Reviewerly/exHarmony huggingface dataset:
Clear Skies Ahead: Towards Real‐Time Automatic Sky Replacement in Video

Abstract Digital videos such as those captured by a smartphone often exhibit exposure inconsistencies, a poorly exposed sky, or simply suffer from an uninteresting or plain looking sky. Professionals may edit these videos using advanced and time‐consuming tools unavailable to most users, to replace the sky with a more expressive or imaginative sky. In this work, we propose an algorithm for automatic replacement of the sky region in a video with a different sky, providing nonprofessional users with a simple yet efficient tool to seamlessly replace the sky. The method is fast, achieving close to real‐time performance on mobile devices and the user's involvement can remain as limited as simply selecting the replacement sky.
Alternative ways to handle missing values problem: A case study in earthquake dataset

Abstract Dataset is a basic foundation that is often used in understanding a problem. It provides information for researchers to get solutions to the problem. In the data retrieval process, some errors may occur and cause the data to be incomplete for any reason. It was a problem in how to recover the missing values in a dataset. The first step is to look at the characteristics of the data. In this paper, we proposed three alternative ways to obtain the missing values of the dataset. In this case, we used the earthquake dataset that has special properties. We then present the results to see the performance of the proposed methods. The results show a good agreement for the missing data. This is a preliminary result of our research related to missing data in the earthquake dataset. This study has some limitations such as if the missing values occur in a large enough data block, the methods need to be improved.
Weakly supervised segmentation via instance-aware propagation

Peak Response Map (PRM) highlighting the discriminative regions can be extracted from a pre-trained classification network. We can accurately localize instances of each class with the help of these response maps. However, these maps cannot provide reliable information for segmentation even with off-the-shelf object proposals. This is because neither PRM nor the proposals know which regions can be regarded as a complete instance. In this paper, we tackle this problem by proposing an Instance-aware Cue-propagation Network (ICN) with a new proposal-matching strategy. In particular, the ICN aims to filter out background distractions and cover the complete instance, while our proposed proposal-matching strategy adds a re-balancing constraint on the contributions of multi-scale object proposals. Extensive experiments conducted on the PASCAL VOC 2012 dataset show the superior performance of our method over weakly-supervised state-of-the-arts for both semantic and instance segmentation.
A Multi-Layer Capsule-based Forensics Model for Fake Detection of Digital Visual Media

The dangers generated from synthesized multimedia are increasing every day. The creation of the so-called Deepfakes multimedia is vastly evolving, making the detection task harder every day. Researchers and corporations are interested in exploring the technology limits and are coming up with new tools every year to create more robust fake media. In this paper, a new enhanced fake video detection model is introduced addressing many of the face-swapping threats and the low generalization problem. A preprocessing stage is proposed to minimize the noise in the data to enhance their quality. The proposed architecture uses a modified application of capsule neural networks (CapsNet) with an enhanced routing technique. It does not require a lot of training data and generates a small number of training parameters making it fast to build. The model was trained and tested using the DFDC-P dataset and the results have proven that it outperformed other detectors in terms of detection recall, weighted precision, and F1 score.
Non‐linear activation function approximation using a REMEZ algorithm

Here a more accurate piecewise approximation (PWA) scheme for non-linear activation function is proposed. It utilizes a precision-controlled recursive algorithm to predict a sub-range; after that, the REMEZ algorithm is used to find the corresponding approximation function. The PWA realized in three ways: using first-order functions, that is, piecewise linear model, second-order functions (piecewise non-linear model), and hybrid-order model (a mixture of first-order and second-order functions). The hybrid-order approximation employs the second-order derivative of non-linear activation function to decide the linear and non-linear sub-regions, correspondingly the first-order and second-order functions are predicted, respectively. The accuracy is compared to the present state-of-the-art approximation schemes. The multi-layer perceptron model is designed to implement XOR-gate, and it uses an approximate activation function. The hardware utilization is measured using the TSMC 0.18-μm library with the Synopsys Design Compiler. Result reveals that the proposed approximation scheme efficiently approximates the non-linear activation functions.
Automatic, dynamic, and nearly optimal learning rate specification via local quadratic approximation

