Reviewerly/exHarmony huggingface dataset:
A Framework for Anomaly Identification Applied on Fall Detection

Automatic systems to monitor people and subsequently improve people's lives have been emerging in the last few years, and currently, they are capable of identifying many activities of daily living (ADLs). An important field of research in this context is the monitoring of health risks and the identification of falls. It is estimated that every year, one in three persons older than 65 years will fall, and fall events are associated with high mortality rates among the elderly. We propose an anomaly identification framework to detect falls, which incorporates a spatial-temporal convolutional graph network (ST-GCN) as a feature extractor and uses an encoder process to reconstruct ADLs and identify falls as anomalies. As the publicly available fall datasets are few and generally unbalanced, training a reliable model using approaches that need explicit labeling is challenging. Thus, a focus on learning without external supervision is desirable. Treating a fall as an exception of ADLs allows us to recognize falls as anomalies without explicit labels. Given its modular architecture, our framework can robustly represent visual information and use the encoder's reconstruction error to identify falls as anomalies. We assess our framework's ability to recognize falls by training it with only ADLs. We perform three types of experiments: single dataset training and evaluation that consists of separate 90% of the data to train the model 5% to adjust the model, and the rest to the test. A joint dataset experiment, where we combine two datasets to increase the number of samples our model is trained on, and a cross-dataset evaluation, where we train on one dataset and evaluate using another one. Besides presenting state-of-the-art results on our experiments, particularly on the cross-dataset one, the model also presents a low number of false events, which makes it an ideal candidate for real-world application.
Three-objective optimization of boiler combustion process based on multi-objective teaching–learning based optimization algorithm and ameliorated extreme learning machine

The combustion optimization problem of Circulation Fluidized Bed Boiler (CFBB) can be regarded as a constrained dynamic multi-objective optimization problem, so it has become a hot research to solve the problem for saving energy and reducing polluting gas. However, it is difficult to optimize the combustion process based on traditional optimization method due to a variety of complex characteristics of boiler, such as non-linearity, strong coupling , large lag. In order to address the boiler combustion optimization problem, a kind of multi-objective modified teaching–learning-based optimization (namely MMTLBO) is proposed. For the MMTLBO, a constrained mechanism is firstly introduced into MMTLBO. Finally, the MMTLBO and ameliorated extreme learning machine (AELM) are utilized to optimize the CFBB’s combustion process for increasing the thermal efficiency and reducing the NOx/SO2 emissions concentration. The AELM is used to establish the comprehensive model of the thermal efficiency and NOx/SO2 emissions. The model accuracy and standard deviation can arrive 10−2 and 10−4, separately. So the model shows high generalization ability and good stability. Based on the model, the MMTLBO is applied to optimize the boiler’s combustion process parameters. Experiment results show that the MMTLBO can find several groups reasonable combustion parameters which increase the thermal efficiency and reduce the NOx/SO2 emissions concentration. Therefore, the AELM and MMTLBO are the effective artificial intelligence algorithms.
Exploring Visual Context for Weakly Supervised Person Search

Person search has recently emerged as a challenging task that jointly addresses pedestrian detection and person re-identification. Existing approaches follow a fully supervised setting where both bounding box and identity annotations are available. However, annotating identities is labor-intensive, limiting the practicability and scalability of current frameworks. This paper inventively considers weakly supervised person search with only bounding box annotations. We propose to address this novel task by investigating three levels of context clues (i.e., detection, memory and scene) in unconstrained natural images. The first two are employed to promote local and global discriminative capabilities, while the latter enhances clustering accuracy. Despite its simple design, our CGPS boosts the baseline model by 8.8% in mAP on CUHK-SYSU. Surprisingly, it even achieves comparable performance with several supervised person search models. Our code is available at https://github. com/ljpadam/CGPS.
Image Retrieval Method Based on Image Feature Fusion and Discrete Cosine Transform

This paper presents a new content-based image retrieval (CBIR) method based on image feature fusion. The deep features are extracted from object-centric and place-centric deep networks. The discrete cosine transform (DCT) solves the strong correlation of deep features and reduces dimensions. The shallow features are extracted from a Quantized Uniform Local Binary Pattern (ULBP), hue-saturation-value (HSV) histogram, and dual-tree complex wavelet transform (DTCWT). Singular value decomposition (SVD) is applied to reduce the dimensions of ULBP and DTCWT features. The experimental results tested on Corel datasets and the Oxford building dataset show that the proposed method based on shallow features fusion can significantly improve performance compared to using a single type of shallow feature. The proposed method based on deep features fusion can slightly improve performance compared to using a single type of deep feature. This paper also tests variable factors that affect image retrieval performance, such as using principal component analysis (PCA) instead of DCT. The DCT can be used for dimensional feature reduction without losing too much performance.
Motivations, Barriers and Risk-Taking When Investing in Cryptocurrencies

