Reviewerly/exHarmony huggingface dataset:
Towards high-quality thermal infrared image colorization via attention-based hierarchical network

Colorization is an effective technology to improve the imaging quality of thermal infrared sensors, which is of great importance to environmental perception systems. Recently, colorization for thermal infrared images has realized obvious improvement with the development of deep learning. However, the performance of current designs is not adequate to achieve satisfied colorization quality. This is mainly because that thermal infrared image colorization requires the abilities of both color prediction and grayscale detail recovery, which poses severe challenges for feature extraction due to the poor quality of thermal infrared images. Furthermore, most of the current networks are based on generative adversarial network, suffering the risks of training instability and mode collapse. In this paper, we propose a high-quality colorization network for thermal infrared images. By leveraging hierarchical architecture, attention mechanism, and proposing a composite loss function, our network not only significantly improves the quality of colorized thermal infrared images but also achieves a speed of 32 fps on NVIDIA 2080Ti GPU platform. In evaluation experiments, PSNR and SSIM can be increased by at least 0.87 dB and 0.0277 compared with previous methods on publicly available datasets. Experimental results demonstrate that the proposed network can produce state-of-the-art colorized thermal infrared images both in objective metrics and subjective quality.
A Three-Level Gateway protocol for secure M-Commerce Transactions using Encrypted OTP

Mobile commerce, which began in ancient times as the trade system, has grown in the ultramodern age due to improvements in technology, the Internet, the use of Financial Coffers, and the lives of mortal humans. There was a significant extemporization in the tackle industry and concurrent software development that led to a wide range of uses for computers and mobile phones. Ninety percent of people who take prescription drugs do so on handhelds, laptops, or cellphones, and the lives of mortals have been profoundly altered as a result of the smart operations available on mobile systems, which may be utilized at any time and from any location (Everything through one device). With mobile dispatching, so-called trade has been transformed into mobile commerce. Mobile commerce, or m-commerce, is a term used to describe all aspects of a possible marketable commercial agreement that are carried out over wireless or mobile networks, such as text messaging and email. Mobile Commerce tackles the mobile bias of electronic commerce, where the customer does not view any product that is being purchased physically. Mobile Commerce He has just a virtual representation of the thing being purchased. As a consumer and as a producer, the advantages of mobile commerce are vast, but there are also many challenges and security considerations that are keeping many mobile druggies from making the leap. Despite the adoption of several cryptographic security techniques and communication protocols, there are still a few ifs and buts. Three-Level Gateway Protocol for Secure M-Commerce Deals is presented in this paper, which uses any conventional or public-crucial algorithm and the Advanced Message Queuing Protocol (AMQP), an open standard for passing messages between operations or associations, and an Encrypted OTP in the payment process to ensure confidentiality of da and to prevent Replay Attacks, Man in the Middle Attacks, and Masking attacks of Cryptography systems.
ALVS: Adaptive Live Video Streaming using deep reinforcement learning

Achieving a high Quality of Experience (QoE) in live event streaming is a challenging problem given a low-latency requirement and time-varying network conditions. Adaptive video bitrate and adaptive playback speed techniques are two separate control knobs to address this challenge. In this paper, we consider these two control parameters in a joint optimization problem and present a deep reinforcement learning (DRL) framework to maximize QoE for live streaming without any assumption about the environment or fixed rule-based heuristics. With the proposed DRL framework, our approach (ALVS) constructs the inference model to make a joint decision of adaptive playback speed and video quality level for the next video segment. Simulation results through real network traces show that ALVS outperforms both state-of-the-art DRL-based and rule-based algorithms in terms of QoE without sacrificing live latency and skipping any content.
Boosting learning ability of overdamped bistable stochastic resonance system based physical reservoir computing model by time-delayed feedback

Physical reservoir computing (RC), which can be implemented by various physical systems, is a low-cost neuromorphic framework with a fast learning capability. In the previous studies, an overdamped bistable system-based RC (OBRC) inspired by the FitzHugh-Nagumo neuron model has been proposed to construct an outstanding physical RC system. Benefitting from the stochastic resonance effect, the OBRC requires less power and has stronger noise robustness than many conventional physical RC systems. However, compared with conventional physical RC systems, its learning ability is not superior. To boost the performance of the OBRC, we propose an OBRC with time-delayed feedback (TOBRC). In this work, the TOBRC is implemented in a physical setting with time-multiplexing nodes design and simulated on a conventional computer. Moreover, we adopt a powerful optimization algorithm to automatically determine the optimal hyperparameters for both the OBRC and TOBRC; thus, a more precise quantitative discussion on the upper limit of the system can be made. To compare the TOBRC and OBRC, we conducted short-term memory and parity check tasks to assess the short-term memory ability and nonlinearity, which are the two core abilities of physical RC for learning. The results prove that the short-term memory ability and nonlinearity of the proposed TOBRC are 6.46 and 2.15 times higher than those of the OBRC, respectively. Moreover, the TOBRC outperforms the OBRC under different noise conditions. On the MNIST handwritten digit recognition benchmark, the TOBRC exhibited a lower error rate than the OBRC; it was comparable with that of advanced physical RC systems. Our study confirms that the TOBRC can exhibit excellent learning ability in practical problems.
When a RF beats a CNN and GRU, together—A comparison of deep learning and classical machine learning approaches for encrypted malware traffic classification

