Reviewerly/exHarmony huggingface dataset:
A Comparative Study of Internet Architecture and Applications of Online Music Streaming Services: The Impact on The Global Music Industry Growth

On-demand music streaming has grown remarkably driving the growth in digital to compensate for the decline in downloads. Additionally, Technology and innovation has donated immensely to the Internet architecture and applications of audio streaming services. More recently, the rise in paid music streaming service companies such as Apple Music, MelOn, YouTube Music, Spotify, and SoundCloud was responsible for the increase of the on-demand music streaming services revenue. The rapid birth of these new audio streaming services relied on not only the mobile or online applications but also on the market penetration strategy of companies from different industries motivated to take part of the Music Industry. This comparative study of major music streaming services illustrates the significant impact of on-demand music streaming services on the global growth of the Recorded Music Industry. Additionally, the study underlines Internet technology advancement, connectivity including Wireless, and dynamic and diverse markets as the driving forces behind the rapid growth of on-demand music streaming services. From a practical perspective, this research study provides insights on the current state of the Music Industry and its future direction that can be valuable to new entrants, current market players, investors, stakeholders, and R & D.
Quantum speedups of some general-purpose numerical optimisation algorithms

Abstract We give quantum speedups of several general-purpose numerical optimisation methods for minimising a function <?CDATA $f:{\mathbb{R}}^{n}\to \mathbb{R}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>f</mml:mi> <mml:mo>:</mml:mo> <mml:msup> <mml:mrow> <mml:mi mathvariant="double-struck">R</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>n</mml:mi> </mml:mrow> </mml:msup> <mml:mo>→</mml:mo> <mml:mi mathvariant="double-struck">R</mml:mi> </mml:math> . First, we show that many techniques for global optimisation under a Lipschitz constraint can be accelerated near-quadratically. Second, we show that backtracking line search, an ingredient in quasi-Newton optimisation algorithms, can be accelerated up to quadratically. Third, we show that a component of the Nelder–Mead algorithm can be accelerated by up to a multiplicative factor of <?CDATA $O\left(\sqrt{n}\right)$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>O</mml:mi> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mrow> <mml:msqrt> <mml:mrow> <mml:mi>n</mml:mi> </mml:mrow> </mml:msqrt> </mml:mrow> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> </mml:math> . Fourth, we show that a quantum gradient computation algorithm of Gilyén et al can be used to approximately compute gradients in the framework of stochastic gradient descent. In each case, our results are based on applying existing quantum algorithms to accelerate specific components of the classical algorithms, rather than developing new quantum techniques.
Temperature impacts on utility-scale solar photovoltaic and wind power generation output over Australia under RCP 8.5

Climate change has the potential to impact the generation of renewable energy significantly subject to location and equipment specifications. As the penetration of renewable energy in the energy systems keeps increasing, this impact needs be systematically assessed so that investment and reliability information is accurate. Australia represents an ideal study case characterized by its frequency of extreme weather events and the recent and planned growth in the renewable energy sector. In this study, we model and quantify the long-term temperature de-rating impact of utility-scale solar photovoltaic and wind power generation over Australia. Using climate projections simulated by six Global Circulation Models and the CSIRO's Cubic Conformal Atmospheric Model, we analyze half-hourly time series of key weather variables such as temperature, surface solar irradiance, and wind speed for 1980–2060 at two sites where variable renewable generators are located, or are likely to be located in the future based on the current Integrated System Plan by the Australian Energy Market Operator. We also built power conversion models for the temperature de-rating of solar and wind power with added focus on high temperature scenarios. We found that the general temporal trends in annual solar and wind power generation due to climate change are small, being at the order of 0.1% of their average production per decade. However, for peak temperature events, which coincide with the peak power demand and, generally, high prices, the temperature de-rating impact can be much more substantial and disruptive.
Analytical Design and Experimental Verification of Geofencing Control for Aerial Applications

