Reviewerly/exHarmony huggingface dataset:
Efficient L-Diversity Algorithm for Preserving Privacy of Dynamically Published Datasets

Although most conventional methods of preserving data privacy focus on static datasets, which remain unchanged after processing, real-world datasets may be dynamically modified often.Therefore, privacy-preservation methods must maintain data privacy after dataset modification.Re-anonymization of entire datasets is inefficient when large datasets are frequently modified.Although several previous studies have addressed data privacy for incremental data updates (i.e., record insertions), they have not adequately it for dynamic changes made to existing datasets (i.e., record updates and deletions).Therefore, we identified limitations of data-privacy preservation for dynamically evolving datasets and used anatomy instead of generalization and suppression to develop a more efficient l-diversity algorithm for preserving privacy of such datasets.We also used a Cuckoo filter, a new probabilistic data structure for approximate set-membership tests, to improve data-processing efficiency.Experimental results demonstrated that our proposed data-anonymization algorithm processed data more efficiently than other conventional algorithms, requiring much less running time than conventional re-anonymization of entire datasets.The Cuckoo-filtered algorithm was especially efficient, dramatically reducing operation execution times while maintaining privacy of dynamically evolving datasets.
Recurrent convolutional video captioning with global and local attention

Video captioning with encoder–decoder structures has been extensively studied in the recent literature, where a great deal of work focuses on multimodal features and attention mechanisms. Most of the previous work uses only the global temporal features, such as image, motion, and audio features, and ignores the local semantic features existing extensively in the video data. Furthermore, it is difficult to fully utilize the local features due to frame-to-frame redundancy. In this paper, we propose to combine global temporal features and local object-based features in a complementary way to develop a multimodal attention mechanism (global–local attention mechanism). Based on this attention mechanism, we introduce a novel video captioning method, Recurrent Convolutional Video Captioning with Global and Local Attention (RCGL). Further, both LSTM and 1D CNN are incorporated into the decoder to improve the long-range dependency. The experimental results on two standard datasets, MSVD and MSR-VTT, demonstrate that RCGL outperforms the state-of-the-art in four common metrics.
Real-Time Stereo Matching Network with High Accuracy

In this paper, we present a novel stereo matching network aimed at real-time with high accuracy. Current deep architectures form a massive cost volume in order to leverage global context information. However, forming the cost volume is time consuming and it acts as a bottleneck of the network. We form the smaller cost volume than previously used for speed gain. However, the down-scaled cost volume leads to accuracy degradation at areas of thin structures and homogeneous regions. To overcome this limitation, we use focal loss that handles hard negative examples. Moreover, we ease multi-modal distribution problem by using top-k argmin operation when regressing disparity. We call our proposed network RT-SNet, which runs over 40 FPS on color stereo images using NVIDIA Tesla P100. We evaluate our proposed network on KITTI 2015 dataset, experimental results show that RTSNet outperforms other networks with similar runtime.
Fit-me: Image-Based Virtual Try-on With Arbitrary Poses

The image-based virtual try-on system has raised research attention recently, but it still requires to upload an image of a user with the target pose. We present a novel learning model, Fit-Me network, to seamlessly fit in-shop clothing into a person image and simultaneously transform the pose of the person image to another given one. The proposed Fit-Me network helps users not only save the time used to change clothes physically but also provide comprehensive information about how suitable the clothes are. By facilitating the arbitrary pose transformation, we can generate consecutive poses to help users get more information for deciding whether to buy the clothes or not from different aspects.
Adapting TPC-C benchmark to measure performance of multi-document transactions in MongoDB

MongoDB is a popular distributed database that supports replication, horizontal partitioning (sharding), a flexible document schema and ACID guarantees on the document level. While it is generally grouped with "NoSQL" databases, MongoDB provides many features similar to those of traditional RDBMS such as secondary indexes, an ad hoc query language, support for complex aggregations, and new as of version 4.0 multi-statement, multi-document ACID transactions. We looked for a well understood OLTP workload benchmark to use in our own system performance test suite to establish a baseline of transaction performance to enable flagging performance regressions, as well as improvements as we continue to add new functionality. While there exist many published and widely used benchmarks for RDBMS OLTP workloads, there are none specifically for document databases. This paper describes the process of adapting an existing traditional RDBMS benchmark to MongoDB query language and transaction semantics to allow measuring transaction performance. We chose to adapt the TPC-C benchmark even though it assumes a relational database schema and SQL, hence extensive changes had to be made to stay consistent with MongoDB best practices. Our goal did not include creating official TPC-C certifiable results, however, every attempt was made to stay consistent with the spirit of the original benchmark specification as well as to be compliant to all specification requirements where possible. We discovered that following best practices for document schema design achieves better performance than using required normalized schema. All the source code used and validation scripts are published in github to allow the reader to recreate and verify our results.
Scalable Facial Image Compression with Deep Feature Reconstruction

