Reviewerly/exHarmony huggingface dataset:
Application of Computational Intelligence in Visual Quality Optimization Watermarking and Coding Tools to Improve the Medical IoT Platforms Using ECC Cybersecurity Based CoAP Protocol

To ensure copyright protection and authenticate ownership of media or entities, image watermarking techniques are utilized. This technique entails embedding hidden information about an owner in a specific entity to discover any potential ownership issues. In recent years, several authors have proposed various ways to watermarking. In computational intelligence contexts, however, there are not enough research and comparisons of watermarking approaches. Soft computing techniques are now being applied to help watermarking algorithms perform better. This chapter investigates soft computing-based image watermarking for a medical IoT platform that aims to combat the spread of COVID-19, by allowing a large number of people to simultaneously and securely access their private data, such as photos and QR codes in public places such as stadiums, supermarkets, and events with a large number of participants. Therefore, our platform is composed of QR Code, and RFID identification readers to ensure the validity of a health pass as well as an intelligent facial recognition system to verify the pass’s owner. The proposed system uses artificial intelligence, psychovisual coding, CoAP protocol, and security tools such as digital watermarking and ECC encryption to optimize the sending of data captured from citizens wishing to access a given space in terms of execution time, bandwidth, storage space, energy, and memory consumption.
PEMBELAJARAN BIOLOGI BERBASIS LITERASI INFORMASI BERORIENTASI WIKIPEDIA PADA MATERI BAKTERI

One example of a web search that is quite often visited is wikipedia.Wikipedia-based learning can teach students how information literacy skills are used, especially in today's era.By learning biology based on wikipedia-oriented information literacy on bacterial material, it is hoped that students can learn about information literacy so that they can choose the right information.This study aims to determine the application of wikipedia-oriented information literacy-based biology learning on bacteria.The method used is pre experimental, with a pretest -posttest one group design.The test in this instrument was conducted to determine the ability of students before and after being given treatment in the learning process.The instrument used in the form of multiple choice questions with a total of 15 questions and has 5 alternative answer choices.The average pretest score is 44.52 and the posttest average score is 83.70.The N-Gain result obtained in this study is 0.69, this result is included in the medium category.
Learning quantum-state feedback control with backpropagation-free stochastic optimization

High-fidelity state preparation represents a fundamental challenge in the application of quantum technology. While the majority of optimal control approaches use feedback to improve the controller, the controller itself often does not incorporate explicit state dependence. Here, we present a general framework for training deep feedback networks for open quantum systems with continuous weak measurement that allows a variety of system and control structures that are prohibitive by many other techniques and can in effect react to unmodeled effects through nonlinear filtering. Our approach benefits from characteristics of both stochastic sampling and gradient-based optimization methods yet does not require differentiability as in backpropagation approaches. We demonstrate that this method is efficient due to inherent parallelizability, robust to open system interactions, and outperforms landmark state-dependent feedback control results in simulation.
Enhanced unsupervised neural machine translation by cross lingual sense embedding and filtered back-translation for morphological and endangered Indic languages

The fast growth of communication technology has brought nations and their cultures closer together, and the demand for cross-language communication has risen tremendously. There is a different learning method to connect the source language to the target language in which unsupervised learning is a blessing for low-resource languages. The unsupervised machine translation is always problematic to those languages which are morphologically rich and low resources languages. Morphologically rich and low-resource language does not provide good results in machine translation if the translation is from morphologically less complex language to morphologically more complex languages. In this paper, we have improved the unsupervised neural machine translation by tackling the ambiguity problem and the quality of pseudo-parallel sentence pairs generated through back-translation for morphologically rich languages. The ambiguity problem is solved by taking the cross-lingual sense embedding at the source side instead of cross-lingual word embedding. By giving more weight to better pseudo-parallel sentence pairs in the back-translation step, the quality of pseudo-parallel sentences is increased. Different evaluation metrics have been used to check the robustness of the model and compared with different baseline models. The experiment is performed on different morphologically rich languages English-Hindi, English-Tamil, English-Telegu, and one low-resource endangered kangri language.
Multi-resolution dictionary learning for discrimination of hidden features: A case study of atmospheric gravity waves

