Reviewerly/exHarmony huggingface dataset:
A Novel Ship Detection Method in Remote Sensing Images via Effective and Efficient PP-YOLO

Ship detection in the remote sensing images could provide valuable information for military and civilian applications. The deep learning method has shown its power on object detection in images. Among the existing methods, You Only Look Once (YOLO) has been paid much attention in this domain. Especially, YOLO-v3 and YOLO-v4 are two typical branches that can realize relatively ideal detection results. However, the large image size and complex algorithm bring challenges to implement ship detection effectively and efficiently. This article studies a novel ship detection method, which utilizes PP-YOLO to achieve this objective. Two kinds of remote sensing images are utilized in the evaluation experiments, including visible light and synthetic aperture radar. Compared with YOLO-v3 and YOLO-v4, the proposed method has advantages of average accuracy and detection speed. To be specific, the average accuracy is increased by 10.8% and 32.7%, respectively. And the detection speed is improved by 26.6% and 75.6%, respectively.
Deep Vigilante: A deep learning network for real-world crime detection

Identification/recognition of assault, fighting, shooting, and vandalism from video sequence using deep 2D and 3D convolutional neural networks (CNNs) is explored in this paper. Recent wave of extensive unrestricted urbanization has not only uplifted the standard of living, but has also threatened the safety of a common man leading to an extraordinary rise in crime rate. Although Closed-circuit television (CCTV) footage provides a monitoring framework, yet, it’s useless without an auto volume crime detection system. The system proposed in this work is an effort to eradicate volume crimes through accurate detection in real-time. Firstly, a fine-grained annotated dataset including instance and activity information has been developed for real-world volume crimes. Secondly, a comparison between 3D CNN and 2D CNN network has been presented to identify the malicious event from the video sequence. This is carried out to explore the significance of spatial and temporal information present in the video for event recognition. It has been observed that 2D CNN even with lesser parameters achieved a promising classification accuracy of 91.2%and Area under the curve (AUC) of 95.2%on four classes. The system also reduces false alarm rate in comparison to state-of-the-art approaches.
Modelling extreme wide‐angle lens cameras

Abstract The use of consumer cameras fitted with extreme wide‐angle (EWA) lenses for photogrammetric measurement is increasing. Conventional modelling of EWA systems relies on the pinhole camera model and up to five radial lens distortion terms. Aiming to reduce model complexity, this paper reports on an investigation into an alternate approach using fisheye lens models for EWA systems, despite them not falling strictly into the fisheye category. Four fisheye models were tested on four different cameras under laboratory conditions. The self‐calibration results show superior model fit for all fisheye models over the pinhole‐plus‐radial model in terms of residual RMS. The number of radial distortion terms required for the fisheye models was lower in all cases, so model complexity was reduced. Independent assessment revealed very similar 3D reconstruction accuracy for all models. The results suggest that fisheye modelling is an advantageous alternative for EWA lens systems.
Stealth Updates of Visual Information by Leveraging Change Blindness and Computational Visual Morphing

We present an approach for covert visual updates by leveraging change blindness with computationally generated morphed images. To clarify the design parameters for intentionally suppressing change detection with morphing visuals, we investigated the visual change detection in three temporal behaviors: visual blank, eye-blink, and step-sequential changes. The results showed a robust trend of change blindness with a blank of more than 33.3 ms and with eye blink. Our sequential change study revealed that participants did not recognize changes until an average of 57% morphing toward another face in small change steps. In addition, changes went unnoticed until the end of morphing in more than 10% of all trials. Our findings should contribute to the design of covert visual updates without consuming users’ attention by leveraging change blindness with computational visual morphing.
UniNet: A Unified Scene Understanding Network and Exploring Multi-Task Relationships through the Lens of Adversarial Attacks

Scene understanding is crucial for autonomous systems which intend to operate in the real world. Single task vision networks extract information only based on some aspects of the scene. In multi-task learning (MTL), on the other hand, these single tasks are jointly learned, thereby providing an opportunity for tasks to share information and obtain a more comprehensive understanding. To this end, we develop UniNet, a unified scene understanding network that accurately and efficiently infers vital vision tasks including object detection, semantic segmentation, instance segmentation, monocular depth estimation, and monocular instance depth prediction. As these tasks look at different semantic and geometric information, they can either complement or conflict with each other. Therefore, understanding inter-task relationships can provide useful cues to enable complementary information sharing. We evaluate the task relationships in UniNet through the lens of adversarial attacks based on the notion that they can exploit learned biases and task interactions in the neural network. Extensive experiments on the Cityscapes dataset, using untargeted and targeted attacks reveal that semantic tasks strongly interact amongst themselves, and the same holds for geometric tasks. Additionally, we show that the relationship between semantic and geometric tasks is asymmetric and their interaction becomes weaker as we move towards higher-level representations.
Leakage and Tamper Resilient Permutation-Based Cryptography