In this paper, we propose a scalable image compression scheme, including the base layer for feature representation and enhancement layer for texture representation. More specifically, the base layer is designed as the deep learning feature for analysis purpose, and it can also be converted to the fine structure with deep feature reconstruction. The enhancement layer, which serves to compress the residuals between the input image and the signals generated from the base layer, aims to faithfully reconstruct the input texture. The proposed scheme can feasibly inherit the advantages of both compress-then-analyze and analyze-then-compress schemes in surveillance applications. The performance of this framework is validated with facial images, and the conducted experiments provide useful evidences to show that the proposed framework can achieve better rate-accuracy and rate-distortion performance over conventional image compression schemes.
Efficient Privacy-Preserving Certificateless Provable Data Possession Scheme for Cloud Storage

Cloud computing is a burgeoning paradigm that offers reliable and adaptive infrastructure to the data owner who shares his data with the data user through the cloud server.In the data sharing process, the integrity of the data stored in the cloud faces serious threats.The provable data possession schemes ensure that a cloud server provider proves to a third party auditor that it is truthfully storing data from the data owner.However, the PKI-based provable data possession scheme suffers the management issue of the certificates and the identity-based provable data possession scheme causes an inherent key escrow problem.In this paper, we put forward an efficient privacy-preserving certificateless provable data possession scheme based on certificateless cryptography and elliptic curve cryptography, which has been proved to be unforgeable against adaptive chosen message attacks in the random oracle model.At the same time, the data integrity can be audited by the third party auditor without downloading the whole data.The proposed scheme gives away no information of the stored data to the third party auditor during the audit phase and the data owner's identity privacy is protected.The experiment results show that the proposed scheme is of efficiency and feasibility as far as computation and communication costs are concerned.
Semantic Segmentation on Automotive Radar Maps

As radar sensors can measure an object's range and velocity with a high degree of precision, moving objects can be successfully classified, as well. Classifying stationary objects still needs a lot of research, however. In this paper, we use popular semantic segmentation networks in order to classify the vehicle's immediate infrastructure. To this end, a full 3D measurement is performed with a test vehicle equipped with four high resolution corner radar sensors. A preprocessed point cloud is transformed into various radar maps for input to a neural network. Simulations as well as real-world measurements show an overall intersection over union of 84 and 77%, respectively, as well as an overall accuracy of 95 and 90%, respectively, being a new benchmark for this young research field.
Detecting Anomalous Driving Behavior using Neural Networks

The ability to robustly detect abnormal driving behavior has the potential to limit traffic accidents and save many lives. Abnormal driving behavior that threatens road safety includes aggressive, anxious, nervous, and unstable driving. Any of these can lead to dangerous situations in traffic. Therefore, we aim to provide a robust mechanism to detect such abnormal driving behavior. In this paper, we present our work in this regard which focuses on neural networks-based anomaly detection approaches. We consider autoencoder replicator neural networks and long short-term memory networks; comparing them to a previously employed Isolation Forest. We show that introducing a post-processing approach, that takes into account the recent history of a vehicle, reliable anomaly detection for driving behavior can be achieved based on the recurrent neural network. Its performance is well suited for application in a large scale detection system for driver assistance or autonomous vehicles.
Scalable logo detection by self co-learning

Existing logo detection methods usually consider a small number of logo classes, limited images per class and assume fine-gained object bounding box annotations. This limits their scalability to real-world dynamic applications. In this work, we tackle these challenges by exploring a web data learning principle without the need for exhaustive manual labelling. Specifically, we propose a novel incremental learning approach, called Scalable Logo Self-co-Learning (SL2), capable of automatically self-discovering informative training images from noisy web data for progressively improving model capability in a cross-model co-learning manner. Moreover, we introduce a very large (2,190,757 images of 194 logo classes) logo dataset “WebLogo-2M” by designing an automatic data collection and processing method. Extensive comparative evaluations demonstrate the superiority of SL2 over the state-of-the-art strongly and weakly supervised detection models and contemporary web data learning approaches.
Disam: Density Independent and Scale Aware Model for Crowd Counting and Localization

