Reviewerly/exHarmony huggingface dataset:
A Comprehensive study of Blockchain and its various Applications

Blockchain is most promising and trustworthy decenterized distributed database technology where all the information is maintained on all the nodes and provide transparent and secure system. Number of application uses the Blockchain technology like cryptocurrency, banking, e-voting, Security of IoT devices etc. The proposed paper lists the various features, structures and prominent application of Blockchain and its applications .
Approximative compactness of linear combinations of characteristic functions

Best approximation by the set of all n-fold linear combinations of a family of characteristic functions of measurable subsets is investigated. Such combinations generalize Heaviside-type neural networks. Existence of best approximation is studied in terms of approximative compactness, which requires convergence of distance-minimizing sequences. We show that for (Ω,μ) a measure space, in Lp(Ω,μ) with 1≤p≤∞ and for all n≥1, compact families of characteristic functions of sets (of finite measure for p<∞) generate approximatively compact n-fold linear spans. Results are illustrated by examples of continuously parametrized sets.
Entity Summarization in Knowledge Graphs: Algorithms, Evaluation, and Applications

Knowledge graphs (KGs) encapsulate entities and relationships that describe the entities. The concise representation format and graph nature of KGs have resulted in creating many novel Web and industrial applications and enhancing existing ones. However, in a KG, dozens or hundreds of facts describing an entity could exceed the capacity of a typical user interface and overload users with excessive amounts of information. This has motivated fruitful research on entity summarization—automated generation of compact summaries for entities to satisfy users’ information needs efficiently and effectively. Over the recent years, researchers have contributed to this problem by proposing approaches ranging from pure ranking and mining techniques to machine and deep learning techniques. The state of the art has continuously improved and at the same time made it harder for the community and new comers to the problem to keep up with the recent contributions and basic building blocks in the space. This tutorial aims to fill this gap.
Towards an Economic Cyber Loss Index for Parametric Cover Based on IT Security Indicator: A Preliminary Analysis

As cyber events have virtually no geographical limitations and can result in economic losses on a global scale, the assessment of return periods for such economic losses is currently debated among experts. The potential accumulation of consequential insurance losses due to intrusions or viruses is one of the major reasons why the (re-)insurance industry has limited risk appetite for cyber related risks. In order to increase the risk appetite for cyber risk and based on a first batch of data provided by Symantec, the goal of this article is to: Check if IT activity, i.e., the number of virus or intrusions being blocked by Norton on end-user computers could be used as an index for parametric covers that reinsurance companies could propose to their cedants; Look into the correlations of this IT activity across different regions, thereby confirming the absence of geographical limitations for cyber risk, and hence confirming the systemic nature of this risk. This first study on the Symantec dataset shows that a cyber index based on IT activity could be a useful tool to design parametric reinsurance product.
Entity Resolution in Dynamic Heterogeneous Networks

Networks evolve continuously over time not only with the addition and deletion of links and nodes but also with changes in the importance of edges. Even though many networks contain this type of temporal weightings, vast majority of research in network representation learning and classification has focused on static snapshots of the graph, while largely ignoring the temporal dynamics. In this work, we describe two approaches for incorporating weighted temporal information into network embedding methods such as Graph Convolutional Networks (GCNs). While the first approach aggregates time-weighted edges and nodes, the second approach uses temporal random walks to find relevant convolution nodes. With experiments on public and proprietary datasets, we demonstrate the effectiveness of the proposed TimeSage for link prediction tasks. By applying these predictions, we show improvements in our task of identifying fraudulent actors on a large e-commerce website selling software as subscriptions.
Energy efficient fuzzy-based DASH adaptation algorithm

With the expansive demand for video streaming over mobile networks, it is necessary to adopt schemes that balance the need for high video quality with the available network resources when streaming or downloading the video. Several approaches were proposed in the literature, including Dynamic Adaptive Streaming over HTTP (DASH). In this work, we consider an approach in which we place sufficient emphasis on the constrained battery resources in mobile devices when making decisions on the quality (or bitrate) of the video to be requested. This is done by using a fuzzy logic controller that enhances the performance of the Fuzzy-based DASH (FDASH) scheme. Simulation results show that our proposed approach conserves more energy than its predecessor while maintaining similar video quality and avoiding playback interruptions.
Effective Marketing Analysis on Gender and Age Classification with Hyperparameter Tuning

