Reviewerly/exHarmony huggingface dataset:
Factoring Personalization in Social Media Recommendations

Nowadays, since social media sites and online social networks have created big media data, it is thus complex and time-consuming for users to find the preferred social media from a large media catalog. Social media recommender systems are therefore emerged to recommend personalized media objects. However, most media recommender systems only focus on one aspect of social media. It is lacking a big picture of how to build an effective social media recommender system. Therefore, this paper tackles this challenge first for specifying the distinct features of media object that can be used for recommender systems, and then discusses five critical aspects that can affect the design of social media recommender systems. This paper further indicates how to assemble these critical aspects and concludes that when we apply traditional recommender algorithms in the media context, those are the critical aspects to improve and optimize social media recommneder systems.
Comprehensive Overview of Neural Networks and Its Applications in Autonomous Vehicles

Deep learning and Artificial intelligence (AI) have been trending these days due to the capability and state-of-the-art results that they provide. They have replaced some highly skilled professionals with neural network-powered AI, also known as deep learning algorithms. Deep learning majorly works on neural networks. This chapter discusses about the working of a neuron, which is a unit component of neural network. There are numerous techniques that can be incorporated while designing a neural network, such as activation functions, training, etc. to improve its features, which will be explained in detail. It has some challenges such as overfitting, which are difficult to neglect but can be overcome using proper techniques and steps that have been discussed. The chapter will help the academician, researchers, and practitioners to further investigate the associated area of deep learning and its applications in the autonomous vehicle industry.
Insights on spatial variography and outcrop-subsurface relationship of sandstone reservoir properties: A case study from the Late Triassic Minjur Formation, central Saudi Arabia

Understanding the variography of outcrop properties may give a guide for predicting the subsurface reservoir quality, more significantly for the more stable and resistant reservoir rocks such as sandstones compared to the carbonates. This study was planned to investigate the lithofacies and reservoir properties of different sandstone lithofacies of the Late Triassic Minjur Formation outcrop, and attempted to develop a relationship between these aspects in outcrop and subsurface settings. Based on the field and petrographic analyses, sandstone lithofacies of Minjur Formation were categorized into five types upon their textures. These lithofacies were intensively investigated for porosity and permeability. Variography analyses were applied on composite outcrop lithofacies, and also on porosity and permeability data. A good relationship was observed between porosity and permeability, which coincides with the univariate statistics of each and confirmed by variography analysis. Combination of outcrop and subsurface reservoir properties showed an acceptable correlation between outcrop reservoir properties and data from central Saudi Arabia and Rub’ Al-Khali Basin in southern and southeastern Saudi Arabia. This correlation was further highlighted by the global sense of subsurface sandstone reservoir properties. In general, the current study reflects the possibility of sandstone outcrop data to act as a representative analog for its equivalent subsurface reservoir. This possibility, which is mainly controlled by the similarity in depositional setting, can be also attained for reservoir properties and quality. Obtaining more qualitative and quantitative data is always advantageous to get better and more robust correlation.
“It Only Tells Me How I Slept, Not How to Fix It”: Exploring Sleep Behaviors and Opportunities for Sleep Technology

We present an online survey study examining people's sleep behaviors as well as their strategies and tools to improve sleep health. Findings show that certain demographic features and sleep behaviors may impact sleep quality, and that current sleep technology is not as effective in promoting sleep health as expected. We discuss the importance of understanding sleep behaviors, design insights for future sleep technology, and the value of a holistic approach to sleep technology design.
Segment Scheduling Scheme for Efficient Bandwidth Utilization of HTTP Adaptive Streaming in Multipath Environments

HyperText Transfer Protocol (HTTP) adaptive streaming has been receiving attention to provide efficient video streaming services by adaptively adjusting the transmission rate.With increased demand for high-quality video streaming, since mobile devices are now equipped with multiple wireless interfaces connected to different networks (3G/4G or Wi-Fi), one possible solution to provide a better quality of experience (QoE) is to use multipath-based transmission schemes.In conventional HTTP adaptive streaming systems, the segment request policy initially operates in a buffering state and switches to a steady state once the playout buffer reaches a threshold.In the steady state, the client generates an ON/OFF traffic pattern that degrades bandwidth utilization and user QoE in multipath environments.In this paper, we propose a segment scheduling scheme for efficient bandwidth utilization in multipath environments.In our scheme, we first propose a collective segment request policy to improve bandwidth utilization and reducing the frequency of OFF periods.To improve network responsiveness, we present a method for adaptively adjusting the request interval.Subsequently, we propose a segment scheduler to prevent the out-oforder problem in multipath environments.Finally, a rate adaptation algorithm for the proposed request policy is proposed.Through simulation, we prove that our scheme significantly improves bandwidth utilization and increases the average bitrate.
Educational Component of Doctoral Training at Engineering University