In this work, we propose Multi-Resolution Dictionary Learning by promoting sparsity, and Instantaneous Frequency Estimation technique for the discrimination of measured parameter from the effect of unknown sources like noise, and natural phenomena that occur in background during the data acquisition. The Dictionary Learning technique using Multi-Resolution Analysis is performed in the Wavelet Analysis domain using Singular Value Decomposition. In this paper, we aim for the discrimination of contribution from unknown sources in the measured parameter in the amplitude domain and frequency domain by promoting sparsity and Instantaneous Frequency Estimation respectively. We implement the proposed technique and discuss its potential with a case study on the discriminating the Doppler shift in Atmospheric Gravity Waves that occur due to the horizontal wind during data acquisition.
Semi-supervised adaptive kernel concept factorization

Kernelized concept factorization (KCF) has shown its advantage on handling data with nonlinear structures; however, the kernels involved in the existing KCF-based methods are empirically predefined, which may compromise the performance. In this paper, we propose semi-supervised adaptive kernel concept factorization (SAKCF), which integrates the data representation and kernel learning into a unified model to make the two learning processes adapt to each other. SAKCF extends traditional KCF in a semi-supervised manner, which encourages the high-dimensional representation to be consistent with both the limited supervisory and local geometric information. Besides, an alternating iterative algorithm is proposed to solve the resulting constrained optimization problem. Experimental results on six real-world data sets verify the effectiveness and advantages of our SAKCF over state-of-the-art methods when applied on the clustering task.
The recovery scheme of computer-generated holography encryption–hiding images based on deep learning

In order to improve the security of ciphertext images in transmission, a scheme of using neural network is proposed to restore the encryption-hiding images based on chaotic iris phase mask and computer-generated holography (CGH), which solve the great difficulty of illegal attacks in the symmetric-asymmetric hybrid encryption system. Firstly, the ciphertext image based on CGH and chaotic iris phase mask is generated, and the ciphertext image is hidden into a carrier image. A large number of hidden image and plaintext image pairs are produced as a dataset. Next, the neural network is built by continuous training and testing. Finally, the established neural network can fit the mapping relationship between the hidden image and the plaintext image. It is not necessary to extract the ciphertext image from the hidden image before decrypting. We compare the image recovered by the neural network with the plaintext image. The experimental results show that the average cross-correlation coefficient (CC) is 0.994, the average peak signal-to-noise ratio (PSNR) is 70.2 dB, and the average structural similarity (SSIM) is 0.929. This scheme can directly realize the decryption of the hidden image. The scheme (encryption-decryption-hiding) are elaborated in detail. The simulation experiments show that the scheme is feasible and has good robustness. • A recovery scheme of encryption-hiding images is proposed. • The scheme can recover plaintext images directly from hidden images. • ResNet can well fit the logical relationship between the hidden image and the plaintext image. • This scheme has a positive effect on improving the noise resistance of image hiding technology in the spatial domain.
An objective method for pedestrian occlusion level classification

Pedestrian detection is among the most safety-critical features of driver assistance systems for autonomous vehicles. One of the most complex detection challenges is that of partial occlusion, where a target object is only partially available to the sensor due to obstruction by another foreground object. A number of current pedestrian detection benchmarks provide annotation for partial occlusion to assess algorithm performance in these scenarios, however each benchmark varies greatly in their definition of the occurrence and severity of occlusion. In addition, current occlusion level annotation methods contain a high degree of subjectivity by the human annotator. This can lead to inaccurate or inconsistent reporting of an algorithm’s detection performance for partially occluded pedestrians, depending on which benchmark is used. This research presents a novel, objective method for pedestrian occlusion level classification for ground truth annotation. Occlusion level classification is achieved through the identification of visible pedestrian keypoints and through the use of a novel, effective method of 2D body surface area estimation. Experimental results demonstrate that the proposed method reflects the pixel-wise occlusion level of pedestrians in images and is effective for all forms of occlusion, including challenging edge cases such as self-occlusion, truncation and inter-occluding pedestrians.
A collaborative method for code clone detection using a deep learning model

