Reviewerly/exHarmony huggingface dataset:
Analysis of Quality Website Content and Twitter Content Quality as Public Service Tools at the Local Level in DKI Jakarta Province, Indonesia

Currently in Industry 4.0, in creating good governance, the government must optimize and guarantee the quality of the website to create services that are truly for the community. Social media (Twitter) is also an important medium for public information. This study aims to see how the quality of local government websites is in applying the principles of transparent, accountable, effective, and efficient governance as well as the cost features available on the official government website and Twitter activities and issues that are often discussed. The research was conducted by a survey and observation on six district/city government websites in DKI Jakarta Province. The data was collected by browsing the district/city government websites. Measuring website performance using tools provided by Google, namely Page Speed Insights, website loading speed is rated on a percent scale. The research results reveal that the quality of the websites of each district/city government varies according to the aspects of Features, Transparency, Accountability, Effectiveness and Efficiency. Moreover, official Twitter account of the local government is one of the social media that greatly benefits from the government in coordinating discussing sharing issues and as a means of public information. Ensuring the quality of the website and using Twitter is very important for the welfare of the community.
The Visualization System of Image Search Base on Convolutional Spherical Self Organizing Map Implemented Using WebGL

Recently, many digital devices from small IoT devices to large systems of computers create a huge number of data. To help the understanding by human, it is important to visualize the relations among the data. As one of a method, Convolutional Spherical Self Organizing Map (Convolutional Spherical SOM) is proposed in this research. Additionally, Javascript and WebGL are used to implement the 3D drawing and manipulation of Spherical SOM, thus it becomes possible to execute on many devices using Web browsers. The effectiveness of this method is confirmed by implementing a system that can visualize the relations among the images searched from the Internet on Spherical SOM.Dozono, Hiroshi Toyozumi, Kenta Yoshioka, Koki Niina, Gen
Student-Teacher Oneness: A Storage-efficient approach that improves facial expression recognition

We present Student-Teacher Oneness (STO), a simple but effective approach for online knowledge distillation improves facial expression recognition, without introducing any extra model parameters. Stochastic sub-networks are designed to replace the multi-branch architecture component in current online distillation methods. This leads to a simplified architecture, and yet competitive performances. Under the "teacher-student" framework, we construct both teacher and student within the same target network. Student network is the sub-networks which randomly skipping some portions of the full (target) network. The teacher network is the full network, can be considered as the ensemble of all possible student networks. The training process is performed in a closed-loop: (1) Forward prediction contains two passes that generate student and teacher predictions. (2) Backward distillation allows knowledge transfer from the teacher back to students. Comprehensive evaluations show that STO improves the generalization ability of a variety of deep neural networks to a significant margin. The results prove our superior performance in facial expression recognition task on FER-2013 and RAF.
Underwater image super-resolution using SRCNN

In recent years, energy minerals have become more important due to the rapid industrialization worldwide. Due to the rapid industrialization on a global scale, there is a shortage of mineral resources, and there are more opportunities to rely on alternative energy sources. Therefore, the exploration of marine resources, which are abundant in the ocean, is being promoted. However, it is dangerous and impractical for humans to dive and search for marine resources by hand. Therefore, it is possible to proceed with underwater exploration safely by having a robot do the work instead. Robots have been used as a mainstream search tool in the underwater environment due to the existence of various hazardous environmental conditions. However, there are several problems associated with robot control in underwater environments, one of which is poor visibility in the water. One of the problems is the poor visibility in the water. To improve the visibility in the water, we are trying to increase the resolution of underwater images by using super-resolution technology. In this paper, we conduct experiments using SRCNN, which is a basic super-resolution technique for underwater images. In addition, we investigate the effectiveness of "Mish", which has been attracting attention in recent years for its potential to surpass the performance of "ReLU", although "ReLU" is a typical activation function of neural networks, on SRCNN.
WakaVT: A Sequential Variational Transformer for Waka Generation

