Reviewerly/exHarmony huggingface dataset:
A Cost-Efficient Auto-Scaling Algorithm for Large-Scale Graph Processing in Cloud Environments with Heterogeneous Resources

Graph processing model is being adopted extensively in various domains such as online gaming, social media, scientific computing and Internet of Things (IoT). Since general purpose data processing tools such as MapReduce are shown to be inefficient for iterative graph processing, many frameworks have been developed in recent years to facilitate analytics and computing of large-scale graphs. However, regardless of distributed or single machine based architecture of such frameworks, dynamic scalability is always a major concern. It becomes even more important when there is a correlation between scalability and monetary cost - similar to what public clouds provide. The pay-as-you-go model that is used by public cloud providers enables users to pay only for the number of resources they utilize. Nevertheless, processing large-scale graphs in such environments has been less studied and most frameworks are implemented for commodity clusters where they will not be charged for the resources that they consume. In this paper, we have developed algorithms to take advantage of resource heterogeneity in cloud environments. Using these algorithms, the system can automatically adjust the number and types of virtual machines according to the computation requirements for convergent graph applications to improve the performance and reduce the monetary cost of the entire operation. Also, a smart profiling mechanism along with a novel dynamic repartitioning approach helps to distribute graph partitions expeditiously. It is shown that this method outperforms popular frameworks such as Giraph and decreases more than 50 percent of the dollar cost compared to Giraph.
A LPSO-SGD algorithm for the Optimization of Convolutional Neural Network

In recent years, Convolutional Neural Networks (CNN) perform very well in many complex tasks. When we train CNN, the Stochastic Gradient Descent (SGD) algorithm is widely used to optimize the loss function of CNN. However, SGD algorithm has some disadvantages such as being easy to fall into local optimum and vanishing gradient problems that need to be solved. In this paper, we propose a new hybrid algorithm that aims to tackle the disadvantage mentioned above by combining the advantages of the Lclose Particle Swarm Optimization (LPSO) and SGD algorithm. Particle Swarm Optimization (PSO) is a Global optimization algorithm, but it does not perform very well in optimizing the loss function of the neural network because of the neural network's high dimensional weight parameters and the infinite search area. To take advantage of the excellent global search capability of LPSO and the rapid convergence capability, we design the LPSO-SGD algorithm. In the experimental part, we construct the LeNet-5 deep CNN to classify the MNIST data set and the experimental results demonstrate that the proposed algorithm perform better than standard SGD algorithm.
A Matrix Factorization Algorithm for Efficient Recommendations in Social Rating Networks Using Constrained Optimization

In recent years the emergence of social media has become more prominent than ever. Social networking has become the de facto tool used by people all around the world for information discovery. Consequently, the importance of recommendations in a social network setting has urgently emerged, but unfortunately, many methods that have been proposed in order to provide recommendations in social networks cannot produce scalable solutions, and in many cases are complex and difficult to replicate unless the source code of their implementation has been made publicly available. However, as the user base of social networks continues to grow, the demand for developing more efficient social network-based recommendation approaches will continue to grow as well. In this paper, following proven optimization techniques from the domain of machine learning with constrained optimization, and modifying them accordingly in order to take into account the social network information, we propose a matrix factorization algorithm that improves on previously proposed related approaches in terms of convergence speed, recommendation accuracy and performance on cold start users. The proposed algorithm can be implemented easily, and thus used more frequently in social recommendation setups. Our claims are validated by experiments on two real life data sets, the public domain Epinions.com dataset and a much larger dataset crawled from Flixster.com.
SET-OT

We propose a new secure equality testing (SET) protocol, namely SET-OT, for two-party setting by using a recently introduced Private Set Membership Protocol (PSM) based on Oblivious Transfer (OT) as a building block. We designed our equality test in such a way that the test result will not be revealed in clear text, which is desired in several cryptographic protocols. The advantage of using OT is that with the help of OT Extension (OTE) protocols, the cost of asymmetric operations per OT operations reduces when the number of OT executions increases. This makes our protocol competitive especially for the cases where the number of equality tests to be invoked is high. When the number of equality test increases, the time complexity of SET-OT converges to one asymmetric key decryption operation, this operation is the dominant part in terms of computational cost. SET-OT has a better performance in terms of the communication rounds and data transmission cost than state-of-the-art solutions: three communication rounds and 2.9 KB of data transmission are the communication costs of performing equality testing protocol for 20-bit string pairs. In addition to our complexity analysis, we also present test results to validate our claim on performance.
Significant EHR Feature-Driven T2D Inference: Predictive Machine Learning and Networks

