Miracle-dz/UniFAR-FTUs huggingface dataset:
Hyperspectral anomaly detection of hidden camouflage objects based on convolutional autoencoder

The rising availability of hyperspectral data has increased the attention of anomaly detection for various applications. Anomaly detection aims to find a small number of pixels in the hyperspectral data for which the spectral signatures differ significantly from the background. However, for anomalies like camouflage objects in a rural area, the spectral signatures distinguish only by small features. For this purpose, we use a 1D-Convolutional Autoencoder, which extracts the background spectra’s most specific features to reconstruct the spectral signature by minimizing the loss function’s error. The difference between the original and the reconstructed data can be exploited for anomaly detection. Since the loss function is minimized based on predominant background spectra, areas with anomalies exhibit higher error values. The proposed anomaly detection method’s performance is tested on hyperspectral data in the range of 1000 to 2500 nm. The data was recorded with a drone-based Headwall sensor at approximately 80 m over a rural area near Greding, Germany. The anomalies consist mainly of camouflage materials and vehicles. We compare the performance of a 1D-Convolutional Autoencoder trained on a data set without the target anomalies for different models. This is done to quantify the number of anomalies in the data set before they inhibit the detection process. Additionally, the detection results are compared to the state-of-the-art Reed-Xiaoli anomaly detector. We present the results by counting the correct detections in relation to the false positives with the receiver operating characteristic and discuss more suitable evaluation approaches for small targets. We show that the 1D-CAE outperforms the Reed-Xiaoli anomaly detector for a false alarm rate of 0.1% by reconstructing the background with a low error and the anomalies with a higher error. The 1D-CAE is suitable for camouflage anomaly detection.
SHIP target image recognition based on FAST detector and faster-RCNN

This article proposes a ship target recognition method based on FAST detector and Faster-RCNN. Firstly, the FAST detector is used to extract the feature points of the ship target. Then, using the method of increasing the sliding window, improving the convolutional layer structure of Faster R-CNN, using suitable anchor points to identify the target; designed a recognition method based on the combination of the real-world model identification frame and the area suggestion to obtain the target information. Finally, the method of non-maximum suppression is used to filter and remove the redundant rectangular identification frame, so as to realize the accurate identification of the ship's real-world target. Through experimental comparison and analysis, this method has application advantages in extracting feature points with greater recognition utility and recognition rate.
Bridging the gap from academia to application: rapid algorithm design and deployment for artificial intelligence (RADD-AI)

Machine Learning (ML) and Artificial intelligence (AI) have led to an increase in automation potential within defense applications such as border protection, compound security, and surveillance applications. Recent academic advances in deep learning aided computer vision have yielded impressive results on object detection, and recognition, necessary capabilities to increase automation in defense applications. These advances are often open-sourced, enabling the opportunistic integration of state-of-the-art (SOTA) algorithms into real systems. However, these academic achievements do not translate easily to engineered systems. Academics often are looking at a single capability with metrics such as accuracy or F1 score without consideration of system-level performance and how these algorithms must integrate or what level of computational performance is required. An engineered system is developed as a system of algorithms that must work in conjunction with each other with deployment constraints. This paper describes a system, called Rapid Algorithm Design and Deployment for Artificial Intelligence (RADD-AITM), developed to enable the rapid development of systems of algorithms incorporating these advances in a modular fashion using networked Application Programming Interfaces (APIs). The inherent modularity mitigates the assumption of monolithic integration within a single ecosystem that creates vendor lock. This monolith assumption does not account for the reality that frameworks are usually targeted toward different types of problems and learning vs inference capabilities. RADD-AI makes no such assumption. If a different framework solves subsets of the system more eloquently, they can be integrated into the larger pipeline. RADD-AI enables the integration of state-of-the-art ML into deployed systems while also supporting the necessary ML engineering tasks, such as transfer learning, to operationalize academic achievements. To motivate how RADD-AI enables applications of ML/AI, we detail how this system is used to implement a defense application, a border surveillance capability, via the integration of detection, recognition, and tracking algorithms. This system, implemented and developed within RADD-AI, utilizes several SOTA models and traditional algorithms within multiple frameworks bridging the gap from academic achievement to fielded system.
Photon counting mode for advanced real-time detection of bioaerosols

