Reviewerly/exHarmony huggingface dataset:
Design of an Active-Reliable Grasping Mechanism for Autonomous Unmanned Aerial Vehicles

This paper presents a novel design of an active grasping mechanism for autonomous Unmanned Aerial Vehicles (UAVs) aimed to carry ferromagnetic objects in assembly tasks. The proposed design uses electromagnets along with a combination of sensors to provide fast and reliable feedback. The designed gripper with its control system is aimed to be fully autonomous and will be employed in a task in the Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2020 competition where a group of autonomous UAVs cooperatively build a wall. The design is optimized for the Tarot 650 drone platform and for outdoor operation, while taking into consideration robustness of performance and resilience to aerial maneuvers. We describe the design of the gripper, the overall system and the approach used to obtain the feedback from the sensors which is crucial for robust aerial grasping and for high level planning of the assembly task. Various outdoor experiments were conducted on fabricated bricks to verify the proposed approach and to demonstrate the ability of the system to autonomously build a wall.
TYPES OF MOBILE EDUCATIONAL GAMES FOR CHILDREN IN PRIMARY SCHOOL

M. Gocheva, E. Somova, V. Angelova, N. KasaklievUniversity of Plovdiv "Paisii Hilendarski" (BULGARIA)
Review of Flower Pollination Algorithm: Applications and Variants

Flower Pollination Algorithm (FPA) is a nature inspired metaheuristic algorithm that mimics the behavior of pollination of flowering plants. FPA has been created by Yang in 2012 showing its better performance than other metaheuristic algorithms. Many variants of FPA have been established since 2012 to solve complex problems of different research domains. In this paper, application of different FPA variants are discussed based on modifications, tuning parameters and hybridizing FPA with other algorithms. This paper is very useful for researchers as it introduces general pollination process of flowers, its conversion to algorithm, application of FPA in different domains and particularly to parameter estimation problem of PV cells and modules. Future recommendations have also been given for new researchers.
Lateral bipolar junction transistor on a silicon photonics platform

Integration of active electronics into photonic systems is necessary for large-scale photonic integration. While heterogeneous integration leverages high-performance electronics, a monolithic scheme can coexist by aiding the electronic processing, improving overall efficiency. We report a lateral bipolar junction transistor on a commercial silicon photonics foundry process. We achieved a DC current gain of 10 with a Darlington configuration, and using measured S-parameters for a single BJT, the available AC gain was at least 3dB for signal frequencies up to 1.1 GHz. Our single BJT demonstrated a transimpedance of 3.2mS/ μ m, which is about 70 times better than existing literature.
An Improved Genetic Algorithm

Aiming at the disadvantage of premature convergence of basic genetic algorithm, an adaptive simulated annealing genetic tabu search algorithm is proposed. This algorithm fully combines the global convergence and adaptability of simulated annealing algorithm and the strong climbing ability and high efficiency of tabu search strategy. It has strong convergence and adaptability. The simulation results of the adaptive simulated annealing genetic tabu search algorithm are given and compared with the basic genetic algorithm and simulated annealing algorithm. The simulation results show that the algorithm has better convergence and optimization performance, and can better solve the combinatorial optimization problem.
Framework for Automated Earthquake Event Detection Based on Denoising by Adaptive Filter

Automated detection of the P-wave arrival for real-time earthquake-early-warning-system employing conventional short-time-average/long-time-average (STA/LTA) algorithm is prone to suffer from erroneous detection due to high background noise. This paper introduces an enhanced variable step-size least mean square (EVSSLMS) algorithm for considerably improving the P-wave detection. The proposed event detection scheme employs the EVSSLMS algorithm for de-noising the seismic data followed by conventional STA/LTA algorithm to detect arrival of the P-wave. The EVSSLMS algorithm outperforms the existing trigonometric least mean square (TLMS), variable step-size LMS (VSSLMS), least mean logarithmic square (LMLS) and variable α-LMLS algorithms in terms of convergence speed and steady-state error and achieves a significant enhancement in detection accuracy by 42%, 33%, 25% and 23% at low signal-to-noise ratio (SNR) as compared to the existing TLMS, VSSLMS, LMLS, and variable-α LMLS algorithms, respectively. Moreover, a high-speed and low-complexity very-large-scale-integrated (VLSI) architecture has been implemented on both field-programmable-gate array (FPGA) and application-specific-integrated circuit (ASIC) platforms for realtime applications. Comparison of architectural performance of the proposed scheme with that of architecture designed to realize variable-α LMLS algorithm exhibits 36% less slice-delay-product whereas, its ASIC implementation exhibits 7.27% less areadelay-product, 9.31% less energy-per-sample with 5.2% more maximum achievable frequency.
An Extended Method for Saccadic Eye Movement Measurements Using a Head-Mounted Display