Implementation attacks such as power analysis and fault attacks have shown that, if potential attackers have physical access to a cryptographic device, achieving practical security requires more considerations apart from just cryptanalytic security. In recent years, and with the advent of micro-architectural or hardware-oriented attacks, it became more and more clear that similar attack vectors can also be exploited on larger computing platforms and without the requirement of physical proximity of an attacker. While newly discovered attacks typically come with implementation recommendations that help counteract a specific attack vector, the process of constantly patching cryptographic code is quite time consuming in some cases, and simply not possible in other cases.
High-fidelity quantum information transmission using a room-temperature nonrefrigerated lossy microwave waveguide

Quantum microwave transmission is key to realizing modular superconducting quantum computers and distributed quantum networks. A large number of incoherent photons are thermally generated within the microwave frequency spectrum. The closeness of the transmitted quantum state to the source-generated quantum state at the input of the transmission link (measured by the transmission fidelity) degrades due to the presence of the incoherent photons. Hence, high-fidelity quantum microwave transmission has long been considered to be infeasible without refrigeration. In this study, we propose a novel method for high-fidelity quantum microwave transmission using a room-temperature lossy waveguide. The proposed scheme consists of connecting two cryogenic nodes (i.e., a transmitter and a receiver) by the room-temperature lossy microwave waveguide. First, cryogenic preamplification is implemented prior to transmission. Second, at the receiver side, a cryogenic loop antenna is placed inside the output port of the waveguide and coupled to an LC harmonic oscillator located outside the waveguide. The loop antenna converts quantum microwave fields to a quantum voltage across the coupled LC harmonic oscillator. Noise photons are induced across the LC oscillator including the source generated noise, the preamplification noise, the thermal occupation of the waveguide, and the fluctuation-dissipation noise. The loop antenna detector at the receiver is designed to extensively suppress the induced photons across the LC oscillator. The signal transmittance is maintained intact by providing significant preamplification gain. Our calculations show that high-fidelity quantum transmission (i.e., more than [Formula: see text]) is realized based on the proposed scheme for transmission distances reaching 100 m.
Curriculum Learning-Based Artificial Neural Network Model for Solving Differential Equations

This chapter is dedicated to studying the impact of the curriculum learning process and the Swish activation function for finding the solution of Differential Equations (DEs) with initial conditions. Then we have compared the result of the proposed training algorithm and the usual training algorithm. Also, we have compared the neural result using Swish, Tanh, and Sigmoid activation functions. The artificial neural network (ANN) trial solution of the differential equation is written as a sum of two terms. Here the first term satisfies boundary conditions and the second term involves containing adjustable parameters so as the trial solution can solve the differential equations. In this investigation, first we have trained our neural network in a small domain and gradually expanded the domain. Feedforward neural network (FFNN) and error backpropagation algorithm have been used to minimize the error function and modification of weights and biases. Finally, several problems have been solved to illustrate the proposed training method, and analytical results have been compared with neural results.
New Steganography Method of Reversible Data Hiding With Priority to Visual Quality of Image

Data hiding is the science of concealing a secret information in a digital image called a cover image or other multimedia files. This allows the transmit of secret data undetected through open communication channels. Target this article is the development of a new reversible method of data hiding with the priority of preserving the visual quality of the stego image at sufficient hiding capacity. The problem is solved in two stages. In the first step the input image is divided into blocks 2×2, within which the smallest pixel is chosen. The difference between the remaining three pixels and the smallest pixel is then calculated. The decimal values of the resulting difference are converted into binaries. The second stage involves the concealing of bits of secret information into calculated binary pixels using the LSB to increase the payload capacity. The results of the experiment confirmed the effectiveness of the proposed method, which consists of an acceptable value of data hiding capacity value and in the best visual quality of the stego image.
Oralizations in e-mail consultations: A study of general practitioners’ use of non-verbal cues in written doctor–patient communication

It is well-known that non-verbal cues are essential in doctor–patient communication. As doctor–patient communication is turning increasingly digital and written, it becomes relevant to explore the role of non-verbal cues in such communication genres. One more recent genre is the doctor–patient e-mail consultation. Research has found that while patients like e-mail consultations, they also miss facial expressions, eye contact, etc. In this study, we explored the different ways in which Danish GPs use non-verbal cues in e-mail consultations. We analysed 633 e-mail consultations written by 22 GPs. We applied the concept of oralization , which includes the use of emoticons and non-standard use of grammar and spatial arrangement. We found that the dominant types of oralizations were non-corrected spelling errors and lack of attention to capitalization. Overall, GPs used a limited number of other non-verbal cues. We discuss how these findings relate to norms of formality and professional context.
Skeleton Joints Moment (SJM): A Hand Gesture Dimensionality Reduction for Central Nervous System Interaction

