Reviewerly/exHarmony huggingface dataset:
Calibration of Imaging Sensor and Fiber Optic Taper-Caused Distortion in an X-Ray Intensified CMOS Camera

An integrated calibration method of the imaging sensor and the fiber optic taper-caused distortion in an intensified Complementary Metal-Oxide-Semiconductor (CMOS) camera for the X-ray capture application is proposed. Generally, four kinds of distortions need to be corrected in this system: the column noise, the inconsistent pixel intensity response of whole frame, the nonlinear intensity response of each pixel, and the geometry distortion of whole frame. To conquer these problems, first, the column noise is estimated and subtracted from the captured image. The multiple measurement data processing method and the strip noise estimation technique are used. Second, a kind of two-step correction technique of the pixel intensity response is proposed to solve middle two distortion problems. The first step builds the ideal intensity response line for all pixels in image; and the second step estimates and compensates the correction error for each pixel by a polynomial function. Third a polynomial fitting method is also utilized to correct the geometry distortion. The RANdom SAmple Consensus (RANSC) computation mechanism is used to improve the calibration precision further. The optic indices of fiber optic taper and camera are considered to assist the selection of key parameters of these calibration method above. Many experiment results have proved the correctness and effectiveness of proposed method.
YOLOv3-VD

Deep neural networks (DNNs) are currently state-of-the-art methods in many important AI tasks. However, DNNs usually contain a lot of parameters that make them prone to overfitting and slow in inference. In this paper, we apply variational dropout to sparsify YOLOv3 network for vehicle detection. We then prune redundant layers and compress the network to reduce the memory size and accelerate the inference speed of the model. Experiments show that we can eliminate up to 91% weights in the original YOLOv3 with a negligible decrease of accuracy
Blockchain-Based Cross-Organizational Integrated Platform for Issuing and Redeeming Reward Points

For the sake of enhancing repurchase rate and customer loyalty, plenty of enterprises have issued reward points. However, as there are more enterprises issuing reward points, customers become less and less interested to collect them. Even though there are companies forming a business alliance to make reward points more attractive, it is usually managed by a central leading company. In other words, there still exists a security concern for a centralized system. In this paper, we propose a blockchain-based cross-organizational integrated platform (BCOIP) for issuing and redeeming reward points. BCOIP provides customers an electronic reward point system that is easier to collect and manage and could circulate in many enterprises. Besides, since BCOIP deploys the smart contract to operate the reward point and make all point transactions stored on the permissioned blockchain, it provides more interoperability and security to enterprises and more credibility for costumers. Moreover, goods providers joining the BCOIP could not only improve the diversity of the reward market but also reach more costumers. Finally, we conduct two experiments to evaluate the efficiency and stability of the BCOIP. Results indicate that points transactions are stable regardless of the number of parallel processes and the other shows the number of nodes has nothing to do with the efficiency of this system.
Fast Distance-based Outlier Detection in Data Streams based on Micro-clusters

Continuous outlier detection in data streams is one important topic in data mining. It has many applications in public health, network intrusion detection, and fraud detection. Over the last two decades of research, many studies have been conducted on distance-based outlier detection algorithms which are viable, scalable, and parameter-free approaches. Because streaming data points arrive and expire over time, the challenge is to monitor the outlier status of data points with time and space efficiency. In this study, we propose three algorithms: O-MCOD, U-MCOD, and M-MCOD. These algorithms improve upon the state-of-the-art algorithm in distance-based outlier detection in data streams, i.e., MCOD, by relaxing the constraints of micro-clusters and using the minimal probing principal. With extensive experiments on synthetic and real-world datasets, we show that the proposed algorithms are superior in time and space efficiency. Specially, our proposed algorithms are 1.5 to 95 times faster than MCOD, require as low as 25% peak memory compared to MCOD, and outperform the most recent algorithm NETS.
Augmented Reality Applied in Astronomy Subject

Abstract Mobile augmented reality is one of the most potential technology for education. Science astronomy is one of the subject that need to develop by mobile augmented reality technology. Purpose in this research is to create mobile augmented reality based on astronomy subject in Indonesian curriculum in junior high school. Animation that has been show in this application follows Kepler’s law about planetary motion. This research is R&amp;D research and it follow instructional design type ADDIE. This researcher collected the data qualitatively from 2 experts, 2 teachers and 2 students. Each of participant will give suggestion to improve the quality of the application. Finally the application has been made with 16 marker. 11 explain about member of solar system, 3 marker about rotation and revolution of earth and moon, and 2 marker is about solar eclipse and moon eclipse. The application size is 47 Mb with Apk format. It can be use at least in android version 4.1 jelly bean and camera 13 MP. This application is ready to use for more large participant.
Adaptive Treatment Assisting System for Patients Using Machine Learning