Didactic aspects of the educational component of doctoral training appear highly significant for modern doctoral education. The educational component involves two parts: research and teaching training. The dual nature of training, which includes different and, at the same time, interrelated types of learning activities, requires the development of appropriate research and methodological support of doctoral education. The methodological basis of designing and implementation of the educational component of doctoral training at an engineering university is an integrated approach. A comprehensive system of training is based on the combining of learning material in a certain semantic environment. This semantic environment is represented by the personal development of PhD students, along with the development of her/his professional mobility, competence, increasing competitiveness and improving the quality of training. The processes of designing and implementation of the educational component entail the use of a systematic approach, whereby the designed objects (preparation for the research along with the preparation for teaching activities) are considered as separate pedagogical systems; competence and activity approaches aimed at the development of general and professional competencies within research and teaching activities; personality-oriented approach focused on the educational component for personal development of PhD students within the research and teaching activities in accordance with their individual curricula. The authors discuss the elements of the pedagogical model of PhD students training at engineering universities.
Improving V-Nets for multi-class abdominal organ segmentation

Segmentation is one of the most important tasks in medical image analysis. With the development of deep leaning, fully convolutional networks (FCNs) have become the dominant approach for this task and their extension to 3D achieved considerable improvements for automated organ segmentation in volumetric imaging data, such as computed tomography (CT). One popular FCN network architecture for 3D volumes is V-Net, originally proposed for single region segmentation. This network effectively solved the imbalance problem between foreground and background voxels by proposing a loss function based on the Dice similarity metric. In this work, we extend the depth of the original V-Net to obtain better features to model the increased complexity of multi-class segmentation tasks at higher input/output resolutions using modern large-memory GPUs. Furthermore, we markedly improved the training behaviour of V-Net by employing batch normalization layers throughout the network. In this way, we can efficiently improve the stability of the training optimization, achieving faster and more stable convergence. We show that our architectural changes and refinements dramatically improve the segmentation performance on a large abdominal CT dataset and obtain close to 90% average Dice score.
Content-based retrieval of video segments from minimally invasive surgery videos using deep convolutional video descriptors and iterative query refinement

Despite a strong evidence of the clinical and economic benefits of minimally invasive surgery (MIS) for many common surgical procedures, there is a gross underutilization of MIS in many US hospitals, potentially due to its steep learning curve. Intraoperative videos captured using a camera inserted into the body during MIS procedures are emerging as an invaluable resource for MIS education, skill assessment and quality assurance. However, these videos often have a duration of several hours and there is a pressing need for automated tools to help surgeons quickly find key semantic segments of interest within MIS videos. In this paper, we present a novel integrated approach for facilitating content-based retrieval of video segments that are semantically similar to a query video within a large collection of MIS videos. We use state-of-theart deep 3D convolutional neural network (CNN) models pre-trained on large public video classification datasets to extract spatiotemporal features from MIS video segments and employ an iterative query refinement (IQR) strategy where in a support vector machine (SVM) classifier trained online based on relevance feedback from the user is used to refine the search results iteratively. We show that our method outperforms the state-of-the-art on the SurgicalActions160 dataset containing 160 video clips of typical surgical actions in gynecologic MIS procedures.
What is the student response to using the weblogs for learning resources?

The utilization of the internet on learning is growing rapidly including on physics learning at every level. The aim of this research is to develop the weblogs to facilitate more various and easier learning resources. The weblogs development used 4D methods (define, design, develop and disseminate). The test of the product response was applied on 22 students. The questionnaire covered four aspects which were the language, the subject matter content, the layout, and the usefulness. The response items were measured using the Likert scale from 1 to 4 scales. The analysis technique was descriptive statistics. Results of analysis in the aspect of 3.09, 3.14, 3.21, and 3.28 from scale 4 respectively. Overall the result of student response analysis indicates that this weblog is good (3.18 out of 4) for use in learning. The further research should study on the impact of this media to the learning performance.
Modeling and Computational Characterization of Twitter Customer Service Conversations

