Reviewerly/exHarmony huggingface dataset:
A Hierarchical MdMC Approach to 2D Video Game Map Generation

In this paper we describe a hierarchical method for procedurally generating 2D game maps using multi-dimensional Markov chains (MdMCs). Our method takes a collection of 2D game maps, breaks them into small chunks and performs clustering to find a set of chunks that correspond to high-level structures (high-level tiles) in the training maps. This set of high-level tiles is then used to re-represent the training maps, and to fit two sets of MdMC models: a high-level model captures the distribution of high-level tiles in the map, and a set of low-level models capture the internal structure of each high-level tile. These two sets of models can then be used to hierarchically generate new maps. We test our approach using two classic games, Super Mario Bros. and Loderunner, and compare the results against other existing map generators.
ColorMap<sup>ND</sup>: A Data-Driven Approach and Tool for Mapping Multivariate Data to Color

A wide variety of color schemes have been devised for mapping scalar data to color. We address the challenge of color-mapping multivariate data. While a number of methods can map low-dimensional data to color, for example, using bilinear or barycentric interpolation for two or three variables, these methods do not scale to higher data dimensions. Likewise, schemes that take a more artistic approach through color mixing and the like also face limits when it comes to the number of variables they can encode. Our approach does not have these limitations. It is data driven in that it determines a proper and consistent color map from first embedding the data samples into a circular interactive multivariate color mapping display (ICD) and then fusing this display with a convex (CIE HCL) color space. The variables (data attributes) are arranged in terms of their similarity and mapped to the ICD's boundary to control the embedding. Using this layout, the color of a multivariate data sample is then obtained via modified generalized barycentric coordinate interpolation of the map. The system we devised has facilities for contrast and feature enhancement, supports both regular and irregular grids, can deal with multi-field as well as multispectral data, and can produce heat maps, choropleth maps, and diagrams such as scatterplots.
CLASS: Cloud Log Assuring Soundness and Secrecy Scheme for Cloud Forensics

User activity logs can be a valuable source of information in cloud forensic investigations; hence, ensuring the reliability and integrity of such logs is crucial. Most existing solutions for secure logging are designed for conventional systems rather than the complexity of a cloud environment. In this paper, we propose the Cloud Log Assuring Soundness and Secrecy (CLASS) process as an alternative scheme for the securing of logs in a cloud environment. In CLASS, logs are encrypted using the individual user's public key so that only the user is able to decrypt the content. In order to prevent unauthorized modification of the log, we generate proof of past log (PPL) using Rabin's fingerprint and Bloom filter. Such an approach reduces verification time significantly. Findings from our experiments deploying CLASS in OpenStack demonstrate the utility of CLASS in a real-world context.
Streaming High-Definition Real-Time Video to Mobile Devices with Partially Reliable Transfer

Delivering High-Definition (HD) real-time video to mobile devices is challenged with stringent constraints in delay and reliability. In the presence of network dynamics (e.g., channel errors and bandwidth fluctuations), the existing error-control mechanisms [e.g., Automatic Repeat reQuest (ARQ) and Forward Error Correction (FEC)] frequently induce deadline violations and quality degradations. To strike an effective balance between delay and reliability in real-time video transmission, this research presents an application-layer solution dubbed Partial rEliability based Real-timEStreaming (PERES) to perform partially reliable transfer. First, we develop an analytical framework to model the delay-constrained partial reliability for acknowledgement (ACK) and negative acknowledgement (NAK) based real-time video streaming. Second, we propose scheduling algorithms for video-aware reliability adaptation and network-adaptive buffer control. PERES is able to maximize the transmission reliability of high-priority video frames within stringent delay constraint. We implement the proposed transmission scheme in embedded video monitoring systems and evaluate the efficacy over different wireless network environments. Evaluation results demonstrate PERES achieves appreciable improvements over the reference schemes in perceptual video quality, delay performance, and bandwidth efficiency.
Changing the circuit-depth complexity of measurement-based quantum computation with hypergraph states

While the circuit model of quantum computation defines its logical depth or ``computational time'' in terms of temporal gate sequences, the measurement-based model could allow totally different temporal ordering and parallelization of logical gates. By developing techniques to analyze Pauli measurements on multiqubit hypergraph states generated by the controlled-controlled-Z (ccz) gates, we introduce a deterministic scheme of universal measurement-based computation. In contrast to the cluster-state scheme where the Clifford gates are parallelizable, our scheme enjoys massive parallelization of ccz and swap gates, so that the computational depth grows with the number of global applications of Hadamard gates, or, in other words, with the number of changing computational bases. A logarithmic-depth implementation of an $N$-times controlled-Z gate illustrates a trade-off between space and time complexity.
Scalable Access Control For Privacy-Aware Media Sharing

