Reviewerly/exHarmony huggingface dataset:
A feasibility study of using virtual reality as a pre-occupancy evaluation tool for the elderly

This research aims to investigate the potential adoption of virtual reality (VR) as a pre-occupancy evaluation (PreOE) tool applied for older people, focusing on technology adoption and spatial cognition. An experimental study was conducted to explore simulator sickness, presence, and spatial cognition in immersive VR and non-immersive VR conditions. Thirty-two healthy older participants aged 60 and over were randomly divided equally into two groups, immersive VR and Non-immersive VR. The results indicated that immersive VR is promising to be applied safely for older people with no considerable recorded symptom of simulation sickness (SS). The presentation using immersive VR also provided a higher sense of presence and more accurate spatial cognition. Interestingly, the qualitative data shows positive aspects from the subjects' viewpoint. To conclude, immersive VR is feasible and effective to be used in coupling with conventional methods for the PreOE purpose among older people.
Multi-Sensor Surveillance System Based on Integrated Video Analytics

Image-based sensing is a powerful source of information for a wide range of applications such as surveillance, monitoring, and automation. The volume of data collected by surveillance devices is enormous; therefore, images are processed to extract application-driven data, moreover, centralized processing is unfeasible when several cameras are distributed across broad geographical regions. In such scenario, with processing limitations and bandwidth availability being important restrictions, an automated process is required for extracting meaningful information from the collected data during surveillance process and reducing operator fatigue. The proposed design, which continuously processes the image stream to meet real-time requirements, provides integrated video analytics with enhancement, panoramic views, and automated motion detection. This system enhances pictorial information for human interpretation and analyzes scene data using machine-vision modules. These features aid in the use of surveillance system for continuous monitoring of border areas without the need for human intervention under adverse-weather conditions. The proposed system architecture is based on distributed video-analytics using visual and infrared-band sensors. The system has a visual detection range of 5 km with 80% detection probability for a 2.3 m X 2.3 m target under standard clear visibility conditions. The preliminary results indicate that the system is capable of processing up to 25 video frames per second. Additionally, the implemented system records data for post-analysis and uses video analytics to reduce the bandwidth requirement of communication link. Finally, this system can be distinguished because it is flexible, compact, affordable, and scalable, and therefore, can be considered for monitoring and surveillance.
A comprehensive meta-analysis of emerging swarm intelligent computing techniques and their research trend

This study presents an extensive analysis of ten emerging swarm intelligence metaheuristic techniques, namely Emperor Penguins Colony (EPC), Harris Hawks Optimizer (HHO), Butterfly Optimization Algorithm (BOA), Spotted Hyena Optimizer (SHO), Crow search algorithm (CSA), Whale optimization algorithm (WOA), Red Deer Algorithm (RDA), Ant Lion Optimizer (ALO), Dragonfly Algorithm (DA) and Grey wolf optimization (GWO). Here, a Quad–fold review strategy comprised of planning, shortlisting, extraction, and execution have been adhered to compile this meta-analysis. The mathematical models and working principles of these techniques have been briefly elucidated. The variants of these meta-heuristic techniques have also been explored and presented. The research trend of these metaheuristic methods has also been highlighted. The findings indicate that these methods are widely used to solve different problems viz: image segmentation, optimal power flow, air pollution forecasting, drug design, wireless sensor networks, disease diagnosis, transport, and routing. Furthermore, in the healthcare sector, the use of SI techniques in selecting optimal features for diagnosis of different diseases like Cancer, Alzheimer's, Kidney disease, Anemia, Viral infection, Skin diseases have also been highlighted. Moreover, it is observed that the education-related optimization problems have been deeply explored by these meta-heuristic techniques whereas, weather forecasting is recognized as the least explored area. The binary, chaotic, and hybrid variants of EPC, HHO, BOA, SHO, CSA, WOA RDA, ALO, DA, and GWO of these metaheuristics techniques need to be deeply explored in healthcare for skin diseases, ophthalmology, viral infection, allergy along with distinct mental disorders. Finally, for better performance, the exploitation and exploration phases of these methods need to be carefully balanced.
Automated Design of Robust Genetic Circuits: Structural Variants and Parameter Uncertainty