Background. Electronic Health Records (EHR) play an important role for the redefinition of phenotypes in view of the wealth and heterogeneity of information now available from disparate data sources. A recent cross-sectional retrospective study has described the potential of EHR toward type 2 diabetes mellitus (T2D) screening when ad hoc models are used. About 10,000 US patients have been analyzed through a variety of inference techniques applied to all records with a variable degree of completeness. The analyses conducted in the reference study have indicated that EHR phenotypes significantly improved T2D detection. Methods. With these US patients and the T2D data evidenced in the above study, we propose an integrative inference approach that leverages the prediction power of EHR features selected by two well-known methods, Random Forests and Lasso. The goal is twofold: reducing the big data redundancies potentially harmful to the predictive learning task and exploiting the inter-connectivity of EHR features. A mutual information (MI) network is the inference tool used to identify communities useful to prioritize significant T2D features underlying the similarity between patients. Results. Endowed with a different degree of granularity, the communities detected after the application of both methods were centered especially on T2D comorbidities and risk factors. As such, they appear very relevant for assessment of two main issues, T2D disease burden and prevention. Conclusions. Our analytical approach offers a solution for managing the EHR scale factor in a complex disease context. EHR are rich sources of phenotypic diversity through which novel stratifications of patients are expected. To enable these results, both pre-screening of variables and calibration of risk prediction methods become necessary steps in EHR analyses. We have presented networks identifying major T2D communities. The specific significance assigned to comorbidities and risk factors in relation to T2D can be inferred with accuracy from just a suitably reduced number of EHR features.
Survey on Plagiarism Detection Systems and Their Comparison

Plagiarism occurs when a person uses someone's work, ideas, words, expressions without giving the required attribution. Plagiarism is a common problem in fields like academia, Research papers, Publications, Patents, etc. In this paper, we deliberate the techniques for detecting the extrinsic plagiarism. These techniques are based on linguistic features, Semantic role labelling, vector space model, Fuzzy semantic string matching, and N-gram approach. And are tested on PAN plagiarism corpus 2009 and PAN plagiarism corpus 2011.
Leveraging Confident Points for Accurate Depth Refinement on Embedded Systems

Despite the notable progress in stereo disparity estimation, algorithms are still prone to errors in challenging conditions. Thus, heuristic disparity refinement techniques are usually deployed to improve accuracy. Moreover, state-of-the-art methods rely on complex CNNs requiring power hungry GPUs not suited for many practical applications constrained by limited computing resources. In this paper, we propose a novel technique for disparity refinement leveraging on confidence measures and a novel, automatic learning-based selection method to discard outliers. Then, a non-local strategy infers missing disparities by analyzing the closest reliable points. This framework is very fast and does not require any hand-tuned thresholding. We assess the performance of our Non-Local Anchoring (NLA), standalone refinement techniques and methods leveraging on confidence measures inside the stereo algorithm. Our evaluation with two popular stereo algorithms shows that our proposal significantly ameliorates their accuracy on Middlebury v3 and KITTI 2015 datasets. Moreover, since our method relies only on cues computed in the disparity domain, it is suited even for COTS stereo cameras coupled with embedded systems, e.g. nVidia Jetson TX2.
Improving collaborative filtering recommender system results and performance using satisfaction degree and emotions of users

Recommender Systems (RSs) are provided as web 2.0 services that give support to electronic commerce. Today, social information has been used for improving the performances of RSs. One of the popular social networks is Facebook which recently developed a new reaction button. This button provides new opportunists to analyze and understand the user’s emotions and behavior. This paper proposes a new social-based RS that benefits from social information to improve the performance of the collaborative filtering. This RS depends on satisfaction degree and emotions of users. Each user can rate an item to express his satisfaction degree with this item. Also, the user able to express his feelings toward this item through the Facebook reaction button. The proposed algorithm is experimentally compared to alternative techniques. The results obtained shows that the proposed algorithm outperforms these algorithms in recommendation quality by 40% and performance by 29%.
Image Collection Summarization: Past, Present and Future

With recent trends in data, it is very evident that more and more of it will be continued to be generated. It is suspected that our limit to provide services to customers will be limited by the type of analysis and knowledge that we can extract from the data. Images constitute a fair share of information in the large form of media that is used for communication. For example text, video, audio to name other few along with their meaningful combinations. While Summarization of videos and events have been of recent interest to computer vision and multimedia research community. Recent advances in the field of optimization especially deep learning have shown significant improvements in video summarization. Image Collection Summarization is an important task that continues to elude because of the inherent challenges and its differences from video summarization. Since the video has a lot of temporal link between the frames that can be exploited using some temporal neural networks like Long Short-Term Memory (LSTM) or Recurrent Neural Networks (RNNs) they prove to be useful in case of designing deep learning based architecture for the event and video summarization. Similarly, for text, it can be acknowledged that a long passage and sentences will have a high-level temporal sequence between them which can be exploited for summarization. While in case of a collection of images there is no temporal sequence between two images to be exploited by the network [, ]. This has resulted in the problem being esoteric in nature. To remedy this, the following article plans to bring the challenges in the field of image collection summarization, the need for gold standards in the definition of summarization, datasets and quantitative evaluation metrics based on those datasets and also major papers in the area that have aimed to solve the problem in past.
Transpiling Applications into Optimized Serverless Orchestrations