Machine learning techniques have achieved a lot of success in many healthcare related aspects including personalized treatment, medical imaging, diagnostic systems etc. In present work, we propose a medicine reminder system which can assist patients in their treatment process at home. In case of treatment at home of patients with different physical and/or mental disabilities, a key challenge is the choice of the proper message for each specific patient taking into account his/her needs. To this aim, the proposed system can use a variety of messages (i.e audio, visual and textual) to communicate with patients taking into account their specific physical and/or mental disabilities. We apply Upper Confidence Bound method which enables the guidance system to adapt according to a patient`s mental and physical skills. To demonstrate the feasibility of the proposed treatment assisting system, a sample treatment plan are implemented in an simulated experiment. The results show that our proposed system through interactions with the patient of simulated physical and mental skills is capable of selecting right mode of message which will lead the patient to the right medicine box.
SIST: Online Scale-Adaptive Object tracking with Stepwise Insight

Correlation filter (CF) has achieved great success in the tracking community due to its high efficiency and effectiveness. However, in most existing CF trackers: (i) they mainly focus on object localization and cannot handle scale variations well; (ii) the learned CF is unavoidably deteriorated by the boundary effects, especially the background pixels inside the bounding box; (iii) there exists a localization gap between the original image and feature map. To handle these issues, we propose an online scale-adaptive correlation tracker by collaboratively learning a target-insight model (TIM) for object localization and a scale-insight model (SIM) for progressive refinement. In TIM, we introduce a target likelihood map to impose discriminative weights on the image, and then formulate a target-insight correlation filter for encoding the target as well as its surrounding context, which can handle boundary effect and distractors. Furthermore, we developed a SIM based on multiple instance learning and structured-labelled samples, which realizes the scale estimation and position refinement over time. Specifically, a hard negatives mining strategy is introduced to update the SIM rather than random sampling, which helps realize long-term tracking with re-capturing the missing target even tracking failure occurs. Extensive experiments on four benchmarks demonstrate that our method outperforms several state-of-the-art methods.
Robust Compression Technique for YOLOv3 on Real-Time Vehicle Detection

For vehicle detection, YOLOv3 has shown promising accuracy. Since the number of parameters in this network can be more than ten million parameters, it cannot be fit into a commodity camera. In this paper, we propose a compression mechanism designed specifically for YOLOv 3's network by removing unnecessary filters. Since YOLOv3 composes of two network components: backbone and pyramid networks, we propose a robust pruning mechanism to prune filters of each network separately. This can help to avoid over-pruning the network in some part of the model making our model more robust. There are two main pruning criteria investigated: Average Percentage of Zero (APoZ) and Sum Magnitude Weight. The experiment was conducted on UA-DETRAC. The results show that our compression mechanism with APoZ criterion can reduce more than 90% of the network size, while the accuracy is even higher than the full model for about 2%.
VisWall: Visual Data Exploration Using Direct Combination on Large Touch Displays

An increasing number of data visualization tools are being designed for touch-based devices ranging from smartwatches to large wall-sized displays. While most of these tools have focused on exploring novel techniques to manually specify visualizations, recent touch-based visualization systems have begun to explore interface and interaction techniques for attribute-based visualization recommendations as a way to aid users (particularly novices) during data exploration. Advancing this line of work, we present a visualization system, VisWall, that enables visual data exploration in both single user and co-located collaborative settings on large touch displays. Coupling the concepts of direct combination and derivable visualizations, VisWall enables rapid construction of multivariate visualizations using attributes of previously created visualizations. By blending visualization recommendations and naturalistic interactions, VisWall seeks to help users visually explore their data by allowing them to focus more on aspects of the data (particularly, data attributes) rather than specifying and reconfiguring visualizations. We discuss the design, interaction techniques, and operations employed by VisWall along with a scenario of how these can be used to facilitate various tasks during visual data exploration.
A CNN based invisible QR code generator for human living space

In this paper, the authors aim at embedding and restoring arbitrary information in an arbitrary image using CNN. In order to achieve this objective, we propose a model composed of two CNNs of different roles. In the proposed method, the QR code is used as the medium of information to be embedded. Thanks to the error correction nature of QR code, embedded information is expected to be restored without errors. We hope to use this technology to integrate QR codes into human living space, thereby enabling us to hide arbitrary information without damaging the atmosphere of it. The proposed CNN model was trained to test whether it is possible to embed a QR code image in a color image. As a result, it was possible to embed the QR code without deteriorating the image quality of the input color image. However, it has been found that embedding a QR code into a sharp color image can not restore the QR code correctly. From this result, it turns out that the proposed method can only adapt to blurry images at present, and we will aim to solve this problem in future research.
Pattern Matching with Variables