Genetic design automation methods for combinational circuits often rely on standard algorithms from electronic design automation in their circuit synthesis and technology mapping. However, those algorithms are domain-specific and are hence often not directly suitable for the biological context. In this work we identify aspects of those algorithms that require domain-adaptation. We first demonstrate that enumerating structural variants for a given Boolean specification allows us to find better performing circuits and that stochastic gate assignment methods need to be properly adjusted in order to find the best assignment. Second, we present a general circuit scoring scheme that accounts for the limited accuracy of biological device models including the variability across cells and show that circuits selected according to this score exhibit higher robustness with respect to parametric variations. If gate characteristics in a library are just given in terms of intervals, we provide means to efficiently propagate signals through such a circuit and compute corresponding scores. We demonstrate the novel design approach using the Cello gate library and 33 logic functions that were synthesized and implemented in vivo recently (Nielsen, A., et al., Science, 2016, 352 (6281), DOI: 10.1126/science.aac7341). Across this set of functions, 32 of them can be improved by simply considering structural variants yielding performance gains of up to 7.9-fold, whereas 22 of them can be improved with gains up to 26-fold when selecting circuits according to the novel robustness score. We furthermore report on the synergistic combination of the two proposed improvements.
An Efficient Certificate-Based Data Integrity Auditing Protocol for Cloud-Assisted WBANs

With the evolution of wireless body area networks (WBANs), wearable equipment will improve the human healthcare service. However, the medical data generated in WBANs increase dramatically with time, and the massive data cause the storage burden. With the help of cloud computing, the cloud service provider (CSP) can assist data owners in storing these data collected by sensors. By keeping their data in the CSP, the data integrity and authenticity is a big concern of data owners. To date, many data integrity auditing protocols have been proposed to address this issue. Most of them rely on traditional public-key mechanism, or identity-based cryptography (IBC), or certificateless cryptography (CLC). However, they suffer from the heavy cost of certificate management, key escrow, or the requirement of a secret channel for each user, respectively. To solve these drawbacks, we propose an efficient certificate-based data integrity auditing protocol for cloud-assisted WBANs. In our protocol, the computation cost in tag generation for a data block is fixed, and is independent of the size of the data block. We prove our protocol is secure in the random oracle model (ROM) and use the Java pairing-based cryptography library (JPBC) to implement the protocol. The experimental results show that our protocol is computationally efficient and practical.
Enhancing keyphrase extraction from academic articles with their reference information

With the development of Internet technology, the phenomenon of information overload is becoming more and more obvious. It takes a lot of time for users to obtain the information they need. However, keyphrases that summarize document information highly are helpful for users to quickly obtain and understand documents. For academic resources, most existing studies extract keyphrases through the title and abstract of papers. We find that title information in references also contains author-assigned keyphrases. Therefore, this article uses reference information and applies two typical methods of unsupervised extraction methods (TF*IDF and TextRank), two representative traditional supervised learning algorithms (Naïve Bayes and Conditional Random Field) and a supervised deep learning model (BiLSTM-CRF), to analyze the specific performance of reference information on keyphrase extraction. It is expected to improve the quality of keyphrase recognition from the perspective of expanding the source text. The experimental results show that reference information can increase precision, recall, and F1 of automatic keyphrase extraction to a certain extent. This indicates the usefulness of reference information on keyphrase extraction of academic papers and provides a new idea for the research on automatic keyphrase extraction.
Research on anomaly detection method of nuclear power plant operation state based on unsupervised deep generative model