Poetry generation has long been a challenge for artificial intelligence. In the scope of Japanese poetry generation, many researchers have paid attention to Haiku generation, but few have focused on Waka generation. To further explore the creative potential of natural language generation systems in Japanese poetry creation, we propose a novel Waka generation model, WakaVT, which automatically produces Waka poems given user-specified keywords. Firstly, an additive mask-based approach is presented to satisfy the form constraint. Secondly, the structures of Transformer and variational autoencoder are integrated to enhance the quality of generated content. Specifically, to obtain novelty and diversity, WakaVT employs a sequence of latent variables, which effectively captures word-level variability in Waka data. To improve linguistic quality in terms of fluency, coherence, and meaningfulness, we further propose the fused multilevel self-attention mechanism, which properly models the hierarchical linguistic structure of Waka. To the best of our knowledge, we are the first to investigate Waka generation with models based on Transformer and/or variational autoencoder. Both objective and subjective evaluation results demonstrate that our model outperforms baselines significantly.
Towards an Advanced Integrated Security Framework for Cloud Computing

Cloud computing (CC) has become a technological change enabling amongst other small and medium enterprises (SMEs) in obtaining access to emerging ICTs at lower costs. The services that a user requires are often located remotely. Security, therefore, becomes critical as research indicates there have been several security failures, vulnerabilities and threats to cloud security. The literature demonstrates that the present cloud security frameworks are often unable to detect attacks to cloud computing resources. In this paper, we develop an enhanced cloud security framework coined AIC2S which employs the strengths of popular frameworks and further integrates aspects of virtualisation, autonomic computing security, and cloud forensics. These enable the framework to self-detect, isolate and investigate security breaches to cloud services, stored data, as well as the cloud hypervisor.
Flexible Bayesian Inference by Weight Transfer for Robust Deep Neural Networks

Adversarial attacks are viewed as a danger to Deep Neural Networks (DNNs), which reveal a weakness of deep learning models in security-critical applications. Recent findings have been presented adversarial training as an outstanding defense method against adversaries. Nonetheless, adversarial training is a challenge with respect to big datasets and large networks. It is believed that, unless making DNN architectures larger, DNNs would be hard to strengthen the robustness to adversarial examples. In order to avoid iteratively adversarial training, our algorithm is Bayes without Bayesian Learning (BwoBL) that performs the ensemble inference to improve the robustness. As an application of transfer learning, we use learned parameters of pretrained DNNs to build Bayesian Neural Networks (BNNs) and focus on Bayesian inference without costing Bayesian learning. In comparison with no adversarial training, our method is more robust than activation functions designed to enhance adversarial robustness. Moreover, BwoBL can easily integrate into any pretrained DNN, not only Convolutional Neural Networks (CNNs) but also other DNNs, such as Self-Attention Networks (SANs) that outperform convolutional counterparts. BwoBL is also convenient to apply to scaling networks, e.g., ResNet and EfficientNet, with better performance. Especially, our algorithm employs a variety of DNN architectures to construct BNNs against a diversity of adversarial attacks on a large-scale dataset. In particular, under l∞ norm PGD attack of pixel perturbation ε=4/255 with 100 iterations on ImageNet, our proposal in ResNets, SANs, and EfficientNets increase by 58.18% top-5 accuracy on average, which are combined with naturally pretrained ResNets, SANs, and EfficientNets. This enhancement is 62.26% on average below l2 norm C&W attack. The combination of our proposed method with pretrained EfficientNets on both natural and adversarial images (EfficientNet-ADV) drastically boosts the robustness resisting PGD and C&W attacks without additional training. Our EfficientNet-ADV-B7 achieves the cutting-edge top-5 accuracy, which is 92.14% and 94.20% on adversarial ImageNet generated by powerful PGD and C&W attacks, respectively.
End-to-end dehazing of traffic sign images using reformulated atmospheric scattering model

As an advanced machine vision task, traffic sign recognition is of great significance to the safe driving of autonomous vehicles. Haze has seriously affected the performance of traffic sign recognition. This paper proposes a dehazing network, including multi-scale residual blocks, which significantly affects the recognition of traffic signs in hazy weather. First, we introduce the idea of residual learning, design the end-to-end multi-scale feature information fusion method. Secondly, the study used subjective visual effects and objective evaluation metrics such as Visibility Index (VI) and Realness Index (RI) based on the characteristics of the real-world environment to compare various traditional dehazing and deep learning dehazing method with good performance. Finally, this paper combines image dehazing and traffic sign recognition, using the algorithm of this paper to dehaze the traffic sign images under real-world hazy weather. The experiments show that the algorithm in this paper can improve the performance of traffic sign recognition in hazy weather and fulfil the requirements of real-time image processing. It also proves the effectiveness of the reformulated atmospheric scattering model for the dehazing of traffic sign images.
Open practices in our science and our courtrooms