Internet traffic classification plays a crucial role in Quality of Experience (QoE), Quality of Services (QoS), intrusion detection, and traffic-trend analyses. While there is no theoretical guarantee that deep learning (DL)-based solutions perform better than classic machine learning (ML)-based ones, DL-based models have become the common default. This paper compares well-known DL-based and ML-based models and shows that in the case of malicious traffic classification, state-of-the-art DL-based solutions do not necessarily outperform the classical ML-based ones. We exemplify this finding using two well-known datasets for a varied set of tasks, such as: malware detection, malware family classification, detection of zero-day attacks, and classification of an iteratively growing dataset. Note that, it is not feasible to evaluate all possible models to make a concrete statement, thus the above finding is not a recommendation to avoid DL-based models, but rather an empirical finding that in some cases, there are more simplistic solutions, that may perform even better.
A space-embedding strategy for anomaly detection in multivariate time series

Anomaly detection of time series has always been a hot topic in academia and industry. However, many existing multivariant time series methods suffer from common challenges due to increased dimensionality. In this study, we developed a space-embedding strategy for anomaly detection in multivariant time series (SES-AD). As a hybrid model, SES-AD did not directly search the discords from the original time series, but projected the raw sequence to a lower dimensional space so that the significant abrupt change points in the new space can be easily captured from the dissimilarity vector. Finally, the potential abnormities were determined by a statistical strategy. To verify the performance of our method, SES-AD was applied to an extensive number of multivariate time series. The experimental results suggest that SES-AD is more efficient than five existing approaches. Overall, the SES-AD model is suitable for solving anomaly detection for high-dimensional datasets and guarantees computational effectiveness and accuracy.
End-to-end digitization of image format piping and instrumentation diagrams at an industrially applicable level

Abstract This study proposes an end-to-end digitization method for converting piping and instrumentation diagrams (P&amp;IDs) in the image format to digital P&amp;IDs. Automating this process is an important concern in the process plant industry because presently image P&amp;IDs are manually converted into digital P&amp;IDs. The proposed method comprises object recognition within the P&amp;ID images, topology reconstruction of recognized objects, and digital P&amp;ID generation. A data set comprising 75 031 symbol, 10 073 text, and 90 054 line data was constructed to train the deep neural networks used for recognizing symbols, text, and lines. Topology reconstruction and digital P&amp;ID generation were developed based on traditional rule-based approaches. Five test P&amp;IDs were digitalized in the experiments. The experimental results for recognizing symbols, text, and lines showed good precision and recall performance, with averages of 96.65%/96.40%, 90.65%/92.16%, and 95.25%/87.91%, respectively. The topology reconstruction results showed an average precision of 99.56% and recall of 96.07%. The digitization was completed in &amp;lt;3.5 hours (8488.2 s on average) for five test P&amp;IDs.
Modeling chaotic systems: Dynamical equations vs machine learning approach

Chaotic systems are ubiquitous in the real world, but often analytical models remain inaccessible. We find that a machine-learning method known as "reservoir computing" provides an alternative feasible way for modeling chaotic systems rather than conventional dynamical equations. Specifically, we show that recurrence in temporal and spatial scales of the trained reservoir system are indistinguishable from that of an observed chaotic system. Furthermore, by sharing a common signal, dual synchronization between a chaotic system and its learned reservoir system can be achieved successfully. In the same manner, we show that the identical synchronization also emerges on their coupled system. These findings reveal that reservoir computing approach would be excellent candidate for modeling a great variety of chaotic systems.
Cracking the code: Co-coding with AI in creative programming education

This paper presents a study of a group of university students using generative machine learning to translate from natural language to computer code. The study explores how the use of the AI tool can be understood in terms of co-creation, focusing on the one hand on how the tool may serve as a resource for understanding and learning, and on the other hand how the tool affects the creative processes. Findings show how the participants search for a 'correct' syntax in their instructions to the machine learning tool, and how the inconsistent and erroneous behavior can work as a way to generate clues and inspiration for generating creative expressions. The notion of friction is used to describe how systems like this can serve to both lower thresholds for programming, and also interfere with the creative processes, encouraging reflection and exploration of alternative solutions.
The Magic of Carousels: Single vs. Multi-List Recommender Systems

