Reviewerly/exHarmony huggingface dataset:
TinyGAN: Distilling BigGAN for Conditional Image Generation

Generative Adversarial Networks (GANs) have become a powerful approach for generative image modeling. However, GANs are notorious for their training instability, especially on large-scale, complex datasets. While the recent work of BigGAN has significantly improved the quality of image generation on ImageNet, it requires a huge model, making it hard to deploy on resource-constrained devices. To reduce the model size, we propose a black-box knowledge distillation framework for compressing GANs, which highlights a stable and efficient training process. Given BigGAN as the teacher network, we manage to train a much smaller student network to mimic its functionality, achieving competitive performance on Inception and FID scores with the generator having \(16\times \) fewer parameters. (The source code and the trained model are publicly available at https://github.com/terarachang/ACCV_TinyGAN).
Digitalization as the “new normal” of higher education

Abstract The research emphasis of the article is focused on the urgent problem of digitalization of education. Depending on the degree of development of states, the digitalization of education is uneven. In the context of the COVID 19 pandemic, digitalization has accelerated, consolidated and approved the distance learning system, while revealing a number of problems: insufficient technology, lack of appropriate software, unprofessional teaching staff, unwillingness of students to study using distance learning technologies, etc. The article highlights the main barriers to the development and promotion of digitalization of the Russian educational environment in the "new reality". The dependence of the digitalization of the Russian educational environment on internal and external factors determining the competitiveness of universities in providing their services in a digital format is determined. Analysis of the work of higher school teachers during the period of distance learning revealed some patterns. Teachers used traditional forms of work even when switching to a distance format; a significant part of communications with students was carried out through e-mail and popular training sites. Sometimes work with students was limited to issuing a mandatory task, which must be solved on the proposed portals. Some large higher educational institutions have their own specialized corporate portals through which work with students is carried out: lectures, webinars, tests and tests are held. Attention is focused on the gamification of educational activities as an effective teaching tool. A low degree of student involvement in the educational process, weak motivation and the inability to organize effective control of students’ knowledge are stated. To enhance the effect of digitalization in education, the above barriers should be removed.
Camcording-Resistant Forensic Watermarking Fallback System Using Secondary Watermark Signal

Forensic watermarking is used to track down digital pirates after they illegally redistribute video content. Although existing algorithms often resist common signal processing attacks, they are not always robust against camcording attacks. As a solution in the state of the art, registration methods are used to align the attacked video to the original one. However, watermark detection still fails when the quality is sufficiently decreased or when exposed to targeted attacks. Therefore, this paper proposes a novel fallback system that aims to detect the watermark when traditional methods fail. More concretely, we demonstrate that a primary watermark embedded by a traditional scheme indirectly creates a secondary watermark signal during video encoding. This secondary watermark consists of compression artifacts and is detected by the fallback system. Additionally, the proposed system incorporates video registration to cope with camcording attacks. The experimental results indicate that the fallback system has a striking increase in robustness compared to the existing methods. For example, the observed false-negative rate for targeted attacks improves from 100% to 0%. Moreover, the fallback is camcording resistant even when the traditional method combined with registration is not. In conclusion, the proposed system can be used as a fallback when traditional detection fails.
Solar Panel Position Control and Monitoring System For Maximum Power Generation

The research on power generation renewable energy sources are increasing In this paper the proposing automatic position control system of solar panel is introduced as the position of sun is changing through out the day, in order to maximize the generation I.e, maximizing the conversion of solar energy to electrical energy. The solar panel has to be faced towards the sun in order to get maximum solar energy. The LDR (light dependent resistors) is used as the sensor to detect the intensity of sunlight. And the main heart of this project is arduino Uno, where all the processing is done to know the position of the sun. Servo motor is used to move the solar panel based on signal received from arduino. This system is eco-friendly and is operating at low-cost. Finally the result will show the effectiveness of this system when compared to regular solar system.
Rock or Roll – Locomotion Techniques with a Handheld Spherical Device in Virtual Reality

We investigate the use of a handheld spherical object as a controller for locomotion in VR. Rotating the object controls avatar movement in two different ways: As a zero order controller, it is continuously rotated to the target position as if rolling a ball on the floor. As a first order controller, it is tilted like a joystick to determine the direction and speed of movement. We describe how our prototype was built from low-cost commercially available hardware and discuss our design decisions. Then we evaluate both locomotion techniques in a user study (N=20) and compare them to established methods using handheld VR controllers. Our prototype matched and in some cases outperformed these methods regarding task time and accuracy. All results were obtained without any usage instructions, indicating easy learnability. Some of our insights may transfer to interaction with other naturally shaped objects in VR experiences.
Completely blind image quality assessment via contourlet energy statistics

