 1-input single gate regardless of the number of qubits used in the circuit In addition, our architecture helps to reduce simulation time by skipping calculation and data transfer. The proposed architecture is implemented on FPGA and the experimental results show that our architecture reduces 79.2% of the multiplication process when we simulate 20-qubit quantum Fourier transform with a small hardware size.
Revisiting TLS-Encrypted Traffic Fingerprinting Methods for Malware Family Classification

Transport Layer Security (TLS) is a well-known end-to-end encryption protocol for secure communication, and the use of TLS is continuously increasing, which influences that currently most of the Web traffic is delivered through a secure channel. Unfortunately, the use of secure channel for malware is also substantially increasing so that both application layer payload and metadata from unencrypted header fields become unavailable for malicious traffic classification. While several TLS fingerprinting methods, namely JA3 and Mercury, are available, the approaches are more suitable for exact matching than for machine learning-based classification. To deal with this, in this paper, we revisit Markov chain-based fingerprinting from packet length sequences to classify TLS-encrypted malware traffic into malware families. We especially, evaluate Markov chain-based and the existing fingerprinting methods (JA3 and Mercury) with several classifiers including k-nearest neighbor and convolutional neural network. Our results show that our Markov chain-based approach has better performance than other fingerprinting methods with an appropriate classifier.
The Scientific Review of AI Functions of Enhancement English Learning and Teaching

This paper explores and reviews AI functions enhancing English use, learning, and teaching. According to this research paper, AI functions that could promote English learning and teaching are a capable process of the AI sub-fields involving natural language processing (NLP), machine learning, and deep learning. The reviews found that AI enhancement of English learning and teaching included a data warehouse system and data mart, digital platforms for communicative channels such as blogs, social networking (social media), wikis, and multi-player online games. AI real simulation dialogue platforms through AI-based teaching systems were modified by using decision tree algorithms, Expert AI system knowledge, or multidimensional variables in information points applied to English memory and learning processes. In addition, the Applications with the NLP process widely used are Grammarly, Duolingo, and Google Translate. According to the reviews, it was found that AI should be functioned and be modified further in cooperation with English teachers and students in the virtual classroom as long as AI functions should be more interactive and able to understand human feelings and emotions exactly.
Performance Analysis of Sequence-based Deep Learning Model for LPI Radar Waveform Recognition in Fading Channel

Although many studies have been conducted for the recognition of low probability of intercept (LPI) radar waveforms, only a few consider fading channels because the recognition in the fading channel is more challenging than that in the additive white Gaussian noise channel. In this paper, we investigate the recognition performance of the sequence-based deep learning model for LPI radar waveforms in a fading channel. As inputs of the model, we consider the received radar waveform, its discrete Fourier transform, and its autocorrelation, respectively. Simulation results show that it is advantageous to exploit the discrete Fourier transform of the received radar waveform as the input of the sequence-based recognition model in the fading channel.