Code cloning (CC) is the process of copying and reconfiguring a code fragment and using it in another part of a software project. This clones increases the running overhead of the software. As a result, Code Clone Detection (CCD) has become an active research area in software development research. The detection of Large-Variance Code Clones (LV-CCs) is very difficult when the lines of codes (LOCs) in the source code are very large. The distance metrics have been used in LV-CC detection by calculating the distance between training feature sets of source codes and testing feature sets of source codes. However, threshold selection for detecting clones is a challenging issue in distance-based LV-CC detection. To solve this, a Collaborative CCD using Deep Learning (CCCD-DL) is developed in this paper by utilising lexical, syntactic, semantic and structural features for identifying all types of clones together. A lexical feature is extracted from Clone Pairs (CPs) identified by LV-Mapper. Syntactic and semantic features are identified by the Abstract Syntax Tree (AST) and Control Flow Graph (CFG). The structural features are extracted by code size metrics (CZMs) and object-oriented metrics (OOMs). All features are coordinated and fed into the input layer of DNN. The hidden layer then transforms the inputs into the neural vertices in the multi-classification stage using linear transformation preceded by suppressing non-linearity. This process can generate a complicated and non-hypothetical prototype with a weight matrix for fitting the training sequence. Thus, the feed-forward step has been successfully completed. This model then uses back-propagation in the following element to modify the weight matrix based on the training set. Finally, a softmax layer converts the clone detection task into a classification process. The results of the experiments show that the proposed method solves distance-based problems more quickly and effectively than the traditional methods for the CCD.
Historian: A Large-Scale Historical Film Dataset with Cinematographic Annotation

Developing automated tools for sustainable film preservation of extensive historical film collections assumes an understanding of fundamental cinematographic settings. In order to be able to investigate new approaches to detect and classify cinematographic settings, this paper proposes a novel large-scale historical film dataset with cinematographic annotations (HISTORIAN), i.e., shot boundaries, shot types, camera movements. The dataset consists of 98 digitized original analog film reels related to the Second World War and 10593 film shots manually annotated with human film experts. Moreover, annotations for overscan areas such as sprocket holes are included. A baseline film analysis pipeline is introduced and evaluated. To the best of our knowledge, HISTORIAN is the first dataset that covers the challenges and characteristics of historical film documentaries and provides novel possibilities for exploring automatic film analysis tools.
A Path Planning Approach for Tractor-Trailer System based on Semi-Supervised Learning

Autonomous semi-trucks (tractor-trailer combinations) call for dedicated path planners for on-road driving due to their unique articulated structures and large dimensions, which are often ignored in the current path/trajectory planner design for normal autonomous cars, and which could lead to off-tracking without proper modification. While numerical optimization approaches such as model predictive control-based path planning algorithms do exist, these complex planners usually require large computation resources to meet real-time requirements. In this paper, we propose a novel path planning approach based on semi-supervised learning. We train an encoder-decoder type of deep neural network to generate / plan paths with the objective to minimize the off-track of the tractor-trailer swept area. The encoder encodes input information such as lane markings, static obstacles, and potentially other features, and pass it to the decoder to generate a planned path. The key to our approach is the construction of a path cost function that scores / penalizes each network-generated path based on its deviation from the lane center, the path smoothness and collision with any static obstacles, and backpropagates the cost of the paths through the encoder-decoder network to train it. As the path cost function acts as a critic of the path quality, our approach requires no collected data from expert driving for training, but only randomly generated samples of many possible combinations of lane shapes and obstacles arrangements. The proposed approach is finally verified in simulation environment.
A Model Driven Approach for Web Service Composition

We propose a model driven approach for web services composition, a way allowing specifying web services and their composition at a high level of abstraction and generating the implementation of such composition. Web services are described by WSDL (Web Service Description Language), and then translated into high-level models, which can be composed according to a composition scheme. The MDA (Model Driven Architecture) is used to support our approach for generating BPEL code and WSDL Description from the high-level web service models. A case study highlights the benefits of our proposal and illustrates the automatic generation of web services composition.
Correlations dynamics of two-spin XYZ Heisenberg model via negativity and skew information

We introduce a new framework for quantum two-spin correlations based on a quantum skew information quantity and long-negativity. The quantum correlations for an anisotropic two-qubit Heisenberg [Formula: see text] model are affected by physical parameters such as intrinsic decoherence, Werner states and time-dependent magnetic fields. In particular, the joint effects of Werner states, magnetic fields and anisotropic parameters on quantum correlation robustness are investigated in the presence of intrinsic decoherence. For an initially uncorrelated state, robust entanglement is generated while skew information between the two quantum bits no longer exists. It is shown that the magnetic field makes the system strongly correlated with incredibly significant steady-state values in uncorrelated states. The phenomenon of sudden death and sudden birth appear during the evolution process of initial conditions. Finally, if the system starts from a mixed state or a Werner state, the magnetic field does not play a significant role in the monotonically decreasing correlations. By leveraging our framework, one can create a kind of stability between the uncertainty-induced quantum nonlocality and the local quantum uncertainty correlations.
A storytree-based model for inter-document causal relation extraction from news articles

