Reviewerly/exHarmony huggingface dataset:
Recommending Packages of Multi-Criteria Items to Groups

Most recommender services help individual users by recommending items within a single category based on the items' overall ratings. However, this may not be sufficient for group activities. For example, a group of friends using an online travel website look for a weekend getaway package (with hotel and restaurant), where the group members have different preferences over the characteristics (e.g., price, service, ambient) of these items. We call items with multiple characteristics as multi-criteria items. The items may come from different categories (e.g., hotel, restaurant). This paper proposes a novel problem of recommending packages of multi-criteria items to a group of users by leveraging users' preferences over categories. As far as we know, our work is the first paper studying this problem. We propose two models to measure the preference of a group to a package. The first model utilizes users' preferences for all the items and all categories, while the second model further leverages the influence of different group members to user preferences. We further introduce a new metric, to measure the fairness of the recommendations to different group members. We present an approach that utilizes co-clustering to incorporate items' characteristics in the calculation of user preferences and creating recommendations. Finally, we conduct extensive experiments with three real datasets. The experiments show that the second model can find packages that balance better the preferences of all the group members.
Multi-Level Residual Up-Projection Activation Network for Image Super-Resolution

Although convolutional neural networks (CNNs) have received great attention in image super-resolution (SR), most SR networks do not take full account of the hierarchical features of the original low-resolution (LR) images, including the spatial feature information and the channel-wise feature information. To solve this problem, we propose a multi-level residual up-projection activation network (MRUAN) consisting of residual up-projection group (RUG), upscale module and residual activation block (RAB). Specifically, RUG uses recursive method to mine hierarchical LR feature information and HR residual information. Subsequently, the upscale module adopts multi-level LR feature information as input to obtain HR features. Furthermore, we improve the original residual block with spatial-and-channel attention mechanism, which adaptively recalibrates features by considering the spatial relationships within channel and the pixel-wise inter-dependencies between channels simultaneously. Experiments on benchmark datasets show that our MRUAN achieves favorable performance against state-of-the-art methods.
A Survey on QoS in Cloud Computing Environment

Cloud computing is emerging as a predominant technology in many organizations, data centers, where private and hybrid cloud-based storage constructions were adapted to a greater extent. The cloud has already been simplified by the ability of the computing method and possess various challenges towards the area of Service Quality management. As the quality of service is one of the most important challenges for the cloud computing environment. In this paper, we have presented a survey on quality of service in cloud computing in terms of techniques used and provides the comparative study for various well-known quality of services in the cloud computing paradigm based on the techniques used, advantages, and disadvantages. Further, presented a comparison of various scheduling techniques based on the mechanism used, proposed models and their weaknesses. This paper will be helpful for the new researcher to easily get an overview of QoS in cloud computing.
Towards Human-Like Bots Using Online Interactive Case-Based Reasoning

The imitation of human playing style has been gaining relevance in both the Artificial Intelligence for Games research community and the Digital Game industry over the last decade, achieving a special importance in recent years. The goal of these virtual players is to deceive real players and be perceived just as another human player. Although this challenge can be addressed using different Imitation Learning techniques, classic supervised learning approaches do not usually work well due to the violation of the independent and identically distributed assumption for random variables. No regret algorithms in online learning settings seem to outperform previous approaches. In this work we describe an interactive and online case-based reasoning system in which the bot gives control to the human player when it reaches game states that are not well represented by cases in its case base, and regains control when the game states are known again. Results show that (1) the amount of human intervention decreases rapidly, (2) the case base needed to achieve reasonable imitation is considerable smaller than that used in a non-interactive approach (3) the resulting agent outperforms other agents using non-interactive CBR.
Age Estimation of Real-Time Faces Using Convolutional Neural Network