Carousel-based interfaces with multiple topic-focused item lists have emerged as a de-facto standard for presenting recommendation results to end-users in real-life recommender systems. In this paper, we attempt to formalize and explain the "magic" power of carousel-based interfaces from a traditional hypertext prospect of navigability. By applying both, formal analysis and a data-driven evaluation, we demonstrate and measure the benefits offered by the carousel-based organization of recommendations. We hope that this work will benefit the researchers in both hypertext and recommender systems communities, where the research on carousel-based interfaces is gaining popularity.
Goldwork Embroidery

Within the emerging field of e-textiles, goldwork embroidery (also known as metalwork) which uses metal threads and materials is an underexplored area, despite being a centuries-old practice in traditional crafts of different cultures. In this paper, we explore the material culture of textile goldwork to better understand how e-textile researchers can leverage their material properties, palette, and practices. First, we provide a historical background of English goldwork to give HCI researchers context on this craft field including technological and cultural influences. Then, we interview 13 contemporary goldwork practitioners on their creative practice to better understand the tools, techniques, and skills they employ. Our study findings show how goldwork practitioners deal with the unique constraints of metal threads and materials, and how these materials need to be handled differently than regular non-metal threads and fibers. This paper contributes an analysis of goldwork practices for HCI audiences with suggestions on how we can leverage these practices for the future of e-textile hybrid crafts.
A secure and efficient data deduplication framework for the internet of things via edge computing and blockchain

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Payment Channel Networks: Single-Hop Scheduling for Throughput Maximization

Payment channel networks (PCNs) have emerged as a scalability solution for blockchains built on the concept of a payment channel: a setting that allows two parties to safely transact between themselves in high frequencies by updating pre-committed balances. Transaction requests in PCNs may be declined because of unavailability of funds due to temporary uneven distribution of the channel balances. In this paper, we investigate how to alleviate unnecessary payment blockage via proper prioritization of the transaction execution order. Specifically, we consider the scheduling problem in a payment channel: as transactions continuously arrive on both sides, nodes need to decide which ones to process and when, in order to maximize channel throughput. We introduce a stochastic model to capture the dynamics of a payment channel under discrete stochastic arrivals, with incoming transactions potentially held in buffers up until some deadline in order to enable more elaborate processing decisions. We describe a scheduling policy that maximizes the channel success rate/throughput, formally prove its optimality for fixed-amount transactions, and also show its superiority in the case of heterogeneous amounts via experiments in our discrete event simulator. Overall, our work is a step in the direction of formal research on improving PCN performance.
An extreme learning machine for unsupervised online anomaly detection in multivariate time series

Unsupervised anomaly detection in time series remains challenging, due to the rare and complex patterns of anomalous data. Previous change point detection methods based on extreme learning machine and mutual information (ELM-MI) are potential solutions for this problem. However, the kernels in these methods are randomly initialized on test data, which imposes a constraint that these methods can only be used for offline inference. Moreover, these methods are limited in utilizing temporal contexts, and require the ensemble of multiple models to improve the robustness. To tackle these problems, we introduce a multivariate ELM-MI framework, and combine it with a dynamic kernel selection method, which performs a hierarchical clustering procedure on unlabeled training data and utilizes the clusters to determine the kernels in ELM-MI. In this way, our method can tackle the unsupervised online detection of various anomalous (e.g., point anomalies and group anomalies) and reduce the computational cost. Extensive experiments on three public datasets and our collection of real-life 4G Long-Term Evolution data demonstrate that the proposed method outperforms state-of-the-art methods in terms of effectiveness and efficiency. For demo, see this link: https://personal.ntu.edu.sg/ezplin/NC-demo.htm.
Caching-based Multicast Message Authentication in Time-critical Industrial Control Systems

Attacks against industrial control systems (ICSs) often exploit the insufficiency of authentication mechanisms. Verifying whether the received messages are intact and issued by legitimate sources can prevent malicious data/command injection by illegitimate or compromised devices. However, the key challenge is to introduce message authentication for various ICS communication models, including multicast or broadcast, with a messaging rate that can be as high as thousands of messages per second, within very stringent latency constraints. For example, certain commands for protection in smart grids must be delivered within 2 milliseconds, ruling out public-key cryptography. This paper proposes two lightweight message authentication schemes, named CMA and its multicast variant CMMA, that perform precomputation and caching to authenticate future messages. With minimal precomputation and communication overhead, C(M)MA eliminates all cryptographic operations for the source after the message is given, and all expensive cryptographic operations for the destinations after the message is received. C(M)MA considers the urgency profile (or likelihood) of a set of future messages for even faster verification of the most time-critical (or likely) messages. We demonstrate the feasibility of C(M)MA in an ICS setting based on a substation automation system in smart grids.
Towards Optimal Multi-Modal Federated Learning on Non-IID Data with Hierarchical Gradient Blending