An aim of completely blind image quality assessment (BIQA) is to develop algorithms which can grade image quality without any prior knowledge of the images. Here, a new contourlet energy statistics based completely on blind opinion-unaware BIQA (OU-BIQA) method is proposed, which can predict the perceptual severity of a range of image distortion types without requiring any prior knowledge. According to the energy distribution of the contourlet sub-bands of natural images in log-domain, the lower-scale sub-band energy can be predicted by the corresponding higher-scale sub-band energies of distorted images. A quality model is then constructed by quantifying the difference between predicted energy and realistic energy. Meanwhile, an effective method for adjusting and compensating an undesired distortion is integrated into the quality model. Experimental results show that the proposed new method outperforms state-of-the-art OU-BIQA models on relevant portions of TID2013 database, and is competitive on the LIVE IQA database. Moreover, the proposed model is very fast, suggesting a real-time solution to high-performance BIQA.
High-Accuracy 3-D Sensor for Rivet Inspection Using Fringe Projection Profilometry with Texture Constraint

Riveted workpieces are widely used in manufacturing; however, current inspection sensors are mainly limited in nondestructive testing and obtaining the high-accuracy dimension automatically is difficult. We developed a 3-D sensor for rivet inspection using fringe projection profilometry (FPP) with texture constraint. We used multi-intensity high dynamic range (HDR) FPP method to address the varying reflectance of the metal surface then utilized an additional constraint calculated from the fused HDR texture to compensate for the artifacts caused by phase mixture around the stepwise edge. By combining the 2-D contours and 3-D FPP data, rivets can be easily segmented, and the edge points can be further refined for diameter measurement. We tested the performance on a sample of riveted aluminum frame and evaluated the accuracy using standard objects. Experiments show that denser 3-D data of a riveted metal workpiece can be acquired with high accuracy. Compared with the traditional FPP method, the diameter measurement accuracy can be improved by 50%.
End-to-End Learning of Social Behaviors for Humanoid Robots

Social robots should understand the user's nonverbal behavior and respond appropriately. Machine learning is one way of implementing the social intelligence. It provides the ability to automatically learn and improve from experience instead of explicitly telling the robot what to do. This paper proposes an end-to-end machine learning method to learn social behaviors for humanoid robots. We adapt sequence-to-sequence architecture consisting of two long short-term memory (LSTM) units. One is an LSTM encoder for encoding the previous sequence of human poses, and the other is an LSTM decoder for generating the next sequence of robot poses. The weights of the LSTMs are trained using human-human interaction data such as greeting and handshaking. The trained model is implemented in a humanoid robot, Pepper, to show its feasibility. Experimental results show that the robot can generate gestures appropriate to the situation and recognize subtle differences in user behavior. In addition, when a user's behavior changes, the transition to another behavior occurs naturally.
A Reverse Hash Chain Path-Based Access Control Scheme for a Connected Smart Home System

As smart home systems increasingly utilize AI technology, a user-friendly and intelligent smart home system becomes increasingly popular. However, a home generally consists of various devices with different capabilities from different vendors. Hence, it is difficult to apply high-performance security solutions to such a smart home system. Recently, although a blockchain is mentioned as an alternative, it needs a high computation resource because of the mining process of proof of work (PoW). Therefore, to apply a blockchain to a smart home system, it is necessary to address such a high resource problem. This article proposes a reverse hash chain as an alternative PoW for a blockchain-based smart home authentication. Since this proposed scheme needs small numbers of hash value computations for PoW, a user can apply a blockchain to a smart home.
Pull Request Prioritization Algorithm based on Acceptance and Response Probability

Pull requests (PRs) prioritization is one of the main challenges faced by integrators in pull-based development. This is especially true for large open-source projects where hundreds of pull requests are submitted daily. Indeed, managing these pull requests manually consumes time and resources and may lead to delays in the reaction (i.e., acceptance or response) to enhancements or bug fixes suggested in the codebase by contributors. We propose an approach, called AR-Prioritizer (Acceptance and Response based Prioritizer), integrating a PRs prioritization mechanism that considers these two aspects. The results of our study demonstrate that our approach can recommend top@5, top@10, and top@20 most likely to be accepted and responded pull requests with Mean Average Precision of 95.3%, 89.6%, and 79.6% and Average Recall of 40%, 65.7%, and 92.9%. Moreover, AR-Prioritizer has outperformed the baseline models with a statistical significance in prioritizing the most likely to be accepted and responded to PRs.
An Autoencoder-embedded Evolutionary Optimization Framework for High-dimensional Problems

