Reviewerly/exHarmony huggingface dataset:
Video Generation from Text Employing Latent Path Construction for Temporal Modeling

Video generation is one of the most challenging tasks in Machine Learning and Computer Vision fields of study. In this paper, we tackle the text to video generation problem, which is a conditional form of video generation. Humans can listen/read natural language sentences, and can imagine or visualize what is being described; therefore, we believe that video generation from natural language sentences will have an important impact on Artificial Intelligence. Video generation is relatively a new popular task in Computer Vision (CV), which is far from being solved. The majority of recent works deal with synthetic datasets or real datasets with very limited types of objects, scenes and motions. To the best of our knowledge, this is the very first work on the text (free-form sentences) to video generation on more realistic video datasets like Actor and Action Dataset (A2D) or UCF101. We tackle the complicated problem of video generation by regressing the latent representations of the first and last frames and employing a context-aware interpolation method to build the latent representations of in-between frames. We propose a stacking "upPooling" block to sequentially generate RGB frames out of each latent representations and progressively increase the resolution. Moreover, our proposed Discriminator encodes videos based on single and multiple frames.
Collaborative Filtering Recommendation Algorithm Based on Similarity of Co-Rating Sequence

In order to improve the accuracy of the recommendation system, we study the influence of the number of co-rating items between users and the sequence associations between similar users on user ratings. When calculating the user similarity, we can not only consider the influence of user ratings, but also consider the similarity between user association sequences consisting of the number of co-rating items. And on this basis, we propose a more accurate user similarity measurement method, and get a more accurate user rating prediction method.The experimental results show that the proposed similarity calculation method combined with the co-rating sequence can more accurately characterize the user similarity, the user ratings prediction mean square error is smaller and the recommended effect is effectively improved compared with other algorithms. While the algorithm is based on a large number of experimental foundations, does not include deep learning into the category, so the choice of fusion coefficient may not be optimal.
Refining Single Low-Quality Facial Depth Map by Lightweight and Efficient Deep Model

Consumer depth sensors have become increasingly common, however, the data are rather coarse and noisy, which is problematic to delicate tasks, such as 3D face modeling and 3D face recognition. In this paper, we present a novel and lightweight 3D Face Refinement Model (3D-FRM), to effectively and efficiently improve the quality of such single facial depth maps. 3D-FRM has an encoder-decoder structure, where the encoder applies depth-wise, point-wise convolutions and the fusion of features of different receptive fields to capture original discriminative information, and the decoder exploits sub-pixel convolutions and the combination of low- and high-level features to achieve strong shape recovery. We also propose a joint loss function to smooth facial surfaces and preserve their identities. In addition, we contribute a large dataset with low- and high-quality 3D face pairs to facilitate this research. Extensive experiments are conducted on the Bosphorus and Lock3DFace datasets, and results show the competency of the proposed method at ameliorating both visual quality and recognition accuracy. Code and data will be available at https://github.com/muyouhang/3D-FRM.
Efficient Implementation of Smoothing Filters Using FPGA

Image smoothing filters are applied to reduce the amount of noise in digital images by applying the convolution process on image pixels. However, the implementation of these filters in real time systems involves the complexity of implementation as floating numbers calculation and division. In this paper, the implementation of two smoothing filters, Mean and Gaussian is achieved using FPGA Spartan 3E device. The implementation was performed in a simplified and way that does not require division, and resultant images were displayed on LCD using configured VGA ports. Two filters were effectively implemented by utilizing 880 slices (5%) of Spartan 3E- XC3S160E device with maximum frequency of 56.6Mhz.
A lightweight network for monocular depth estimation with decoupled body and edge supervision

Learning depth from a single image is a challenging task in computer vision. Many recent works on monocular depth estimation explore increasingly large convolutional neural networks to learn monocular cues implicitly. Such methods may fail to generalize well around object boundaries as large networks tend to distort the fine details (such as edges and corners) in low-resolution layers, leading to a poor depth prediction near object edges. To reduce depth loss near object boundaries, this paper proposes to explicitly decouple depth features for the body and edges of objects corresponding to low and high-frequency regions of an image, respectively. To this end, we learn a flow field to warp depth features into consistent body features and residual edge features. Afterward, decoupled supervision is employed on both sets of features to learn body and edge depth maps explicitly. Moreover, we also propose a lightweight encoder-decoder network that efficiently combines features at multiple scales to alleviate the loss of fine details in the final feature map. Extensive experiments on NYUD-v2 and KITTI datasets demonstrate that our proposed lightweight network with depth decoupling performs comparably to state-of-the-art methods while drastically reducing the number of parameters.
Feature Extraction and Matching of 3D Face Model Based on Facial Landmark Detection