The prevalence of social networks has made it easier than ever for users to share their photos, videos, and other media content with anybody from anywhere. However, the easy access of user-generated media content also brings about privacy concerns. Traditional access control mechanisms, where a single access policy is made for a specific piece of content, cannot satisfy the user privacy requirements in large-scale media sharing systems. Instead, configuring multiple levels of access privileges for the shared media content is desired. On one hand, it conforms to the principle of social networks in information propagation. On the other hand, it accords with the diverse and complex social relationship among social network users. In this paper, we propose a scalable media access control (SMAC) system to enable such a configuration in a secure and efficient manner. The proposed SMAC system is empowered by the scalable ciphertext policy attribute-based encryption algorithm as well as a comprehensive key-management scheme. We provide formal security proof to prove the security of the proposed SMAC system. In addition, we conduct extensive experiments on mobile devices to demonstrate its efficiency.
Kerman: A Hybrid Lightweight Tracking Algorithm to Enable Smart Surveillance as an Edge Service

Edge computing pushes the cloud computing boundaries beyond uncertain network resource by leveraging computational processes close to the source and target of data. Time-sensitive and data-intensive video surveillance applications benefit from on-site or near-site data mining. In recent years, many smart video surveillance approaches are proposed for object detection and tracking by using Artificial Intelligence (AI) and Machine Learning (ML) algorithms. However, it is still hard to migrate those computing and data-intensive tasks from Cloud to Edge due to the high computational requirement. In this paper, we envision to achieve intelligent surveillance as an edge service by proposing a hybrid lightweight tracking algorithm named Kerman (Kernelized Kalman filter). Kerman is a decision tree based hybrid Kernelized Correlation Filter (KCF) algorithm proposed for human object tracking, which is coupled with a lightweight Convolutional Neural Network (L-CNN) for high performance. The proposed Kerman algorithm has been implemented on a couple of single board computers (SBC) as edge devices and validated using real-world surveillance video streams. The experimental results are promising that the Kerman algorithm is able to track the object of interest with a decent accuracy at a resource consumption affordable by edge devices.
Uncertainty-Based Active Learning via Sparse Modeling for Image Classification

Uncertainty sampling-based active learning has been well studied for selecting informative samples to improve the performance of a classifier. In batch-mode active learning, a batch of samples are selected for a query at the same time. The samples with top uncertainty are encouraged to be selected. However, this selection strategy ignores the relations among the samples, because the selected samples may have much redundant information with each other. This paper addresses this problem by proposing a novel method that combines uncertainty, diversity, and density via sparse modeling in the sample selection. We use sparse linear combination to represent the uncertainty of unlabeled pool data with Gaussian kernels, in which the diversity and density are well incorporated. The selective sampling method is proposed before optimization to reduce the representation error. To deal with ${l}_{0}$ norm constraint in the sparse problem, two approximated approaches are adopted for efficient optimization. Four image classification data sets are used for evaluation. Extensive experiments related to batch size, feature space, seed size, significant analysis, data transform, and time efficiency demonstrate the advantages of the proposed method.
Unsupervised Stylish Image Description Generation via Domain Layer Norm

Most of the existing works on image description focus on generating expressive descriptions. The only few works that are dedicated to generating stylish (e.g., romantic, lyric, etc.) descriptions suffer from limited style variation and content digression. To address these limitations, we propose a controllable stylish image description generation model. It can learn to generate stylish image descriptions that are more related to image content and can be trained with the arbitrary monolingual corpus without collecting new paired image and stylish descriptions. Moreover, it enables users to generate various stylish descriptions by plugging in style-specific parameters to include new styles into the existing model. We achieve this capability via a novel layer normalization layer design, which we will refer to as the Domain Layer Norm (DLN). Extensive experimental validation and user study on various stylish image description generation tasks are conducted to show the competitive advantages of the proposed model.
EXPLORE: EXPLainable item-tag CO-REcommendation

Tag-based recommendation has become increasingly important in recent years owing to the popularization of social tagging systems. Related studies on this subject can be categorized into item recommendation and tag recommendation based on the objective of the recommendation. In both categories, most existing recommendation approaches focus on improving prediction accuracy. But they ignore that the explanations of recommendations also greatly affect the decision-making of users. In social tagging systems, tags not only behave as auxiliary information of items but also show the implicit preferences of users. Therefore, they can be used to improve prediction accuracy and provide explanations to recommended items. Items and tags have interrelation and mutual effects. By focusing only on either item recommendation or tag recommendation, users may miss some information and can only achieve marginal gains. Fusing the two types of recommendations can improve the performance of both approaches. On the basis of the above ideas, in this study, we propose an EXPLainable item-tag CO-REcommendation (EXPLORE) framework that jointly recommends items and the corresponding tags. Different from conventional recommendations that utilize a single source of content, EXPLORE takes advantage of users’ interests, item contents, and item tags. The experiments conducted on three real-world datasets demonstrate that EXPLORE outperforms the state-of-the-art methods. Importantly, the recommended tags can provide explanations to the recommended items, thus making the recommendation results explainable.
Registration Center Based User Authentication Scheme for Smart E-Governance Applications in Smart Cities

