Reviewerly/exHarmony huggingface dataset:
Triplet Attention Network for Video-Based Person Re-Identification

Video-based person re-identification (re-ID) aims at retrieving person across non-overlapping camera and has achieved promising results owing to deep convolutional neural network. Due to the dynamic properties of the video, the problems of background clutters and occlusion are more serious than image-based person Re-ID. In this letter, we present a novel triple attention network (TriANet) that simultaneously utilizes temporal, spatial, and channel context information by employing the self-attention mechanism to get robust and discriminative feature. Specifically, the network has two parts, where the first part introduces a residual attention subnetwork, which contains channel attention module to capture cross-dimension dependencies by using rotation and transformation and spatial attention module to focus on pedestrian feature. In the second part, a time attention module is designed to judge the quality score of each pedestrian, and to reduce the weight of the incomplete pedestrian image to alleviate the occlusion problem. We evaluate our proposed architecture on three datasets, iLIDS-VID, PRID2011 and MARS. Extensive comparative experimental results show that our proposed method achieves state-of-the-art results.
Tensor Data Representation, Privacy Protection and Heterogeneous Computing for Internet of Things: a Survey

The Internet of Things (IoT) is the Internet of everything connected. In the era of rapid development of the IoT, the paper summarizes the current progress of the IoT, and then finds out the high-dimensional tensor data generated by the IoT. However, with the increase of tensor data, the computing speed of the computer will decrease, and there will be corresponding privacy protection and compute problems. Therefore, this paper lists two applications that deal with different IoT scenarios, namely privacy protection, and heterogeneous computing. This paper holds that while processing data in parallel efficiently, protecting the security of data is the key point of the future development trend of the IoT.
A Prospective Model of a Digital Epidemiological Surveillance 4.0 Based on the Peruvian Public Health System

The health-disease situation of the population and health care delivery services are continuously monitored by epidemiological surveillance systems and health information systems, but since the first cases of COVID-19 appeared in December 2019 and spread globally, each country tried to control the virus by activating their own health protocols, but they became insufficient to face the pandemic and every control attempts seem impossible to all humanity, even more without the support of information technologies, which provides a rapid response for decision-making. Being that the reason why surveillance systems should help mitigate the spread of diseases and, at the same time, plan resources to protect the health status of citizens. Therefore, the aim of this article is to propose a model of a digital epidemiological surveillance 4.0, considering the current technological revolution and the constant evolution of the Internet. This model is divided into 4 phases: situational analysis, planning, execution and evaluation, and supported on 4 key components: logistic support, digital privacy and data protection, communication and technology, and disruptive innovation. A quantitative type of research was applied in a sample of 58 participants, all working as health staff in Chiclayo-Peru, to whom a data collection instrument, based on digital forms and interviews with medical epidemiologists, was applied, that information, which mainly considers the perception and perspectives of the epidemiological surveillance system, served as the basis for the proposed model.
Cryogenic Adiabatic Transistor Circuits for Quantum Computer Control

Adiabatic and reversible computing have a previously unappreciated benefit that may make them important for quantum computing applications. While adiabatic and reversible methods did not catch on initially, they demonstrated ways to manage the location where waste energy is turned into heat. This work shows how to exploit this degree of freedom, explaining it with introduction of a new adiabatic logic family called Quiet 2-Level Adiabatic Logic (Q2LAL). In a hybrid room-temperature/cryogenic computing environment, such as a quantum computer, moving energy from the cryostat to room temperature before turning it into heat can save orders of magnitude in refrigeration energy and reduce electrical noise. The combination of the Q2LAL circuit and energy management strategy increases the range of applications suitable for adiabatic computing.
Annotating a Chinese Corpus for Aspectual Study

The best way to test a theory is to resort to the real data. In this chapter, I will first present a guideline for annotating a Chinese corpus based on the theory I proposed in Chaps. 3 and 4 .
Total Variation Gamma Correction Method for Tone Mapped HDR Images

Tone mapping methods aim to display a high dynamic range (HDR) image on a common 8-bit liquid crystal display by compressing its dynamic range. Both color rendering and contrast are two important issues in the development of tone mapping methods. In this paper, we propose a variational method to generate low dynamic range (LDR) images by using localized Gamma correction for HDR images to deal with color rendering and contrast issues. Our idea is to employ a weight map that controls localized Gamma correction in each pixel, and the weights are determined by minimizing the differences between the contrast of the original HRD image and that of the LDR image at nearby pixels. By imposing the regularization of the weight map, the total variational term for the weights is incorporated in the objective function for Gamma correction process. Numerical results based on widely-used HDR images are reported to illustrate the effectiveness of the proposed method and the visibility of the details in tone mapped images compared with the other testing methods.
UAV Training Control and Evaluation Method Based on Dynamic Task Construction