A pattern ɑ (i.e., a string of variables and terminals) matches a word w , if w can be obtained by uniformly replacing the variables of ɑ by terminal words. The respective matching problem, i.e., deciding whether or not a given pattern matches a given word, is generally NP-complete, but can be solved in polynomial-time for restricted classes of patterns. We present efficient algorithms for the matching problem with respect to patterns with a bounded number of repeated variables and patterns with a structural restriction on the order of variables. Furthermore, we show that it is NP-complete to decide, for a given number k and a word w , whether w can be factorised into k distinct factors. As an immediate consequence of this hardness result, the injective version (i.e., different variables are replaced by different words) of the matching problem is NP-complete even for very restricted classes of patterns.
Craquelure as a Graph: Application of Image Processing and Graph Neural Networks to the Description of Fracture Patterns

Cracks on a painting is not a defect but an inimitable signature of an artwork which can be used for origin examination, aging monitoring, damage identification, and even forgery detection. This work presents the development of a new methodology and corresponding toolbox for the extraction and characterization of information from an image of a craquelure pattern. The proposed approach processes craquelure network as a graph. The graph representation captures the network structure via mutual organization of junctions and fractures. Furthermore, it is invariant to any geometrical distortions. At the same time, our tool extracts the properties of each node and edge individually, which allows to characterize the pattern statistically. We illustrate benefits from the graph representation and statistical features individually using novel Graph Neural Network and hand-crafted descriptors correspondingly. However, we also show that the best performance is achieved when both techniques are merged into one framework. We perform experiments on the dataset for paintings' origin classification and demonstrate that our approach outperforms existing techniques by a large margin.
Less is More: From Multi-objective to Mono-objective Refactoring via Developer's Knowledge Extraction

Refactoring studies either aggregated quality metrics to evaluate possible code changes or treated them separately to find trade-offs. For the first category of work, it is challenging to define upfront the weights for the quality objectives since developers are not able to express them upfront. For the second category of work, the number of possible trade-offs between quality objectives is large which makes developers reluctant to look at many refactoring solutions. In this paper, we propose, for the first time, a way to convert multi-objective search into a mono-objective one after interacting with the developer to identify a good refactoring solution based on his preferences. The first step consists of using a multi-objective search to generate different possible refactoring strategies by finding a trade-off between several conflicting quality attributes. Then, an unsupervised learning algorithm clusters the different trade-off solutions, called the Pareto front, to guide the developers in selecting their region of interests and to reduce the number of refactoring options to explore. Finally, the extracted preferences from the developer are used to transform the multi-objective search into a mono-objective one by taking the preferred cluster of the Pareto front as the initial population for the mono-objective search and generating an evaluation function based on the weights that are automatically computed from the position of the cluster in the Pareto front. Thus, the developer will just interact with only one refactoring solution generated by the mono-objective search. We selected 32 participants to manually evaluate the effectiveness of our tool on 7 open source projects and one industrial project. The results show that the recommended refactorings are more accurate than the current state of the art.
An Improved Arnold Image Scrambling Algorithm

Abstract In recent years, with the rapid development of the Internet, our life has undergone tremendous changes, and the Internet has become an indispensable part of us. With the popularity of the Internet, more and more people use the Internet to transmit multimedia information such as images [1]. Studies have shown that about 75% of human information from the outside world comes from the visual system, that is, from images. Image is not unfamiliar to us. However, in recent years, the security of image information in the transmission process has become increasingly prominent. Such image information as cases, design drawings and so on need to be encrypted when they are transmitted on the Internet. Scrambling technology is an image encryption technology. There are many commonly used image scrambling algorithms. This paper first introduces the image scrambling algorithm based on Arnold transform. Then, in order to improve the image scrambling performance, the application of Arnold transform matrix in image scrambling is analyzed. On this basis, a double scrambling image encryption algorithm based on two-dimensional Arnold transform is proposed. This algorithm uses two-dimensional Arnold transform at the same time. Gray scale and position scrambling [3]. The simulation results show that the improved algorithm can achieve better scrambling effect and encryption effect, which is conducive to the study of image encryption.
Predictive Resource Allocation with Interference Coordination by Deep Learning