Building a high-quality 3D face model is one of the important issues in computer graphics. High-fidelity 3D face model can be obtained through 3D reconstruction methods from measured data, but the reconstructed models are often flawed with missing measurement data. In order to improve the usability and robustness of the 3D face models, repair and optimization of the face model are critically important. The traditional solutions rely on geometric heuristics to complete repairing work. Considering the characteristics of the face, it is possible to utilize the semantic information to improve the quality of repairing process for the three-dimensional face models, thereby improving the accuracy of the repaired models. In this paper, we combine artificial intelligence and computer graphics methods, and proposes a feature extraction and matching of 3D face model method based on facial landmark detection. Our method combines the facial feature recognition algorithm of 2D facial image and the mapping relationship of 3D face mesh model, and realizes the function of semanticsegmentation and extraction of the 3D face model features. It can be used for the next step of 3D face mesh repair research.
Quantum state sharing of an arbitrary<i>m</i>-particle state using Einstein–Podolsky–Rosen pairs and application in quantum voting

In this paper, we propose a scheme where Alice shares an arbitrary m-particle unknown state with her agents, Bob and Charlie. Alice starts by distributing 2m Einstein–Podolsky–Rosen pairs with her agents and performs m joint three-particle Greenberger–Horne–Zeilinger state measurements on her particles. Bob, who acts as the controller, performs a product measurement [Formula: see text] on his m qubit states while Charlie retrieves the original state by performing unitary operations on his m particles. Subsequently, we demonstrate our proposed scheme’s feasibility by applying it in electronic voting by sharing an arbitrary single-particle state.
The New World of Blockchain Economics: Consensus Mechanism as a Core Element

A consensus mechanism is at the core of each distributed ledger technology. This paper provides a brief overview of the two most prominent consensus protocols from the blockchain universe, namely proof-of-work and proof-of-stake. We describe the fundamentals from a design perspective, highlight some crucial differences between the protocols and discuss their implications for the validity and security of the blockchain. The paper concludes by pointing to Byzantine-Fault-Tolerance consensus, which in the future may become a promising alternative to established consensus mechanisms.
Black-Box Diagnosis and Calibration on GAN Intra-Mode Collapse: A Pilot Study

Generative adversarial networks (GANs) nowadays are capable of producing images of incredible realism. Two concerns raised are whether the state-of-the-art GAN’s learned distribution still suffers from mode collapse and what to do if so. Existing diversity tests of samples from GANs are usually conducted qualitatively on a small scale and/or depend on the access to original training data as well as the trained model parameters. This article explores GAN intra-mode collapse and calibrates that in a novel black-box setting: access to neither training data nor the trained model parameters is assumed. The new setting is practically demanded yet rarely explored and significantly more challenging. As a first stab, we devise a set of statistical tools based on sampling that can visualize, quantify, and rectify intra-mode collapse . We demonstrate the effectiveness of our proposed diagnosis and calibration techniques, via extensive simulations and experiments, on unconditional GAN image generation (e.g., face and vehicle). Our study reveals that the intra-mode collapse is still a prevailing problem in state-of-the-art GANs and the mode collapse is diagnosable and calibratable in black-box settings. Our codes are available at https://github.com/VITA-Group/BlackBoxGANCollapse .
Source Class Selection With Label Propagation For Partial Domain Adaptation

In traditional unsupervised domain adaptation problems, the target domain is assumed to share the same set of classes as the source domain. In practice, there exist situations where target-domain data are from only a subset of source-domain classes and it is not known which classes the target-domain data belong to since they are unlabeled. This problem has been formulated as Partial Domain Adaptation (PDA) in the literature and is a challenging task due to the negative transfer issue (i.e. source-domain data belonging to the irrelevant classes harm the domain adaptation). We address the PDA problem by detecting the outlier classes in the source domain progressively. As a result, the PDA is boiled down to an easier unsupervised domain adaptation problem which can be solved without the issue of negative transfer. Specifically, we employ the locality preserving projection to learn a latent common subspace in which a label propagation algorithm is used to label the target-domain data. The outlier classes can be detected if no target-domain data are labeled as these classes. We remove the detected outlier classes from the source domain and repeat the process for multiple iterations until convergence. Experimental results on commonly used datasets Office31 and Office-Home demonstrate our proposed method achieves state-of-the-art performance with an average accuracy of 98.1% and 75.4% respectively.
Introducing an evolutionary-decomposition model for prediction of municipal solid waste flow: application of intrinsic time-scale decomposition algorithm