In the field of traditional industrial control, anomaly detection method is mainly used to identify data items that do not match the normal operation state of the system. The traditional machine learning algorithm needs the transient operation data of normal and accident conditions to identify the abnormal state of nuclear power plant. The transient operation data of nuclear power plant during normal condition is sufficient, but it is lacks of transient operation data in accident conditions. To solve the above problems, an abnormal operation state detection method of nuclear power plant based on unsupervised deep generative model is established by using Variational Auto Encoders (VAE) and Isolation Forest (iForest) in this paper. The biggest advantage of this method is that it can only make use of the normal operation data of the nuclear power plant to make the nuclear power plant control system effectively identify whether the current state of nuclear power plant is normal operation or in accident condition. In the unsupervised deep generative model, VAE is used for data preprocessing, and iForest is used to identify abnormal operation data. Then, the method is verified in seven variable and accident conditions, such as power reduction condition, steam generator tube rupture accident and so on. The verification results show that the anomaly detection method can recognize the current abnormal condition immediately when the accident happens. And the time consumed to identify a group of operation parameters corresponding to the operation state of the nuclear power plant is about 3 ms, which can satisfy the real-time requirements of the control system. Therefore, the anomaly detection method based on unsupervised deep generative model can distinguish the normal or abnormal operation state of the nuclear power plant in real time and effectively, and provide judgment basis for accident classification and subsequent rescue.
Development of a Wearable Gesture Recognition System Based on Two-Terminal Electrical Impedance Tomography

This paper proposes a low-cost, wearable gesture recognition system based on the two-terminal electrical impedance tomography (EIT) technique. The system includes a wearable EIT sensor of eight electrodes, a hardware device, and gesture recognition software running on a PC. Nine different gestures can be stably identified from the measured impedance changes through machine learning algorithms. Experimental results show that the Quadric Discriminator algorithm has the highest recognition rate of 98.49% for the filtered validation set. Besides, the recognition results in the two-terminal mode and transformed four-terminal mode are compared by applying a two-to-four-terminal mapping to the two-terminal EIT system, and the recognition rate decreases with the most classification models in the latter mode. Thus, it is supposed that contact impedance plays an important role in gesture recognition. By analyzing the data characteristics with variance inflation factor (VIF) test and principal component analysis (PCA), the supposition is explained and verified, proving the merit of a two-terminal EIT system in gesture recognition.
An innovative eye-tracker: Main features and demonstrative tests

We present a set of results obtained with an innovative eye-tracker based on magnetic dipole localization by means of an array of magnetoresistive sensors. The system tracks both head and eye movements with a high rate (100-200 Sa/s) and in real time. A simple setup is arranged to simulate head and eye motions and to test the tracker performance under realistic conditions. Multimedia material is provided to substantiate and exemplify the results. A comparison with other available technologies for eye-tracking is drawn, discussing advantages (e.g., precision) and disadvantages (e.g., invasivity) of the diverse approaches, with the presented method standing out for low cost, robustness, and relatively low invasivity.
Using Gaze Behavior and Head Orientation for Implicit Identification in Virtual Reality

Identifying users of a Virtual Reality (VR) headset provides designers of VR content with the opportunity to adapt the user interface, set user-specific preferences, or adjust the level of difficulty either for games or training applications. While most identification methods currently rely on explicit input, implicit user identification is less disruptive and does not impact the immersion of the users. In this work, we introduce a biometric identification system that employs the user’s gaze behavior as a unique, individual characteristic. In particular, we focus on the user’s gaze behavior and head orientation while following a moving stimulus. We verify our approach in a user study. A hybrid post-hoc analysis results in an identification accuracy of up to 75 % for an explainable machine learning algorithm and up to 100 % for a deep learning approach. We conclude with discussing application scenarios in which our approach can be used to implicitly identify users.
Supervised Two-Dimensional Functional Principal Component Analysis with Time-to-Event Outcomes and Mammogram Imaging Data

Screening mammography aims to identify breast cancer early and secondarily measures breast density to classify women at higher or lower than average risk for future breast cancer in the general population. Despite the strong association of individual mammography features to breast cancer risk, the statistical literature on mammogram imaging data is limited. While functional principal component analysis (FPCA) has been studied in the literature for extracting image-based features, it is conducted independently of the time-to-event response variable. With the consideration of building a prognostic model for precision prevention, we present a set of flexible methods, supervised FPCA (sFPCA) and functional partial least squares (FPLS), to extract image-based features associated with the failure time while accommodating the added complication from right censoring. Throughout the article, we hope to demonstrate that one method is favored over the other under different clinical setups. The proposed methods are applied to the motivating data set from the Joanne Knight Breast Health cohort at Siteman Cancer Center. Our approaches not only obtain the best prediction performance compared to the benchmark model, but also reveal different risk patterns within the mammograms.
Insurers’ responses to cyber crime: Evidence from Russia