Recent breakthroughs with numerous visual experiences using mobile devices encourage the research of human-computer interaction (HCI) involving hand gesture recognition for Holograms, Virtual Reality, and Augmented Reality. The rise of these technologies allows educators in medical segments to apply new pedagogy by interacting with virtual content in a coherent learning environment. This paper proposed the Central Nervous System (CNS) interaction using the Skeleton Joints Moment (SJM) approach for dimension reduction with k Nearest Neighbour (k-NN) for hand gesture classification. Over the past few decades, researchers have proposed various techniques in dimension reduction. One of the methods is principal component analysis (PCA). Experimental results indicated that the SJM technique has similar accuracy to PCA, where both methods showed 96% of prediction using hand skeleton joints data. In addition, PCA has a higher uncertainty of mean error 0.75 compared to SJM at only 0.01. Furthermore, PCA has the worst complexity of O(min(p3,n3)) where SJM O(n/d). Evaluation results using the T-Test showed a significant difference between SJM and PCA where p < 0.05. Thus, there is evidence to reject the null hypothesis.
FRGAN: A Blind Face Restoration with Generative Adversarial Networks

Recent works based on deep learning and facial priors have performed well in superresolving severely degraded facial images. However, due to the limitation of illumination, pixels of the monitoring probe itself, focusing area, and human motion, the face image is usually blurred or even deformed. To address this problem, we properly propose Face Restoration Generative Adversarial Networks to improve the resolution and restore the details of the blurred face. They include the Head Pose Estimation Network, Postural Transformer Network, and Face Generative Adversarial Networks. In this paper, we employ the following: (i) Swish-B activation function that is used in Face Generative Adversarial Networks to accelerate the convergence speed of the cross-entropy cost function, (ii) a special prejudgment monitor that is added to improve the accuracy of the discriminant, and (iii) the modified Postural Transformer Network that is used with 3D face reconstruction network to correct faces at different expression pose angles. Our method improves the resolution of face image and performs well in image restoration. We demonstrate how our method can produce high-quality faces, and it is superior to the most advanced methods on the reconstruction task of blind faces for in-the-wild images; especially, our 8 × SR SSIM and PSNR are, respectively, 0.078 and 1.16 higher than FSRNet in AFLW.
Human-Algorithm Interaction: Algorithmic Pricing in Hybrid Laboratory Markets

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Comparison of Logistic Regression and XGBoost for Predicting Potential Debtors

Income earned from loan interest is the main income for banks, especially conventional banks. However, currently the population ratio between total customers and customers who have credit facilities is very far away. Moreover, the marketing strategy undertaken still relies on the referral system, so it does not reach potential debtors who have no relations with other debtors. This study aims to predict the potential of customers in taking credit facilities or becoming a debtor. The study is conducted by comparing logistic regression model and XGBoost. The accuracy of the testing data on the logistic regression model is 88% while the XGBoost is 92%. The comparison results show that the XGBoost method has better results based on four evaluation indicators namely accuracy, sensitivity, specificity, and precision. Because those values on the XGBoost are greater than logistic regression.
Error suppression for speckle correlation-based 3D shape measurement via dynamic shape function selection

Speckle correlation-based 3D shape measurement technology has an increasing number of applications in the fields of medical assistance, cultural relic protection, product manufacturing, and inspection. The foremost process of this method is the stereo matching of speckle image pairs using digital image correlation (DIC). It is challenging to improve the matching precision of DIC for complex shapes with large or abrupt curvature changes. Because the local curvature change and surface shape complexity of different objects vary, first-order and second-order shape functions have different abilities to describe the mapping relationship from the reference subset to the target subset. As a result, the systematic errors caused by subset under-matching and over-matching are also different. In this study, a pixel-by-pixel dynamic shape function selection algorithm is proposed for different measurement objects or different regions on the surface of the same object. Experimental results show that the proposed algorithm can effectively suppress the measurement error caused by the limited description ability of a specific shape function.
Design and Development of Website Dr.Changkitchen Diet Catering Using SDLC Waterfall Model

