Reviewerly/exHarmony huggingface dataset:
Protecting Deep Learning: Could the New EU-Trade Secrets Directive Be an Option for the Legal Protection of Artificial Neural Networks?

Deep learning based on artificial neural networks is currently the most promising machine learning method in the field of AI. This paper distinguishes four legal protection objects of artificial neural networks per se: the training data, the topology, the weights as an expression of the trained network, and the specific training method. Both archetypical intellectual property (IP) rights, copyright and patent law, fall to some extent short of protecting these objects. This article examines whether and to what extent trade secret protection could be a suitable or supplementary legal protection tool. Trade secret protection is, among other advantages, flexible. Its greatest weakness, however, is that it allows for reverse engineering which in turn limits its application as a legal protection tool. In the case of an adaptation, trade secret law could at least temporarily supplement patent law and partially replace the classical anthropocentric copyright law in the field of deep learning.
Extract Accurate 3D Human Skeleton from Video

Extracting 3D skeletons from video faces more problems than extracting 3D skeletons from images. For example, there will be more motion blur and occlusion in the video. But video also has its own unique properties and there is a strong similarity between frames in the video. In this article, we focus on maximizing the utilization of temporal information in the video to extract the accurate 3D human skeleton. Our system is divided into three parts. The first part utilizes unsupervised learning method to split the video into a serious of sub-video according to the content. Human pose has high similarity in this sub-video. The second part is to detect the 2D skeleton in this divided sub-video. In order to establish the connection between the frames and ensure the efficiency, we adopt the convLSTM model [38] in this module. The last part is to map the 2D skeleton sequence detected in the previous step into 3D space, the input is 2D joint point sequence, and the output is the corresponding 3D joint point sequence. In this module, we choose one-dimensional convolution model. This model can build the relationship between frames in the nearby areas of each frame.
Word Graph Network: Understanding Obscure Sentences on Social Media for Violation Comment Detection

Violation comment detection aims to recognize the texts that may violate the governing laws/regulations and cause adverse effect on social media. To avoid being intercepted, violation comments always informal and incomplete in an obscure expression poses challenge to violation detection algorithms. To tackle the problem, we introduce a new language representation model namely Word Graph Network (WGN). By introducing word graph, WGN integrates more syntactic structure information thus is qualified with stronger association and completion capability on detecting informal and incomplete violation comments in social networking scenarios. Our experimental results show that WGN outperforms than the existing state-of-the-art models and even performs best in simulation of real online environment.
Discovering Emerging Patterns from Medical Opinions about the Decrease of Autopsies Performed in a Mexican Hospital

Emerging Pattern Mining (EPM) is a data mining task that finds discriminative characteristics between classes or data sets. In this paper, several EPM algorithms were applied to a data set which contains the opinions of the medical staff from a Mexican hospital about the decrease of autopsies. We consider two attributes as class labels: motives for autopsy acceptance and motives for autopsy rejection in order to find aspects like medical training and medical experience that imply that physicians consider reasons for requesting or rejecting autopsies.
Implementation of Blockchain for Fair Polling System

This paper introduces an idea proposed for the voting system that is completely transparent and tamper-proof. The conventional way of an election in the country presently deals with the problem of vote tampering which raises concerns for every citizen. The time spent in administrative paperwork can be reduced to almost negligible. With the help of Blockchain, Machine Learning, and the Internet of Things polling can be made fairer by using a decentralized network. Moreover, the voter has the power to verify their casted vote for authenticity. The paper presents the requirements for building software-based voting systems that enhance security and reduces the cost of conducting a nationwide election.
Analysis of Adaptive Mutation in Crazy Particle Swarm Optimization

This paper contributes an investigation in the utilization of adaptive mutation in a very prominent edition of particle swarm optimization technique i.e, crazy particle swarm optimization for the betterment of finding out the global best solution. In the field of swarm intelligence, particle swarm optimization is recognized as one of the popular techniques, which have shown an accepted performance in various real world engineering problems. Simultaneously, it has some cons which inspire the researchers for further modifications to get a better output with the improvement of its convergence quality. Here we proposed one enhanced version of Crazy PSO i.e. Adaptive mutated Crazy PSO. After the results of simulating experiment, it has been observed that Adaptive mutated Crazy PSO has exhibited improved results as compared to the basic Crazy PSO. The addition of Adaptive mutation helps in the achievement of better performance in Crazy PSO which has been tested on various benchmark functions
Optical backpropagation training method and its applications