In view of the problems such as high cost, low training efficiency and lack of versatility, a design scheme of UAV simulation training system based on dynamic mission construction is proposed, and the reproducing dynamic mission environment simulation technology and UAV training control and result evaluation and method are described in detail. The evaluation system of UAV simulation training results based on multi-factor influence data is constructed, which can effectively guide the simulation training of multi-type UAV and improve the overall training effect of UAV operators by synthesizing historical training data and real-time training data.
DQN Algorithm Based on Target Value Network Parameter Dynamic Update

In the deep Q-network of the deep reinforcement learning algorithm, the parameters of the action-value network are copied to the target value network after every <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> iterations, and the interval <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> of the update network is unchanged. However, this method simply updates the parameters of the target value network, causing the traditional DQN algorithm to converge slowly and become unstable after convergence. In response to this issue, this paper proposes a DQN algorithm based on target value network parameter dynamic update. Use episode rewards to control the speed of updating the target value network parameters to speed up the algorithm's convergence speed and improve its stability. The TDU-DQN algorithm and the DQN algorithm are applied to Mountain Car and CartPol problems. The experimental results show that the TDU-DQN algorithm has a faster convergence speed and higher stability than the traditional DQN algorithm. It proves the effectiveness of the TDU-DQN algorithm.
A Relevant SNC Application for Data Computation using Python Programming

Multiple applications of cloud servicing can be seen in the field of logical programming as well as IT industries. Complex computations over local machines may demand for plenty of system resources thereby delaying the data processing operations. In order to achieve speed in processing one must opt for cloud computing techniques. Extensive maneuver of cloud services is desirable for scientific computation of user data and application. This will require a platform designed in a way to meet the specific requirements of individual users, providing an ease for moving their data and applications over different devices. Symbolic-Numeric Computation using cloud service platform is presented in the paper. In this approach user tasks are presented in the form of symbolic expressions using languages like Java, C/C++, APIs etc. Proposed work employs Python programming for carrying out compilation process.
Approximate Gradient Coding with Optimal Decoding

In distributed optimization problems, a technique called gradient coding, which involves replicating data points, has been used to mitigate the effect of straggling machines. Recent work has studied approximate gradient coding, which concerns coding schemes where the replication factor of the data is too low to recover the full gradient exactly. Our work is motivated by the challenge of creating approximate gradient coding schemes that simultaneously work well in both the adversarial and stochastic models. To that end, we introduce novel approximate gradient codes based on expander graphs, in which each machine receives exactly two blocks of data points. We analyze the decoding error both in the random and adversarial straggler setting, when optimal decoding coefficients are used. We show that in the random setting, our schemes achieve an error to the gradient that decays exponentially in the replication factor. In the adversarial setting, the error is nearly a factor of two smaller than any existing code with similar performance in the random setting. We show convergence bounds both in the random and adversarial setting for gradient descent under standard assumptions using our codes. In the random setting, our convergence rate improves upon block-box bounds. In the adversarial setting, we show that gradient descent can converge down to a noise floor that scales linearly with the adversarial error to the gradient. We demonstrate empirically that our schemes achieve near-optimal error in the random setting and converge faster than algorithms which do not use the optimal decoding coefficients.
The Condition and Strategies to Improve Knowledge Management Capabilities of Hospital (Indonesian Context)

Hospitals as the main health service institutions must be able to improve the quality of their services, especially to increase knowledge management skills, this research will focus on the areas of social interaction capabilities, IT characteristics, and knowledge leadership. Based on primary data collected through questionnaires from hospital leaders (33 hospitals) in DKI Jakarta (Indonesia) which were analyzed through descriptive statistics, it is found that the conditions of factors that affect the hospital's knowledge management capabilities. And based on the current condition analysis, the results of this study provide recommendations on what action plans the hospital should take to improve service quality through increased knowledge management capabilities.
Model of Mind Mapping to Compilation of Audit Report

Auditing is a judgmental process that requires the professional judgments of auditors in both the performance and reporting stages. Psychological theories (dual code theory and load theory) state that visual information can make better judgments and decisions than its linear functions (states) by stimulating the nonlinear functions of the human brain. Thus, in the present study, the mental mapping model based on the above-mentioned psychological theories has been analyze to compile the audit report using statistical tests such as binomial test, t-test, Kolmogorov test and Friedman test. In order to design a mind mapping model, 53 independent auditors and academic experts were surveyed in 2020 to first extract data related to the main factors influencing reporting and then to use it to develop and design a mind mapping model. The model of mind map extracted and compiled in the present study can be use as an effective tool in compiling the auditor's report as a desirable and reliable adjustment of the audit report.
Artificial Intelligence Based Music Composition System-Multi Algorithmic Music Arranger(MAGMA)