Advocates of transparency in science often point to the benefits of open practices for the scientific process. Here, we focus on a possibly underappreciated effect of standards for transparency: their influence on non-scientific decisions. As a case study, we consider the current state of probabilistic genotyping software in forensics.
A Deep Cross-Modal Hashing Technique for Large-Scale SAR and VHR Image Retrieval

Cross-modal hashing is an important technology for large-scale very high resolution (VHR) and synthetic-aperture radar (SAR) image retrieval. Current cross-modal hashing methods fail to effectively preserve the intra-class similarities and the inter-class discriminations between VHR and SAR images when learning common semantic representation of these cross-modal images. This is because these methods use derived signals to implicitly guide hashing learning, which leads to low discrimination of generated hash codes. To address the drawback, this paper proposes an explicit semantic preserving-based deep hashing method, which can fully learn the intra-class and inter-class semantic structure. Specifically, we design a novel objective function to explicitly preserve the intra-class and inter-class semantic structure directly with class labels. Extensive experiments on a VHR-SAR dataset demonstrate that our method outperforms various state-of-the-art cross-modal hashing methods.
Development of Digital Comic Technology Applications Design to Increase Children's Literature Reading Interest in Elementary School

Comics are favorite reading for elementary school students. Interesting digital comics are suitable for learning media. This study aims to develop and utilize the application technology of making comics media on children's literature material to increase the reading interest of elementary school students. The approach used is a type of development research with the ADDIE model (Analysis, Design, Development, Implementation, and Evaluation). The data obtained consisted of qualitative and quantitative data. Qualitative data in the form of suggestions and responses, and input from expert and user assessments through questionnaires. Quantitative data in the form of percentage values from expert assessment questionnaires prepared on a multilevel scale. Data collection techniques through observation, interviews, questionnaires, and tests. Trial for students to get learning outcomes above the minimum criterion value. These results indicate that comics learning media digital declared very feasible and able to increase students' reading interest to be implemented in learning.
A Visualization Method for Training Data Comparison

With the diversification of machine learning applications, the quality verification and comparison of training data has been an important process. For example, while performing transfer learning, verification the difference in the quality between the source and the target data can prevent the accuracy of the model from deteriorating. However, training datasets for deep learning is getting larger and larger, and analysis of such datasets is not always easy. As a solution to this problem, we are working on the visualization for training data validation. In this study, we apply dimensionality reduction to the training datasets and display them as scatterplots to realize a visual analysis that can easily detect differences in the quality. Our current implementation draws the regions where the points are concentrated as semitransparent polygons for each label in the scatterplot. Also, the implementation provides a slider to set a threshold for the interactive adjustment of polygon generation. This allows us to observe the differences in the distribution of labels among the training data.
Alleviating the independence assumptions of averaged one-dependence estimators by model weighting

Of numerous proposals to refine naive Bayes by weakening its attribute independence assumption, averaged one-dependence estimators (AODE) has been shown to be able to achieve significantly higher classification accuracy at a moderate cost in classification efficiency. However, all one-dependence estimators (ODEs) in AODE have the same weights and are treated equally. To address this issue, model weighting, which assigns discriminate weights to ODEs and then linearly combine their probability estimates, has been proved to be an efficient and effective approach. Most information-theoretic weighting metrics, including mutual information, Kullback-Leibler measure and the information gain, place more emphasis on the correlation between root attribute (value) and class variable. We argue that the topology of each ODE can be divided into a set of local directed acyclic graphs (DAGs) based on the independence assumption, and multivariate mutual information is introduced to measure the extent to which the DAGs fit data. Based on this premise, in this study we propose a novel weighted AODE algorithm, called AWODE, that adaptively selects weights to alleviate the independence assumption and make the learned probability distribution fit the instance. The proposed approach is validated on 40 benchmark datasets from UCI machine learning repository. The experimental results reveal that, AWODE achieves bias-variance trade-off and is a competitive alternative to single-model Bayesian learners (such as TAN and KDB) and other weighted AODEs (such as WAODE).
Faithful Simulation of Distributed Quantum Measurements With Applications in Distributed Rate-Distortion Theory