Market intelligence to crowded event is critical in decision making to advertising campaigns and support planning. In this paper describes gender classification and age estimation based on hyperparameter tuning optimization Computer Vision and Machine Learning Approach. The images of Myanmar People are quite challenging with existing famous IMDBs dataset due to different ethnics and nations. The proposed system presented to achieve improving better analysis with using hyperparameter optimization by Deep Learning Fast-R-CNN method on three Datasets, IMDBs, Asia Image Dataset and new Myanmar Image Dataset. Specially, this system adapted fast-R-CNN with hyper parameter optimization using Nelder-Mead methods. Experiences result demonstrated that hybrid method outperformed than other method and achieved the state-of-art-accurate results on real-world Myanmar Video streaming to achieve target audience.
Storage on the Edge: Evaluating Cloud Backed Edge Storage in Cyberphysical Systems

Effective control of emerging cyberphysical systems such as smart transportation, smart health-care, etc. requires edge computing infrastructure that is often organized into three layers, namely edge (IoT) devices, edge controllers (ECs) and the cloud. In large infrastructures, ECs must be deployed densely in the proximity of edge devices and need to satisfy strict constraints on cost, size, cooling, etc. Thus, ECs cannot host large amounts of local storage and instead must make use of cloud storage in the background to provide an impression of large, fast local storage to host the IoT device data needed for online and real-time queries. In this paper, we provide insights into the configuration issues of such an edge storage infrastructure (ESI) based on the evaluation of commercial ESIs on several real-world edge computing workloads. We also show that the current ESI designs are lacking in several respects, and suggest some approaches for enhancing their capabilities to meet the stringent requirements of emerging edge computing applications.
Quantitative analysis of the joint variability of rock texture and composition in thin section: The Generalized Griffiths Descriptor and its application to sediment generation from granitoids

The general protocol for quantitative petrographic description of clastic sediments envisioned by J.C. Griffiths approximately 60 years ago is built on specification of the following five statistically independent fundamental properties of aggregates: modal composition, spatial arrangement, size distributions, shape distributions, and orientation distributions of the elements. Each element is represented by a grain falling into one of an exhaustive set of petrographic classes. We introduce the so-called Generalized Griffiths Descriptor (GGD) which extends this protocol to igneous and metamorphic rocks. Statistical independence of the fundamental properties permits rigorous testing of petrogenetic hypotheses as well as simulation of rocks based on any conceivable combination of them. The benefits of a unifying scheme for description of crystalline parent rocks and their weathering products are illustrated by exploratory analysis of a petrographic data set derived from five granodiorites from across North America, comprising the parent rocks and three size fractions (coarse, medium, and fine) of their detritus. Compositional data analysis is used to estimate sediment generation and evolution (SGE) trajectories for each of the plutons sampled. The bulk mineral composition and the mineral composition of the rock-fragment assemblage display a common trend of relative depletion of quartz and K-feldspar with decreasing grain size, compensated for by relative enrichment of biotite and accessory minerals. The evolution of crystal interface composition suggests that the balance between chemical and mechanical weathering may be reflected in relative stabilities of crystal interfaces. We propose a computationally efficient stereological inversion procedure for estimating the parameters of crystal-size probability distributions in rocks based on the assumption of lognormality. The lognormal distribution is an acceptable default model for crystal size in granitoids at the level of individual thin sections (about 87% passes the tests for lognormality), but less so at the level of entire plutons (only 62% success ratio). The current implementation of the GGD is not yet complete, because it deals explicitly with the first three fundamental properties only, as a consequence of adopting the simplifying assumption that all elements which make up a rock are spherical. Shape and orientation distributions may be included in future implementations of the GGD. Dedicated data sets comprising sediments generated from a single parent rock under different conditions will be needed to advance SGE studies by shedding more light on the balance between chemical and mechanical weathering.
Combining Gaze Estimation and Optical Flow for Pursuits Interaction

Pursuit eye movements have become widely popular because they enable spontaneous eye-based interaction. However, existing methods to detect smooth pursuits require special-purpose eye trackers. We propose the first method to detect pursuits using a single off-the-shelf RGB camera in unconstrained remote settings. The key novelty of our method is that it combines appearance-based gaze estimation with optical flow in the eye region to jointly analyse eye movement dynamics in a single pipeline. We evaluate the performance and robustness of our method for different numbers of targets and trajectories in a 13-participant user study. We show that our method not only outperforms the current state of the art but also achieves competitive performance to a consumer eye tracker for a small number of targets. As such, our work points towards a new family of methods for pursuit interaction directly applicable to an ever-increasing number of devices readily equipped with cameras.
QnAMaker: Data to Bot in 2 Minutes