Age classification of an individual from an unconstrained real-time face image is rapidly gaining more popularity and this is because of its many possible applications from security control, surveillance monitoring to forensic art. Several solutions have been proposed in the past few years in solving this problem. Many of the existing traditional methods addressed age classification from face images taken from a controlled environment, only a few studied an unconstrained imaging conditions problem from real-time faces. However, deep learning methods have proven to be effective in solving this problem especially with the availability of both a large amount of data for training and high-end machines. In view of this, we propose a deep learning solution to age estimation from real-life faces. A novel six-layer deep convolutional neural network (CNN) architecture, learns the facial representations needed to estimate ages of individuals from face images taken from uncontrolled ideal environments. In order to further enhance the performance and reduce overfitting problem, we pre-trained our model on a large IMDB-WIKI dataset to conform to face image contents and then tuned the network on the training portions of MORPH-II and OIU-Adience datasets to pick-up the peculiarities and the distribution of the dataset. Our experiments demonstrate the effectiveness of our method for age estimation in-the-wild when evaluated on OIU-Adience benchmark that is known to contain images of faces acquired in ideal and unconstrained conditions, where it achieves better performance than other CNN methods. The proposed age classification method achieves new state-of-the-art results with an improvement of $$8.6\%$$ (Exact) and $$ 3.4\%$$ (One-off) acccuracy over the best-reported result on OIU-Adience dataset.
Multifrequency Microwave Radiometric Method of Detection and Control of Dangerous Atmospheric Weather Events, Resistant to Changing Measurement Conditions

The paper develops and substantiates the application of the multifrequency microwave remote sensing method aimed at detecting convective clouds and controlling the formation and development of dangerous and unfavorable atmospheric meteorological phenomena associated with their evolution under conditions of undetermined changes in background radiation. The basic requirements and limitations to the directional properties of the antenna system of a multifrequency microwave measuring system are given, as well as the basic mathematical relations describing the functioning of the proposed method.
Design of a Simple Orthogonal Multiwavelet Filter by Matrix Spectral Factorization

We consider the design of an orthogonal symmetric/antisymmetric multiwavelet from its matrix product filter by matrix spectral factorization. As a test problem, we construct a simple matrix product filter with desirable properties and factor it using Bauer's method, which in this case can be done in closed form. The corresponding orthogonal multiwavelet function is derived using algebraic techniques which allow symmetry to be considered. This leads to the known orthogonal multiwavelet SA1, which can also be derived directly. We also give a lifting scheme for SA1, investigate the influence of the number of significant digits in the calculations, and show some numerical experiments.
Hierarchical Graph-Rnns for Action Detection of Multiple Activities

In this paper, we propose an approach that spatially lo-calizes the activities in a video frame where each person can perform multiple activities at the same time. Our approach takes the temporal scene context as well as the relations of the actions of detected persons into account. While the temporal context is modeled by a temporal recurrent neural network (RNN), the relations of the actions are modeled by a graph RNN. Both networks are trained together and the proposed approach achieves state of the art results on the AVA dataset.
TriResNet: A Deep Triple-Stream Residual Network for Histopathology Grading

While microscopic analysis of histopathological slides is generally considered as the gold standard method for performing cancer diagnosis and grading, the current method for analysis is extremely time consuming and labour intensive as it requires pathologists to visually inspect tissue samples in a detailed fashion for the presence of cancer. As such, there has been significant recent interest in computer aided diagnosis systems for analysing histopathological slides for cancer grading to aid pathologists to perform cancer diagnosis and grading in a more efficient, accurate, and consistent manner. In this work, we investigate and explore a deep triple-stream residual network (TriResNet) architecture for the purpose of tile-level histopathology grading, which is the critical first step to computer-aided whole-slide histopathology grading. In particular, the design mentality behind the proposed TriResNet network architecture is to facilitate for the learning of a more diverse set of quantitative features to better characterize the complex tissue characteristics found in histopathology samples. Experimental results on two widely-used computer-aided histopathology benchmark datasets (CAMELYON16 dataset and Invasive Ductal Carcinoma (IDC) dataset) demonstrated that the proposed TriResNet network architecture was able to achieve noticeably improved accuracies when compared with two other state-of-the-art deep convolutional neural network architectures for histopathology grading. Based on these promising results, the hope is that the proposed TriResNet network architecture could become a useful tool to aiding pathologists increase the consistency, speed, and accuracy of the histopathology grading process.
Research on Path Planning Algorithm of Two-Machine Cooperative Wall Climbing and Sanding Robot Based on Ant Colony Algorithm

In the process of climbing and sanding for our wall climbing robot, it is of great practical value and significance to find the optimal kinematic precept plan. Therefore, in this paper we base on the ant colony algorithm and set the minimum moving time of the sanding robot as the optimization goal. Apart from it, we also set the reducing of the no-load walking of the robot, the necessary sanding surface quality and the working safety of the robot as the constraint conditions. As a consequence of it, we finally solve the problem of path planning for the wall climbing and sanding robots effectively with consideration of the efficiency and automatic.
Context-Aware Enforcement of Privacy Policies in Edge Computing