Owing to the importance of municipal waste as a determining factor in waste management, developing data-driven models in waste generation data is essential. In the current study, solid waste generation is taken as the function of several parameters, namely month, rainfall, maximum temperature, average temperature, population, household size, educated man, educated women, and income. Two different stand-alone computational models, namely, gene expression programming and optimally pruned extreme machine learning techniques, are used in this study to establish their reliability in municipal solid waste generation forecasting, followed by Mallow’s coefficient feature selection method. The lowest Mallow’s coefficient defines the optimal parameters in solid waste generation forecasting. The novel hybrid models of intrinsic time-scale decomposition-gene expression programming and intrinsic time-scale decomposition- optimally pruned extreme machine learning methods based on Monte-Carlo resampling are employed, and an empirical equation is presented for solid waste generation prediction. For examining the reliability of these models, five statistical criteria, namely coefficient of determination, root mean square error, percent mean absolute relative error, uncertainty at 95% and Willmott’s index of agreement, are implemented. Considering Willmott’s index, the Monte Carlo-intrinsic time-scale decomposition-gene expression programming model attains the closest value (0.957) to the ideal value in the training stage and 0.877 in the testing stage. The hybrid ensemble model of intrinsic time-Scale decomposition-gene expression programming presented lower values of root mean square error (12.279) and percent mean absolute relative error (4.310) in the training phase and in the testing, phase compared to gene expression programming with (12.194) and (5.195), respectively. Overall, the prediction results of the hybrid model of intrinsic time-scale decomposition-gene expression programming using Monte-Carlo resampling technique agrees well with the observed solid waste generation data.
A revocable attribute-based access control system using blockchain

Abstract Attribute-based access control is an effective cryptographic mechanism that allows a data owner to perform attribute-level access control over users’ access capabilities. In the application background of blockchain, it is urgent to use attribute-based access control to solve a series of security problems. Although there exist several similar schemes, they do not consider attribute revocation. By adopting the attribute revocation technique based on binary trees, we extended Waters’ ciphertext policy attribute-based encryption (CP-ABE) scheme into a revocable CP-ABE (RCP-ABE) scheme. By combining our RCP-ABE with blockchain, we constructed a revocable attribute-based access control system. The new system supports expressive access control policies and allows the attribute authority to revoke users’ attributes or part of their attributes. The security analysis shows that the new system satisfies several ideal properties, i.e., forward security, backward security, confidentiality, and integrity.
Kernel-based Attribute-aware Self adaptation and Multi thresholding for Rating Prediction

Abstract In recommender systems, our main task is to predict the rating of a new product from the authorized user and then return the best rating for the particular item and this technique reduced the existing prediction error rate. Our proposed system is user-item rating matrix prediction based on Synergetic filtering techniques and this technique is more efficiency compared to other technique. Proposed System are providing personalized recommendation for help the users accord with information overburden problem. However, the techniques are the data insufficiency of the user-item rating matrix underlying for brand-new items and users are severely affected by Synergetic filtering technique. Since the character of common links and items between more accessible by the users in the Internet and this paper exploits the common links of users and the character of items to overcome the existing problems and to ease the rating insufficient effect. However they may need excessive computational moment, and they often accost the insufficient problem which negatively modify the ability of the system. Specifically, we initially propose a Kernel-based Attribute-aware Self adaptation and multi thresholding model to blend the character information of items into matrix factorization and then introduce self-adaptation and multi thresholding. KASM can find the indefinite interactions among characters, users, and items, which reduce the rating insufficient effect for brand-new items by nature. In this paper we suggest a quick recommendation algorithm based on self-adaptation and multi thresholding. Self-adaptation in its genuine meaning is a state-of-the-art method to alter the setting of control specification. It is called self-adaptive because the algorithm controls the setting of these specification itself sink them into a distinctive genome and emerging them. It is construct to deal with the specified drawbacks and enhance the prediction quality. Extended analysis on two real world data sets establish that our proposed method can attain necessarily improve performance than other state-of the-art-methods. In this method we get the accuracy rate of predicting user rating will be 95%
Semisupervised Learning-Based Sensor Ontology Matching