Saccadic eye movement is an important ability in our daily life and is especially important in driving and sports. Traditionally, the Developmental Eye Movement (DEM) test and the King–Devick (K-D) test have been used to measure saccadic eye movement, but these only involve measurements with “adjusted time”. Therefore, a different approach is required to obtain the eye movement speed and reaction rate in detail, as some are rapid eye movements, while others are slow actions, and vice versa. This study proposed an extended method that can acquire the “rest time” and “transfer time”, as well as the “adjusted time”, by implementing a virtual reality-based DEM test, using a FOVE virtual reality (VR) head-mounted display (HMD), equipped with an eye-tracking module. This approach was tested in 30 subjects with normal vision and no ophthalmologic disease by using a 2-diopter (50-cm) distance. This allowed for measurements of the “adjusted time” and the “rest time” for focusing on each target number character, the “transfer time” for moving to the next target number character, and recording of the gaze-tracking log. The results of this experiment showed that it was possible to analyze more parameters of the saccadic eye movement with the proposed method than with the traditional methods.
On Parameter Tuning in Meta-Learning for Computer Vision

Learning to learn plays a pivotal role in meta-learning (MTL) to obtain an optimal learning model. In this paper, we investigate image recognition for unseen categories of a given dataset with limited training information. We deploy a zero-shot learning (ZSL) algorithm to achieve this goal. We also explore the effect of parameter tuning on performance of semantic auto-encoder (SAE). We further address the parameter tuning problem for meta-learning, especially focusing on zero-shot learning. By combining different embedded parameters, we improved the accuracy of tuned-SAE. Advantages and disadvantages of parameter tuning and its application in image classification are also explored.
(M)ad to See Me?

Advertising is an unavoidable albeit a frustrating part of mobile interactions. Due to limited form factor, mobile advertisements often resort to intrusive strategies where they temporarily block the user's view in an attempt to increase effectiveness by forcing the user's attention. While such strategies contribute to advertising awareness and effectiveness, they do so at the cost of degrading the user's overall experience and can lead to frustration and annoyance. In this paper, we contribute by developing Perceptive Ads as an intelligent advertisement placement strategy that minimizes disruptions caused by ads while preserving their effectiveness. Our work is the first to simultaneously consider the needs of users, app developers, and advertisers. Ensuring the needs of all stakeholders are taken into account is essential for the adoption of advertising strategies as users (and indirectly developers) would reject strategies that are disruptive but effective, while advertisers would reject strategies that are non-disruptive but inefficient. We demonstrate the effectiveness of our technique through a user study with N = 16 participants and two representative examples of mobile apps that commonly integrate advertisements: a game and a news app. Results from the study demonstrate that our approach improves perception towards advertisements by 43.75% without affecting application interactivity while at the same time increasing advertisement effectiveness by 37.5% compared to a state-of-the-art baseline.
Quantum Copy-Protection from Hidden Subspaces.

Quantum copy-protection is an innovative idea that uses the no-cloning property of quantum information to copy-protect programs and was first put forward by [Aar09]. The general goal is that a program distributor can distribute a quantum state $|\Psi\rangle$, whose classical description is secret to the users; a user can use this state to run the program P on his own input, but not be able to pirate this program P or create another state with the same functionality. In the copy-protection with oracle setting, the user has access to a public oracle and can use the given quantum state and the oracle to compute on his/her own input for polynomially many times. However, the user is not able to produce an additional program(quantum or classical) that computes the same as P on almost all inputs. We present a first quantum copy protection scheme with a classical oracle for any unlearnable function families. The construction is based on membership oracles for hidden subspaces in $\mathbb{F}_2^n$, an idea derived from the public key quantum money scheme in[Aar12]. We prove the security of the scheme relative to a classical oracle, namely, the subspace membership oracle with the functionality of computing the secret function we want to copy-protect. The security proof builds on the quantum lower bound for the Direct-Product problem ([Aar12],[BDS16]) and the unlearnability of the copy-protected functions. We also show that existence of quantum copy protection and the quantum hardness of Learning-with-Errors (LWE) will imply publicly verifiable quantum money.
Introduction to Machine Learning with Python

Earlier in this book, we mentioned that one reason to learn Python is its popularity in the scientific computing and machine learning communities. This chapter introduces machine learning by demonstrating how to build a classifier from first principles. The lab section then introduces a set of popular libraries for data analysis.
A Cluster Autoscaler Based on Multiple Node Types in Kubernetes

Kubernetes, as a production-grade container orchestration system, has been used in varieties of fields on a large scale. However, as used more and more widely, the appropriate selection of auto scaling schemes in kubernetes also faces considerable challenges in their respective application fields. Based on multiple node types with image pre-loaded, this paper proposes an improved automatic scaling scheme that combines the advantages of different types of nodes in the scaling process. The results show that, compared with the default auto scaler, it can improve the performance of the system under rapid load pressure and reduce the instability within the running clusters.
Anomaly Detection in Smart Homes Using Bayesian Networks