The cryptocurrency market is very young, volatile, and highly risky. By the end of 2020, a new bull run started, and the prices of several cryptocurrencies reached record-breaking highs. The factors affecting this rise of cryptocurrencies include the impacts of the COVID-19 pandemic, the economic crisis and the global increase in the inflation rate, as well as the gradual acceptance and adoption of cryptocurrencies by people worldwide. This exploratory research is focused on this last factor, i.e., using cryptocurrency and with it, the associated support of its ecosystem (e.g., mining, staking). A survey was carried out investigating the motivational factors and barriers to investment in cryptocurrency for Czech representatives of Generations Y and Z (18–42 years; n = 468). The geographic scope was nationwide, and quota sampling was used. Notably, this survey was carried out prior to the global COVID-19 pandemic outbreak, and it is thus not affected by the pandemic and its related economic impacts. The article investigates the dependency between the individual motivational factors and barriers from the perspective of the tendency to take risks (using the risk propensity scale), according to gender and representation of Generations Y and Z. The lack of information on this form of investment is considered as the main barrier to investment in cryptocurrency, with respect to sex and generations. Compared to that, a negative experience with investment in cryptocurrency constitutes the most minor barrier. Respondents that have a tendency to take risks are mostly put off by their lack of experience with investment in general. The main motivational factor for investment in cryptocurrency, with respect to sex and generations, is considered to be the speed of increase in cryptocurrency value. On the other hand, the least encouraging factor is the opportunity to use the high volatility of cryptocurrency for speculative trading. Interestingly, this factor mostly encourages respondents that do not have a tendency to take risks. The findings are discussed, along with the presentation of their implications for practice and the directions of further explanatory research.
RETRACTED ARTICLE: Recurrent Neural Network for Genome Sequencing for Personalized Cancer Treatment in Precision Healthcare

For the purpose of the detection of disease through genome sequencing, the complete genome sequence of the infected individuals is now technically feasible. Identifying all coding exons can be used to identify new diseases that involve disease-related variants and genes. The objective of this paper is to propose deep neural network inspired approach in interpreting genome sequencing with optimization of Long Short Term Memory, LSTM method is in focus for personalized cancer treatment. A distinguishing feature of this proposed work is that it includes optimization technique for LSTM. It is evident from evaluation results that the proposed approach has yielded higher accuracy outcome when compared with conventional hybrid classifiers.
Breaking Masked Implementations with Many Shares on 32-bit Software Platforms

We explore the concrete side-channel security provided by state-of-theart higher-order masked software implementations of the AES and the (candidate to the NIST Lightweight Cryptography competition) Clyde, in ARM Cortex-M0 and M3 devices. Rather than looking for possibly reduced security orders (as frequently considered in the literature), we directly target these implementations by assuming their maximum security order and aim at reducing their noise level thanks to multivariate, horizontal and analytical attacks. Our investigations point out that the Cortex-M0 device has so limited physical noise that masking is close to ineffective. The Cortex-M3 shows a better trend but still requires a large number of shares to provide strong security guarantees. Practically, we first exhibit a full 128-bit key recovery in less than 10 traces for a 6-share masked AES implementation running on the Cortex-M0 requiring 232 enumeration power. A similar attack performed against the Cortex-M3 with 5 shares require 1,000 measurements with 244 enumeration power. We then show the positive impact of lightweight block ciphers with limited number of AND gates for side-channel security, and compare our attacks against a masked Clyde with the best reported attacks of the CHES 2020 CTF. We complement these experiments with a careful information theoretic analysis, which allows interpreting our results. We also discuss our conclusions under the umbrella of “backwards security evaluations” recently put forwards by Azouaoui et al. We finally extrapolate the evolution of the proposed attack complexities in the presence of additional countermeasures using the local random probing model proposed at CHES 2020.
Automatic Tempered Posterior Distributions for Bayesian Inversion Problems