Training an artificial neural network with backpropagation algorithms requires an extensive computational process. Our recent work proposes to implement the backpropagation algorithm optically for in-situ training of both the linear and nonlinear diffractive optical neural networks which enables the acceleration of training speed and improvement on the energy efficiency on core computing modules. We numerically validated that the proposed in-situ optical learning architecture achieves comparable accuracy to the in-silico training with an electronic computer on the task of object classification and matrix-vector multiplication, which further allows adaptation to the system imperfections. Besides, the self-adaptive property of our approach facilitates the novel application of the network for all-optical imaging through scattering media. The proposed approach paves the way for the robust implementation of large-scale diffractive neural networks to perform distinctive tasks all-optically.
Incorporating the Knowledge Distillation to Improve the EfficientNet Transfer Learning Capability

Due to the Deep Learning requirement for a large training dataset, Transfer Learning has become a central method in the field of Computer Vision, which heavily used Deep Learning. Since the adoption of transfer learning in the field, the performance of the models in computer vision is significantly upgraded. The common transfer learning practice in computer vision research is to use state-of-the-art architectures in the ImageNet dataset as the backbone network. The best performing architecture in ImageNet is currently held by EfficientNet, which is a set of architectures that were progressively grown from a baseline architecture. By observing the EfficientNet development approach, it is natural to hypothesize that the smaller EfficientNet architecture is capable to contain the knowledge of the larger architecture. This hypothesis comes from the fact that the larger EfficientNet architecture was grown from the smaller architecture. Therefore, in this study, we experimented if it is beneficial to transfer the knowledge from the larger architecture to the smaller architecture of the EfficientNet. To achieve this goal, we proposed a transfer learning method that uses Knowledge Distillation as the knowledge transfer mechanism from the larger to smaller architecture. We found that the proposed method is able to upgrade the performance of each of the EfficientNet architecture. Several smaller architectures are even able to outperform the larger architecture that was trained using the standard transfer learning method.
Generation of Pseudo-random Numbers Based on Network Traffic

The Pseudo Random Numbers generators can be based on chaotic maps; they are still deterministic functions, so it is possible to predict future results. On the other hand, we have the real random number generators that comply with the mentioned characteristics, this is possible at the cost of high latency and slowness by the use of physical processes. In this article, the use of network traffic in the generation of random sequences is tested. An equation is used to improve the statistical properties of the method. It is verified that network traffic tends to be more chaotic in spaces with a larger number of users. Results show that the method generates very different sequences, but with unequal bit generation. We present a method for the generation of pseudo random numbers based on network traffic, minimizing the repetition of generated sequences.
Implementation of a SVM on an Embedded System: A Case Study on Fall Detection

Edge Computing seeks to bring Machine Learning as close as possible to the source events of interest, providing an almost instant interpretation to data acquired by sensors giving sense to raw data while addressing concerns of particular applications such as latency, privacy and server stress relieve. Due to a lack of research on this particular type of application, we are faced with difficulties both in software and hardware as embedded systems are known to possess serious limitations on its available processing resources. To address this, we make use of the concepts of edge computing and offline programming to accomplish a reliable machine learning model deployment on the microprocessor. By studying real case problem, we can get measurements on the resources required by such an application as well as its performance. In this study, we address the implementation of such an application in an embedded system focusing on the detection of human falls.
Using the Gini Index for a Gaussian Mixture Model

A Gaussian mixture model is a weighted sum of parametric Gaussian components. These parametric density functions are widely used in data mining and pattern recognition. In this work we propose an efficient method to model a density function as a Gaussian mixture through an iterative algorithm that allow us to estimate the parameters of the model for a given data set. For this purpose we use the Gini Index, a measure of the inequality degree between two probability distributions. The Gini Index is obtained by finding the solution of an optimization problem. Our model consists in minimizing the Gini Index between an empirical distribution and a parametric distribution that is a Gaussian mixture. We will show some simulated examples and real data examples, with two widely used datasets, to observe the efficiency and properties of our model.
Blood Flow Velocity Detection of Nailfold Microcirculation Based on Spatiotemporal Analysis