Smart cities and E-governance in smart cities has been the emerging topic in this twenty-first century. The rapid developments and experiences in advancing and utilizing the applications of smart city for the continuous smart E-governance have evolved as an interesting topic to concentrate. According to Information and Communication Technology (ICT), the aims and paradigm of the smart city are to provide an integrated infrastructure and manage proper E-governance with a mission of next-generation cities. The smart delivery services which are opting the ICT-based E-governance are facilitated with an interactivity, say Internet through which the citizens can enjoy the best facilities of smart city. As the growing popularity of smart city and its applications, the communication between the government and the citizens is also raising the possibility of infringement attempts. To address this issue, we come up with an advanced multi-factor user authentication scheme which can be utilized for the smart E-governance applications in smart cities. The lightweight nature of our scheme and resistance to many network attacks, including low computational overhead show beyond doubt that our scheme is efficient and applicable to E-governance applications in smart city. A formal verification performed using AVISPA tool confirms the security of the proposed scheme.
Two-Stage Label Embedding via Neural Factorization Machine for Multi-Label Classification

Label embedding has been widely used as a method to exploit label dependency with dimension reduction in multilabel classification tasks. However, existing embedding methods intend to extract label correlations directly, and thus they might be easily trapped by complex label hierarchies. To tackle this issue, we propose a novel Two-Stage Label Embedding (TSLE) paradigm that involves Neural Factorization Machine (NFM) to jointly project features and labels into a latent space. In encoding phase, we introduce a Twin Encoding Network (TEN) that digs out pairwise feature and label interactions in the first stage and then efficiently learn higherorder correlations with deep neural networks (DNNs) in the second stage. After the codewords are obtained, a set of hidden layers is applied to recover the output labels in decoding phase. Moreover, we develop a novel learning model by leveraging a max margin encoding loss and a label-correlation aware decoding loss, and we adopt the mini-batch Adam to optimize our learning model. Lastly, we also provide a kernel insight to better understand our proposed TSLE. Extensive experiments on various real-world datasets demonstrate that our proposed model significantly outperforms other state-ofthe-art approaches.
Violence Rating Prediction from Movie Scripts

Violent content in movies can influence viewers’ perception of the society. For example, frequent depictions of certain demographics as perpetrators or victims of abuse can shape stereotyped attitudes. In this work, we propose to characterize aspects of violent content in movies solely from the language used in the scripts. This makes our method applicable to a movie in the earlier stages of content creation even before it is produced. This is complementary to previous works which rely on audio or video post production. Our approach is based on a broad range of features designed to capture lexical, semantic, sentiment and abusive language characteristics. We use these features to learn a vector representation for (1) complete movie, and (2) for an act in the movie. The former representation is used to train a movie-level classification model, and the latter, to train deep-learning sequence classifiers that make use of context. We tested our models on a dataset of 732 Hollywood scripts annotated by experts for violent content. Our performance evaluation suggests that linguistic features are a good indicator for violent content. Furthermore, our ablation studies show that semantic and sentiment features are the most important predictors of violence in this data. To date, we are the first to show the language used in movie scripts is a strong indicator of violent content. This offers novel computational tools to assist in creating awareness of storytelling.
A CRNN module for hand pose estimation

Hand pose estimation plays an important role in human–computer interaction. The traditional way is to deal with a video stream frame by frame. However, since the gesture in the video is changing continuously, the adjacent frames must be highly related to each other. Therefore, the input of the neural network in this paper was set to be a series of contiguous video frames in order to make use of the relevance that the adjacent frames have. In this paper, a convolutional recurrent neural network (CRNN) module is proposed, which combines the characteristics of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), and can significantly improve the accuracy of the network.The impact of the location of the CRNN/RNN module in the network is also discussed in this paper. Finally, we demonstrated that our approach significantly outperforms the current state-of-the-art techniques in the NYU Hand dataset.
Arbitrary two-particle high-dimensional Bell-state measurement by auxiliary entanglement