The serverless computing paradigm promises increased development productivity by abstracting the underlying hardware infrastructure and software runtime when building distributed cloud applications. However, composing a serverless application consisting of many tiny functions is still a cumbersome and inflexible process due to the lack of a unified source code view and strong coupling to non-standardized function-level interfaces for code and configuration. In our vision, developers can focus on writing readable source code in a logical structure, which then gets transformed into an optimized multi-function serverless orchestration. Our idea involves transpilation (i.e., source-to-source transformation) based on an optimization model (e.g., cost optimization) by dynamically deciding which set of methods will be grouped into individual deployment units. A successful implementation of our vision would enable a broader range of serverless applications and allow for dynamic deployment optimization based on monitoring runtime metrics. Further, we would expect increased developer productivity by using more familiar abstractions and facilitating clean coding practices and code reuse.
Simulation and Evaluation of Three-User Redirected Walking Algorithm in Shared Physical Spaces

Shifting from single-person experiences to multi-user interactions is an inevitable trend of virtual reality technology. Existing methods primarily address the problem of one- or two-user redirected walking and do not respond to additional challenges related to potential collisions among three or more users who are moving both virtually and physically. To apply redirected walking to multiple users who are immersed in virtual reality experiences, we present a novel algorithm of three-user redirected walking in shared physical spaces. In addition, we present the steps to apply three-user redirected walking to multiplayer VR scene, where the users are divided into different groups based on the users' motion states. Therefore, this strategy can be applied to each group to address the challenges of redirected walking when there are more than three users. The results show that sharing a space using our three-user redirected walking algorithm is completely feasible.
Using Inputs and Context to Verify User Intentions in Internet Services

An open security problem is how a server can tell whether a request submitted by a client is legitimately intended by the user or fakes by malware that has infected the user's system. This paper proposes Attested Intentions (AINT), to ensure user intention is properly translated to service requests. AINT uses a trusted hypervisor to record user inputs and context, and uses an Intel SGX enclave to continuously verify that the context, where user interaction occurs, has not been tampered with. After verification, AINT also uses SGX enclave for execution protection to generate the service request using the inputs collected by the hypervisor. To address privacy concerns over the recording of user inputs and context, AINT performs all verification on the client device, so that recorded data is never transmitted to a remote party.
Evaluation of Two-Phase Virtual Machine Placement Algorithms for Green Cloud Datacenters

Cloud Computing Datacenters represent a power-intensive industry with well-known economical and ecological challenges. This work focusses on Virtual Machine Placement (VMP) problems as a valid alternative to address mentioned challenges. An experimental evaluation of 36 VMP optimization algorithms for power consumption minimization is presented. Algorithms were evaluated under uncertainty of 4 different dynamic parameters, considering 400 experimental scenarios and taking into account an average objective function cost as evaluation criterion. Experimental results indicate that two-phase algorithms considering prediction-based VMPr Triggering and update-based VMPr Recovering methods are best suited for power consumption minimization.
Image Stitching Algorithm for Drones Based on SURF-GHT

Abstract Aiming at the problem that the amount of single remote sensing picture information is limited due to the limited angular range of single drone, this paper proposes SURF-GHT algorithm based on SURF algorithm and generalized Hough transform based on the existing image stitching algorithm. After the feature points are extracted by the SURF algorithm for the pictures to be spliced, the decision tree forest in the generalized Hough transform is used to filter the set of feature points, and then the fading image fusion algorithm is used for image splicing. The experimental results show that the SURF-GHT algorithm improves the quality of image stitching on the basis of higher stitching efficiency, and better satisfies the subsequent identification of remote sensing images of drones.
Color Theme--based Aesthetic Enhancement Algorithm to Emulate the Human Perception of Beauty in Photos