Currently, one of the most important application of flow cytometry is the real-time analysis of aerosols, in particular, to ensure biosafety. In most cases, such analysis is aimed at detecting fluorescent signals from aerosol particles corresponding to the light emission of tryptophan and nicotinamide adenine dinucleotide (NADH). Further development of the method is largely related to the improvement of the light detecting systems for recording and processing of fluorescence and scattered light signals. In this work, a comparative analysis of flow cytometers for bioaerosols detection based on photo-multiplier tubes (PMT) and avalanche photodiodes (APD) operating in analog and photon-counting modes was carried out. The limit of detection (LOD) of bioaerosols, response time and ability to detect particles with low scattering and fluorescence cross section were calculated and examined. The calculations were carried out for the well-known optical scheme of fluorescence detection based on discrete photodetectors and dichroic mirrors combined with an air flow chamber equipped with elliptical and spherical mirrors. An ultraviolet light emission diode (LED) was used as a model source of exciting radiation. To estimate the optical properties of aerosol particles, experimental results obtained for a model bovine serum albumin bioaerosol and published data on various other bioaerosols were used. The calculation of the total number of fluorescent photons, emitted by particles of various sizes while passing the flow chamber was carried out. The obtained data were compared with parameters of photodetectors operating in analog and photon-counting modes. The critical particle size was determined for the effective registration in a photon-counting mode. Considering the size distribution of aerosol particles, it was concluded that application of the photon-counting mode will reduce the LOD of bioaerosols by more than an order of magnitude.
Ant-eating twigs and stalks: the natural prey of Tmarus and Monaeses (Araneae: Thomisidae) in the Western Palaearctic, analysed by using online-accessible wildlife photography

Abstract. The diet of the Western Palaearctic species of the closely related crab spider genera Tmarus Simon, 1875 and Monaeses Thorell, 1869 is only little known. A quantitative analysis of images showing spiders with prey, collected from the World Wide Web and the literature, suggests that both genera mainly prey on ants (Hymenoptera: Formicidae), while occasionally capturing other arthropods, such as spiders (Araneae). Most images depict feeding spiders that hold ants anterior-dorsally at the articulation of head and pronotum. Tmarus galapagosensis Baert, 2013, from the Galapagos Islands, is known to attack ants by a bite to this region, which, in combination with the photographic evidence, supports the hypothesis that Western Palaearctic representatives of both genera attack ants in a similar way. Three species of Tmarus and two of Monaeses were identified from the images feeding on ants, demonstrating that probably all species of both genera at least include ants in their diet. In conclusion, Western Palaearctic Tmarus and Monaeses are myrmecophagous predators and probably have a narrow trophic niche in nature. Furthermore, both genera represent interesting model organisms for the study of trophic specialisation in widespread taxonomic clades.
In This Issue: Intersection of Realist Inquiry and Structural Equation Modeling, Multilevel Participatory Social Justice Mixed Methods Design, Qualitative Vignette Experiments, Mixed Methods Research Systematic Methodological Reviews, and Mixed Methods in Randomized Controlled Trials

Journal of Mixed Methods Research 2021, Vol. 15(4) 467–469 © The Author(s) 2021 Article reuse guidelines: sagepub.com/journals-permissions DOI: 10.1177/15586898211049622 journals.sagepub.com/home/mmr In This Issue: Intersection of Realist Inquiry and Structural Equation Modeling, Multilevel Participatory Social Justice Mixed Methods Design, Qualitative Vignette Experiments, Mixed Methods Research Systematic Methodological Reviews, and Mixed Methods in Randomized Controlled Trials
Comparison of the QuikRead go® point-of-care faecal immunochemical test for haemoglobin with the FOB Gold Wide® laboratory analyser to diagnose colorectal cancer in symptomatic patients

Abstract Objectives Faecal immunochemical testing for haemoglobin (FIT) is used to triage patients for colonic investigations. Point-of-care (POC) FIT devices on the market have limited data for their diagnostic accuracy for colorectal cancer (CRC). Here, a POC FIT device is compared with a laboratory-based FIT system using patient collected samples from the urgent referral pathway for suspected CRC. Methods A prospective, observational cohort study. Patients collected two samples from the same stool. These were measured by POC QuikRead go® (Aidian Oy, Espoo, Finland) and laboratory-based FOB Gold Wide® (Sentinel Diagnostics, Italy). Faecal haemoglobin <10 μg haemoglobin/g of faeces was considered as negative. At this threshold, comparisons between the two systems were made by calculating percentage agreement and Cohen’s kappa coefficient. Proportion of negative results were compared with Chi squared testing. Sensitivities for CRC were calculated. Results A total of 629 included patients provided paired samples for FIT to compare the QuikRead go® and FOB Gold Wide®. The agreement around the negative threshold was 83.0% and Cohen’s kappa coefficient was 0.54. The QuikRead go® reported 440/629 (70.0% of samples) as negative compared to 523/629 (83.1%) for the FOB Gold Wide®, this difference was significant (p-value<0.001). Sensitivities for CRC detection by the QuikRead go® and FOB Gold Wide® were 92.9% (95% confidence interval (CI): 68.5–98.7%) and 100% (CI: 78.5–100%) respectively. Conclusions Both systems were accurate in their ability to detect CRC. Whilst good agreement around the negative threshold was identified, more patients would be triaged to further colonic investigation if using the QuikRead go®.
Redundancy of Routing Information on the Distributed Key-Value Store Based on Order Preserving Linear Hashing and Skip Graph with the Load Balancing Method