Having a bot for seamless conversations is a much-desired feature that products and services today seek for their websites and mobile apps. These bots help reduce traffic received by human support significantly by handling frequent and directly answerable known questions. Many such services have huge reference documents such as FAQ pages, which makes it hard for users to browse through this data. A conversation layer over such raw data can lower traffic to human support by a great margin. We demonstrate QnAMaker, a service that creates a conversational layer over semi-structured data such as FAQ pages, product manuals, and support documents. QnAMaker is the popular choice for Extraction and Question-Answering as a service and is used by over 15,000 bots in production. It is also used by search interfaces and not just bots.
MLPGI: Multilayer Perceptron-Based Gender Identification Over Voice Samples in Supervised Machine Learning

The goal of this proposed work is to design a gender identification system that identifies the gender of the speaker. Gender classification is an emerging area of research for the accomplishment of efficient interaction between human and machine using speech files. Numerous ways have been proposed for the gender classification in the past. Speech recognition serves as a prime approach for the identification of the source. Other means for the gender classification includes gait of person, lips shape, facial recognition, iris code, etc. In this paper, the gender has been classified for machine learning-based systems using speech files. These systems may be deployed for the critical investigations areas like crime scene. There are various challenges in this field of speech recognition like determining the multilingual segments added in the speech stream and the gender of the speaker. To resolve these problems and to identify the gender of the speaker many different algorithms are used like frequency estimation, matrix representation, Gaussian mixture models, pattern matching algorithm, hidden Markov model, vector quantization, decision trees and neural networks. In this work, we used machine learning methods of neural network and decision trees for the classification of gender that are explained further in literature review.
Ranked document selection

Let D be a collection of string documents of n characters in total. The top-k document retrieval problem is to preprocess D into a data structure that, given a query (P,k), can return the k documents of D most relevant to pattern P. The relevance of a document d for a pattern P is given by a predefined ranking function w(P,d). Linear space and optimal query time solutions already exist for this problem. In this paper we consider a novel problem, document selection, in which a query (P,k) aims to report the kth document most relevant to P (instead of reporting all top-k documents). We present a data structure using O(nlogϵ⁡n) space, for any constant ϵ>0, answering selection queries in time O(log⁡k/log⁡log⁡n), and a linear-space data structure answering queries in time O(log⁡k), given the locus node of P in a (generalized) suffix tree of D. We also prove that it is unlikely that a succinct-space solution for this problem exists with poly-logarithmic query time, and that O(log⁡k/log⁡log⁡n) is indeed optimal within O(npolylogn) space for most text families. Finally, we present some additional space-time trade-offs exploring the extremes of those lower bounds.
Noise2Weight: On detecting payload weight from drones acoustic emissions

The increasing popularity of autonomous and remotely-piloted drones has paved the way for several use-cases and application scenarios, including merchandise delivery, surveillance, and warfare, to cite a few. In many application scenarios, estimating with zero-touch the weight of the payload carried by a drone before it approaches could be of particular interest, e.g., to provide early tampering detection when the weight of the payload is sensitively different from the expected one. To the best of our knowledge, we are the first to investigate the possibility to remotely detect the weight of the payload carried by a commercial drone by analyzing its acoustic fingerprint. Rooted on a sound methodology and validated by an extensive experimental on-field campaign carried out on a reference 3DR Solo drone, we characterize how the differences in the thrust needed by a drone to carry different payloads affect the speed of the motors and the blades and, in turn, introduces significant variations in the resulting acoustic fingerprint. We applied the above findings to different use-cases and scenarios, characterized by different computational capabilities of the detection system. Results are striking: using the Mel-Frequency Cepstral Coefficients (MFCC) components of the audio signal and different Support Vector Machine (SVM) classifiers, we showed that it is possible to achieve a minimum classification accuracy of 98% in the detection of the specific payload class carried by the drone, using an acquisition time of only 0.25 s—performances improve when using longer time acquisitions. All the data used for our analysis have been released as open-source, to enable the community to validate our findings and use such data as a ready-to-use basis for further investigations.
Adaptive hybrid optimization enabled stack autoencoder-based MapReduce framework for big data classification

The rapid development in the field of network and technology has led to the creation of massive digital information. The main problem found in the big data context is the classification of the task. In this paper, a big data classification model based on the optimization-enabled MapReduce framework was developed for the effective management of data. The optimization algorithm, namely the adaptive Exponential Bat algorithm (adaptive E-Bat) is used, which is the integration of the adaptive concept along with the E-Bat algorithm. The entire operation for big data classification is briefed as follows: The data from the various sources is provided to the Mapper, which performs feature selection. The mapper has the adaptive E-Bat algorithm for selecting the suitable features from the database, where the adaptive nature of the algorithm facilitates the effective handling of the real-time data. Then, the selected features are provided to the reducer for actual big data classification. The reducer uses a deep stack autoencoder, which is trained using the adaptive E-Bat algorithm. Thus, the big data classification is facilitated using the proposed adaptive E- Bat-based stack autoencoder. The performance is analyzed in terms of accuracy and TPR. The proposed method outperformed the existing methods with the TPR and accuracy of 0.9519 and 0.8784, respectively.
Selection Algorithms with Small Groups