With more and more news articles appearing on the Internet, discovering causal relations between news articles is very important for people to understand the development of news. Extracting the causal relations between news articles is an inter-document relation extraction task. Existing works on relation extraction cannot solve it well because of the following two reasons: (1) most relation extraction models are intra-document models, which focus on relation extraction between entities. However, news articles are many times longer and more complex than entities, which makes the inter-document relation extraction task harder than intra-document. (2) Existing inter-document relation extraction models rely on similarity information between news articles, which could limit the performance of extraction methods. In this paper, we propose an inter-document model based on storytree information to extract causal relations between news articles. We adopt storytree information to integer linear programming (ILP) and design the storytree constraints for the ILP objective function. Experimental results show that all the constraints are effective and the proposed method outperforms widely used machine learning models and a state-of-the-art deep learning model, with F1 improved by more than 5% on three different datasets. Further analysis shows that five constraints in our model improve the results to varying degrees and the effects on the three datasets are different. The experiment about link features also suggests the positive influence of link information.
Feature Space Disentangling Based on Spatial Attention for Makeup Transfer

Makeup transfer aims at rendering the makeup style from a given reference image to a source image. Most existing works have achieved promising progress by disentangled representation. However, these methods do not consider the spatial distribution of makeup style, which inevitably change the makeup-irrelevant regions. To solve the problem, we introduce a novel feature space disentangling framework based on spatial attention mechanism for makeup transfer. In particular, we first utilize a single encoder to extract all the features of the image. Then we propose a learnable spatial semantic classifier to classify the extracted features into makeup-specific and makeup-irrelevant features. Finally, we complete makeup transfer by swapping the classified features. Experiments demonstrate that the makeup-specific features precisely signify the spatial distribution of makeup style. The superiority of our approach is well demonstrated by the experiment that it produces promising visual results and keeps those makeup-irrelevant regions unchanged.
Selecting Adequate Basis Model for Adaptation in Pedestrian Detection

The emerging machine learning methods and models require large and well-annotated benchmark datasets in order to increase their generalization capacity. Pedestrian detection is one of the major research fields that suffer from data set dependency and over-fitting. A significant portion of the ongoing studies uses transfer learning or similar adaptation strategies based on deep models, which are initially trained on a benchmark dataset. This paper searches for an answer. Unfortunately, the selection of an adequate model is usually based on trial and error, which requires a tedious and time-consuming simulation through potential candidates. This paper proposes a computationally simple analysis strategy for selecting a good candidate using Shannon Entropy and Gray Level Co-occurrence Matrices, which are utilized to determine the similarity and usability of a pre-trained data set on an independent test dataset. For this purpose, a new dataset is also presented together with a detailed analysis of its characteristics. The results show that the similarity of entropy distribution of annotations (i.e. bounding boxes) has the most important characteristic to achieve robust adaptation after training,
Private Location Sharing for Decentralized Routing Services

Data-driven methodologies offer many exciting upsides, but they also introduce new challenges, particularly in the realm of user privacy. Specifically, the way data is collected can pose privacy risks to end users. In many routing services, a single entity (e.g., the routing service provider) collects and manages user trajectory data. When it comes to user privacy, these systems have a central point of failure since users have to trust that this entity will not sell or use their data to infer sensitive private information. With this as motivation, we study the problem of using location data for routing services in a privacy-preserving way. Rather than having users report their location to a central operator, we present a protocol in which users participate in a decentralized and privacy-preserving computation to estimate travel times for the roads in the network in a way that no individuals' location is ever observed by any other party. The protocol uses the Laplace mechanism in conjunction with secure multi-party computation to ensure that it is cryptogrpahically secure and that its output is differentially private. The protocol is computationally efficient and does not require specialized hardware; all it needs is GPS, which is included in most mobile devices. A natural question is if privacy necessitates degradation in accuracy or system performance. We show that if a road has sufficiently high capacity, then the travel time estimated by our protocol is provably close to the ground truth travel time. We validate the protocol through numerical experiments which show that using the protocol as a routing service provides privacy guarantees with minimal overhead to user travel time.