Privacy is a fundamental concern that confronts systems dealing with sensitive data. The lack of robust solutions for defining and enforcing privacy measures continues to hinder the general acceptance and adoption of these systems. Edge computing has been recognized as a key enabler for privacy enhanced applications, and has opened new opportunities. In this paper, we propose a novel privacy model based on context-aware edge computing. Our model leverages the context of data to make decisions about how these data need to be processed and managed to achieve privacy. Based on a scenario from the eHealth domain, we show how our generalized model can be used to implement and enact complex domain-specific privacy policies. We illustrate our approach by constructing real world use cases involving a mobile Electronic Health Record that interacts with, and in different environments.
Datalignment

We focus on the problem of aligning ontology relations, namely finding relation names that correspond to the same or related concepts. Such alignment is a prerequisite to the integration of the multiple available Knowledge Bases many of which include similar concepts, differently termed. We propose a novel approach for this problem, by leveraging association rules - originally mined in order to enrich the ontological content. Here, we treat the rules as Datalog programs and look for bounded-depth sub-programs that are contained in (or equivalent to) each other. Heads of such programs intuitively correspond to related concepts, and we propose them as candidates for alignment. The candidate alignments require further verification by experts; to this end we accompany each aligned pair with explanations based on the provenance of each relation according to its sub-program. We have implemented our novel solution in a system called Datalignment. We propose to demonstrate Datalignment, presenting the aligned pairs that it finds, and the computed explanations, in context of real-life Knowledge Bases.
Time-Asymmetric 3d Convolutional Neural Networks for Action Recognition

Three-dimensional (3D) convolutional neural network (CNN) is widely used for action recognition. However, it performs same on time and space, which dose not match asymmetry of time. To overcome this problem, we propose the time-asymmetric 3D CNN based on the hypothesis that the early frames persist. Our time-asymmetric 3D CNN performs better on Kinetics-400 than normal 3D CNN with less parameters. And it can get comparable results on UCF101 among leading methods, which indicates that our time-asymmetric 3D CNN is a good choice for action recognition.
Fashion Recommendation on Street Images

Learning the compatibility relationship is of vital importance to a fashion recommendation system, while existing works achieve this merely on product images but not on street images in the complex daily life scenario. In this paper, we propose a novel fashion recommendation system: Given a query item of interest in the street scenario, the system can return the compatible items. More specifically, a two-stage curriculum learning scheme is developed to transfer the semantics from the product to street outfit images. We also propose a domain-specific missing item imputation method based on style and color similarity to handle the incomplete outfits. To support the training of deep recommendation model, we collect a large dataset with street outfit images. The experiments on the dataset demonstrate the advantages of the proposed method over the state-of-the-art approaches on both the street images and the product images.
Moving Target, Deception, and Other Adaptive Defenses

Moving target defenses raise the cost of an attack to make it more difficult or infeasible. Strategies to do so include implementing diversity, movement, and obfuscation at the platform, network, runtime environment, application, or data layer. Doing so, however, often requires an investment in software, hardware, procedure, or overhead (such as training) and can also increase the complexity of infrastructures being defended. In industrial control system contexts, this complexity and its impact upon performance and reliability might present obstacles to implement such defensive technologies. As the scope of possible domains for introducing moving target defense concepts is now well-defined and the considerations are largely enumerated, consideration must additionally be given to systems that can dynamically select optimal strategies in response to attacks. In this chapter, we will survey the foundations, principles, and domains of moving target defense, consider specific implementation examples, and evaluate the considerations for implementing deceptive and responsive strategies in industrial control systems applications.
UPDCNN: A New Scheme for Image Upsampling and Deblurring Using a Deep Convolutional Neural Network

Restoration of a blurred and subsampled image is an ill-posed problem. In this paper, a two-stage convolutional network is proposed to carry out the processes of upsampling and deblurring to restore the original image. The main idea in the proposed scheme is that the deblurring process is attempted on a high PSNR image obtained after removing the ringing effect that is necessarily caused by the upsampling process. The evaluation of the proposed scheme is carried out using a benchmark dataset in terms of PSNR. The scheme is shown to outperform the state-of-the-art schemes, namely, the sparse coding network, the non-local means filters and the centralized sparse representation.
Temporal Feature Correlation for Human Pose Estimation in Videos