In this paper, redundancy of routing information on the distributed key-value store based on order preserving linear hashing and Skip Graph with the load balancing method is evaluated. In this system, data are divided by order preserving linear hashing and Skip Graph is used for overlay network. For load balancing, by storing many Skip Graph nodes in one physical node, any highest-load Skip Graph can be divided. By this method, load balancing can be done. But the number of Skip Graph nodes becomes very many, redundancy of routing information is expected. In this paper, redundant routing elements are classified and redundancy of the routing information is experimentally evaluated.
Improving Readers’ Awareness of Divergent Viewpoints by Displaying Agendas of Comments in Online News Discussions

Readers of online news articles often hold related discussions in social media, sharing comments with various news-relevant topics, which we denote as ‘agendas’. Online discussions that span diverse agendas can help readers mitigate the biased influences from news media. However, displaying comments based on popular voting often fails to reveal such diversities to readers. In this paper, we designed a proof-of-concept Hagendas that aims to improve awareness of divergent agendas in online news discussions. It presents possible agendas automatically derived from news articles, and enables readers to view and filter the comments accordingly. We evaluated how Hagendas would affect users’ online news discussion practices through a within-subjects experiment with 95 online participants from MTurk. While the agenda tags and filtering features did not significantly increase the number of distinct agendas people identified through reading comments, 77.9% of participants preferred having those features for exploring online news discussion.
Zurich Like New: Analyzing Open Urban Multimodal Data

Citizen-driven platforms for enhancing local public services have been adopted in several countries like the UK and Switzerland. Local governments use data collected from these platforms to solve reported issues. Data can also be used by governments for data-driven decision-making and to improve the operation of the platforms themselves. In particular, as citizen reports become increasingly popular, there is a need to handle them more efficiently. In this paper, we present an analysis of ZüriWieNeu, a map-based website helping people in Zurich, Switzerland to report urban issues related to waste, broken streetlamps, or graffiti, among others. Our contributions are two-fold. First, we analyze what machine-extracted textual, visual, spatial and temporal features reveal about the dynamics of reporting and the content of each report category. This analysis provides a snapshot of the common patterns of urban issues in the Zurich area. Second, we perform classification to automatically infer the category of reports, achieving promising performance. Our work contributes towards developing machine learning-based systems to classify report categories, with the ultimate goal of supporting both users and platform operation.
InuAid Chest X-rays AI Product: Respiratory diseases detection using Artificial Intelligence to significantly improve productivity and quality of diagnosis

The spread of Covid-19 virus around the world has taken many lives, quarantined people and shattered many industries. Due to high transmissibility of the virus and its silent incubation period in human beings, detection of the virus plays an important role to control its spread and to plan diagnostic and preventive measures. Laboratory tests such as Polymerase Chain Reaction (PCR) take more time and hence there is a need for rapid and accurate diagnostic methods to detect the virus to prevent its spread and combat it. Today PCR tests were used for diagnosing purposes and the chest x-ray was only used as the follow up of patients, hence these studies on the chest x-rays of patients with Covid-19 pneumonia or any other disease are still limited to the literature and must be improved in the future. In this project, the goal is to build an application for healthcare workers to monitor the health of lungs using the chest x-ray images of patients. The algorithm must be very accurate because it deals with the lives of people. Here we used computer vision and deep learning techniques in this project. The focus is to classify chest x-ray images and segment the abnormal region and to get more insights on the images from the available datasets. The diagnostic accuracy is the challenging part and to increase the detection efficiency due to the limited open-source data available. The data was collected from the internet. On classification, the trained model was able to achieve 93.10% accuracy and F1 Score of 0.93 after using transfer learning technique with pneumonia images. On segmentation, the Intersection Over Union value was found to be 0.91 on the validation data.
Facial Expression Intensity Estimation using Deep Convolutional Neural Network

Facial expression recognition has been applied in various fields, but it is not enough to express complex emotions. It is required to detect the intensity of facial expression because the intensity of facial expression varies depending on the intensity of the emotion. In this research, we propose a method for estimating the facial expression intensity using deep convolutional neural networks (deep CNN). First, we collect facial expression intensity labels by experiments and evaluate the accuracy of the labels. Next, we train the deep CNN model by fine-tuning due to the small dataset. As a result, we show that the deep CNN method is effective compared to the results of hand-crafted facial features. However, there are still issues of estimation accuracy for facial expressions with small data and overfitting due to shortage of data.
BOND: Efficient and Frugal DL Model Co-design for Botnet detection on IoT Gateways

