Reviewerly/exHarmony huggingface dataset:
Multiple kernel subspace clustering with local structural graph and low-rank consensus kernel learning

Multiple kernel learning (MKL) methods are generally believed to perform better than single kernel learning (SKL) methods in handling nonlinear subspace clustering problem, largely thanks to MKL avoids selecting and tuning a pre-defined kernel. However, previous MKL methods mainly focused on how to define a kernel weighting strategy, but ignored the structural characteristics of the input data in both the original space and the kernel space. In this paper, we first propose a novel graph-based MKL method for subspace clustering, namely, Local Structural Graph and Low-Rank Consensus Multiple Kernel Learning (LLMKL). It jointly learns an optimal affinity graph and a suitable consensus kernel for clustering purpose by elegantly integrating the MKL technology, the global structure in the kernel space, the local structure in the original space, and the Hilbert space self-expressiveness property in a unified optimization model. In particular, to capture the data global structure, we employ a substitute of the desired consensus kernel, and then introduce a low-rank constraint on the substitute to encourage that the structure of linear subspaces is present in the feature space. Moreover, the data local structure is explored by building a complete graph, where each sample is treated as a node, and an edge codes the pairwise affinity between two samples. By such, the consensus kernel learning and the affinity graph learning can promote each other such that the data in resulting Hilbert space are both self-expressive and low-rank. Experiments on both image and text clustering well demonstrate that LLMKL outperforms the state-of-the-art methods.
Privacy-preserving range query over multi-source electronic health records in public clouds

Range query is an important data search technique in cloud-based electronic healthcare (eHealth) systems. It enables authorized doctors to retrieve target electronic health records (EHRs) that are generated and outsourced by patients from the cloud server. In reality, patients always encrypt their EHRs before outsourcing, making the range query impossible. In this paper, we identify three threats in real cloud-based eHealth systems, i.e., privacy leakage, frequency analysis, and identical data inference. To capture the security properties that resist these threats, we define a security notion of indistinguishability under multi-source ordered chosen plaintext attack (IND-MSOCPA). Then, we propose a multi-source order-preserving encryption (MSOPE) scheme for cloud-based eHealth systems to enable range queries over encrypted EHRs from multiple patients. Security analysis proves that the MSOPE scheme is IND-MSOCPA secure. We also conduct comprehensive performance evaluations, which demonstrate the high efficiency of the MSOPE scheme.
Perturbed stochastic fractal search for solar PV parameter estimation

Following the widespread use of solar energy all over the world, the design of high quality photovoltaic (PV) cells has attracted strong research interests. To properly evaluate, control and optimize solar PV systems, it is crucial to establish a reliable and accurate model, which is a challenging task due to the presence of non-linearity and multi-modality in the PV systems. In this work, a new meta-heuristic algorithm (MHA), called perturbed stochastic fractal search (pSFS), is proposed to estimate the PV parameters in an optimization framework. The novelty lies in two aspects: (i) employ its own searching operators, i.e., diffusion and updating, to achieve a balance between the global exploration and the local exploitation; and (ii) incorporate a chaotic elitist perturbation strategy to improve the searching performance. To examine the effectiveness of pSFS, this method is applied to solve three PV estimation problems for different PV models, including single diode, double diode and PV modules. Experimental results and statistical analysis show that the proposed pSFS has improved estimation accuracy and robustness compared with several other algorithms recently developed.
Momentum trading in cryptocurrencies: Short-term returns and diversification benefits

We test for the presence of momentum effects in cryptocurrency market and estimate dynamic conditional correlations (DCCs) of returns between momentum portfolios of cryptocurrencies and traditional assets. First, investment portfolios are constructed adherent to the classic J∕K momentum strategy, using daily data from twelve cryptocurrencies for over a period of three years. We identify the existence of momentum effect, which is highly significant for short-term portfolios but disappears over the longer term. Second, we show that cross correlations of weekly returns between momentum portfolio of cryptocurrencies and traditional assets are unlike correlations of returns between traditional assets. Third, we find that momentum portfolios of cryptocurrencies not only offer diversification benefits but also can be a hedge and safe haven for traditional assets.
Blockchain adoption for information sharing: risk decision-making in spacecraft supply chain

This research studies the risk decision-making (RDM) problem faced by participants in a spacecraft supply chain, considering the adoption of the blockchain technology to facilitate information sharing. We investigate a three-level spacecraft supply chain composed of a spacecraft builder, supplier, and logistics service integrator with the Stackelberg game under the scenarios of decentralized, partially centralized, and fully centralized decision-making. For each scenario, we show how the spacecraft builder's optimal order quantity and the supplier's application degree of the blockchain technology change with the spacecraft builder's risk coefficient. We also compare the overall profit of the supply chain for three decision-making scenarios.
A Survey on Cryptography: Comparative Study between RSA vs ECC Algorithms, and RSA vs El-Gamal Algorithms