In deep learning tasks, the update step size determined by the learning rate at each iteration plays a critical role in gradient-based optimization. However, determining the appropriate learning rate in practice typically relies on subjective judgment. In this work, we propose a novel optimization method based on local quadratic approximation (LQA). In each update step, we locally approximate the loss function along the gradient direction by using a standard quadratic function of the learning rate. Subsequently, we propose an approximation step to obtain a nearly optimal learning rate in a computationally efficient manner. The proposed LQA method has three important features. First, the learning rate is automatically determined in each update step. Second, it is dynamically adjusted according to the current loss function value and parameter estimates. Third, with the gradient direction fixed, the proposed method attains a nearly maximum reduction in the loss function. Extensive experiments were conducted to prove the effectiveness of the proposed LQA method.
Robust Semantic Segmentation By Dense Fusion Network On Blurred VHR Remote Sensing Images

Robust semantic segmentation of VHR remote sensing images from UAV sensors is critical for earth observation, land use, land cover or mapping applications. Several factors such as shadows, weather disruption and camera shakes making this problem highly challenging, especially only using RGB images. In this paper, we propose the use of multi-modality data including NIR, RGB and DSM to increase robustness of segmentation in blurred or partially damaged VHR remote sensing images. By proposing a cascaded dense encoder-decoder network and the SELayer based fusion and assembling techniques, the proposed RobustDenseNet achieves steady performance when the image quality is decreasing, compared with the state-of-the-art semantic segmentation model.
Supervised Attention for Speaker Recognition

The recently proposed self-attentive pooling (SAP) has shown good performance in several speaker recognition systems. In SAP systems, the context vector is trained end-to-end together with the feature extractor, where the role of context vector is to select the most discriminative frames for speaker recognition. However, the SAP underperforms compared to the temporal average pooling (TAP) baseline in some settings, which implies that the attention is not learnt effectively in end-to-end training. To tackle this problem, we introduce strategies for training the attention mechanism in a supervised manner, which learns the context vector using classified samples. With our proposed methods, context vector can be boosted to select the most informative frames. We show that our method outperforms existing methods in various experimental settings including short utterance speaker recognition, and achieves competitive performance over the existing baselines on the VoxCeleb datasets.
Towards Pick and Place Multi Robot Coordination Using Multi-agent Deep Reinforcement Learning

Recent advances in deep reinforcement learning are enabling the creation and use of powerful multi-agent systems in complex areas such as multi-robot coordination. These show great promise to help solve many of the difficult challenges of rapidly growing domains such as smart manufacturing. In this position paper we describe our early-stage work on the use of multi-agent deep reinforcement learning to optimise coordination in a multi-robot pick and place system. Our goal is to evaluate the feasibility of this new approach in a manufacturing environment. We propose to adopt a decentralised partially observable Markov Decision Process approach and to extend an existing cooperative game work to suitably formulate the problem as a multiagent system. We describe the centralised training/decentralised execution multi-agent learning approach which allows a group of agents to be trained simultaneously but to exercise decentralised control based on their local observations. We identify potential learning algorithms and architectures that we will investigate as a base for our implementation and we outline our open research questions. Finally we identify next steps in our research program.
A consistent deep registration network with group data modeling

Medical image registration is a critical process for automated image computing, and ideally, the deformation field from one image to another should be smooth and inverse-consistent in order to bidirectionally align anatomical structures and to preserve their topology. Consistent registration can reduce bias caused by the order of input images, increase robustness, and improve reliability of subsequent quantitative analysis. Rigorous differential geometry constraints have been used in traditional methods to enforce the topological consistency but require comprehensive optimization and are time consuming. Recent studies show that deep learning-based registration methods can achieve comparable accuracy and are much faster than traditional registration. However, the estimated deformation fields do not necessarily possess inverse consistency when the order of two input images is swapped. To tackle this problem, we propose a new deep registration algorithm by employing the inverse consistency training strategy, so the forward and backward deformations of a pair of images can consistently align anatomical structures. In addition, since fine-tuned deformations among the training images reflect variability of shapes and appearances in a high-dimensional space, we formulate a group prior data modeling framework so that such statistics can be used to improve accuracy and consistency for registering new input image pairs. Specifically, we implement the wavelet principle component analysis (w-PCA) model of deformation fields and incorporate such prior constraints into the inverse-consistent deep registration network. We refer the proposed algorithm as consistent deep registration with group data modeling. Experiments on 3D brain magnetic resonance (MR) images showed that the unsupervised consistent deep registration and data modeling strategy yield consistent deformations after switching the input images and tolerated image variations well.
Hybrid Encryption Algorithm for the Data Security of ESP32 based IoT-enabled Robots