Predictive resource allocation with interference coordination can provide high throughput for densely deployed cellular network by harnessing future average channel gains of mobile users in a minute-long time window. However, except for the necessity of learning the future information, finding the optimal policy incurs prohibitive computational complexity. In this paper, we resort to deep neural networks (DNNs)to learn the optimal policy for heterogeneous networks. To show the potential of the DNN-based solution, we first assume that future information in the prediction window is known perfectly. For the considered problem of coordinating interference by muting base stations and user association, we find that there exists dataset bias issue, where the optimal policy contains much more “0” than “1”. To deal with this issue, we design a DNN to learn the intermediate result of the original optimization problem. Then, we design a user association algorithm. We proceed to extend the designed DNN, such that the DNN can learn from the historical data in an end-to-end manner. Simulation results show that the proposed DNN-based solution performs closely to the numerically-obtained optimal solution with much lower computational complexity, even when the input of the DNN is the historical data.
Adversarial Attack Defense Based on the Deep Image Prior Network

Several recent studies have shown that artificial intelligence (AI) systems can be malfunctioned by deliberately crafted data entering through the normal route. For example, a well-crafted sticker attached on a traffic sign can lead a self-driving car to misinterpret the meaning of a traffic sign from its original one. Such deliberately crafted data which cause the AI system to misjudge are called adversarial examples. The problem is that current AI systems are not stable enough to defend adversarial examples when an attacker uses them as means to attack an AI system. Therefore, nowadays, many researches on detecting and removing adversarial examples are under way. In this paper, we proposed the use of the deep image prior (DIP) as a defense method against adversarial examples using only the adversarial noisy image. This is in contrast with other neural network based adversarial noise removal methods where many adversarial noisy and true images have to be used for the training of the neural network. Experimental results show the validness of the proposed approach.
A Revocable Group Signature Scheme with Scalability from Simple Assumptions

Group signatures are signatures providing signer anonymity where signers can produce signatures on behalf of the group that they belong to. Although such anonymity is quite attractive considering privacy issues, it is not trivial to check whether a signer has been revoked or not. Thus, how to revoke the rights of signers is one of the major topics in the research on group signatures. In particular, scalability, where the signing and verification costs and the signature size are constant in terms of the number of signers N, and other costs regarding signers are at most logarithmic in N, is quite important. In this paper, we propose a revocable group signature scheme which is currently more efficient compared to previous all scalable schemes. Moreover, our revocable group signature scheme is secure under simple assumptions (in the random oracle model), whereas all scalable schemes are secure under q-type assumptions. We implemented our scheme by employing a Barreto-Lynn-Scott curve of embedding degree 12 over a 455-bit prime field (BLS-12-455), and a Barreto-Naehrig curve of embedding degree 12 over a 382-bit prime field (BN-12-382), respectively, by using the RELIC library. We showed that the online running times of our signing algorithm were approximately 14msec (BLS-12-455) and 11msec (BN-12-382), and those of our verification algorithm were approximately 20msec (BLS-12-455) and 16msec (BN-12-382), respectively. Finally, we showed that our scheme (with a slight extension) is applied to an identity management system proposed by Isshiki et al.
Attribute-Aware Loss Function for Accurate Semantic Segmentation Considering the Pedestrian Orientations

Numerous applications such as autonomous driving, satellite imagery sensing, and biomedical imaging use computer vision as an important tool for perception tasks. For Intelligent Transportation Systems (ITS), it is required to precisely recognize and locate scenes in sensor data. Semantic segmentation is one of computer vision methods intended to perform such tasks. However, the existing semantic segmentation tasks label each pixel with a single object's class. Recognizing object attributes, e.g., pedestrian orientation, will be more informative and help for a better scene understanding. Thus, we propose a method to perform semantic segmentation with pedestrian attribute recognition simultaneously. We introduce an attribute-aware loss function that can be applied to an arbitrary base model. Furthermore, a re-annotation to the existing Cityscapes dataset enriches the ground-truth labels by annotating the attributes of pedestrian orientation. We implement the proposed method and compare the experimental results with others. The attribute-aware semantic segmentation shows the ability to outperform baseline methods both in the traditional object segmentation task and the expanded attribute detection task.
Multimedia Image Retrieval System by Combining CNN With Handcraft Features in Three Different Similarity Measures

The authors propose WNAHVF, a combined weighted and normalized AlexNet with handcrafted visual features for extracting features from images and using those vectors for image retrieval and classification. The authors test the WNAHVF method on two general datasets, Corel-1k and Corel-10k, and one medical dataset. The outcomes demonstrate combining Bag of Features and Local Neighbor patterns with AlexNet enhances the accuracy and gives better results in general and medical image datasets in retrieval and classification problems. This algorithm gives results that are superior to existing strategies.