Given the increasing popularity of customer service dialogue on Twitter, analysis of conversation data is essential to understanding trends in customer and agent behavior for the purpose of automating customer service interactions. In this work, we develop a novel taxonomy of fine-grained “dialogue acts” frequently observed in customer service, showcasing acts that are more suited to the domain than the more generic existing taxonomies. Using a sequential SVM-HMM model, we model conversation flow, predicting the dialogue act of a given turn in real time, and showcase this using our “PredDial” portal. We characterize differences between customer and agent behavior in Twitter customer service conversations and investigate the effect of testing our system on different customer service industries. Finally, we use a data-driven approach to predict important conversation outcomes: customer satisfaction, customer frustration, and overall problem resolution. We show that the type and location of certain dialogue acts in a conversation have a significant effect on the probability of desirable and undesirable outcomes and present actionable rules based on our findings. We explore the correlations between different dialogue acts and the outcome of the conversations in detail using an actionable-rule discovery task by leveraging a state-of-the-art sequential rule mining algorithm while modeling a set of conversations as a set of sequences. The patterns and rules we derive can be used as guidelines for outcome-driven automated customer service platforms.
Detecting anomalies in data center physical infrastructures using statistical approaches

Data center physical infrastructures including electrical system, cooling system, and other secondary systems support the developments of modern information technology. Early detection of the unexpected observations in physical infrastructures is of great significance to prevent the breakdown of the system and further losses. However, the state of the art technique for identifying anomalies in existing infrastructure monitoring platform mainly depends on fixed threshold method. An obvious drawback of the method is that it usually leads to a high misdetection rate. In this study, statistical anomaly detection approach is introduced to physical infrastructure monitoring. First, three important types of anomalies encountered in infrastructure monitoring platform are addressed, namely naïve point anomalies, contextual point anomalies, and level shifts. Then, a method based on Gaussian model is put forward to detect the above three anomalies. Because the proposed method can only effectively detect the naïve point anomalies; an improved approach combining the statistical test results on original and first-differenced monitoring data is provided. Performances of the proposed methods on a real data set are evaluated. Results show that the optimized anomaly detection approach has a good precision and can significantly lower the misdetection rate. In conclusion, this study will not only contribute to the improvement of existing monitoring platform but also benefit the preventive maintenance of data center physical infrastructures.
An Improved Seed Point Selection-Based Unsupervised Color Clustering for Content-Based Image Retrieval Application

Abstract The images involved in the content-based image retrieval (CBIR) applications are collectively represented by features such as color, texture and shape. The precision of the CBIR application relies on the key features used in image representation and its similarity measure. In CBIR, dominant color feature extraction is affected by the predefined intervals used in color quantization. The proposed work mainly concentrates on extracting the dominant color information of the image using the clustering process. The clustering process is initiated by the proposed seed point’s selection approach. This approach derives the number of seed points using the first order statistical measure and maximum range of the distributed pixel values. Moreover, this work gives equal priority to dominant color and its occurrence information in calculating the similarity between query and database images. Finally, the standard databases such as SIMPLIcity, Corel-10k, OT-scene, Oxford flower and GHIM are taken to investigate the performance of the proposed dominant color based image retrieval application.
A Performance Evaluation Scheme for Multiple Object Tracking with HFSWR

High-frequency surface wave radar (HFSWR) can detect and continuously track ship objects in real time and beyond the horizon. When ships navigate in a sea area, their motions in a time period form a scenario. The diversity and complexity of the motion scenarios make it difficult to accurately track ships, in which failures such as track fragmentation (TF) are frequently observed. However, it is still unclear how and to what degrees the motions of ships affect the tracking performance, especially which motion patterns can cause tracking failures. This paper addresses this problem and attempts to undertake a first step towards providing an intensive quantitative performance assessment and vulnerability detection scheme for ship-tracking algorithms by proposing an evolutionary and data-mining-based approach. Low-dimensional scenarios in terms of multiple maneuvering ship objects are generated using a grammar-based model. Closed-loop feedback is introduced using evolutionary computation to efficiently collect scenarios that cause more and more tracking performance loss, which provides diversified cases for analysing using data-mining technique to discover indicators of tracking vulnerability. Results on different tracking algorithms show that more cluster and convergence patterns and longer duration of our convoy and cluster patterns in the scenarios can cause severer TF to HFSWR ship tracking.
Research on pedestrian tracking algorithm based on deep learning framework

Pedestrian tracking is a hot topic in the field of computer vision. Current pedestrian tracking methods may treat the reappear target as a new target to tracking, which can lead to tracking failure. For this purpose, this paper uses the deep learning framework to complete the whole algorithm of pedestrian tracking to solve this problem. The pedestrian tracking algorithm is divided into pedestrian detection and tracking. In the detection part, the Faster-RCNN framework is used to detect pedestrians; and in the tracking part, we use the Person-ReID method to track pedestrians, converted the pedestrians tracking problem to feature extraction and matching problem between different frames, and improved the tracking results effectively according to the deep learning framework. According to the experiment results on the simple standard dataset and RGB-D People dataset, the tracking mAP of our algorithm get 92.51% and 76.9%.
Mixed Reality Visualizations of Urban Data