We revisit the selection problem, namely that of computing the [Formula: see text]th order statistic of [Formula: see text] given elements, in particular the classic deterministic algorithm by grouping and partition due to Blum, Floyd, Pratt, Rivest, and Tarjan (1973). Whereas the original algorithm uses groups of odd size at least [Formula: see text] and runs in linear time, it has been perpetuated in the literature that using smaller group sizes will force the worst-case running time to become superlinear, namely [Formula: see text]. We first point out that the usual arguments found in the literature justifying the superlinear worst-case running time fall short of proving this claim. We further prove that it is possible to use group size smaller than [Formula: see text] while maintaining the worst case linear running time. To this end we introduce three simple variants of the classic algorithm, the repeated step algorithm, the shifting target algorithm, and the hyperpair algorithm, all running in linear time.
Attention-Based Dense Decoding Network for Monocular Depth Estimation

Depth estimation is a classic computer vision task and provides rich representation of objects and environment. In recent years, the performance of end-to-end depth estimation has been significantly improved. However, the stack of convolutions and pooling operations result in losing local detail spatial information, which is extremely important to monocular depth estimation. In order to overcome this problem, in this work, we propose an encoder-decoder framework with skip connections. Based on the self-attention mechanism, we apply the channel-spatial attention module as a transition layer, which captures the depth and spatial positional relationship and improves the presentation ability of channel and space. Then we propose a dense decoding module to make full use of the attention features of different scale ranges in the decoding process. It achieves a more massive and denser receptive field while obtaining multi-scale information. Finally, a novel distance-aware loss is introduced to predict more meticulous edges and local details in the distance. Experiments demonstrate that the proposed method outperforms the state-of-the-art on KITTI and NYU Depth V2 datasets.
Cohort selection for clinical trials using multiple instance learning

• Cohort selection requires the information across all of the patient’s health records. • MILs under the bag- and the embedded-space paradigms are generally better. • MILs generally perform better than their single instance counterpart approaches. • MIL approach is more effective in criteria requiring overall comprehension. • Other approaches perform well for criteria with unambiguous definitions. Identifying patients eligible for clinical trials using electronic health records (EHRs) is a challenging task usually requiring a comprehensive analysis of information stored in multiple EHRs of a patient. The goal of this study is to investigate different methods and their effectiveness in identifying patients that meet specific eligibility selection criteria based on patients’ longitudinal records. An unstructured dataset released by the n2c2 cohort selection for clinical trials track was used, each of which included 2–5 records manually annotated to thirteen pre-defined selection criteria. Unlike the other studies, we formulated the problem as a multiple instance learning (MIL) task and compared the performance with that of the rule-based and the single instance-based classifiers. Our official best run achieved an average micro-F score of 0.8765 which was ranked as one of the top ten results in the track. Further experiments demonstrated that the performance of the MIL-based classifiers consistently yield better performance than their single-instance counterparts in the criteria that require the overall comprehension of the information distributed among all of the patient’s EHRs. Rule-based and single instance learning approaches exhibited better performance in criteria that don’t require a consideration of several factors across records. This study demonstrated that cohort selection using longitudinal patient records can be formulated as a MIL problem. Our results exhibit that the MIL-based classifiers supplement the rule-based methods and provide better results in comparison to the single instance learning approaches.
Quantum image sharpness estimation based on the Laplacian operator

In order to solve the high complexity of classical image processing, a quantum scheme for image sharpness estimation based on the Laplacian operator is proposed. The mean of grayscale gradients of all pixels is regarded as the sharpness estimation metric. A new quantum image representation model is presented by extending the Novel Enhanced Quantum Representation (NEQR) model, which is greatly useful for quantum image convolution. In quantum platforms, it has been shown that the mean calculation of numbers is rather difficult because the numbers are stored in a quantum superposition state. In order to solve this problem, we put forward an algorithm which essential idea is cyclically shifting the superposition state and iteratively calculating the mean of the new one and the original state. The mean can be obtained from the superposition state by only one quantum measurement. By analyzing the space complexity and time complexity, the scheme is far superior to classical ones in terms of resource consumption and execution speed. In addition, the results of simulation experiments show that for noiseless images, the performance of the scheme is consistent with subjective visual perception of images sharpness.