Internet security and data protection should be guaranteed for all users. Therefore, security is a major concern when exposing information to networks. In todays world, the information is never enough protected the way it should be. Cryptography is one of the most effective and efficient components of network security. Cryptography is a technique to apply ensure the secure and reliable transaction between the sender and the receiver when transferring unintelligible information. Moreover, only the authorized receiver can have the right to decrypt the information that was sent and encrypted by the sender. The cryptography plays an essential role in order to provide security to these networks. In this paper, we observe encryption as well as decryption time of different algorithms with the random size of data packets. Firstly, this work introduces the fundamental concepts of Cryptography: encryption and decryption operations. Secondly, compares between the most popular algorithms RSA , El-Gamal and Elliptic Curve (ECC). Our comparison is based on key size length that affects the running time. Finally, we conclude our survey by focusing on the different outcomes between the RSA and Elliptic curve algorithms.
JointRec: A Deep-Learning-Based Joint Cloud Video Recommendation Framework for Mobile IoT

In the era of Internet of Things (IoT), watching videos on mobile devices has been a popular application in our daily life. How to recommend videos to users is one of the most concerned problem for Internet video service providers (IVSPs). In order to provide better recommendation service to users, they deploy cloud servers in a geo-distributed manner. Each server is responsible for analyzing a local area of user data. Therefore, these cloud servers form information islands and the characteristics of data present nonindependent and identically distribution (non-i.i.d). In this scenario, it is difficult to provide accurate video recommendation service to the minority of users in each area. To tackle this issue, we propose JointRec, a deep learning-based joint cloud video recommendation framework. JointRec integrates the JointCloud architecture into mobile IoT and achieves federated training among distributed cloud servers. Specifically, we first design a dual-convolutional probabilistic matrix factorization (Dual-CPMF) model to conduct video recommendation. Based on this model, each cloud can recommend videos by exploiting the user's profiles and description of videos that users rate, thereby providing more accurate video recommendation services. Then, we present a federated recommendation algorithm which enables each cloud to share their weights and train a model cooperatively. Furthermore, considering the heavy communication costs in the process of federated training, we combine low-rank matrix factorization and 8-bit quantization method to reduce uplink communication costs and network bandwidth. We validate the proposed approach on the real-world data set, and the experimental results indicate the effectiveness of our proposed approach.
LeviProps

LeviProps are tangible structures used to create interactive mid-air experiences. They are composed of an acoustically- transparent lightweight piece of fabric and attached beads that act as levitated anchors. This combination enables real- time 6 Degrees-of-Freedom control of levitated structures which are larger and more diverse than those possible with previous acoustic manipulation techniques. LeviProps can be used as free-form interactive elements and as projection surfaces. We developed an authoring tool to support the creation of LeviProps. Our tool considers the outline of the prop and the user constraints to compute the optimum locations for the anchors (i.e. maximizing trapping forces), increasing prop stability and maximum size. The tool produces a final LeviProp design which can be fabricated following a simple procedure. This paper explains and evaluates our approach and showcases example applications, such as interactive storytelling, games and mid-air displays.
Distributed Gradient Descent Algorithm Robust to an Arbitrary Number of Byzantine Attackers

Due to the growth of modern dataset size and the desire to harness computing power of multiple machines, there is a recent surge of interest in the design of distributed machine learning algorithms. However, distributed algorithms are sensitive to Byzantine attackers who can send falsified data to prevent the convergence of algorithms or lead the algorithms to converge to value of the attackers' choice. Some recent work proposed interesting algorithms that can deal with the scenario when up to half of the workers are compromised. In this paper, we propose a novel algorithm that can deal with an arbitrary number of Byzantine attackers. The main idea is to ask the parameter server to randomly select a small clean dataset and compute noisy gradient using this small dataset. This noisy gradient will then be used as a ground truth to filter out information sent by compromised workers. We show that the proposed algorithm converges to the neighborhood of the population minimizer regardless the number of Byzantine attackers. We further provide numerical examples to show that the proposed algorithm can benefit from the presence of good workers and achieve better performance than existing algorithms.
Morphlour

In this paper, we explore personalized morphing food that enhances traditional food with new HCI capabilities, rather than replacing the chef and authentic ingredients (e.g. flour) with an autonomous machine and heterogeneous mixtures (e.g. gel). Thus, we contribute a unique transformation mechanism of kneaded and sheeted flour-based dough, with an integrated design strategy for morphing food during two general cooking methods: dehydration (e.g. baking) or hydration (e.g. water boiling). We also enrich the design space of morphing food by demonstrating several applications. We end by discussing hybrid cooking between human and a design tool that we developed to ensure accuracy while preserving customizability for morphing food.
Improved itracker combined with bidirectional long short-term memory for 3D gaze estimation using appearance cues