Effectively utilizing temporal information is critical for human pose estimation in videos. Recent methods either neglect the displacements of keypoints in the video frames, or rely on time-consuming optical flow estimation when fusing temporal information. By contrast, we propose a flow-free and displacement-aware algorithm for pose estimation in videos. Our method is based on the observation that the appearance of the body keypoints remains almost unchanged throughout a video. This motivates us to exploit temporal visual consistency of keypoints via temporal feature correlation to establish sparse correspondences between the keypoints in neigh-boring frames. Specifically, we first extract keypoint features from the previous frame, which can be treated as exemplars to search on the intermediate feature map of the current frame. Then we conduct temporal feature correlation for the keypoint search, and the obtained correlation maps are combined with the convolutional features to further guide heatmap estimation. Extensive experiments demonstrate that the proposed method compares favorably against state-of-the-art approaches on both sub-JHMDB and Penn Action datasets. More importantly, our method is robust to large keypoint displacements and could be applied to videos under fast motion.
Video Tampering Detection Algorithm Based on Spatial Constraints and Stable Feature

Most traditional video passive forensics methods only utilize the similarity between adjacent frames. They usually suffer from high false detection rate for the videos with severe motion. To overcome this issue, a novel coarse-to-fine video tampering detection method that combines spatial constraints with stable feature is proposed. In the coarse detection phase, both the low-motion region and the high-texture region are extracted by using spatial constraint criteria. The above two regions are merged to obtain the regions with rich quantitative correlation, which are then used for extracting video optimal similarity features. The luminance gradient component of the optical flow is computed and considered as relatively stable feature. Then, the suspected tampered points are found by combining the above two features. In the fine detection phase, the precise tampering points are located. The similarity of the gradient structure based on the characteristics of the human visual system is utilized to further reduce the false detections. This method is tested on three public video data sets. The experimental results show that compared with the existing works, this method not only has lower false detection rate and higher accuracy for the videos with severe motion, but also has high robustness to regular attacks, such as additive noise, blur and filtering.
Photo Style Transfer With Consistency Losses

We address the problem of style transfer between two photos and propose a new way to preserve photorealism. Using the single pair of photos available as input, we train a pair of deep convolution networks (convnets), each of which transfers the style of one photo to the other. To enforce photorealism, we introduce a content preserving mechanism by combining a cycle-consistency loss with a self-consistency loss. Experimental results show that this method does not suffer from typical artifacts observed in methods working in the same settings [1], [2]. We then further analyze some properties of these trained convnets. First, we notice that they can be used to stylize other unseen images with same known style. Second, we show that retraining only a small subset of the network parameters can be sufficient to adapt these convnets to new styles.
Does the Order of Attributes Play an Important Role in Classification?

This paper proposes a methodology to feature sorting in the context of supervised machine learning algorithms. Feature sorting is defined as a procedure to order the initial arrangement of the attributes according to any sorting algorithm to assign an ordinal number to every feature, depending on its importance; later the initial features are sorted following the ordinal numbers from the first to the last, which are provided by the sorting method. Feature ranking has been chosen as the representative technique to fulfill the sorting purpose inside the feature selection area. This contribution aims at introducing a new methodology where all attributes are included in the data mining task, following different sortings by means of different feature ranking methods. The approach has been assessed in ten binary and multiple class problems with a number of features lower than 37 and a number of instances below than 106 up to 28056; the test-bed includes one challenging data set with 21 labels and 23 attributes where previous works were not able to achieve an accuracy of at least a fifty percent. ReliefF is a strong candidate to be applied in order to re-sort the initial characteristic space and C4.5 algorithm achieved a promising global performance; additionally, PART -a rule-based classifier- and Support Vector Machines obtained acceptable results.
Cognitive Compliance: Analyze, Monitor and Enforce Compliance in the Cloud

IT compliance is an area of increasing attention and capital spend in enterprise IT environments. Enforcing compliance is a complex process, which involves following regulatory requirements coming from many, often overlapping sources, and mapping those requirements against a controls framework that implements them on the ground. In this paper, we propose a solution for streamlining the process of analyzing, monitoring and enforcing compliance in the cloud. We rely on text classification methodologies to match both regulatory requirements and controls against a common hierarchy. Finally, we explain how to use the text classification techniques to analyze the regulatory requirements, and match them to executable code that enforces these requirements in the cloud infrastructure components, such as in virtual machines and containers.