Nailfold microcirculation can reflect the state of health. It is an important research topic to detect the change of microcirculation blood flow velocity. In order to save cost, people usually choose the low-cost micro camera to collect samples. However, the microvascular video collected by such camera is often disturbed by various noises, resulting in poor contrast and clarity of the image. Due to the large number of microvessels, it is time-consuming and laborious to detect the blood flow rate manually, which is inefficient and difficult to accurately detect the subtle changes of the blood flow velocity. At present, the previous research of blood flow velocity detection is mainly focused on the clearer microcirculation video. It is difficult to find any related research of blood flow velocity detection for noise image. For the low-quality nailfold microcirculation video collected by the low-cost microscope camera, we propose an automatic detection method of blood flow velocity based on projection analysis of spatiotemporal image. The method is as follows: firstly, video preprocessing, correlation matching are used to remove jitter and image blur is eliminated by deconvolution, the row mean is used to eliminate reflective area by analyzing the characteristics of noise distribution; secondly, we use cumulative background modeling to segment blood vessels and propose an automatic detection algorithm of blood vessel centerline; thirdly, the direction of binary spatiotemporal image is detected by using rotation projection, and then the blood flow velocity is calculated. The experimental results show that the proposed method can detect the blood flow velocity of microcirculation automatically and efficiently. Meanwhile, the average correlation coefficient between the proposed method and the manual measurement standard value is 0.935.
Image Memorability Prediction Based on Machine Learning

With the rapid development and popularization of computer network and multimedia technology, we are continuously exposed to photographs and images. Some images are easy to be remembered, but others are ignored or quickly forgotten. Previous studies have shown that image memorability is an intrinsic property of an image, which is used to measure the degree to which an image is later remembered or forgotten. Many works have shown that visual factors that influence the image memorability, such as highly memorable visual content, image depth information. Prior research has shown that image depth information has a positive relationship with its memorability score. Based on AMNet, this paper makes few improvements and utilizes depth cues to predict the image memorability scores. In this paper, the depth cues of the images are combined with the original image features to predict the final memorability score for the given image by utilizing the late fusion methodology. Experiments conducted on public image memorability datasets evaluate the effectiveness of the proposed model.
Research on Technology of Embedded System Security Protection Component

With the development of the Internet of Things (IoT), it has been widely deployed. As many embedded devices are connected to the network and massive amounts of security-sensitive data are stored in these devices, embedded devices in IoT have become the target of attackers. The trusted computing is a key technology to guarantee the security and trustworthiness of devices' execution environment. This paper focuses on security problems on IoT devices, and proposes a security architecture for IoT devices based on the trusted computing technology. This paper implements a security management system for IoT devices, which can perform integrity measurement, real-time monitoring and security management for embedded applications, providing a safe and reliable execution environment and whitelist-based security protection for IoT devices. This paper also designs and implements an embedded security protection system based on trusted computing technology, containing a measurement and control component in the kernel and a remote graphical management interface for administrators. The kernel layer enforces the integrity measurement and control of the embedded application on the device. The graphical management interface communicates with the remote embedded device through the TCP/IP protocol, and provides a feature-rich and user-friendly interaction interface. It implements functions such as knowledge base scanning, whitelist management, log management, security policy management, and cryptographic algorithm performance testing.
Extracting the Collaboration of Entity and Attribute: Gated Interactive Networks for Aspect Sentiment Analysis

Aspect-based sentiment analysis (ABSA) is composed of aspect term sentiment analysis (ATSA) and aspect category sentiment analysis (ACSA). In the task of ACSA, some existing methods simply bound the aspect category (entity and attribute) as an integrated whole or adopt a randomly initialized embedding to represent the aspect category, which introduces a defective representation of aspect and leads to the ignorance of independent contextual sentiment of entity and attribute. Some other methods only consider the entity and disregard the attribute in predicting the sentiment polarity of aspect category, which leads to the ignorance of the collaboration between the entity and attribute. To this end, we propose a Gated Interactive Network (GIN) for aspect category sentiment analysis in this paper. To be specific, for each context and the corresponding aspect, we adopt two attention-based networks to learn the contextual sentiment for the entity and attribute independently and interactively. Further, based on the interactive attentions learned from entities and attributes, the coordinative gate units are exploited to reconcile and purify the sentiment features for the aspect sentiment prediction. Experimental results on two benchmark datasets demonstrate that our proposed model achieves state-of-the-art performance in the task of ACSA.
Trust and Reputation in Heterogeneous Multi-Agent System for Production Processes Based on the Market Economy