The health and safety of elderly and disabled patients who cannot live alone is an important issue.Timely detection of sudden events is necessary to protect these people, and anomaly detection in smart homes is an efficient approach to extracting such information.In the real world, there is a causal relationship between an occupant's behaviour and the order in which appliances are used in the home.Bayesian networks are appropriate tools for assessing the probability of an effect due to the occurrence of its causes, and vice versa.This paper defines different subsets of random variables on the basis of sensory data from a smart home, and it presents an anomaly detection system based on various models of Bayesian networks and drawing upon these variables.We examine different models to obtain the best network, one that has higher assessment scores and a smaller size.Experimental evaluations of real datasets show the effectiveness of the proposed method.
Tackling COVID-19 is a crucible for privacy

"Tackling COVID-19 is a crucible for privacy." Journal of Technology in Human Services, 38(2), pp. 89–90
Personality Measurement Design for Ontology Based Platform using Social Media Text

Human behavior quantification is an essential part of psychological science.One of the cases is measuring human personality.Social media provide rich text, which can be beneficial as a data source to get valuable insight.Previous researches show that social media offered favorable circumstances for psychological researchers by tracking, analyzing, and predicting human character.In this research, we propose a personality measurement design to help to assess human character through linguistic usage from human digital traces.We construct our model by classifying social media text to the pre-determined personality facet from Big Five personality traits, mapping the knowledge to the ontology model, and implementing the model as a platform dictionary.Our model is based on the Indonesian language, which to the best of our knowledge is the first in the subject area.The platform is running effectively by using a well-established sorting algorithm, called the radix tree.Our objective is to support psychological science in adapting to a new technological era.
Pretraining on Non-linguistic Structure as a Tool for Analyzing Learning Bias in Language Models

We propose a novel methodology for analyzing the encoding of grammatical structure in neural language models through transfer learning. We test how a language model can leverage its internal representations to transfer knowledge across languages and symbol systems. We train LSTMs on non-linguistic, structured data and test their performance on human language to assess which kinds of data induce generalizable encodings that LSTMs can use for natural language. We find that models trained on structured data such as music and Java code have internal representations that help in modelling human language, and that, surprisingly, adding minimal amounts of structure to the training data makes a large difference in transfer to natural language. Further experiments on transfer between human languages show that zero-shot performance on a test language is highly correlated with syntactic similarity to the training language, even after removing any vocabulary overlap. This suggests that the internal representations induced from natural languages are typologically coherent: they encode the features and differences outlined in typological studies. Our results provide insights into how neural networks represent linguistic structure, and also about the kinds of structural biases that give learners the ability to model language.
Bitcoin Governance as a Decentralized Financial Market Infrastructure

Bitcoin is the oldest and most widely established cryptocurrency network with the highest market capitalization among all cryptocurrencies. Although bitcoin (with lowercase b) is increasingly viewed as a digital asset belonging to a new asset class, the Bitcoin network (with uppercase B) is a decentralized financial market infrastructure (dFMI) that clears and settles transactions in its native asset without relying on the conventional financial market infrastructures (FMIs). To be a reliable asset class as well as a dFMI, however, Bitcoin needs to have robust governance arrangements; whether such arrangements are built into the protocol (i.e., on-chain governance mechanisms) or relegated to the participants in the Bitcoin network (i.e., off-chain governance mechanisms), or are composed of a combination of both mechanisms (i.e., a hybrid form of governance).This paper studies Bitcoin governance with a focus on its alleged shortcomings. In so doing, after defining Bitcoin governance and its objectives, the paper puts forward an idiosyncratic governance model whose main objective is to preserve and maximize the main value proposition of Bitcoin, i.e., its censorship-resistant property, which allows participants to transact in an environment with minimum social trust. Therefore, Bitcoin governance, including the processes through which Bitcoin governance crises have been resolved and the standards against which the Bitcoin Improvement Proposals (BIPs) are examined, should be analyzed in light of the prevailing narrative of Bitcoin as a censorship-resistant store of value and payment infrastructure. Within such a special governance model, this paper seeks to identify the potential shortcomings in Bitcoin governance by reference to the major governance crises that posed serious threats to Bitcoin in the last decade. It concludes that the existing governance arrangements in the Bitcoin network have been largely successful in dealing with Bitcoin’s major crises that would have otherwise become existential threats to the Bitcoin network.
Application of Blockchain in Agri-Food Supply Chain