We propose a novel adaptive importance sampling scheme for Bayesian inversion problems where the inference of the variables of interest and the power of the data noise is split. More specifically, we consider a Bayesian analysis for the variables of interest (i.e., the parameters of the model to invert), whereas we employ a maximum likelihood approach for the estimation of the noise power. The whole technique is implemented by means of an iterative procedure, alternating sampling and optimization steps. Moreover, the noise power is also used as a tempered parameter for the posterior distribution of the the variables of interest. Therefore, a sequence of tempered posterior densities is generated, where the tempered parameter is automatically selected according to the actual estimation of the noise power. A complete Bayesian study over the model parameters and the scale parameter can be also performed. Numerical experiments show the benefits of the proposed approach.
Online updating extended belief rule-based system for sensor-based activity recognition

Sensor-based activity recognition (AR) is a core problem with the research domain of smart environments.It has, however, the potential to provide solutions to address the problems associated with the growing size and ageing profile of the global population.The work presented within this paper focuses on the extended belief rule-based system (EBRBS), which offered promising performance compared with popular benchmark AR models and exhibited a high robustness in the situation of sensor failure.Nevertheless, efficiency remains one of the major issues to be improved for determining and updating the extended belief rule base (EBRB) within the EBRBS.This is critical for further utilizing the EBRBS in AR situations within dynamic smart environments.An eigendecomposition-based sensor selection method is firstly proposed to select an effective subset of sensors and to also enable efficient implementation to facilitate online AR.A novel domain division-based rule generation method is also proposed to generate and update an EBRB efficiently when new sensor data are available or when some sensors should be included or excluded in the EBRB.The combination of these two methods leads to an enhanced EBRBS, called online updating EBRBS.Two datasets (in a balanced class situation) obtained from simulation and actual environments are studied to provide detailed experimental analysis as a preliminary study and basis to handle further the imbalanced situation of real AR.The experimental results demonstrate an enhanced performance of the online updating EBRBS compared with the original EBRBS and some benchmark AR models, in terms of efficiency and effectiveness.
How do Voices from Past Speech Synthesis Challenges Compare Today?

Shared challenges provide a venue for comparing systems trained on common data using a standardized evaluation, and they also provide an invaluable resource for researchers when the data and evaluation results are publicly released. The Blizzard Challenge and Voice Conversion Challenge are two such challenges for text-to-speech synthesis and for speaker conversion, respectively, and their publicly-available system samples and listening test results comprise a historical record of state-of-the-art synthesis methods over the years. In this paper, we revisit these past challenges and conduct a large-scale listening test with samples from many challenges combined. Our aims are to analyze and compare opinions of a large number of systems together, to determine whether and how opinions change over time, and to collect a large-scale dataset of a diverse variety of synthetic samples and their ratings for further research. We found strong correlations challenge by challenge at the system level between the original results and our new listening test. We also observed the importance of the choice of speaker on synthesis quality.
Intelligent system for human activity recognition in IoT environment

Abstract In recent years, the adoption of machine learning has grown steadily in different fields affecting the day-to-day decisions of individuals. This paper presents an intelligent system for recognizing human’s daily activities in a complex IoT environment. An enhanced model of capsule neural network called 1D-HARCapsNe is proposed. This proposed model consists of convolution layer, primary capsule layer, activity capsules flat layer and output layer. It is validated using WISDM dataset collected via smart devices and normalized using the random-SMOTE algorithm to handle the imbalanced behavior of the dataset. The experimental results indicate the potential and strengths of the proposed 1D-HARCapsNet that achieved enhanced performance with an accuracy of 98.67%, precision of 98.66%, recall of 98.67%, and F1-measure of 0.987 which shows major performance enhancement compared to the Conventional CapsNet (accuracy 90.11%, precision 91.88%, recall 89.94%, and F1-measure 0.93).
Bayesian phase difference estimation: a general quantum algorithm for the direct calculation of energy gaps

Bayesian phase difference estimation (BPDE) is a controlled-time evolution-free quantum algorithm that is capable of computing the energy difference between two electronic states directly, without inspecting the total energies of individual states.
VERCASM-CPS: Vulnerability Analysis and Cyber Risk Assessment for Cyber-Physical Systems

Since Cyber-Physical Systems (CPS) are widely used in critical infrastructures, it is essential to protect their assets from cyber attacks to increase the level of security, safety and trustworthiness, prevent failure developments, and minimize losses. It is necessary to analyze the CPS configuration in an automatic mode to detect the most vulnerable CPS components and reconfigure or replace them promptly. In this paper, we present a methodology to determine the most secure CPS configuration by using a public database of cyber vulnerabilities to identify the most secure CPS components. We also integrate the CPS cyber risk analysis with a Controlled Moving Target Defense, which either replaces the vulnerable CPS components or re-configures the CPS to harden it, while the vulnerable components are being replaced. Our solution helps to design a more secure CPS by updating the configuration of existing CPS to make them more resilient against cyber attacks. In this paper, we will compare cyber risk scores for different CPS configurations and show that the Windows® 10 build 20H2 operating system is more secure than Linux Ubuntu® 20.04, while Red Hat® Enterprise® Linux is the most secure in some system configurations.
Chase Away the Virus: A Character-Based Chatbot for COVID-19

