Reviewerly/exHarmony huggingface dataset:
Classifier for Face Recognition Based on Deep Convolutional - Optimized Kernel Extreme Learning Machine

Face recognition task has been an active research area in recent years in computer vision and biometrics. Feature extraction and classification are the most significant steps for accurate face recognition systems. Conventionally, the Eigenface approach or frequency domain features have been used for feature extraction, but they are not invariant to outdoor conditions like lighting, pose, expression, and occlusion. Multiple convolutional and pooling layers of Deep Learning Networks (DLN) will efficiently extract the face database's high-level features in the present work. These features have given to the Kernel Extreme Learning Machine (KELM) classifier, whose parameters have optimized using Particle Swarm Optimization (PSO). The proposed Deep Convolutional-Optimized Kernel Extreme Learning Machine (DC-OKELM) algorithm leads to better performance results and fast learning speed than classification using deep neural networks. The performance of DC-OKELM has evaluated on four standards face databases: AT&T, CMU-PIE, Yale Faces, and UMIST. Experimental results have compared with other state-of-the-art classifiers in terms of error rate and network training time, which shows the proposed DC-OKELM classifier's effectiveness.
Scalable Architecture for sharing EHR using the Hyperledger Blockchain

Blockchain technology has been applied in several areas, ranging from financial to health. Although this technology offers benefits, for instance the immutability, anonymity, and decentralization of information, its use presents some problems, been the scalability in terms of storage size one of them. This paper presents a scalable architecture for sharing electronic health records using a multi-channel hyperledger blockchain. The architecture uses one blockchain to record patient visits and one blockchain for each health institution to record links that point to Electronic Health Records (EHRs) stored in external systems. Among the main conclusions, the final results highlight the scalability of the proposed architecture, when compared to the related work models in the heterogeneous network.
Associative memory optimized method on deep neural networks for image classification

Deep neural networks have achieved excellent performance in the field of image classification. However, even to state-of-the-art deep neural networks, there are still many critical images that are difficult to be classified effectively. Enlightened by the brain function of associative memory, we propose a novel classification optimization method based on deep neural networks to improve image classifiers. Psychologists have studied associative memory for a long time. A popular theory is that ideas and memory are associated together in the mind through experience. By applying this theory to object recognition, our method focuses on using the association among different images of the same category to improve image classifiers based on deep neural networks. The association, which is memorized by the LSTM network in our method, could infer a sequence of associative images and form inner data augmentation effectively. Further, we introduce the LSTM network into an end-to-end deep learning framework to boost the performance of image classifiers. Experiments on four benchmark datasets reveal that our method produces a consistent improvement to the existing powerful classifiers. Moreover, as far as we know, our method achieves the best classification accuracies of 97.3%, 96.0%, and 89.1% on Flower102, Caltech101, and Caltech256 datasets, respectively.
A Nonlinear Double Model for Multisensor-Integrated Navigation Using the Federated EKF Algorithm for Small UAVs

Aimed at improving upon the disadvantages of the single centralized Kalman filter for integrated navigation, including its fragile robustness and low solution accuracy, a nonlinear double model based on the improved decentralized federated extended Kalman filter (EKF) for integrated navigation is proposed. The multisensor error model is established and simplified in this paper according to the near-ground short distance navigation applications of small unmanned aerial vehicles (UAVs). In order to overcome the centralized Kalman filter that is used in the linear Gaussian system, the improved federated EKF is designed for multisensor-integrated navigation. Subsequently, because of the navigation requirements of UAVs, especially for the attitude solution accuracy, this paper presents a nonlinear double model that consists of the nonlinear attitude heading reference system (AHRS) model and nonlinear strapdown inertial navigation system (SINS)/GPS-integrated navigation model. Moreover, the common state parameters of the nonlinear double model are optimized by the federated filter to obtain a better attitude. The proposed algorithm is compared with multisensor complementary filtering (MSCF) and multisensor EKF (MSEKF) using collected flight sensors data. The simulation and experimental tests demonstrate that the proposed algorithm has a good robustness and state estimation solution accuracy.
Deep learning-based video surveillance system managed by low cost hardware and panoramic cameras