Sensor ontology models the sensor information and knowledge in a machine-understandable way, which aims at addressing the data heterogeneity problem on the Internet of Things (IoT). However, the existing sensor ontologies are maintained independently for different requirements, which might define the same concept with different terms or context, yielding the heterogeneity issue. Since the complex semantic relationship between the sensor concepts and the large-scale entities is to be dealt with, finding the identical entity correspondences is an error-prone task. To effectively determine the sensor entity correspondences, this work proposes a semisupervised learning-based sensor ontology matching technique. First, we borrow the idea of “centrality” from the social network to construct the training examples; then, we present an evolutionary algorithm- (EA-) based metamatching technique to train the model of aggregating different similarity measures; finally, we use the trained model to match the rest entities. The experiment uses the benchmark as well as three real sensor ontologies to test our proposal’s performance. The experimental results show that our approach is able to determine high-quality sensor entity correspondences in all matching tasks.
Visual Hierarchy and Communication Effectiveness in Medical Decision Tools for Surrogate-Decision-Makers of Critically Ill Traumatic Brain Injury Patients

A major goal of decision support tools (decision aids [DAs]) is to help people make informed decisions; hence, they often provide users with important information. This is particularly true for medical shared decision-making tools (decision aids [DAs]) for surrogate-decision-makers. These tools are designed to help individuals make crucial life-or-death decisions for loved ones who, due to a severe illness, are unable to make their own decisions. Making such decisions on behalf of a loved one often requires the processing of a great deal of medical information, typically in a short period of time, while respecting the patient’s values and preferences. Hence, it is particularly important to design such DAs in a way to communicate provided information effectively. In this study, we examined the impact of visual hierarchy on the communication effectiveness of a DA designed for surrogate decision-makers of critically ill traumatic brain injury patients. We compared users’ viewing behavior between two prototypes with different visual hierarchy levels. We also examined the impact of visual hierarchy that was created through images on engagement with the content. Our results show that creating distinct visual hierarchies can have a notable impact on how effectively provided information is communicated to users. The results also show that creating visual hierarchies via images can improve user engagement with textual information in medical tools designed for surrogate-decision-makers.
Answer Passage Ranking Enhancement Using Shallow Linguistic Features

Question Answering (QA) systems play an important role in decision support systems. Deep neural network-based passage rankers have recently been developed to more effectively rank likely answer-containing passages for QA purposes. These rankers utilize distributed word or sentence embeddings. Such distributed representations mostly carry semantic relatedness of text units in which explicit linguistic features are under-represented. In this paper, we take novel approaches to combine linguistic features (such as different part-of-speech measures) with distributed sentence representations of questions and passages. The QUASAR-T fact-seeking questions and short text passages were used in our experiments to show that while ensembling of deep relevance measures based on pure sentence embedding with linguistic features using several machine learning techniques fails to improve upon the passage ranking performance of our baseline neural network ranker, the concatenation of the same features within the network structure significantly improves the overall performance of passage ranking for QA.
A Graph Coloring Based Framework for Views Construction in Multi-View Ensemble Learning

Multi-view ensemble learning (MEL) is new and fast growing area of machine learning. Here, the subsets of multiple features of same dataset are used for the learning and their prediction is finally ensembled. Vertical partition of dataset corresponds to the subset of the feature set is considered as view in a single-sourced dataset. View construction is an important task in MEL because the quantity of views and their quality leads to better performance of MEL. There is a well known graph partitioning technique, called graph coloring that assign the vertices with different color so that no two adjacent vertices have same color. This technique has various applications in different fields. The one of the applications of coloring is in clustering. In this paper, graph coloring is used for the creation of views in multiview ensemble learning. Here uncorrelated (dissimilar) feature clustering is done with the help of coloring. This paper presents a Graph coloring based views creation (GC-VC) method for automatic views creation in multi-view ensemble learning(MEL). To illustrate the performance of the framework, the five different classifiers have been employed namely Support Vector Machine (SVM), K-nearest neighbors (KNN), Naïve Baysian (NB), Neural Network (NN), Decision Tree (DT). The experiments have been performed on ten different datasets from the UCI repository. The results and their non-parametric statistical analysis of MEL have been done. A good enhanced classification accuracy has been achieved by the purposed MEL framework.
A Preventive and Detective Model for Phishing Attack in Small and Medium Size Businesses

Phishing is the fraudulent attempt to obtain sensitive information or data, such as usernames, passwords and credit card details or other sensitive details, by impersonating oneself as a trustworthy entity in an digital communication. Phishers take advantage of the trusting nature of humans who are considered to be the weakest point of the security triangle. Phishing attacks lead to loss of money, reputation, job etc. A lot of research has been made on this regard and many solutions have been made available but hackers still find and develop new method to trick the security measures in place. The study in this paper proposes a process model that we believe can help to reduce the impact of phishing attacks to some extend; it is composed of Business Security Objectives, Preventive Measures, Detective Measures, Awareness Measures, Responsive Measures, Knowledge Base as main components. It also outlines some best practices to follow in order to prevent phishing attacks.