As a consequence of inexpensive sensors, ubiquitous tracking devices, and abundant digital storage, by 2019 smarter cities have become platforms for increasing levels of operational monitoring and data capture. Various government agencies and private interests have generated a tremendous database of urban content, which, despite its availability, remains comprehensively inaccessible. How can new emerging MR technologies relate urban big data back to the physical and spatial conditions of the cities to which they relate? This paper examines data-integrated virtual urban environments, identifies their audience, determines their mechatronic configurations, and reviews strengths and limitations of use before presenting an experimental MR technology interface for translating urban data into accessible information.
Classifying quantum data by dissipation

We investigate a general class of dissipative quantum circuit capable of computing arbitrary Conjunctive Normal Form (CNF) Boolean formulas. In particular, the clauses in a CNF formula define a local generator of Markovian quantum dynamics which acts on a network of qubits. Fixed points of this dynamical system encode the evaluation of the CNF formula. The structure of the corresponding quantum map partitions the Hilbert space into sectors, according to decoherence-free subspaces (DFSs) associated with the dissipative dynamics. These sectors then provide a natural and consistent way to classify quantum data (i.e. quantum states). Indeed, the attractive fixed points of the network allow one to learn the sector(s) for which some particular quantum state is associated. We show how this structure can be used to dissipatively prepare quantum states (e.g. entangled states), and outline how it may be used to generalize certain classical computational learning tasks.
A new code-based public-key cryptosystem resistant to quantum computer attacks

We propose a new type of public-key cryptosystems (PKC) which is based on repetition of different error-correcting codes. We give a brief analysis of some well known attacks on code-based PKC, including structural ones and show that the scheme could be used as a perspective post-quantum PKC.
Graininess characterization by multidimensional scaling

A validation of the graininess attribute was made by means of a psychophysical experiment and the multidimensional scaling algorithm. A visual experiment was designed to obtain graininess differences to be used like the dissimilarity matrix in the MDS algorithm. The results revealed that two dimensions are needed to characterize the graininess effect. The BYK-mac-i instrument and a gonio-hyperspectral imaging system were employed to evaluate the statistical dimensions. On one hand, the first dimension correlated well with the graininess value provided by the BYK-mac-i (r2 = 0.9566). However, we were unable to find a relationship with dimension 2 and any parameter measured by this instrument. Furthermore, the images captured by the gonio-hyperspectral imaging system were processed. A good relationship with the correlation parameter was observed (r2 = 0.8958). However, no relationship was established with dimension 2. Based on these conclusions, further research is necessary that focuses on the new imaging processes and a new visual experiment.
Identity-Based Verifiable Aggregator Oblivious Encryption and Its Applications in Smart Grids

Verifiable aggregator oblivious encryption is a useful notion that not only allows an untrusted aggregator to compute aggregated data without learning any other information but also requires the aggregator to generate a proof of the aggregated data so that anyone can verify the accuracy. Since the aggregator does not learn data that may reveal users' private information and also cannot forge the aggregated data, verifiable aggregator oblivious encryption is suitable for privacy-preserving smart metering. However, NIST has recommended not using public-key-based cryptography in the smart grid because public-key cryptography cannot provide sufficient scalability. Thus, we propose a notion of identity-based verifiable aggregator oblivious encryption and design concrete schemes in this paper. We prove the aggregator oblivious security and aggregator unforgeability of our schemes via the smooth projective hash function and computational Diffie-Hellman assumption. Furthermore, based on our identity-based verifiable aggregator oblivious encryption scheme, we propose an identity-based data aggregation protocol for the smart grid and provide a security analysis in the context of seven typical attacks on smart grids. The implementation of our protocol via the Intel Edison platform shows that it is sufficiently lightweight for resource-constrained smart meters.
Snape

Code executing on the edge needs to run on hardware platforms that feature different memory architectures, virtualization extensions, and using a range of security features. Forcing application code to conform to a monolithic API such as POSIX, or ABI such as Linux, ties developers into large, complex platforms that make it difficult to use such hardware-specific features effectively as well as coming with their own baggage and the attendant security issues. As edge computing proliferates, handling increasingly sensitive and intimate data in our everyday lives, it becomes important for developers to be able to use all the hardware resources of their particular platform, correctly and efficiently.