Keep-in operational envelopes are essential to maintain the safety of unmanned aerial vehicles (UAVs). System properties and constraints, including underactuated dynamics and actuator saturation, dramatically affect the system's maneuverability inside the operational envelope. Moreover, sate-of-the-art safety control depends heavily on the specifications of the operational envelope. Thus, this article focuses on creating a scalable technique to transform safety envelopes into input-constrained barriers along each axis of motion. Then, it is shown that the proposed class of operational envelopes simultaneously guarantees safety and asymptotic stability. The closed-form solution for the safety rule is derived as allowable low and high bounds of the control command, which are calculated in real time. Furthermore, it is shown that the proposed safety design seamlessly integrates with an existing motion control algorithm with minimum modification. The super-twisting control is used to handle the nonlinear complexity of the UAV and parametric uncertainties and achieve a desirable robust behavior for trajectory and attitude control. The control calibration and tuning are carried out on a state-of-the-art experimental system. The experimental results verify the effectiveness of the proposed safety control.
Knowledge creation practices at organizational boundaries: the role of ICT in sickle-cell care for tribal communities

Purpose This paper aims to examine the role of information and communication technology (ICT) in the knowledge creation practices adopted by a health care organization. The organization is delivering care to patients of a genetic disorder, called the sickle cell, in tribal communities. The paper identifies how ICT intermediates knowledge creation practices across the organizational boundaries wherein tribal patients, front-line counselors and expert physicians interact, which then produces context-specific, evidence-based medicine (EBM). Design/methodology/approach The knowledge-in-practice approach is adopted to conduct an ethnographic study of sickle cell care practices in a non-profit health care organization in Western India. The analysis focuses on ICT-mediated interactional practices among the physicians, front-line counselors, tribal patients and their families, for more than a year-long observation. These are supplemented with informal and formal interviews, archival records and vignettes based on several episodes to explicate the key knowledge creation practices. Findings Technology-mediated informative interactions at organizational boundaries can bridge socio-linguistic and interpretive barriers between actors, while also providing a generative structure that leads to the creation of longitudinal clinical evidence about a rare genetic disorder. Three specific ICT-entwined knowledge creation practices emerge, namely, knowing the community, increasing interactional engagement and constructing gradients of socio-clinical history. These practices generate organization-wide knowledge about the social and clinical dimensions of the genetic disorder. The findings are presented through vignettes and a novel conceptual framework. Research limitations/implications This study identifies various useful knowledge creation practices in health care delivery for resource-constrained emerging economy contexts. Further, the study suggests that the involvement of local front-line actors and ICT can become important resources in the delivery of health care in these settings. Originality/value A novel framework is developed which demonstrates knowledge creation at organizational boundaries wherein the actors use ICT-based practices for effective delivery of health care. The proposed framework may be used by health care organizations in similar contexts providing care to marginalized communities.
Semantic granularity metric learning for visual search

Existing metric learning methods often do not consider different granularity in visual similarity. However, in many domains, images exhibit similarity at multiple granularities with visual semantic concepts, e.g.fashion demonstrates similarity ranging from clothing of the exact same instance to similar looks/design or common category. Therefore, training image triplets/pairs inherently possess different degree of information. Nevertheless, the existing methods often treat them with equal importance which hinder capturing underlying granularities in image similarity. In view of this, we propose a new semantic granularity metric learning (SGML) that develops a novel idea of detecting and leveraging attribute semantic space and integrating it into deep metric learning to capture multiple granularities of similarity. The proposed framework simultaneously learns image attributes and embeddings with multitask-CNN where the tasks are linked by semantic granularity similarity mapping to leverage correlations between the tasks. To this end, we propose a new soft-binomial deviance loss that effectively integrates informativeness of training samples into metric-learning on-the-fly during training. Compared to recent ensemble-based methods, SGML is conceptually elegant, computationally simple yet effective. Extensive experiments on benchmark datasets demonstrate its superiority e.g., 1–4.5%[email protected] improvement over the state-of-the-arts (Kim et al., 2018; Cakir et al., 2019) on DeepFashion-Inshop dataset.
Content‐based gastric image retrieval using convolutional neural networks

Abstract The endoscopy procedure has demonstrated great efficiency in detecting stomach lesions, with extensive numbers of endoscope images produced globally each day. The content‐based gastric image retrieval (CBGIR) system has demonstrated substantial potential in gastric image analysis. Gastric precancerous diseases (GPD) have higher prevalence in gastric cancer patients. Thus, effective intervention is crucial at the GPD stage. In this paper, a CBGIR method is proposed using a modified ResNet‐18 to generate binary hash codes for a rapid and accurate image retrieval process. We tested several popular models (AlexNet, VGGNet and ResNet), with ResNet‐18 determined as the optimum option. Our proposed method was valued using a GPD data set, resulting in a classification accuracy of 96.21 ± 0.66% and a mean average precision of 0.927 ± 0.006 , outperforming other state‐of‐art conventional methods. Furthermore, we constructed a Gastric‐Map (GM) based on feature representations in order to visualize the retrieval results. This work has great auxiliary significance for endoscopists in terms of understanding the typical GPD characteristics and improving aided diagnosis.
Chain-Oriented Load Balancing in Microservice System