This study aims to identify effective responses to cyber crime in the insurance industry. Survey responses from Moscow-based employees holding key positions in the leading insurance companies have been collected. The study analyses awareness of, attitudes to, and approaches to cyber security, as well as the incidence and impact of breaches or attacks. According to the experts, complying with laws or regulations and preventing fraud or theft are the main reasons for investing in cyber security. Phishing, viruses, and unauthorised use of computers, networks or servers by staff are the most widely spread threats to cyber security. Russian insurers often undertake additional staff training or change their policies in response to cyber breaches. Strategic recommendations were elaborated for industry professionals.
Salient object detection in hyperspectral images using deep background reconstruction based anomaly detection

Salient object detection is a significant task that forms the basis for many image processing and computer vision applications. In recent years, the research in this area is being extended beyond RGB images to applications involving multispectral and hyperspectral images. But most of the existing algorithms for salient object detection often yield an incomplete representation of the object and often produce saliency maps with blurred edges. In this paper, we propose an efficient hyperspectral image salient object detection method through anomaly detection by combining deep learning autoencoders with one-class support vector machines. Here, the saliency detection problem in hyperspectral images is formulated as an unsupervised deep background spectral reconstruction-based anomaly detection. Our proposed method first employs deep autoencoders to model the background of an input hyperspectral image in terms of spectral reconstruction residuals of the autoencoders and then detect the salient objects from the image through a one-class support vector machine-based anomaly detection. The proposed method was evaluated on a publicly available hyperspectral image dataset for salient object detection. The experimental results show that our proposed method is found to be more efficient and superior over other previous methods in terms of various performance measures.
Source data-free domain adaptation for a faster R-CNN

The existing domain adaptive object detection methods often need to carry a large number of source domain samples for domain adaptation, which is not realistic due to GPU limitations, privacy and physical memory in practical applications. To solve this problem, we propose a source data-free domain adaptive object detection method. Only unlabeled target domain data is used to optimize the source domain model so that it can work better in the target domain. Our method takes Faster R-CNN as baseline. Specifically, we first construct global class prototypes which will be updated in batch iteratively. Then based on the global class prototypes, more accurate pseudo-labels are generated for training the target model. In this way, the source and target domains are also implicitly aligned. Our contributions are 1) a prototype guided domain adaptation method which uses prototypes to mine the semantic category information without accessing the source dataset; 2) a scheme of iteratively updating global class prototype which can handle the class and sample imbalances in the training procedure and 3) a more accurate pseudo-label generation method combining semantic information and image information. On multiple public domain adaptive scenarios, our method achieves the state-of-the-art results in terms of accuracy compared with the Faster R-CNN model and some domain adaptive methods with source datasets.
The Role of Machine Learning in Cardiovascular Pathology

Machine learning has seen slow but steady uptake in diagnostic pathology over the past decade to assess digital whole-slide images. Machine learning tools have incredible potential to standardise, and likely even improve, histopathologic diagnoses, but they are not yet widely used in clinical practice. We describe the principles of these tools and technologies and some successful preclinical and pretranslational efforts in cardiovascular pathology, as well as a roadmap for moving forward. In nonhuman animal models, one proof-of-principle application is in rodent progressive cardiomyopathy, which is of particular significance to drug toxicity studies. Basic science successes include screening the quality of differentiated stem cells and characterising cardiomyocyte developmental stages, with potential applications for research and toxicology/drug safety screening using derived or native human pluripotent stem cells differentiated into cardiomyocytes. Translational studies of particular note include those with success in diagnosing the various forms of heart allograft rejection. For fully realising the value of these tools in clinical cardiovascular pathology, we identify 3 essential challenges. First is image quality standardisation to ensure that algorithms can be developed and implemented on robust, consistent data. The second is consensus diagnosis; experts don't always agree, and thus "truth" may be difficult to establish, but the algorithms themselves may provide a solution. The third is the need for large-enough data sets to facilitate robust algorithm development, necessitating large cross-institutional shared image databases. The power of histopathology-based machine learning technologies is tremendous, and we outline the next steps needed to capitalise on this power.