Many ever-increasingly complex engineering optimization problems fall into the class of High-dimensional Expensive Problems (HEPs), where fitness evaluations are very time-consuming. It is extremely challenging and difficult to produce promising solutions in high-dimensional search space. In this paper, an Autoencoder-embedded Evolutionary Optimization (AEO) framework is proposed for the first time. As an efficient dimension reduction tool, an autoencoder is used to compress high-dimensional landscape to informative low-dimensional space. The search operation in this low-dimensional space can facilitate the population converge towards the optima more efficiently. To balance the exploration and exploitation ability during optimization, two sub-populations coevolve in a distributed fashion, where one is assisted by an autoencoder and the other undergoes a regular evolutionary process. The information between these two sub-populations are dynamically exchanged. The proposed algorithm is validated by testing several 200 dimensional benchmark functions. Compared with the state-of-art algorithms for HEPs, AEO shows extraordinarily high efficiency for these challenging problems.
The Course of Physics at the Technical University Digital Education System

The paper considers the digital education possibilities for a technical university, using the online courses on general physics course sections example. Online courses in physics contain video lectures, seminars, video demonstrations, and test tasks. All course content is available on the “Open BMSTU” distance learning platform. Current students’ knowledge assessment is carried out by testing in remote access mode. Test tasks have three levels of difficulty. Tests of the first complexity level allow you to evaluate the knowledge of physical quantities, laws, formulas definitions and assume the choice of the answer. Tests of the second complexity level contain problems that require knowledge of formulas and basic methods for solving problems. Tests of the third complexity level contain problems, the solution of which requires knowledge of formulas, logical thinking and provides for detailed problem-solving. Performing test task complexity determines the optimal ratio choice between the time taken to complete the test and the task content complexity level in the test. Regular testing allows not only to control but also to merge the information got at lectures, seminars and laboratory sessions. We carried out the statistical processing of experimental groups test results on the general physics course “Electromagnetism” topics using the digital educational environment MOODLE and EDX.
Implementation of Data Augmentation Using Convolutional Neural Network for Batik Classification

CNN has the ability to detect and recognize objects in an image, also can outperform traditional methods in computer vision and pattern recognition tasks. CNN has been implemented in many kinds of work, one of which is to do batik classification. We implemented eight kinds of data augmentation on the batik dataset using the VGG16 pre-trained model with fine-tuning methods. The dataset consists of 500 batik images divided into five classes, namely Ceplok, Kawung, Lereng, Nitik, and Parang. Batik classification by selecting data augmentation was successful in increasing the accuracy of 3.13% from 95.83% (without data augmentation) to 98.96% (by selecting data augmentation).
Pixel-wise ordinal classification for salient object grading

Driven by business intelligence applications for rating attraction of products in shops, a new problem — salient object grading is studied in this paper. In computer vision, plenty of salient object detection approaches have been proposed, while most existing studies detect objects in a binary manner: salient or not. This paper focuses on a new problem setting that requires detecting all salient objects and categorizing them into different salient levels. Based on that, a pixel-wise ordinal classification method is proposed. It consists of a multi-resolution saliency detector which detects and segments objects, an ordinal classifier which grades pixels into different salient levels, and a binary saliency enhancer which sharpens the difference between non-saliency and all other salient levels. Two new image datasets with salient level labels are constructed. Experimental results demonstrate that, on the one hand, the proposed method provides effective salient level predictions and on the other hand, offers very comparable performance with state-of-the-art salient object detection methods in the traditional problem setting.
An Ethical Multi-Stakeholder Recommender System Based on Evolutionary Multi-Objective Optimization

In this work, we propose an ethical multi-stakeholder recommender system that uses a multi-objective evolutionary algorithm to make a trade-off between provider coverage, long-tail services inclusion, and recommendation accuracy. Experimental results on real-world datasets show that the proposed method significantly improves the novelty and diversity of recommended services and the coverage of providers with minor loss of accuracy.
Daily Rainfall Prediction Using One Dimensional Convolutional Neural Networks

Rainfall is influenced by several factors, such as air temperature, humidity, and wind speed. But at this time, the weather conditions in the territory of Indonesia are increasingly uncertain, making them difficult to predict. The machine can learn the prediction of rainfall. This research proposed methods to predict rainfall within 14 days using ID Convolutional Neural Networks. Weather data were obtained from a weather observation station on Meteorological, Climatological, and Geophysical Agency (BMKG) website for ten years. First, data will be interpolated to fill in the missing value. Then the data will be segmented by overlapping and normalized to generalize the value of climate data to 0-1, including taking overlapping data to be an advantage in providing a linkage of sequential data values. Then the training and prediction process uses ID Convolutional Neural Networks. The accuracy generated using the Adam optimization model for testing data is 81.46%, and Loss is 0.0018.
Privacy-Preserving Correlated Data Publication: Privacy Analysis and Optimal Noise Design