With the continuous development of cloud computing and container technology, microservice architecture has received more and more attention. Due to the convenience in the development and maintenance of splitting large applications into fine-grained single services, lots of large applications have evolved from monolithic architecture to microservice architecture. In a microservice system, a request is often processed by a chain composed of multiple different microservices, and these chains often share microservices [2]. Hence, there is competition for resources between the microservices chain. Furthermore, the latency of a long chain that traverses more microservices is often longer than a short chain, and it consumes user patience seriously. Existing research often ignores or simplifies the competition between microservice chains. This paper proposed a multi-priority queue based chain-oriented load balancing method to reduce the latency of the long chain. Our evaluations on docker containers in 6 cloud servers shows that the method proposed in this paper could reduce the latency of long chains without having much impact on the short chains.
Performance of Bitcoin Network With Synchronizing Nodes and a Mix of Regular and Compact Blocks

Compact blocks, and compact block protocol are a recent addition to the Bitcoin (BTC) data propagation protocol that aims to reduce bandwidth requirements and, possibly, reduce latency as well. In this work we evaluate operation of BTC network under a mix of regular, and compact block traffic, assuming that nodes randomly leave, and re-join the network, and perform block and, optionally, transaction pool (mempool) synchronization upon returning. Our analysis begins by evaluating block, and transaction deficits accumulated during the node absence and block synchronization. Then, we analyze mempool behavior, and show that mempool synchronization is necessary since it decreases probability of transaction deficit, and the need for transaction retrieval actions. Finally, we analyze the impact of synchronization activities, and transaction deficit on data distribution times in the BTC network with high, and low bandwidth distribution modes for a mix of compact, and regular blocks. Results demonstrate resilience to node absence, and subsequent synchronization, as well as substantial performance improvements for small protocol changes. Furthermore we show that the low bandwidth mode is more resilient to potential security attacks.
Future LIS Education and Evolving Global Competency Requirements for the Digital Information Environment: An Epistemological Overview

In the context of an evolving digitally-oriented library and/or information science (LIS) discipline and framed by Andrew Abbott’s Chaos of Disciplines theory, this article presents an epistemological overview of evolving competency requirements for a global digital information environment and the implications of this for future LIS education. In doing so it draws from both an international case study of ongoing research by the IFLA BSLISE (Building Strong LIS Education) Working Group into the development of an international framework for the assessment of quality standards in LIS education and a national (South African) case study involving the compilation of an LIS competency index in a highly digitally oriented information environment. The Chaos of Disciplines theory was originally conceptualized to demonstrate the evolution of disciplines in the social sciences. Its core principles of the interstitial character of a discipline and fractal distinctions in time are employed as a heuristic tool to connect the empirical evidence from these two purposively selected case studies to the inherent nature of the LIS discipline and the implications of this for competency requirements for professional practice in a highly digitized global information environment and for future LIS education responding to these competency exigencies.
Human Arm Motion Tracking by Kinect Sensor Using Kalman Filter for Collaborative Robotics

The rising interest in collaborative robotics leads to research solutions in order to increase robot interaction with the environment. The development of methods that permit robots to recognize and track human motion is relevant for safety and collaboration matters. A large quantity of data can be measured in real time by Microsoft Kinect®, a well-known low-cost depth sensor, able to recognize human presence and to provide postural information by extrapolating a skeleton. However, the Kinect sensor tracks motion with relatively low accuracy and jerky behavior. For this reason, the effective use in industrial applications in which the measurement of arm velocity is required can be unsuitable. The present work proposes a filtering method that allows the measurement of more accurate velocity values of human arm, based on row data provided by the Kinect sensor. The estimation of arm motion is achieved by a Kalman filter based on a kinematic model and by the imposition of fixed lengths for the skeleton links detected by the sensor. The development of the method is supported by experimental tests. The achieved results suggest the practical applicability of the developed algorithms.
Information-theoretic privacy in federated submodel learning