We consider the task of faithfully simulating a distributed quantum measurement, wherein we provide a protocol for the three parties, Alice, Bob and Charlie, to simulate a repeated action of a distributed quantum measurement using a pair of non-product approximating measurements by Alice and Bob, followed by a stochastic mapping at Charlie. The objective of the protocol is to utilize minimum resources, in terms of classical bits needed by Alice and Bob to communicate their measurement outcomes to Charlie, and the common randomness shared among the three parties, while faithfully simulating independent repeated instances of the original measurement. To achieve this, we develop a mutual covering lemma and a technique for random binning of distributed quantum measurements, and, in turn, characterize a set of sufficient communication and common randomness rates required for asymptotic simulatability in terms of single-letter quantum information quantities. In the special case, where the Charlie&#x2019;s action is restricted to a deterministic mapping, we develop a one-shot performance characterization of the distributed faithful simulation problem. Furthermore, using these results we address a distributed quantum rate-distortion problem, where we characterize the achievable rate distortion region through a single-letter inner bound. Finally, via a technique of single-letterization of multi-letter quantum information quantities, we provide an outer bound for the rate-distortion region.
An Improvement in Multi-query Person Re-identification Using Cumulative Distance Aggregation

Re-identification of humans is a challenging task due to the unmonitored environment causing illumination variation, pose variation, inter/intra-class variation, occlusion, etc. These elements cause inaccurate features extraction which discriminates the images of individual identities. Hence aggregating the features of multiple images (multi-queries) proves to be superior in re-identification to using a single image. In this paper, we proposed a novel approach to improve the performance of multi-query person re-identification called cumulative distance aggregation. Our hypothesis is that different query images captured in an unsupervised environment have non-identical hard-negatives and hard positives. Cumulative distance of multi-queries increases for non-identical hard-negatives and/or decreases for hard positives. Traditional methods for multi-query person re-identification consider only one probe’s feature based on its pooling strategy (mean or max) and discard the information contained by the features of other probe images, while distance aggregation considers all information contained by each feature of every individual probe to calculate the cumulative distance. We have evaluated our purposed multi-query method on both Market1501 and MARS datasets which have a multi-query strategy. Due to the lack of datasets with a multi-query setting, we further evaluated the proposed multi-query person re-identification on CUHK03 and DukeMTMC to validate the versatility of our proposed method. Evaluation metrics show that our proposed multi-query distance aggregation method outperforms the traditional multi-query feature pooling both in rank-1 and mAP.
Local File Inclusion Vulnerability Scanner with Tor Proxy

Web applications have made communication and services for users extremely simple because of the user-friendly interface, global accessibility, and ease of management. However, careless web application design and implementation are crucial to a security compromise that is incredibly troubling both to the user and web administrators. The weakness in Local File Inclusion (LFI) currently exists in many web applications that result in remote code execution in a host server. Hence, detecting the vulnerability of LFI is becoming extremely important to the web owner in taking effective risk mitigation action. Meanwhile, the current vulnerability scanner that is available nowadays focuses more on SQL injection and cross site scripting but fewer over Local File Inclusion vulnerability. Other than that, users cannot observe what sort of sensitive file or data could be obtained by an attacker and maintain the anonymity of the user because current Vulnerability scanner on the market does not integrate with TOR network out-of-the-box. This project proposed an automated system for the identification of LFI vulnerabilities with obscure for web applications. Therefore, the objective of this project is to develop a system that can detect LFI vulnerabilities within the web application and while still able to maintain user anonymity across the network by covering the source IP address of the scanner using the Tor network and simulates how a real-world hacker attacks web application using LFI vulnerability. Furthermore, there are six phases involved in the methodology to complete this project: information gathering, requirement analysis, system design, development, testing, and documentation. Lastly for documentation, is to make a report about Local File Inclusion Vulnerability Scanner with Tor Onion Router Proxy. From the result testing, it indicates that the project can identify any local file inclusion vulnerabilities that exist over the web application while also having the advantage to observe the point of view of an attacker capable of hiding the scanner source of IP address.
Temporal Feature Networks for CNN based Object Detection

For reliable environment perception, the use of temporal information is essential in some situations. Especially for object detection, sometimes a situation can only be understood in the right perspective through temporal information. Since image-based object detectors are currently based almost exclusively on CNN architectures, an extension of their feature extraction with temporal features seems promising. Within this work we investigate different architectural components for a CNN-based temporal information extraction. We present a Temporal Feature Network which is based on the insights gained from our architectural investigations. This network is trained from scratch without any ImageNet information based pre-training as these images are not available with temporal information. The object detector based on this network is evaluated against the non-temporal counterpart as baseline and achieves competitive results in an evaluation on the KITTI object detection dataset.