Supply chain traceability is one of the most important problems faced by all organizations globally. Tracking any commodity is a tedious job and requires a lot of manual attention. The existing system uses a centralised database system to track a commodity. Blockchain is a decentralised, immutable ledger managed by a cluster of computers. The very nature of blockchain makes it suitable technology for traceability in a supply chain. Blockchain creates immutable records. Once a transaction is stored in the blockchain, it is impossible to tamper it illegally. This immutability of blockchain builds trust among the participants that conduct business in a lack of trust environment. Using consensus protocols, blockchain builds trust between the various participants. Blockchain provides traceability since immutable records are added in blockchain; it is easy to track the source of the product. This paper illustrates the Application of Blockchain in Agri-Food Supply Chain. It unfolds how blockchain technology can be integrated to an Agri Supply Chain and improves the traceability. This integration enables companies to quickly track unsafe products back to their source and see where else they have been distributed. This integration can result to prevent illness and save lives, as well as reduce the cost of product recalls.
Personalized News Recommendation Based on the Text and Image Integration

The personalized news recommendation has been very popular in the news recommendation field. In most research, the picture information in the news is ignored, but the information conveyed to the users through pictures is ... | Find, read and cite all the research you need on Tech Science Press
Discrete Embedding for Latent Networks

Discrete network embedding emerged recently as a new direction of network representation learning. Compared with traditional network embedding models, discrete network embedding aims to compress model size and accelerate model inference by learning a set of short binary codes for network vertices. However, existing discrete network embedding methods usually assume that the network structures (e.g., edge weights) are readily available. In real-world scenarios such as social networks, sometimes it is impossible to collect explicit network structure information and it usually needs to be inferred from implicit data such as information cascades in the networks. To address this issue, we present an end-to-end discrete network embedding model for latent networks DELN that can learn binary representations from underlying information cascades. The essential idea is to infer a latent Weisfeiler-Lehman proximity matrix that captures node dependence based on information cascades and then to factorize the latent Weisfiler-Lehman matrix under the binary node representation constraint. Since the learning problem is a mixed integer optimization problem, an efficient maximal likelihood estimation based cyclic coordinate descent (MLE-CCD) algorithm is used as the solution. Experiments on real-world datasets show that the proposed model outperforms the state-of-the-art network embedding methods.
Systolic-CNN: An OpenCL-defined Scalable Run-time-flexible FPGA Accelerator Architecture for Accelerating Convolutional Neural Network Inference in Cloud/Edge Computing

This paper presents Systolic-CNN, an OpenCLdefined scalable, run-time-flexible FPGA accelerator architecture, optimized for performing the low-latency, energy-efficient inference of various convolutional neural networks (CNNs) in the context of multi-tenancy cloud/edge computing. Systolic-CNN adopts a highly pipelined and parallelized 1-D systolic array architecture, which efficiently explores both spatial and temporal parallelism for accelerating CNN inference on FPGAs. SystolicCNN is highly scalable and parameterized, which can be easily adapted by users to achieve 100% utilization of the coarsegrained computation resources (i.e., DSP blocks) for a given FPGA. In addition, Systolic-CNN is run-time-flexible, which can be time-shared, in the context of multi-tenancy cloud or edge computing, to accelerate a variety of CNN models at run time without the need of recompiling the FPGA kernel hardware nor reprogramming the FPGA. The experiment results based on an Intel Arria 10 GX FPGA Development board show that Systolic-CNN, when mapped with a single-precision data format, can achieve 100% utilization of the DSP block resource and an average inference latency of 10ms, 84ms, 1615ms, and 990ms per image for accelerating AlexNet, ResNet-50, RetinaNet, and Light-weight RetinaNet, respectively. The peak computational throughput is measured at 80–170 GFLOPS/s across the acceleration of different CNN models. Codes are available at https://github.com/PSCLab-ASU/SystolicCNN.
Spherical Motion Dynamics of Deep Neural Networks with Batch Normalization and Weight Decay.

We comprehensively reveal the learning dynamics of deep neural networks (DNN) with batch normalization (BN) and weight decay (WD), named as Spherical Motion Dynamics (SMD). Our theorem on SMD is based on the scale-invariant property of weights caused by BN, and regularization effect of WD. SMD shows the optimization trajectory of weights is like a spherical motion; and a new indicator, angular update is proposed to measure the update efficiency of DNN with BN and WD. We rigorously prove that the angular update is only determined by pre-defined hyper-parameters (i.e. learning rate, WD parameter and momentum coefficient), and provide their quantitative relationship. Most importantly, the quantitative result of SMD can perfectly match the empirical observation in complex and large scale computer vision tasks like ImageNet and COCO with standard training schemes. SMD can also yield reasonable interpretations on some phenomena about BN from an entirely new perspective, including avoidance of vanishing and exploding gradient, no risk of being trapped into sharp minima, and sudden drop of loss when shrinking learning rate. Further, to present the practical significance of SMD, we discuss the connection between SMD and commonly used learning rate tuning scheme: Linear Scaling Principle.