The privacy issue in data publication is critical and has been extensively studied.Correlation is unavoidable in data publication, which universally manifests intrinsic correlations owing to social, physical, behavioral, and genetic relationships.However, most of the existing works assume that private data is independent, i.e., the correlation among data is neglected.In this paper, we investigate the privacy concern of data publication where deterministic and probabilistic correlations are considered, respectively.Specifically, (ε, δ )-multi-dimensional data-privacy (MDDP) is proposed to quantify the correlated data privacy.It characterizes the disclosure probability of the published data being jointly estimated with the correlation under a given accuracy.Then, we explore the effects of deterministic and probabilistic correlations on privacy disclosure, respectively.For both kinds of correlations, it is shown that the privacy disclosure with correlations increases compared to the one without correlation knowledge.Meanwhile, a closed-form expression of disclosure probability and a strict bound of privacy disclosure gain are derived, respectively.To minimize the disclosure probability, we provide the optimal noise distribution in the sense of (ε, δ )-MDDP.Extensive simulations on a real dataset verify our analytical results.
A Toolkit for Security Awareness Training Against Targeted Phishing

The attack landscape is evolving, and attackers are employing new techniques to launch increasingly targeted and sophisticated social engineering attacks that exploit human vulnerabilities. Many organizations provide their employees with security awareness training to counter and mitigate such threats. However, recent studies have shown that current embedded phishing training programs and tools are often ineffective or incapable of addressing modern, tailored social engineering attacks. This paper presents a toolkit for the deployment of sophisticated, tailored phishing campaigns at scale (e.g., to deploy specific training within an organization). We enable the use of highly customizable phishing email templates that can be instantiated with a large range of information about the specific target and a semi-automated process for the selection of the phishing domain name. We demonstrate our tool by showing how tailored phishing campaigns proposed in previous studies can be enhanced to increase the credibility of the phishing email, effectively addressing the very limitations identified in those studies.
Comparative analysis of heavy mineral characteristics of sediments from the Huanghe River and the Changjiang River based on the multiple-window grain size strategy

Identification of detrital heavy minerals was carried out using a multiple-window grain size strategy on 25 representative sandy-silt sediment samples from the estuaries of the Huanghe (including the abandoned Huanghe River) and Changjiang Rivers, with each sample divided into 6–9 fractions (for a total of 196 sub-samples) at 0.5Φ intervals. After characterization of the samples with respect to the abundance of each mineral species, the mean values of content were determined as 37.7% amphiboles, 23.8% Fe-metallic minerals, and 14.4% stable minerals, corresponding to 7.1% garnet, 1.8% apatite, and 1.2% zircon, in the Huanghe River samples. In comparison, the Changjiang River samples were characterized as 54.9% amphiboles, 14.1% Fe-metallic minerals, and 6.5% stable minerals, with 1.7% garnet and 3.0% sphene. The ratio of unstable to stable mineral classes (UM/SM) in the silt sediments of the Huanghe River ranged from 2.3–6.8, the value of epidote group/stable minerals was 0.7–2.3, and the ratio of garnet to sphene (Grt/Spn) ranged from 1.2–2.7. The Changjiang River's corresponding mineral ratios were 6.0–22.8, 2.3–5.2, and 0.4–1.3, respectively. The identification data reveal that the main heavy minerals and stable minerals differ substantially, suggesting that mineral profiling could be used for source identification and differentiation of sediments originating from these two rivers. The multi-particle heavy mineral method better reflects the samples' entire characteristics than single-particle analysis (3–4Φ), thus providing a more reliable end-element index for the distinction between sediments of the Huanghe and Changjiang Rivers.
Distributed embodied evolution over networks

In several network problems the optimum behavior of the agents (i.e., the nodes of the network) is not known before deployment. Furthermore, the agents might be required to adapt, i.e. change their behavior based on the environment conditions. In these scenarios, offline optimization is usually costly and inefficient, while online methods might be more suitable. In this work, we use a distributed Embodied Evolution approach to optimize spatially distributed, locally interacting agents by allowing them to exchange their behavior parameters and learn from each other to adapt to a certain task within a given environment. Our results on several test scenarios show that the local exchange of information, performed by means of crossover of behavior parameters with neighbors, allows the network to conduct the optimization process more efficiently than the cases where local interactions are not allowed, even when there are large differences on the optimal behavior parameters within each agent's neighborhood.