Gaze is an important non-verbal cue for speculating human’s attention, which has been widely employed in many human–computer interaction-based applications. In this paper, we propose an improved Itracker to predict the subject’s gaze for a single image frame, as well as employ a many-to-one bidirectional Long Short-Term Memory (bi-LSTM) to fit the temporal information between frames to estimate gaze for video sequence. For single image frame gaze estimation, we improve the conventional Itracker by removing the face-grid and reducing one network branch via concatenating the two-eye region images. Experimental results show that our improved Itracker obtains 11.6% significant improvement over the state-of-the-art methods on MPIIGaze dataset and has robust estimation accuracy for different image resolutions under the premise of greatly reducing network complexity. For video sequence gaze estimation, by employing the bi-LSTM to fit the temporal information between frames, experimental results on EyeDiap dataset further demonstrate 3% accuracy improvement.
DreamWalker

We explore a future in which people spend considerably more time in virtual reality, even during moments when they transition between locations in the real world. In this paper, we present DreamWalker, a VR system that enables such real-world walking while users explore and stay fully immersed inside large virtual environments in a headset. Provided with a real-world destination, DreamWalker finds a similar path in a pre-authored VR environment and guides the user while real-walking the virtual world. To keep the user from colliding with objects and people in the real-world, DreamWalker's tracking system fuses GPS locations, inside-out tracking, and RGBD frames to 1) continuously and accurately position the user in the real world, 2) sense walkable paths and obstacles in real time, and 3) represent paths through a dynamically changing scene in VR to redirect the user towards the chosen destination. We demonstrate DreamWalker's versatility by enabling users to walk three paths across the large Microsoft campus while enjoying pre-authored VR worlds, supplemented with a variety of obstacle avoidance and redirection techniques. In our evaluation, 8 participants walked across campus along a 15-minute route, experiencing a lively virtual Manhattan that was full of animated cars, people, and other objects.
Human Action Recognition Using Deep Multilevel Multimodal (${M}^{2}$ ) Fusion of Depth and Inertial Sensors

Multimodal fusion frameworks for Human Action Recognition (HAR) using depth and inertial sensor data have been proposed over the years. In most of the existing works, fusion is performed at a single level (feature level or decision level), missing the opportunity to fuse rich mid-level features necessary for better classification. To address this shortcoming, in this paper, we propose three novel deep multilevel multimodal (M2) fusion frameworks to capitalize on different fusion strategies at various stages and to leverage the superiority of multilevel fusion. At input, we transform the depth data into depth images called sequential front view images (SFIs) and inertial sensor data into signal images. Each input modality, depth and inertial, is further made multimodal by taking convolution with the Prewitt filter. Creating “modality within modality” enables further complementary and discriminative feature extraction through Convolutional Neural Networks (CNNs). CNNs are trained on input images of each modality to learn low-level, high-level and complex features. Learned features are extracted and fused at different stages of the proposed frameworks to combine discriminative and complementary information. These highly informative features are served as input to a multi-class Support Vector Machine (SVM). We evaluate the proposed frameworks on three publicly available multimodal HAR datasets, namely, UTD Multimodal Human Action Dataset (MHAD), Berkeley MHAD, and UTD-MHAD Kinect V2. Experimental results show the supremacy of the proposed fusion frameworks over existing methods.
Knowledge Graphs and Knowledge Networks: The Story in Brief

Knowledge Graphs (KGs) represent real-world noisy raw information in a structured form, capturing relationships between entities. However, for dynamic real-world applications such as social networks, recommender systems, computational biology, relational knowledge representation has emerged as a challenging research problem where there is a need to represent the changing nodes, attributes, and edges over time. The evolution of search engine responses to user queries in the last few years is partly because of the role of KGs such as Google KG. KGs are significantly contributing to various AI applications from link prediction, entity relations prediction, node classification to recommendation and question answering systems. This article is an attempt to summarize the journey of KG for AI.
Multimodal Dialog System