Chatbots are becoming an attractive tool for people who seek medical advice due to their constant availability. Multiple healthcare chatbots were developed for different purposes such as delivering advice, booking appointments, and accessing medical records. Additionally, it was found that personalized healthcare chatbots affected the user experience positively, due to the addition of human empathy. Therefore, we propose building a character-based chatbot named “Chasey” for COVID-19, to combat the risk of misinformation amplification during the pandemic. Chasey provides users with various COVID-19 information such as tracking the cases per country, giving advice, answering frequently asked questions, and performing symptoms checking. According to the selected chatbot character, users will receive personalized responses to their inquiries from verified sources. Moreover, we investigate how our chatbot implementation overcomes some of the challenges and limitations of healthcare and COVID-19 chatbots. Finally, an experiment was conducted to evaluate the chatbot's usability, as well as, the likability and trustworthiness of the chatbot characters. Overall, the participants were satisfied with the chatbot features and character change option. Moreover, significant results were found between the likability of the chatbot characters.
Understanding cancel culture: Normative and unequal sanctioning

Cancel culture is a phenomenon where individuals transgressing norms are called out and ostracised on social media and other venues by members of the public. While its effects are decried by some and its existence denied by others, the processes that shape cancel culture are misunderstood. In this article, I argue that cancellation can only occur if participating third parties with oversight over transgressing individuals perform sanctions. Furthermore, I explore how cancel culture affects people unequally by looking at the phenomenon known as the Karens. Using social normative theories, I evaluate how women affected by cancellation are facing misogyny through cancel culture.
Double layer security using crypto-stego techniques: a comprehensive review

Recent advancement in the digital technology and internet has facilitated usage of multimedia objects for data communication. However, interchanging information through the internet raises several security concerns and needs to be addressed. Image steganography has gained huge attention from researchers for data security. Image steganography secures the data by imperceptibly embedding data bits into image pixels with a lesser probability of detection. Additionally, the encryption of data before embedding provides double-layer protection from the potential eavesdropper. Several steganography and cryptographic approaches have been developed so far to ensure data safety during transmission over a network. The purpose of this work is to succinctly review recent progress in the area of information security utilizing combination of cryptography and steganography (crypto-stego) methods for ensuring double layer security for covert communication. The paper highlights the pros and cons of the existing image steganography techniques and crypto-stego methods. Further, a detailed description of commonly using evaluations parameters for both steganography and cryptography, are given in this paper. Overall, this work is an attempt to create a better understanding of image steganography and its coupling with the encryption methods for developing state of art double layer security crypto-stego systems.
Development and testing of an image transformer for explainable autonomous driving systems

Purpose Perception has been identified as the main cause underlying most autonomous vehicle related accidents. As the key technology in perception, deep learning (DL) based computer vision models are generally considered to be black boxes due to poor interpretability. These have exacerbated user distrust and further forestalled their widespread deployment in practical usage. This paper aims to develop explainable DL models for autonomous driving by jointly predicting potential driving actions with corresponding explanations. The explainable DL models can not only boost user trust in autonomy but also serve as a diagnostic approach to identify any model deficiencies or limitations during the system development phase. Design/methodology/approach This paper proposes an explainable end-to-end autonomous driving system based on “Transformer,” a state-of-the-art self-attention (SA) based model. The model maps visual features from images collected by onboard cameras to guide potential driving actions with corresponding explanations, and aims to achieve soft attention over the image’s global features. Findings The results demonstrate the efficacy of the proposed model as it exhibits superior performance (in terms of correct prediction of actions and explanations) compared to the benchmark model by a significant margin with much lower computational cost on a public data set (BDD-OIA). From the ablation studies, the proposed SA module also outperforms other attention mechanisms in feature fusion and can generate meaningful representations for downstream prediction. Originality/value In the contexts of situational awareness and driver assistance, the proposed model can perform as a driving alarm system for both human-driven vehicles and autonomous vehicles because it is capable of quickly understanding/characterizing the environment and identifying any infeasible driving actions. In addition, the extra explanation head of the proposed model provides an extra channel for sanity checks to guarantee that the model learns the ideal causal relationships. This provision is critical in the development of autonomous systems.