People counting in high density crowds is emerging as a new frontier in crowd video surveillance. Crowd counting in high density crowds encounters many challenges, such as severe occlusions, few pixels per head, and large variations in person's head sizes. In this paper, we propose a novel Density Independent and Scale Aware model (DISAM), which works as a head detector and takes into account the scale variations of heads in images. Our model is based on the intuition that head is the only visible part in high density crowds. In order to deal with different scales, unlike off-the-shelf Convolutional Neural Network (CNN) based object detectors which use general object proposals as inputs to CNN, we generate scale aware head proposals based on scale map. Scale aware proposals are then fed to the CNN and it renders a response matrix consisting of probabilities of heads. We then explore non-maximal suppression to get the accurate head positions. We conduct comprehensive experiments on two benchmark datasets and compare the performance with other state-of-theart methods. Our experiments show that the proposed DISAM outperforms the compared methods in both frame-level and pixel-level comparisons.
Image Super-Resolution Using Complex Dense Block on Generative Adversarial Networks

The recent super-resolution (SR) techniques are divided into two directions. One is to improve PSNR and the other is to improve visual quality. We believe improving visual quality is more important and practical than blindly improving PSNR. In this paper we employ a generative adversarial network (GAN) and a new perceptual loss function for photo-realistic single image super-resolution (SISR). Our main contributions are as follows: we propose a new dense block which uses complex connections between each layer to build a more powerful generator. Next, to improve the perceptual quality, we found a new set of feature maps to compute the perceptual loss, which would make the output image look more real and natural. Finally, we compare our results with other methods by subjective evaluation. The subjects rank the image generated by various methods from good to bad. The final results show that our method can generate a more natural and realistic SR image than other state-of-the-art methods.
Center bias outperforms image salience but not semantics in accounting for attention during scene viewing

How do we determine where to focus our attention in real-world scenes? Image saliency theory proposes that our attention is ‘pulled’ to scene regions that differ in low-level image features. However, models that formalize image saliency theory often contain significant scene-independent spatial biases. In the present studies, three different viewing tasks were used to evaluate whether image saliency models account for variance in scene fixation density based primarily on scene-dependent, low-level feature contrast, or on their scene-independent spatial biases. For comparison, fixation density was also compared to semantic feature maps (Meaning Maps; Henderson & Hayes, Nature Human Behaviour, 1, 743–747, 2017) that were generated using human ratings of isolated scene patches. The squared correlations (R2) between scene fixation density and each image saliency model’s center bias, each full image saliency model, and meaning maps were computed. The results showed that in tasks that produced observer center bias, the image saliency models on average explained 23% less variance in scene fixation density than their center biases alone. In comparison, meaning maps explained on average 10% more variance than center bias alone. We conclude that image saliency theory generalizes poorly to real-world scenes.
Alignment of Perception Information for Cooperative Perception

A fundamental for an automated driving car is the awareness of all its surrounding road participants. Current approach to gather this awareness is to sense the environment by on-board sensors, like camera or radar. In the future Vehicle-to-X (V2X) might be able to improve the awareness, due to V2X's communication range superiority compared to the on-board sensors' range. Due to a limited amount of communication partners sharing their own ego states, current research focuses particularly on cooperative perception. This means sharing objects perceived by local on-board sensors of different partners via V2X. In this paper, temporal and spatial alignment of the shared objects is reviewed at track-level. State-of-the-art during the alignment procedure is the compensation of the sender motion in the object state and to perform the coordinate transformation of the object state using the predicted sender state. We propose to use the non-predicted sender state for the transformation and therefore to neglect the sender motion compensation. Finally, both methods are evaluated to figure out the best choice.
Improving Document Binarization Via Adversarial Noise-Texture Augmentation

Binarization of degraded document images is an elementary step in most problems involving document image analysis. The paper re-visits the binarization problem by introducing an adversarial learning approach. We construct a Texture Augmentation Network that transfers the texture element of a degraded reference document image to a clean binary image. In this way, the network creates multiple versions of the same textual content with various noisy textures, thus enlarging the available document binarization datasets. Finally, the newly generated images are passed through a Binarization network to get back the clean version. By jointly training the two networks we can increase the adversarial robustness of our system. The most significant contribution of our framework is that it does not require any paired data unlike other Deep Learning-based methods [1], [2], [3]. Such a novel approach has never been implemented earlier thus making it the very first of its kind in Document Image Analysis community. Experimental results suggest that the proposed method achieves superior performance over widely used DIBCO datasets.
A Systematic Literature Review of Authentication in Internet of Things for Heterogeneous Devices