This article presents an approach in which software agents fulfill transportation tasks in a production facility. Therefore, the coordination of different agents is managed in a decentralized way using a multi-agent system. Various aspects of the market economy are implemented into the behaviors of the agents. Since the heterogeneous system consists not only of automated transport systems but also of the human worker, important features of the interaction between humans, trust and reputation, are implemented into the agents' behaviors. The overall approach is simulated and the benefits of those aspects for the overall system e.g. workload of the transport resources is investigated.
Aerial Coverage Planning for Areas Hidden from the View of a Moving Ground Vehicle

We consider the problem of path planning for a UAV, deployed to provide sensor coverage ahead of a moving ground vehicle. The ground vehicle travels a fixed route through an uncertain environment and requires information about the area ahead. Given this route, the UAV planner calculates the regions to be covered and the time by which each must be covered, as an orienteering Problem with Time Windows (OPTW) and solves it using a Mixed Integer Linear Program (MILP). To improve scalability, we prove that the optimization can be partitioned into a set of smaller problems, each of which may be solved independently without loss of overall solution optimality. Finally, we demonstrate a method of limited loss partitioning, which can perform a trade-off between improved solution time and a bounded objective loss. All of our results are validated in simulation.
Controllable non-Markovianity in phase relaxation

Abstract Recently remarkable progress in quantum technology has been witnessed. In view of this it is important to investigate an open quantum system as a model of such quantum devices. Quantum devices often require extreme conditions for the devices to operate: dynamics can be non-Markovian here. This observation necessitates us to investigate a non-Markovian open quantum system, both theoretically and experimentally. In this paper, we report two important results: (1) exact solution of a simple but non-trivial theoretical model and (2) demonstration of this model by NMR experiments, where non-Markovianity is continuously controllable. We observe qualitative agreement between theory and experiment.
A Valorização da Enfermagem no Enfrentamento da COVID-19

Currently, Nursing is the profession that represents the largest health workforce in the world, according to the World Health Organization (WHO), and was considered, for the 17th consecutive year, according to the research carried out by the Gallup Organization, to most reliable and ethical profession among all evaluated. In Brazil, according to data from the Federal Nursing Council (COFEN), there are more than 2.3 million nursing professionals (including nurses, technicians, assistants and midwives) who work in the scope of care, management, teaching and research.
Extensions of the combinatorial game <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e686" altimg="si201.svg"><mml:mrow><mml:mo>(</mml:mo><mml:mi>K</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math>-Wythoff

(K,t)-Wythoff is a restriction of classical t-Wythoff, where a player is restricted to remove a multiple of K tokens in each move (K is a fixed positive integer). In this paper, we define a class of new games obtained from (K,t)-Wythoff by adjoining to it an appropriate subset of its P-generators as additional moves. For all these extended new games, the sets of their P-positions and the corresponding winning strategies are given. Meanwhile, the equivalences among these new games are analyzed, and the results show that only few special P-generators play a key role in the formation of the new games.
An Exploration of the Communicative Efficacy of HIV/AIDS IEC Materials Among Secondary School Teenagers in Harare

The aim of this contribution is to determine the communicative efficacy of selected print-based HIV and AIDS information education and communication (IEC) materials (posters, leaflets and brochures) among secondary school teenagers (13–19 years) in Harare. Data were collected over a period of 2 months from six schools, selected using multistage cluster sampling. It incorporated use of a self-administered questionnaire involving a sample of 750 teenagers, and 6 focus group discussions (FGDs), each comprised of 10 purposefully sampled participants. The survey investigated a number of indicators of communicative efficacy, wherein the IEC materials were found to be clear by 38.4 per cent ( n = 288), informative by 45.2 per cent ( n = 339), credible by 80.5 per cent ( n = 604), appealing by 64.7 per cent ( n = 485), important by 69.5 per cent ( n = 521) and acceptable by 54 per cent ( n = 405) of the respondents. The outcomes of the FGDs showed that, although HIV and AIDS IECs were generally believed to be appealing and to significantly increase awareness among teenagers, these were also considered somewhat unclear and inaccessible. Moreover, perceptions were strongly inclined towards use of Shona language, social media and elimination of fear appeals in HIV information. Thus, in addition to adopting young people-centred communication modes, finding an appropriate balance between complex language and efficacy of HIV prevention messages is imperative.