Bell-state measurements (BSMs) play a vital role in quantum information. However, there is no definite solution of how to realize high-dimensional Bell-state measurements (HDBSMs). In this paper, we present a scheme for realizing arbitrary high-dimensional two-particle BSMs by auxiliary entanglement. In our scheme, the maximally entangled states with different degrees of freedom are used as auxiliary states, and then the HDBSM can be achieved by implementing a Bell measurement with different degrees of freedom. We present the detailed HDBSM scheme for three- and four-dimensional Bell states, and the general formula for arbitrary high-dimensional Bell states. Furthermore, we design an experimental scheme for three-dimensional BSM, and give a method of arbitrary dimension. This method can promote the high-dimensional quantum information task.
Quantum-Teleportation-Inspired Algorithm for Sampling Large Random Quantum Circuits

Quantum teleportation transfers and processes quantum information through quantum entanglement channels. It is one of the most versatile protocols in quantum information science and leads to many remarkable applications, particularly the one-way quantum computing. Here, we show, for the first time, that the concept of teleportation can also be used to facilitate an important classical computing task, sampling random quantum circuits, which is highly relevant to prove the near-term demonstration of quantum computational supremacy. In our method, the classical computation in the physical-qubit state space is converted to simulate teleportation in logical-qubit state space, resulting in a much smaller number of qubits involved in classical computing. We tested this new method on 1D and 2D lattices up to 1000 qubits. This Letter presents a new quantum-inspired classical computing technology and is helpful to design and optimize classically hard quantum sampling experiments.
Haptic Augmented Reality (HapticAR) for assembly guidance

The use of Augmented Reality (AR) to support assembly tasks has been an area of interest from its origins in the 90s. Since then, the benefits that this technology could bring to assembly-related tasks have been shown. And, although several advances have been done in different areas such as software, hardware, and human interaction, there are still some problems that have not allowed AR to expand and reach its full potential. Thereby, authors propose a real-time vibrotactile guidance method based on the Gestalt continuity principle and developed a Haptic Augmented Reality application with a low-cost configuration to evaluate the support of the proposed method in assembly tasks. Thus, it potentially overcomes the existing visual issues of AR allowing the user to focus on the task and avoid over-reliance into the technology. The proposed system recognizes the different parts and sub-assemblies, generates the instructions to perform the assembly based on the target position and rotation of each part and verifies the assembly. Additionally, a test was conducted to guide the user in positioning a part, obtaining a high accuracy of rotation and location placement. Also, different functions of the application were tested and the results are suitable for supporting the user guidance.
VisDrone-SOT2018: The Vision Meets Drone Single-Object Tracking Challenge Results

Single-object tracking, also known as visual tracking, on the drone platform attracts much attention recently with various applications in computer vision, such as filming and surveillance. However, the lack of commonly accepted annotated datasets and standard evaluation platform prevent the developments of algorithms. To address this issue, the Vision Meets Drone Single-Object Tracking (VisDrone-SOT2018) Challenge workshop was organized in conjunction with the 15th European Conference on Computer Vision (ECCV 2018) to track and advance the technologies in such field. Specifically, we collect a dataset, including 132 video sequences divided into three non-overlapping sets, i.e., training (86 sequences with 69, 941 frames), validation (11 sequences with 7, 046 frames), and testing (35 sequences with 29, 367 frames) sets. We provide fully annotated bounding boxes of the targets as well as several useful attributes, e.g., occlusion, background clutter, and camera motion. The tracking targets in these sequences include pedestrians, cars, buses, and animals. The dataset is extremely challenging due to various factors, such as occlusion, large scale, pose variation, and fast motion. We present the evaluation protocol of the VisDrone-SOT2018 challenge and the results of a comparison of 22 trackers on the benchmark dataset, which are publicly available on the challenge website: http://www.aiskyeye.com/ . We hope this challenge largely boosts the research and development in single object tracking on drone platforms.
Single Shot Multibox Detector With Kalman Filter for Online Pedestrian Detection in Video

Pedestrian detection is a valuable and challenging problem in computer vision. To fully exploit the interframe information to improve the detector’s performance, many frameworks with high complexity for offline detection have been proposed. These methods cannot provide spontaneous responses or alerts. In this paper, we present a Kalman filter-based convolutional neural network (CNN) for online pedestrian detection in videos. First, the single shot multibox detector is implemented as the CNN detector, which incorporates the pedestrian’s aspect ratios. Fusion modules are implemented to improve the detector’s robustness for medium and far scale pedestrians. Then, bounding boxes are propagated according to the prediction from the Kalman filter. Finally, the location and confidence of the bounding boxes are refined by the Kalman filter. Our method is evaluated on two datasets with respect to both the miss rate and speed, and the results show that our method has a lower miss rate, more stable confidence, and a much higher speed.