Internet of Things (IoT) has become one of the most significant technologies in recent years because of possessing the diverse application domains. The variety of applications results in a large amount of users’ private information diffusion that will pose a paramount security concern. User authentication is a significant factor in the IoT environment as it allows the user to communicate with the device securely. Integration of authentication technologies with IoT ensures secure data retrieval and robust access control. This paper provides a comprehensive systematic literature review of various authentication mechanisms for IoT security proposed in the literature. With the comparison of existing authentication mechanisms that are developed for the IoT in terms of security via a multicriteria classification, the open issues that require further research are identified.
A Fully-Connected Ising Model Embedding Method and Its Evaluation for CMOS Annealing Machines

Combinatorial optimization problems with a large solution space are difficult to solve just using von Neumann computers. Ising machines or annealing machines have been developed to tackle these problems as a promising Non-von Neumann computer. In order to use these annealing machines, every combinatorial optimization problem is mapped onto the physical Ising model, which consists of spins, interactions between them, and their external magnetic fields. Then the annealing machines operate so as to search the ground state of the physical Ising model, which corresponds to the optimal solution of the original combinatorial optimization problem. A combinatorial optimization problem can be firstly described by an ideal fully-connected Ising model but it is very hard to embed it onto the physical Ising model topology of a particular annealing machine, which causes one of the largest issues in annealing machines. In this paper, we propose a fully-connected Ising model embedding method targeting for CMOS annealing machine. The key idea is that the proposed method replicates every logical spin in a fully-connected Ising model and embeds each logical spin onto the physical spins with the same chain length. Experimental results through an actual combinatorial problem show that the proposed method obtains spin embeddings superior to the conventional de facto standard method, in terms of the embedding time and the probability of obtaining a feasible solution.
BCEAP - A Blockchain Embedded Academic Paradigm to Augment Legacy Education through Application

Education plays an important role in the economic and social progress of any community. Currently, the higher education system needs immense of enhancements for fulfilling essential needs of the productive and beneficial educational environment. The major challenges faced by the higher education system is the verification of student's data at the time of admission in the university. Current admission system requires a lot of time along with an excess of human resources. there is no mechanism for the authentication of degrees and educational certificates in Pakistan. In this paper, the proposed system based on blockchain technology. This system can validate and verify degree/certificates. The proposed system can verify and validate the student's educational record from respective educational stakeholders like HEC, PEC, IBCC. It allows students to apply for admission using the single platform at a few clicks. Using blockchain, this system is secure, tamper-resistant, time saving, efficient and reliable.
Dynamic path planning for reconfigurable rovers using a multi-layered grid

Autonomy on rovers is desirable in order to extend the traversed distance, and therefore, maximize the number of places visited during the mission. It depends heavily on the information that is available for the traversed surface on other planet. This information may come from the vehicle’s sensors as well as from orbital images. Besides, future exploration missions may consider the use of reconfigurable rovers, which are able to execute multiple locomotion modes to better adapt to different terrains. With these considerations, a path planning algorithm based on the Fast Marching Method is proposed. Environment information coming from different sources is used on a grid formed by two layers. First, the Global Layer with a low resolution, but high extension is used to compute the overall path connecting the rover and the desired goal, using a cost function that takes advantage of the rover locomotion modes. Second, the Local Layer with higher resolution but limited distance is used where the path is dynamically repaired because of obstacle presence. Finally, we show simulation and field test results based on several reconfigurable and non-reconfigurable rover prototypes and a experimental terrain.
Bio-Inspired Representation Learning for Visual Attention Prediction

Visual Attention Prediction (VAP) is a significant and imperative issue in the field of computer vision. Most of existing VAP methods are based on deep learning. However, they do not fully take advantage of the low-level contrast features while generating the visual attention map. In this paper, a novel VAP method is proposed to generate visual attention map via bio-inspired representation learning. The bio-inspired representation learning combines both low-level contrast and high-level semantic features simultaneously, which are developed by the fact that human eye is sensitive to the patches with high contrast and objects with high semantics. The proposed method is composed of three main steps: 1) feature extraction, 2) bio-inspired representation learning and 3) visual attention map generation. Firstly, the high-level semantic feature is extracted from the refined VGG16, while the low-level contrast feature is extracted by the proposed contrast feature extraction block in a deep network. Secondly, during bio-inspired representation learning, both the extracted low-level contrast and high-level semantic features are combined by the designed densely connected block, which is proposed to concatenate various features scale by scale. Finally, the weighted-fusion layer is exploited to generate the ultimate visual attention map based on the obtained representations after bio-inspired representation learning. Extensive experiments are performed to demonstrate the effectiveness of the proposed method.