The Internet of Things (IoT) is one of the growing digital applications in modern times. With the increase of the application of IoT in the fields of networked mobile robotics, Smart Grid, etc., the concern about the security protocols also rises along with it. Cybersecurity is one of the most important network security protocols for the safety of IoT-enabled devices as all the devices are connected and inter-exchanging various data over the internet. Various encryption algorithms for securing data in cybersecurity management are already in existence. RSA, ECC, and other existing encryption algorithms are frequently used in various fields of IoT applications such, however, they have many shortcomings. Among the existing algorithm, AES is one of the best algorithms because it can provide an excellent level of security and its encryption speed is much faster than others. But AES is vulnerable to Key Recovery Attack. In order to overcome this shortcoming and to provide a more secured security protocol, Hybrid Encryption Algorithm (HEA) has been introduced in this paper and implemented in an ESP32 based IoT-enabled mobile robot. The HEA consist of base64 and AES, which is double-layered encryption for data security. The level of security is much higher than the commonly used encryption algorithm. The complexity and runtime are less than other encryption topology. The proposed technique has been computed in ESP32 and the performance was observed to be much better in terms of security level, complexity, execution time, and power consumption of the device.
SOME ISSUESIN THE IMPLEMENTATION OF PROJECT ACTIVITIES IN SCHOOL EDUCATION

The article examines the problems of implementing project activities of students in school education. Project activity allows students to assert themselves, self-actualize on an intellectual and creative level, showing their best qualities and character traits. The law "on education in the Russian Federation" also pays great attention to teaching the basics of project activities to schoolchildren. Today, this issue has gone beyond discussion and has become widespread in practice. Project activities are implemented not only in primary, but also in secondary and high schools. In many regions of the Russian Federation, school teachers are actively learning the basics of project activities in the framework of training platforms, teacher training courses, and the basics of project activities are studied in the curricula of pedagogical universities. However, the practice of implementing project activities in the educational process has caused a number of problems that significantly reduce the potential inherent in it. These include: the lack of internal motivation of students to implement projects, almost complete lack of successful experience in implementing projects by school teachers, the contradiction between the accepted forms of final certification (Unified state exam, General state exam) and the requirements for projects, the inability to assess the quality of completed projects by objective criteria. In this work, the sources of these problems are considered at the system level, the contradictions that cause their occurrence are formulated, and the directions for finding solutions to eliminate the above-described problems are indicated. Solutions are proposed to improve the efficiency and effectiveness of project activities of students at school.
One tag, two codes

Barcodes and NFC have become the de facto standards in the field of automatic identification and data capture. These standards have been widely adopted for many applications, such as mobile payments, advertisements, social sharing, admission control, and so on. Recently, considerable demands require the integration of these two codes (barcode and NFC code) into a single tag for the functional complementation. To achieve the goal of "one tag, two codes" (OTTC), this work proposes CoilCode, which takes advantage of the printed electronics to fuse an NFC coil antenna into a QR code on a single layer. The proposed code could be identified by cameras and NFC readers. With the use of the conductive inks, QR code and NFC code have become an essential part of each other: the modules of the QR code facilitate the NFC chip in harvesting energy from the magnetic field, while the NFC antenna itself represents bits of the QR code. Compared to the prior dual-layer OTTC, CoilCode is more compact, cost-effective, flimsy, flexible, and environment-friendly, and also reduces the fabrication complexity considerably. We prototyped hundreds of CoilCodes and conducted comprehensive evaluations (across 4 models of NFC chips and 8 kinds of NFC readers under 13 different system configurations). CoilCode demonstrates high-quality identification results for QR code and NFC functions on a wide range of inputs and under different distortion effects.
Multi-objective Neural Architecture Search with Almost No Training

In the recent past, neural architecture search (NAS) has attracted increasing attention from both academia and industries. Despite the steady stream of impressive empirical results, most existing NAS algorithms are computationally prohibitive to execute due to the costly iterations of stochastic gradient descent (SGD) training. In this work, we propose an effective alternative, dubbed Random-Weight Evaluation (RWE), to rapidly estimate the performance of network architectures. By just training the last linear classification layer, RWE reduces the computational cost of evaluating an architecture from hours to seconds. When integrated within an evolutionary multi-objective algorithm, RWE obtains a set of efficient architectures with state-of-the-art performance on CIFAR-10 with less than two hours' searching on a single GPU card. Ablation studies on rank-order correlations and transfer learning experiments to ImageNet have further validated the effectiveness of RWE.
A Class of Weak Keys for the QC-MDPC Cryptosystem