Recent advances in federated learning (FL) made it feasible to train a machine learning model across multiple clients, even with non-IID data distributions. In contrast to these uni-modal models that have been studied extensively in the literature, there are few in-depth studies on how multi-modal models can be trained effectively with federated learning. Unfortunately, we empirically observed a counter-intuitive phenomenon that, compared with its uni-modal counterpart, multi-modal FL leads to a significant degradation in performance.Our in-depth analysis of such a phenomenon shows that modality sub-networks and local models can overfit and generalize at different rates. To alleviate these inconsistencies in collaborative learning, we propose hierarchical gradient blending (HGB), which simultaneously computes the optimal blending of modalities and the optimal weighting of local models by adaptively measuring their overfitting and generalization behaviors. When HGB is applied, we present a few important theoretical insights and convergence guarantees for convex and smooth functions, and evaluate its performance in multi-modal FL. Our experimental results on an extensive array of non-IID multi-modal data have demonstrated that HGB is not only able to outperform the best uni-modal baselines but also to achieve superior accuracy and convergence speed as compared to state-of-the-art frameworks.
Estimation of high-resolution solar irradiance data using optimized semi-empirical satellite method and GOES-16 imagery

Semi-empirical satellite method is widely used in estimating global horizontal irradiance (GHI), where various clear-sky models, cloud index (CI) and clear-sky index (CSI) derivation methods are available. This study aims to optimize the semi-empirical satellite model for 5-minute GHI estimation from four aspects: satellite-bands, CI and CSI derivation methods, and clear-sky models. The results show that it achieves better GHI estimates using the blue band, CI derived from monthly fixed upper and lower bounds, and a piecewise CI-to-CSI function. There is no significant difference in all-sky GHI estimation for the clear-sky models regarding normalized root mean squared error (nRMSE, 25.19%–25.53%), which is comparable with the referenced physical model. Clouds cause the largest uncertainty, where the nRMSE is in the range of 37.60%–38.36% in cloudy days and 31.12%–31.54% in cloudy periods. In the application of semi-empirical method with different clear-sky models, Ineichen–Perez has the highest bias of -4.62% in clear days and -3.93% in cloudless periods. REST2 outperforms McClear with slightly lower nRMSE and normalized mean bias error (nMBE) under all sky conditions. McClear is recommended due to its global availability. Modified Ineichen–Perez produces the lowest nRMSE and nMBE using clear-sky GHI as the GHI estimates for clear periods, therefore has the potential for improvements in physical methods.
The Introduction of Artificial Intelligence in the Study of Economic Disciplines in Higher Educational Institutions

This article reveals the importance of teaching artificial intelligence and its application in the study of economic disciplines in higher educational institutions. The article reveals the importance of teaching logical programming languages, including the PROLOG language. The novelty of the article lies in the use of simple spreadsheet editors as prototypes of expert systems, as well as building predictive models using the STATISTICA program. Thanks to such software, it is possible to achieve model accuracy in forecasting, building regression analysis, tending to one hundred percent.
Agile software development one year into the COVID-19 pandemic

As a result of the COVID-19 pandemic, many agile practitioners had to transition into a remote work environment. Despite remote work not being a new concept for agile software practitioners, the forced or recommended nature of remote work is new. This study investigates how the involuntary shift to remote work and how social restrictions imposed by the COVID-19 pandemic have affected agile software development (ASD), and how agile practitioners have been affected in terms of ways of working. An explanatory sequential mixed methods study was performed. Data were collected one year into the COVID-19 pandemic through a questionnaire with 96 respondents and in-depth semi-structured interviews with seven practitioners from seven different companies. Data were analyzed through Bayesian analysis and thematic analysis. The results show, in general, that the aspects of ASD that have been the most affected is communication and social interactions, while technical work aspects have not experienced the same changes. Moreover, feeling forced to work remotely has a significant impact on different aspects of ASD, e.g., productivity and communication, and industry practitioners' employment of agile development and ways of working have primarily been affected by the lack of social interaction and the shift to digital communication. The results also suggest that there may be a group maturing debt when teams do go back into office, as digital communication and the lack of psychological safety stand in the way for practitioners' ability to have sensitive discussions and progress as a team in a remote setting.