Fine Art Photography is one of the most popular art forms, which creates lasting impressions that elicit various human emotional reactions. Photo aesthetic enhancement aims at improving the aesthetic level of the photo to please humans by updating color appearance or modifying the geometry structure of objects within that photo. Even though several aesthetic enhancement methods have been proposed, to our knowledge, there is no research to explore, highlight, and accentuate photos’ intrinsic aesthetic value to elicit a stronger response from the human observer about the photos’ theme. To meet this challenge, a new multimedia technology called automatic color theme--based aesthetic enhancement (CT-AEA) is proposed by leveraging big online data to perform timely collection and learning of humans’ current aesthetic perception-behavior over photos and color themes in art, fashion, and design. Unlike existing aesthetic enhancement that examines the composition, such as the geometric structure of the image contents and color/luminance-related (color tone and luminance distribution) characteristics, this CT-AEA takes into consideration the importance of a suitable color theme, namely a set of dominant colors for the design when assessing the aesthetic appearance of a photo. This algorithm is composed of (1) utilizing the knowledge gained from the human evaluator's perception of beauty from existing online datasets, rather than simply applying prior existing knowledge of color harmony theory; (2) developing a new color theme difference equation that exhibits order-invariance and percentage-sensitive properties; (3) designing an optimal color theme recommendation to maximize the aesthetic performance, while minimizing the color modification cost to solve the problems of color inconsistencies and distortion. Experimental results, quantitative measure, and comparison tests demonstrate the algorithm's effectiveness, advantages, and potential for use in many color-related art and design applications.
Partially Occluded Head Posture Estimation for 2D Images using Pyramid HoG Features

Automatic head posture recognition is essential in the human-centered AI applications such as counting watching people in a bus or lift advertising system. A partially-occluded head posture estimation algorithm based on a histogram of gradient orientation (HoG) in a pyramid setting is proposed. We first apply face detection to a 2D image, and then divide the detected face region into two sub-regions, mouth and eye areas, to predict whether there is an occlusion in these two sub-regions individually. According to the predicted occlusion status, pyramid HoG features are extracted from non-occluded face sub-region. Finally, a support vector machine is applied for model training. Experimental results show that our proposed method has high accuracy, speed, and robustness in terms of illumination, shadow, occlusion, and complex background. The average recognition accuracy on the CMU-PIE dataset is 95.24% (94.22% with full mouth area occlusion), and that on the CAS-PEAL-R1 dataset is 95.61%. In addition, the contribution of the eye area is significantly bigger than that of the mouth area. Our method has potential commercial values and broad application prospects for intelligent human-machine interaction.
Scalable Content-Based Analysis of Images in Web Archives with TensorFlow and the Archives Unleashed Toolkit

We demonstrate the integration of the Archives Unleashed Toolkit, a scalable platform for exploring web archives, with Google's TensorFlow deep learning toolkit to provide scholars with content-based image analysis capabilities. By applying pretrained deep neural networks for object detection, we are able to extract images of common objects from a 4TB web archive of GeoCities, which we then compile into browsable collages. This case study illustrates the types of interesting analyses enabled by combining big data and deep learning capabilities.
Surface Shape Detection with a Single Far-Field Intensity by Combined Amplitude and Phase Retrieval

The efficiency of a reflector antenna highly depends on its surface shape. In order to ensure a good convergence, the conventional phase retrieval based shape detection schemes require that several far-field intensities be scanned, focused, or defocused. For large reflector antennas, the scanning process is time consuming. This paper proposes a new shape detection method that requires only single far-field intensity. Unlike existing shape detection methods, it retrieves both the amplitude and the phase, based on the fact that a deformed shape causes change not only in the aperture phase but also in the aperture amplitude. Through even-odd decomposition analysis, it is found that in the case of small and smooth deformation, “odd-phase” and “even-amplitude” can be directly recovered from one focused far-field intensity. This leads to the recovery of both the odd and the even parts of the antenna surface shape simultaneously. By combining amplitude retrieval and phase retrieval, this work achieves for the first time the shape detection with only one scan.
Dynamic Predictive Modeling Approach of User Behavior in Virtual Reality based Application

Virtual Reality (VR) is considered to be a powerful modern medium for immersive data visualization and exploration. However, few studies have proposed solutions to complement data visualization in immersive environment considering the user's behavior. This paper addresses dynamic modeling of user behavior approach in an interactive VR based application. In this application, real-time data communication is employed to track accurate location and orientation of head mounted display device worn by the user. In our experiment, we use example of collected data and provide a methodology to predict next movements of the user by using nonlinear autoregressive (NAR) and location in the application by the nonlinear autoregressive neural network with exogenous inputs (NARX). Results suggest both neural networks are suitable for performing prediction which can be used to achieve an improved feeling of presence while reducing required high computational power. Data analysis part of the research is also linked to human behaviors to improve studies which are usually performed by traditional survey techniques.