The quasi-cyclic moderate-density parity-check code (QC-MDPC) cryptosystem is one of the recent variants of the original McEliece code-based cryptosystem, that has also been part of the BIKE cryptosystem, which has been submitted to NIST as a post-Quantum cryptosystem candidate. We show that in certain cases the secret key can be recovered from the public key by means of a polynomial factorization. This leads to the concept of "weak keys" for the cryptosystem. Even though the probability of choosing a weak key at random is low, we are able to find weak keys quite easily. This suggests that avoiding weak keys may be introduced as a condition in the implementation of the cryptosystem.
The Ethical Issues of Location-Based Services on Big Data and IoT

Both Internet of Things (IoT) and big data are hot topics in recent years. They indeed have brought about the change of business, promoted the progress of science and technology, and facilitated the lives of human beings. IoT creates the opportunity to connect every item to the Internet, and countless science and technology have supported the achievement of this goal. LBS is one of the indispensable technologies. It brings significant benefits to the business community, the individual, the society, and the national defense. However, at the same time, an individual's personal information is disclosed and even attacked by 'information thieves'. An inevitable reality is that the prerequisite of getting a location service is to expose your position first. Therefore, the privacy-related ethics issues are generated, and the danger is imminent, although there are corresponding protective measures.
A conversational agent system for dietary supplements use

Abstract Background Dietary supplements (DS) have been widely used by consumers, but the information around the efficacy and safety of DS is disparate or incomplete, thus creating barriers for consumers to find information effectively. Conversational agent (CA) systems have been applied to healthcare domain, but there is no such system to answer consumers regarding DS use, although widespread use of DS. In this study, we develop the first CA system for DS use. Methods Our CA system for DS use developed on the MindMeld framework, consists of three components: question understanding, DS knowledge base, and answer generation. We collected and annotated 1509 questions to develop a natural language understanding module (e.g., question type classifier, named entity recognizer) which was then integrated into MindMeld framework. CA then queries the DS knowledge base (i.e., iDISK) and generates answers using rule-based slot filling techniques. We evaluated the algorithms of each component and the CA system as a whole. Results CNN is the best question classifier with an F1 score of 0.81, and CRF is the best named entity recognizer with an F1 score of 0.87. The system achieves an overall accuracy of 81% and an average score of 1.82 with succ@3 + score of 76.2% and succ@2 + of 66% approximately. Conclusion This study develops the first CA system for DS use using the MindMeld framework and iDISK domain knowledge base.
Load Balancing Method Using Server Temperature Prediction Considering Multiple Internal Heat Sources in Data Centers

The increasing demand for cloud computing services to more flexibly use computing resources has led to a severe problem of excessive energy consumption in data centers. In recent research, the energy consumption of computing and cooling equipment has attracted much attention. To reduce the energy consumption in data centers, the energy efficiency of the cooling equipment needs to be considered. This study theoretically modeled the relations between the server's internal heat sources and exhaust temperatures. First, we proposed a model that expands the previous study to describe the temperature change delay and to apply it to multiple internal heat sources. Then, we experimentally evaluated the prediction accuracy of the proposed model using a real server and conducted a simulation to confirm the efficiency of this prediction model for load balancing. The simulation results showed that the proposed method reduced the average maximum server temperature by 0.27 °C compared to the conventional method, which theoretically leads to energy savings of 260 kW per day and 95 MW per year in typical data centers.
Adversarial transfer network with bilinear attention for the detection of adverse drug reactions from social media

The drug safety issues related to adverse drug reactions (ADRs) are gradually becoming more important to the public. With social media booming, increasingly more patients would like to share their reactions to get support from others. These published posts comprise a valuable resource for ADR identification because of their timeliness. However, available social media datasets are rare. Moreover, the informality of the social media text is also a challenge for ADR identification. PubMed and social media differ greatly in expression and syntax. Introducing the PubMed datasets, which are usually normative and numerous, may be helpful to ADR identification in social media. To this end, we propose an adversarial transfer framework for ADR identification that transfers the auxiliary features from PubMed to social media datasets to improve the generalization of the model and mitigate the noise caused by colloquial expression in social media. Additionally, we add dynamic weight to the loss function to offset the training slants caused by imbalanced training data. We experimentally evaluate the method we proposed on two social media datasets and two PubMed datasets. The results show that our proposed method can improve the performance of ADR identification from social media.