A botnet is a network of devices infected by the same malware, acting as a single entity and controlled by a botmaster. They are the biggest cybersecurity threat to carry out large-scale attacks from spamming, ransomware, data exfiltration, and denial-of-service attacks. Lightweight IoT devices without traditional security mechanisms have become favorite victims and agents to carry out botnet attacks. In our work, we seek to detect botnet-infected IoT nodes. This paper presents BOND, a frugal Deep Learning analysis of network traffic for detecting IoT devices infected with botnet(s), correctly classifying Zero-Day attacks and newer benign traffic. BOND is designed considering the constraints of IoT gateways and betters the F1 score of standard benchmark ML algorithms and State-of-The-Art method - Kitsune, by at least 10%, with under 1 millisecond inference time and less than 150 KB of model memory. This paper also presents labeled data-set 27-Botnet spanning 27 IoT botnet families and ten different IoT devices. We believe, it is the first data set with a separate zero-day component.
Development of RISC-V Based Soft-core Processor with Scalable Vector Extension for Embedded System

Recently, the opportunity to use FPGA in the field of embedded computer system has increased. However, the development cost using FPGA is quite high. We can reduce the cost by limiting the development of the dedicated circuit to the essenially required part of the system and by using soft-core processor together. If the performance of the soft-core processor improves, the development cost can be suppressed, since the amount of the dedicated circuit can be reduced. Furthermore, even in the field of embedded system, the opportunity for data parallel processing such as image processing is increasing. We consider that it is important merit of soft-core processor with SIMD extension that the computation performance can be optimized to the system by changing the number of parallel computing units according to the application. However, it is an obstacle that the software for soft-core processor needs to be changed every time we change the number of computing units. Thus, vector processing facility should provide the scalability that means the number of computing units can be changed without changing the program code. In this paper, we develop a RISC-V based soft-core processor with the scalable vector extension.
Distinguishing IM Communication Patterns with Relationship and Conversation Topics

Despite being characterized as constantly on and connected, IM users’ responsiveness varies across different contacts. While research has shown that the relationship between conversation partners plays an important role in influencing their communication patterns, characterization of such patterns simply by using relationship information is limited [7, 8]. In this paper, we identify five distinct clusters of IM patterns using unsupervised learning derived from 46 users’ conversation history. We show that the relationship category sufficed to characterize three clusters of communication patterns, but failed for the most active one. However, considering both relationship and topics would distinguish most communication patterns, including the most active one. This result suggests that future research on IM communication patterns should pay more attention to the topics in users’ conversations.
Emotion Recognition of Chinese Paintings at the Thirteenth National Exhibition of Fines Arts in China Based on Advanced Affective Computing

Today, with the rapid development of economic level, people’s esthetic requirements are also rising, they have a deeper emotional understanding of art, and the voice of their traditional art and culture is becoming higher. The study expects to explore the performance of advanced affective computing in the recognition and analysis of emotional features of Chinese paintings at the 13th National Exhibition of Fines Arts. Aiming at the problem of “semantic gap” in the emotion recognition task of images such as traditional Chinese painting, the study selects the AlexNet algorithm based on convolutional neural network (CNN), and further improves the AlexNet algorithm. Meanwhile, the study adds chi square test to solve the problems of data redundancy and noise in various modes such as Chinese painting. Moreover, the study designs a multimodal emotion recognition model of Chinese painting based on improved AlexNet neural network and chi square test. Finally, the performance of the model is verified by simulation with Chinese painting in the 13th National Exhibition of Fines Arts as the data source. The proposed algorithm is compared with Long Short-Term Memory (LSTM), CNN, Recurrent Neural Network (RNN), AlexNet, and Deep Neural Network (DNN) algorithms from the training set and test set, respectively, The emotion recognition accuracy of the proposed algorithm reaches 92.23 and 97.11% in the training set and test set, respectively, the training time is stable at about 54.97 s, and the test time is stable at about 23.74 s. In addition, the analysis of the acceleration efficiency of each algorithm shows that the improved AlexNet algorithm is suitable for processing a large amount of brain image data, and the acceleration ratio is also higher than other algorithms. And the efficiency in the test set scenario is slightly better than that in the training set scenario. On the premise of ensuring the error, the multimodal emotion recognition model of Chinese painting can achieve high accuracy and obvious acceleration effect. More importantly, the emotion recognition and analysis effect of traditional Chinese painting is the best, which can provide an experimental basis for the digital understanding and management of emotion of quintessence.