A number of problems such as diagnosis, decision-making and optimisation were successfully solved through the methods of artificial intelligence (AI). However, any AI algorithm used for creative problems needs some replacement mechanism for human creative spark, as the computer has no creative potential. Randomness may not be the only mechanism to construct a creative structure, as created by a random number generator. This paper examines AI for music composition. In particular, the paper introduces MAGMA, which uses three different AI algorithms for music generation.
Using binocular vision to measure wave characteristic

The paper presented a new wave acquisition stereo system to estimate significant wave height based on stereo (binocular) vision. Using binocular vision, wave transformation in near shore water can be measured in wave altitude. The total measurement numbers are 1024 times. Wave vertical altitudes can be extracted from the datum according to binocular vision. With the help of 3D Fourier transform, the wave surface data can be turned into wave spectrum. By analyzing the wave spectrum, significant wave height can be estimated. Experimental results have shown that the method is effective.
Análisis de las fuentes de información que influyen en las opiniones del alumnado sobre la vacuna frente a la COVID-19

espanolEl contexto actual de la pandemia de COVID-19 ha producido en la sociedad cambios rapidos que, para una poblacion, con sus actuales niveles de formacion cientifica, son dificiles de asimilar. La informacion sobre el COVID-19 que viaja tanto por las redes como por los medios de comunicacion, o a traves de comunicacion directa por familiares, amistades o profesionales biosanitarios, etc. ejercen una influencia directa sobre la percepcion de la poblacion acerca de la aceptacion o no de la vacunacion. Este trabajo realizado con estudiantes del Grado en Educacion Infantil de la Universidad de Malaga muestra un analisis preliminar, que identifica las distintas fuentes de informacion que estos estudiantes manifiestan sobre las vacunas del COVID-19 y su posible influencia en las decisiones sobre su propia vacunacion. EnglishThe current context of the COVID-19 Pandemic has produced rapid changes in society that are difficult to assimilate for a general population. Information about COVID-19 that travels both through networks and through the media, or through direct communication by family members, friends, or professionals, etc. they exert a direct influence on the perception of the population about the acceptance or not of vaccination. This work carried out with the students of the Degree in Early Childhood Education in the University of Malaga shows a preliminary analysis, which identifies the different sources of information that these students manifest about COVID-19 vaccines and their possible influence on decisions about their vaccination.
One Versus All: Best Practices in Combining Multi-hazard Damage Imagery Training Datasets for Damage Detection for a Deep Learning Neural Network

Accurate and timely damage assessment is important after any natural disaster event. Accurate damage assessments enhance the efficient distribution of resources. Building damage levels are an important outcome of damage assessment, especially in urban areas. Although at present most building damage assessments are collected manually from post-disaster satellite images or aerial photographs, efforts are now underway to automate the process. Some of these efforts use deep learning algorithms to first identify buildings and then to classify them into damage levels. One of these efforts initiated in 2019, through the Defense Innovation Unit (DIU) and with Humanitarian Assistance and Disaster Recovery (HADR) organizations, created a multi-hazard training dataset using high-resolution satellite imagery from pre- and post-event imagery (xBD). Across 19 natural disaster events including tornados, wildfire, earthquake, hurricanes, volcanos, flood, and tsunami, buildings were identified and classified into four classes: no damage, minor damage, major damage, and destroyed. Participants in the challenge were expected to use deep learning algorithms to perform the classification. They were also provided with a base classification algorithm, in which participants were encouraged to improve. The base algorithm contained RESNET50 trained on ImageNet database and three additional convolution and max pooling layers. This project analyzes the quality of the training dataset, discusses the pros and cons of combining training dataset across multiple natural disaster events, and provides recommendations on using the provided training dataset to optimize classification accuracy. Specifically, we will provide recommendations on creating class balance in the training dataset, in which damage labels are the most identifiable. We will also provide an assessment on which natural disasters lead to damage that is most identifiable using satellite imagery in which natural disasters lead to less accurate damage assessments. We will also examine pooling training data across natural disasters to achieve more accurate classifications.
Ability Required of Future Engineers as Lessons from Science and Technology Cases Under Uncertainty