We consider information-theoretic privacy in federated submodel learning, where a global server has multiple submodels. Compared to the privacy considered in the conventional federated submodel learning where secure aggregation is adopted for ensuring privacy, information-theoretic privacy provides the stronger protection on submodel selection by the local machine. We propose an achievable scheme that partially adopts the conventional private information retrieval (PIR) scheme that achieves the minimum amount of download. With respect to computation and communication overhead, we compare the achievable scheme with a naïve approach for federated submodel learning with information-theoretic privacy.
The Teamwork Process Antecedents (TPA) questionnaire: developing and validating a comprehensive measure for assessing antecedents of teamwork process quality

Most models of teamwork describe team behavior and effectiveness using an Input-Process-Output approach. In software engineering, the use of such models has focused on understanding and operationalizing the Process-Output components while less research effort has been applied to define and measure the Input-Process component. To develop and validate a measure of teamwork process antecedents (inputs) that addresses specific characteristics of software teams in industrial practice. First, we reviewed the group work literature, identified and integrated previously described antecedents of work group process, and developed a measure to tap those antecedents. This measure is operationalized in the Teamwork Process Antecedents (TPA) questionnaire, which we then validated with 375 Brazilian software engineers from 100 companies, using exploratory and confirmatory factor analysis. We created a survey to operationalize two multidimensional antecedents of teamwork process, Team Structure and Team Composition, based on well-established models from the literature on work teams. We tailored the response items to the software engineering context to increase construct face validity. We reached a parsimonious set of five dimensions for Team Composition (16 response items) and four dimensions for Team Structure (11 response items). Our results show that our measure of TPA has excellent internal reliability and convergent and discriminant validity. We created a novel measure of antecedents of teamwork process tailored to software teams, that captures the perception of team members about the adequacy of team composition and structure to achieve team goals. Further, we present the development of the TPA measure in the form of a guideline that may be used in the construction of other measurement instruments in empirical software engineering research. We believe both results are important contributions of this work.
Deepfake Detection with Clustering-based Embedding Regularization

In recent months, AI-synthesized face swapping videos referred to as deepfake have become an emerging problem. False video is becoming more and more difficult to distinguish, which brings a series of challenges to social security. Some scholars are devoted to studying how to improve the detection accuracy of deepfake video. At the same time, in order to conduct better research, some datasets for deepfake detection are made. Companies such as Google and Facebook have also spent huge sums of money to produce datasets for deepfake video detection, as well as holding deepfake detection competitions. The continuous advancement of video tampering technology and the improvement of video quality have also brought great challenges to deepfake detection. Some scholars have achieved certain results on existing datasets, while the results on some high-quality datasets are not as good as expected. In this paper, we propose new method with clustering-based embedding regularization for deepfake detection. We use open source algorithms to generate videos which can simulate distinctive artifacts in the deepfake videos. To improve the local smoothness of the representation space, we integrate a clustering-based embedding regularization term into the classification objective, so that the obtained model learns to resist adversarial examples. We evaluate our method on three latest deepfake datasets. Experimental results demonstrate the effectiveness of our method.
A Password-Based Authentication System Based on the CAPTCHA AI Problem

Powerful cryptographic systems based on mathematically hard problems are utilized to ensure tighter security for data communication purposes.However, these traditional cryptographic systems are bound to fail in the ensuing era of quantum computing.Thus, Artificial Intelligence (AI) inspired security methods are needed to secure communications in the era of quantum computing.This article presents a challenge-response password-based authentication system based on the Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) AI hard problem.In this system, a server sends a challenge text to a client, then the client generates a random image and blends the challenge text inside this random image using his password.Then the client sends the generated image to the server.The server extracts the challenge text from the sent image using his copy of the client's password.If the extracted challenge text is the same as the sent challenge text, then both the client's and the server's copies of the password match and the client is authenticated.The efficiency of the proposed system is analyzed and the outcomes prove that the proposed system is efficient in terms of time and space.Also, a security investigation of the proposed system is employed, and the results prove that the system is probabilistic and very sensitive to changes in its parameters.It does not leak any statistical information about the client's password and the generated images cannot be distinguished from random images.In addition, the security of the proposed system is analyzed against two possible attacks; the brute force attack and the replay attack and the results prove that the proposed system is immune to these attacks.Finally, the proposed system is ensured to be indistinguishably secure against an adaptive chosen-challenge text attack (IND-ACCTA), based on the CAPTCHA AI hard problem when the hash function H is modeled as a random oracle.