The design of automated video surveillance systems often involves the detection of agents which exhibit anomalous or dangerous behavior in the scene under analysis. Models aimed to enhance the video pattern recognition abilities of the system are commonly integrated in order to increase its performance. Deep learning neural networks are found among the most popular models employed for this purpose. Nevertheless, the large computational demands of deep networks mean that exhaustive scans of the full video frame make the system perform rather poorly in terms of execution speed when implemented on low cost devices, due to the excessive computational load generated by the examination of multiple image windows. This work presents a video surveillance system aimed to detect moving objects with abnormal behavior for a panoramic 360∘ surveillance camera. The block of the video frame to be analyzed is determined on the basis of a probabilistic mixture distribution comprised by two mixture components. The first component is a uniform distribution, which is in charge of a blind window selection, while the second component is a mixture of kernel distributions. The kernel distributions generate windows within the video frame in the vicinity of the areas where anomalies were previously found. This contributes to obtain candidate windows for analysis which are close to the most relevant regions of the video frame, according to the past recorded activity. A Raspberry Pi microcontroller based board is employed to implement the system. This enables the design and implementation of a system with a low cost, which is nevertheless capable of performing the video analysis with a high video frame processing rate.
An incremental clustering method for anomaly detection in flight data

Safety is a top priority for civil aviation. Data mining in digital Flight Data Recorder (FDR) or Quick Access Recorder (QAR) data, commonly referred to as black box data on aircraft, has gained interest for proactive safety management. New anomaly detection methods, primarily clustering methods, have been developed to monitor pilot operations and detect any risks from such flight data. However, all existing anomaly detection methods are offline learning — the models are trained once using historical data and used for all future predictions. In practice, new flight data are accumulated continuously and analyzed every month at airlines. Clustering such dynamically growing data is challenging for an offline method because it is memory and time intensive to re-train the model every time new data come in. If the model is not re-trained, false alarms or missed detections may increase since the model cannot reflect changes in data patterns. To address this problem, we propose a novel incremental anomaly detection method based on Gaussian Mixture Model (GMM) to identify common patterns and detect outliers in flight operations from digital flight data. It is a probabilistic clustering model of flight operations that can incrementally update its clusters based on new data rather than to re-cluster all data from scratch. It trains an initial GMM model based on historical offline data. Then, it continuously adapts to new incoming data points via an expectation–maximization (EM) algorithm. To track changes in flight operation patterns, only model parameters need to be saved, not the raw flight data. The proposed method was tested on three sets of simulation data and two sets of real-world flight data. Compared with the traditional offline GMM method, the proposed method can generate similar clustering results with significantly reduced processing time (57 %–99 % time reduction in testing sets) and memory usage (91 %–95 % memory usage reduction in testing sets). Preliminary results indicate that the incremental learning scheme is effective in dealing with dynamically growing data in flight data analytics.
Cultivating cybersecurity learning: An integration of self-determination and flow

Because there is a critical shortage of cybersecurity talent, information security professionals and researchers should cultivate cybersecurity skills by encouraging individuals to pursue cybersecurity learning. However, some aspects of cybersecurity require substantial effort and perseverance for conceptual understanding to be gained. We propose motivation as the key to ensuring continuous engagement with and successful learning of such cybersecurity concepts. With a lab-based training program that taught participants about SQL injection attacks, we tested a research model that integrated flow theory and self-determination theory. Within the training program, we captured participants’ persistence in attempting and successfully completing the training exercises while also measuring their perceptions of motivation and flow. We found that flow facilitated motivation and its key antecedents. Flow and task significance had the strongest effects on motivation, while motivation fostered learning persistence and performance. We recommend training programs that maximize flow and task significance.
Manifold Alignment for Semantically Aligned Style Transfer

Most existing style transfer methods follow the assumption that styles can be represented with global statistics (e.g., Gram matrices or covariance matrices), and thus address the problem by forcing the output and style images to have similar global statistics. An alternative is the assumption of local style patterns, where algorithms are designed to swap similar local features of content and style images. However, the limitation of these existing methods is that they neglect the semantic structure of the content image which may lead to corrupted content structure in the output. In this paper, we make a new assumption that image features from the same semantic region form a manifold and an image with multiple semantic regions follows a multi-manifold distribution. Based on this assumption, the style transfer problem is formulated as aligning two multi-manifold distributions and a Manifold Alignment based Style Transfer (MAST) framework is proposed. The proposed frame-work allows semantically similar regions between the output and the style image share similar style patterns. Moreover, the proposed manifold alignment method is flexible to allow user editing or using semantic segmentation maps as guidance for style transfer. To allow the method to be applicable to photorealistic style transfer, we propose a new adaptive weight skip connection network structure to preserve the content details. Extensive experiments verify the effectiveness of the proposed framework for both artistic and photorealistic style transfer. Code is available at https://github.com/NJUHuoJing/MAST.
Identifying a suitable hourly solar diffuse fraction model to generate the typical meteorological year for building energy simulation application