Chemical substances have a great impact on human and animal health, but on the other hand, it is extremely difficult to accurately predict by-products and “negative effects” in advance. Under “uncertainty of science and technology”, cases are increasing in which engineers are forced to make decision. Under such circumstances, recently, similar artificial accidents and scandals are frequently occurring, and improvement about ability required of engineers and engineering education, is demanded. Therefore, the cause elucidation and countermeasures of Minamata Disease and Itai-itai Disease is taken up from the perspective of science and technology under uncertainty. Ability required of future engineers are proposed as Lessons from science and technology cases under uncertainty.
ParallelMOT: Pay More Attention in Tracking

Modern multiple object tracking has made great progress of the JDE model. Because the JDE model uses a shared model, its calculation speed and accuracy have been greatly improved. But using the same network to predict detection and re- ID will affect each other when the network feedback, thereby reducing the MOTA (Evaluation Measures for MOTChallenge) accuracy, and when the network detects the object and ID information separately, it will greatly increase the computing time. We propose a new MOT method named ParallelMOT, which uses two different branches to reduce the mutual influence of network feedback, and uses object information fusion to improve the feature extraction of the object, and uses a new network model to predict embedding for achieving better MOT accuracy.
Dynamic Contact Algebras with a Predicate of Actual Existence: Snapshot Representation and Topological Duality

The paper is in the field of region-based theory of space and time (RBTST). This is an extension of the region-based theory of space (RBTS) with time. Its origin goes back to some ideas of Whitehead, De Laguna, and Tarski and is related to the problem of how to build the theory of space without the use of the notion of point. The notion of contact algebra (CA) presents an algebraic formulation of RBTS. CA is an extension of Boolean algebra, considered as an algebra of spatial regions with an additional relation of contact. Dynamic contact algebra (DCA) considered as an algebraic formulation of RBTST is an extension of CA aiming to study regions changing in time. In this paper, we study a version of DCA incorporating an explicit predicate AE of actual existence. We first develop the representation theory of such DCAs by means of the so-called snapshot models. Second, we introduce topological models of DCA and develop the corresponding topological representation and duality theory.
Features of pedagogical college students’ training in using innovative technologies

The forms and methods of pedagogical college students’ training for innovative activities implementation at school are considered. We analyze the values of innovative activity that are necessary for the professional activity of a future teacher. It is proved that the following innovative learning technologies are the most effective for teaching students: contextual; problem-based; modular learning; technology of full assimilation of knowledge. The specifics of the use of innovative technologies in the educational environment of the college is the presence of a tendency to rebuild, update to the required standards; a new vision of traditional material and modern ways of teaching it to students; the use of active methods for the formation of universal and professional competencies; the modernity of information and communication technologies involved in the educational process. The forms and methods of teaching that contribute to the formation of students initiative, the ability to set and deal with the challenges for solving emerging problems in professional activity, the ability to quickly respond to changes, show flexible skills, be able to work in a team: fishbone, solving practical problems, didactic games, creating interactive presentations, cinquain and essay contests, express research, developing and conducting lesson fragments, searching for original and non-standard methods and techniques for organizing classes, etc.
Improved Soft Duplicate Detection in Search-Based Motion Planning

Search-based techniques have shown great success in motion planning problems such as robotic navigation by discretizing the state space and precomputing motion primitives. However in domains with complex dynamic constraints, constructing motion primitives in a discretized state space is non-trivial. This requires operating in continuous space which can be challenging for search-based planners as they can get stuck in local minima regions. Previous work [1] on planning in continuous spaces introduced soft duplicate detection which requires search to compute the duplicity of a state with respect to previously seen states to avoid exploring states that are likely to be duplicates, especially in local minima regions. They propose a simple metric utilizing the Euclidean distance between states, and proximity to obstacles to compute the duplicity. In this paper, we improve upon this metric by introducing a kinodynamically informed metric, subtree overlap, between two states as the similarity between their successors that can be reached within a fixed time horizon using kinodynamic motion primitives. This captures the intuition that, due to robot dynamics, duplicate states can be far in Euclidean distance and result in very similar successor states, while non-duplicate states can be close and result in widely different successors. Our approach computes the new metric offline for a given robot dynamics, and stores the subtree overlap value for all possible relative state configurations. During search, the planner uses these precomputed values to speed up duplicity computation, and achieves fast planning times in continuous spaces in addition to completeness and sub-optimality guarantees. Empirically, we show that our improved metric for soft duplicity detection in search-based planning outperforms previous approaches in terms of planning time, by a factor of 1.5 to 2× on 3D and 5D planning domains with highly constrained dynamics.