Diet catering is intended for those who have a goal to live healthier and lose weight. This is done by paying attention to the amount of nutrition or nutrition. The writing of this report aims to develop a web-based diet catering business so that the transaction process between the two parties is easier and faster. The web development process consists of several stages: Analysis, Design, Implementation and Testing Deployment, Maintenance. This diet catering web development uses Brackets as the IDE and by implementing the Waterfall software development model. This website is a way to promote our diet catering products and also to simplify and streamline the transaction path between sellers and buyers because all activities and goals of users will be included in the data. Users who can access this website will be divided into 3 users as non-members, members, and admin. The Non-members can view products and testimonials on our homepage and if nonmembers want to make a purchase, they can order by using social media such as Instagram, Whatsapp, and Line. The Members can make orders and transactions through website. Admin will see the existing transaction process and perform maintenance on the web.
r/WatchRedditDie and the politics of reddit’s bans and quarantines

The subreddit r/WatchRedditDie was founded in 2015 after reddit started implementing anti-harassment policies, and positions itself as a “fire alarm for reddit” meant to voyeuristically watch reddit’s impending (symbolic) death. As conversations around platform governance, moderation, and the role of platforms in controlling hate speech become more complex, r/WatchRedditDie and its affiliated subreddits are dedicated in maintaining a version of reddit tolerant of any and all speech, excluding other more vulnerable users from fully participating on the platform. r/WatchReditDie users advocate for no interference in their activities on the platform—meaning that although they rely on the reddit infrastructure to sustain their community, they aim to self-govern to uphold a libertarian and often manipulated interpretation of free expression. Responding to reddit’s evolving policies, they find community with one another by positioning the platform itself as their main antagonist. Through the social worlds framework, I examine the r/WatchRedditDie community’s responses to platform change, bringing up new questions about the possibility of shared governance between platform and user, as well as participatory culture’s promises and perils.
Attention-Based Bidirectional LSTM Network for Target Tracking

One of the major challenges in target tracking is the inaccurate state estimation caused by motion uncertainty of the target. In this paper, a deep neural network-based method is proposed to fit the nonlinear mapping relationship between the filtering state, the measurement, and the real state to achieve effective tracking of the maneuvering trajectory. First, the encoder method is used to mine the favorable information in the target state and radar observation. Next, the bidirectional long short-term memory network (BI-LSTM) is used to memorize and fit the potential laws of the target motion. Then, the attention mechanism is introduced to make the network automatically capture the importance of the trajectory sequence, which greatly improves the efficiency and accuracy of estimation. Finally, we use decoder method to map the complicated functions between the estimated trajectory and the real trajectory. The simulation results verify that our attention-based bidirectional LSTM (ATBI-LSTM) method has a higher estimation accuracy and better dynamic performance than traditional algorithms without prior knowledge and a complex parameter adjustment process.
Multi-key Fully Homomorphic Encryption from Additive Homomorphism

Abstract Fully homomorphic encryption (FHE) allows direct computations over the encrypted data without access to the decryption. Hence multi-key FHE is well suitable for secure multiparty computation. Recently, Brakerski et al. (TCC 2019 and EUROCRYPT 2020) utilized additively homomorphic encryption to construct FHE schemes with different properties. Motivated by their work, we are attempting to construct multi-key FHE schemes via additively homomorphic encryption. In this paper, we propose a general framework of constructing multi-key FHE, combining the additively homomorphic encryption with specific multiparty computation protocols constructed from encryption switching protocol. Concretely, every involved party encrypts his plaintexts with an additively homomorphic encryption under his own public key. Then the ciphertexts are evaluated by suitable multiparty computation protocols performed by two cooperative servers without collusion. Furthermore, an instantiation with an ElGamal variant scheme is presented. Performance comparisons show that our multi-key FHE from additively homomorphic encryption is more efficient and practical.
Second-order Time Delay Reservoir Computing for Nonlinear Time Series Problems

Time Delay Reservoir (TDR) can exhibit effects of high dimensionality and short-term memory based on delay differential equations (DDEs), as well as having hardware-friendly characteristics. However, the predictive performance and memory capacity of the standard TDRs are still limited, and dependent on the hyperparameter of the oscillation function. In this paper, we first analyze these limitations and their corresponding reasons. We find that the reasons for such limitations are fused by two aspects, which are the trade-off between the strength of self-feedback and neighboring-feedback caused by neuron separation, as well as the unsuitable order setting of the nonlinear function in DDE. Therefore, we propose a new form of TDR with second-order time delay to overcome such limitations, incurring a more flexible time-multiplexing. Moreover, a parameter-free nonlinear function is introduced to substitute the classic Mackey-Glass oscillator, which alleviates the problem of parameter dependency. Our experiments show that the proposed approach achieves better predictive performance and memory capacity compared with the standard TDR. Our proposed model also outperforms six other existing approaches on both time series prediction and recognition tasks.