As cooling and heating energy consumptions of buildings are closely related to outdoor climate variations, the reliability of building energy simulation results is significantly influenced by the accuracy of weather data being used. We intend to construct a new typical meteorological year (TMY) for Taipei. As no beam or diffuse solar irradiance data have been recorded at local weather stations, a preliminary study on the influences of currently available hourly solar diffuse fraction models (DFMs) to the building cooling loads was performed. A 2.30%–5.18% range of annual cooling load variation was observed, which drove a need for searching suitable DFMs. To this end, the observed diffuse irradiance data of an in-situ experiment was compared to the DFM modeled values to identify the suitable DFM. It was found that Kuo’s model, which has its coefficient been adapted to the local weather and further uses solar altitude, the daily clearness index as predicting variables, performed best and was used herein. The representativeness against the long-term climate of the three antiquated TMYs and the new one was discussed with a simulation-based comparison from 12 existing buildings. The reliability and accuracy of the new TMY in representing the local climate conditions are much improved.
Spatial mapping of short-term solar radiation prediction incorporating geostationary satellite images coupled with deep convolutional LSTM networks for South Korea

Abstract A practical approach to continuously monitor and provide real-time solar energy prediction can help support reliable renewable energy supply and relevant energy security systems. In this study on the Korean Peninsula, contemporaneous solar radiation images obtained from the Communication, Ocean and Meteorological Satellite (COMS) Meteorological Imager (MI) system, were used to design a convolutional neural network and a long short-term memory network predictive model, ConvLSTM. This model was applied to predict one-hour ahead solar radiation and spatially map solar energy potential. The newly designed ConvLSTM model enabled reliable prediction of solar radiation, incorporating spatial changes in atmospheric conditions and capturing the temporal sequence-to-sequence variations that are likely to influence solar driven power supply and its overall stability. Results showed that the proposed ConvLSTM model successfully captured cloud-induced variations in ground level solar radiation when compared with reference images from a physical model. A comparison with ground pyranometer measurements indicated that the short-term prediction of global solar radiation by the proposed ConvLSTM had the highest accuracy [root mean square error (RMSE) = 83.458 W · m −2 , mean bias error (MBE) = 4.466 W · m −2 , coefficient of determination (R 2 ) = 0.874] when compared with results of conventional artificial neural network (ANN) [RMSE = 94.085 W · m −2 , MBE = −6.039 W · m −2 , R 2 = 0.821] and random forest (RF) [RMSE = 95.262 W · m −2 , MBE = −11.576 W · m −2 , R 2 = 0.839] models. In addition, ConvLSTM better captured the temporal variations in predicted solar radiation, mainly due to cloud attenuation effects when compared with two selected ground stations. The study showed that contemporaneous satellite images over short-term or near real-time intervals can successfully support solar energy exploration in areas without continuous environmental monitoring systems, where satellite footprints are available to model and monitor solar energy management systems supporting real-life power grid systems.
<i>VISE</i>: Combining Intel SGX and Homomorphic Encryption for Cloud Industrial Control Systems

Protecting data-in-use from privileged attackers is challenging. New CPU extensions (notably: Intel SGX) and cryptographic techniques (specifically: Homomorphic Encryption) can guarantee privacy even in untrusted third-party systems. HE allows sensitive processing on ciphered data. However, it is affected by i) a dramatic ciphertext expansion making HE unusable when bandwidth is narrow, ii) unverifiable conditional variables requiring off-premises support. Intel SGX allows sensitive processing in a secure enclave. Unfortunately, it is i) strictly bonded to the hosting server making SGX unusable when the live migration of cloud VMs/Containers is desirable, ii) limited in terms of usable memory, which is in contrast with resource-consuming data processing. In this article, we propose the VIrtual Secure Enclave (VISE), an approach that effectively combines the two aforementioned techniques, to overcome their limitations and ultimately make them usable in a typical cloud setup. VISE moves the execution of sensitive HE primitives (e.g., encryption) to the cloud in a remotely attested SGX enclave, and then performs sensitive processing on HE data-outside the enclave-leveraging all the memory resources available. We demonstrate that VISE meets the challenging security and performance requirements of a substantial application in the Industrial Control Systems domain. Our experiments prove the practicability of the proposed solution.
BodyLights: Open-Ended Augmented Feedback to Support Training Towards a Correct Exercise Execution