On the shoulders of textual dialog systems, the multimodal ones, recently have engaged increasing attention, especially in the retail domain. Despite the commercial value of multimodal dialog systems, they still suffer from the following challenges: 1) automatically generate the right responses in appropriate medium forms; 2) jointly consider the visual cues and the side information while selecting product images; and 3) guide the response generation with multi-faceted and heterogeneous knowledge. To address the aforementioned issues, we present a Multimodal diAloG system with adaptIve deCoders, MAGIC for short. In particular, MAGIC first judges the response type and the corresponding medium form via understanding the intention of the given multimodal context. Hereafter, it employs adaptive decoders to generate the desired responses: a simple recurrent neural network (RNN) is applied to generating general responses, then a knowledge-aware RNN decoder is designed to encode the multiform domain knowledge to enrich the response, and the multimodal response decoder incorporates an image recommendation model which jointly considers the textual attributes and the visual images via a neural model optimized by the max-margin loss. We comparatively justify MAGIC over a benchmark dataset. Experiment results demonstrate that MAGIC outperforms the existing methods and achieves the state-of-the-art performance.
Distill Knowledge From NRSfM for Weakly Supervised 3D Pose Learning

We propose to learn a 3D pose estimator by distilling knowledge from Non-Rigid Structure from Motion (NRSfM). Our method uses solely 2D landmark annotations. No 3D data, multi-view/temporal footage, or object specific prior is required. This alleviates the data bottleneck, which is one of the major concern for supervised methods. The challenge for using NRSfM as teacher is that they often make poor depth reconstruction when the 2D projections have strong ambiguity. Directly using those wrong depth as hard target would negatively impact the student. Instead, we propose a novel loss that ties depth prediction to the cost function used in NRSfM. This gives the student pose estimator freedom to reduce depth error by associating with image features. Validated on H3.6M dataset, our learned 3D pose estimation network achieves more accurate reconstruction compared to NRSfM methods. It also outperforms other weakly supervised methods, in spite of using significantly less supervision.
DewarpNet: Single-Image Document Unwarping With Stacked 3D and 2D Regression Networks

Capturing document images with hand-held devices in unstructured environments is a common practice nowadays. However, "casual" photos of documents are usually unsuitable for automatic information extraction, mainly due to physical distortion of the document paper, as well as various camera positions and illumination conditions. In this work, we propose DewarpNet, a deep-learning approach for document image unwarping from a single image. Our insight is that the 3D geometry of the document not only determines the warping of its texture but also causes the illumination effects. Therefore, our novelty resides on the explicit modeling of 3D shape for document paper in an end-to-end pipeline. Also, we contribute the largest and most comprehensive dataset for document image unwarping to date - Doc3D. This dataset features multiple ground-truth annotations, including 3D shape, surface normals, UV map, albedo image, etc. Training with Doc3D, we demonstrate state-of-the-art performance for DewarpNet with extensive qualitative and quantitative evaluations. Our network also significantly improves OCR performance on captured document images, decreasing character error rate by 42% on average. Both the code and the dataset are released.
Universal Detection of Video Steganography in Multiple Domains Based on the Consistency of Motion Vectors

Digital video provides various types of embedding domains, which lead to a great diversity in video steganography. However, in the detection of video steganography, the existing video steganalytic features all specialize in a particular domain, and are hardly to detect the steganography in other embedding domains. In this paper, we propose a universal feature set which is capable of detecting the video steganography in multiple domains. Two popular embedding domains, i.e., partition mode (PM) domain and motion vector (MV) domain, are considered for steganalysis. The idea is based on the observation that the MVs of the sub-blocks in the same macroblock are usually different from each other, and they will tend to be consistent in values after the MV modifications or PM modifications. Thus the consistency of MVs can be used as an evidence for the steganographic embedding in two domains, and finally a 12-dimensional feature set is designed for universal detection. Extensive experiments are conducted to demonstrate the effectiveness of the proposed feature set. The results show that our feature set achieves superior universality and accuracy in both PM domain and MV domain, and even performs well in mismatched domains, where the detection model trained in one domain can directly be used to attack the steganography in another domain. Besides, the low complexity of the proposed feature set also indicates its advantage in real-time video steganalysis.
End-To-End Supervised Product Quantization for Image Search and Retrieval

Product Quantization, a dictionary based hashing method, is one of the leading unsupervised hashing techniques. While it ignores the labels, it harnesses the features to construct look up tables that can approximate the feature space. In recent years, several works have achieved state of the art results on hashing benchmarks by learning binary representations in a supervised manner. This work presents Deep Product Quantization (DPQ), a technique that leads to more accurate retrieval and classification than the latest state of the art methods, while having similar computational complexity and memory footprint as the Product Quantization method. To our knowledge, this is the first work to introduce a dictionary-based representation that is inspired by Product Quantization and which is learned end-to-end, and thus benefits from the supervised signal. DPQ explicitly learns soft and hard representations to enable an efficient and accurate asymmetric search, by using a straight-through estimator. Our method obtains state of the art results on an extensive array of retrieval and classification experiments.