Technologies targeting a correct execution of physical training exercises typically use pre-determined models for what they consider correct, automatizing instruction and feedback. This falls short on catering to diverse trainees and exercises. We explore an alternative design approach, in which technology provides open-ended feedback for trainers and trainees to use during training. With a personal trainer we designed the augmentation of 18 strength training exercises with BodyLights: 3D printed wearable projecting lights that augment body movement and orientation. To study them, 15 trainees at different skill levels trained three times with our personal trainer and BodyLights. Our findings show that BodyLights catered to a wide range of trainees and exercises, and supported understanding, executing and correcting diverse technique parameters. We discuss design features and methodological aspects that allowed this; and what open-ended feedback offered in comparison to current technology approaches to support training towards a correct exercise execution.
The Designer's Body as Resource in Design: Exploring Combinations of Point-of-view and Tense

The design of wearable, tangible and embedded interactive products requires a focus on bodily/kinesthetic aspects of the user experience, that is, how the product "feels" in use. Although best practice in user-centered design (such as iterative design, prototyping, user testing) also applies for this new type of product, the designer's skill set needs to be supplemented with design methods and practices that utilize bodily intelligence and empathy with the user. We present a framework for categorizing such body-centered design practices based on two dimensions: point-of-view (1st, 2nd, 3rd person) and tense (past, present, future). Inspired by Merleau-Ponty's phenomenology of the body, Shusterman's work on somaesthetics, and Buber's theories on intersubjectivity, the framework provides a language for talking about different ways designers and co-designers can utilize their body as a design resource. The intention is not to be prescriptive on method, but to provide guidance during planning, execution and analysis.
Multi-Volumetric Refocusing of Light Fields

Geometric information of scenes available with four-dimensional (4-D) light fields (LFs) paves the way for post-capture refocusing. Light field refocusing methods proposed so far have been limited to a single planar or a volumetric region of a scene. In this letter, we demonstrate simultaneous refocusing of multiple volumetric regions in LFs. To this end, we employ a 4-D sparse finite-extent impulse response (FIR) filter consisting of multiple hyperfan-shaped passbands. We design the 4-D sparse FIR filter as an optimal filter in the least-squares sense. Experimental results confirm that the proposed filter provides 63% average reduction in computational complexity with negligible degradation in the fidelity of multi-volumetric refocused LFs compared to a 4-D nonsparse FIR filter.
Adhering, Steering, and Queering: Treatment of Gender in Natural Language Generation

Natural Language Generation (NLG) supports the creation of personalized, contextualized, and targeted content. However, the algorithms underpinning NLG have come under scrutiny for reinforcing gender, racial, and other problematic biases. Recent research in NLG seeks to remove these biases through principles of fairness and privacy. Drawing on gender and queer theories from sociology and Science and Technology studies, we consider how NLG can contribute towards the advancement of gender equity in society. We propose a conceptual framework and technical parameters for aligning NLG with feminist HCI qualities. We present three approaches: (1) adhering to current approaches of removing sensitive gender attributes, (2) steering gender differences away from the norm, and (3) queering gender by troubling stereotypes. We discuss the advantages and limitations of these approaches across three hypothetical scenarios; newspaper headlines, job advertisements, and chatbots. We conclude by discussing considerations for implementing this framework and related ethical and equity agendas.
I'm All Eyes and Ears: Exploring Effective Locators for Privacy Awareness in IoT Scenarios

With the proliferation of IoT devices, there are growing concerns about being sensed or monitored by these devices unawares, especially in places perceived as private. We explore the design space of IoT locators to help people physically find nearby IoT devices. We first conducted a survey to understand people's willingness, current practices, and challenges in finding IoT devices. Our survey findings motivated us to design and implement low-cost locators (visual, auditory, and contextualized pictures) to help people find nearby devices. Through an iterative design process and two rounds of experiments, we found that these locators greatly reduced people's search time over a baseline of no locators. Many participants found the visual and auditory locators enjoyable. Some participants also appropriated the use of our system for other purposes, e.g., to learn about new IoT devices, instead of for privacy awareness.
Temporal Segmentation of Creative Live Streams

Many artists broadcast their creative process through live streaming platforms like Twitch and YouTube, and people often watch archives of these broadcasts later for learning and inspiration. Unfortunately, because live stream videos are often multiple hours long and hard to skim and browse, few can leverage the wealth of knowledge hidden in these archives. We present an approach for automatic temporal segmentation of creative live stream videos. Using an audio transcript and a log of software usage, the system segments the video into sections that the artist can optionally label with meaningful titles. We evaluate this approach by gathering feedback from expert streamers and comparing automatic segmentations to those made by viewers. We find that, while there is no one "correct" way to segment a live stream, our automatic method performs similarly to viewers, and streamers find it useful for navigating their streams after making slight adjustments and adding section titles.