Reviewerly/exHarmony huggingface dataset:
Attentive Relational State Representation for Intelligent Joint Operation Simulation

In the multi-agent task, due to the constant changes in the location and state of each agent, the information considered by each agent when making decisions is also constantly changing. This makes it difficult to model cooperatively among agents. Previous methods mainly used average embedding to model feature aggregation. However, this aggregation has the problem of losing permutation invariance or excessive information loss. The feature aggregation method based on attentive relational state representation establishes an insensitive state representation to permutation and problem scale. In our experiments on Intelligent Joint Operation Simulation, experimental results show that attentive relational state representation improves the baseline performance.
SMPG: Adaptive Soft Update for Masked MADDPG

In multi-agent systems, deep reinforcement learning policy gradient algorithms can converge excessively slowly or even fail to converge if the agent size as well as the state information quickly grows. We consequently present a policy gradient algorithm for generalised centralised training and decentralised execution (CTDE) based on the principle of masking. We transform the global state information of the critic network in the original (MADDPG) algorithm to the state information of local random agents as the input of the critic network. In addition, we have changed the way Polyak updates the target network so that it can dynamically and adaptively update the target network. Under the new framework, our approach considerably decreases the training strain on the critic network while taking into consideration the efficiency of agent sample learning and speeding up the multi-agent discovery of superior strategies. Combining these two improvements, our suggested approaches can be extended to any other CTDE-based multi-agent deep reinforcement learning algorithms, rather than being limited to the MADDPG conventional multi-agent reinforcement learning algorithm. We made the code publicly available at https://github.com/ZVEzhangyu/SMPG-master .
Particle Swarm Based Reinforcement Learning

With the vigorous development of computer-related technology, the “perception + decision” paradigm of the combination of deep learning and reinforcement learning has become a research hotspot. Nowadays, deep reinforcement learning algorithms have been successfully applied to the fields of games, industry and commerce. However, deep reinforcement learning algorithms often fall into the dilemma of “exploration” and “exploitation”, and the effect of these algorithms is easily affected by the quality of hyperparameters. In order to make up for the defects mentioned above, this paper introduces the particle swarm based reinforcement learning framework (PRL). Compared with the standard reinforcement learning algorithms, this framework greatly improves the exploration ability and obtains better scores in a series of gym experimental tests.
Action Recognition for Solo-Militant Based on ResNet and Rule Matching

To solve the problem of low accuracy of solo-militant action recognition under small sample data set, a new method of solo-militant behavior analysis based on ResNet and rule matching is proposed in this paper. The militant's action classification is done by 2 levels of classification. Firstly, the skeleton key points are extracted from the militant's combat video frames by OpenPose. Then, the first level classification of militant's action is performed by the ResNet deep learning network based on RGB images and combined with the skeleton key point rule set of militant's action. Next, the second level classification of militant's action is performed by the CNN network based on skeleton map. At last, the final classification of militant's action is output according to the 2 levels of classification. The experimental results show that the proposed method in this paper can achieve more effective recognition rate of solo-militant action under small sample data set.
Laboratory Penetration Test Studies

Penetration testing involves an authorized attempt to gain unauthorized access to a computer system, application, or data. Ethical hacking involves duplicating the strategies and actions of malicious attackers. This practice helps identify security vulnerabilities that can be patched before a malicious attacker has a chance to exploit them. The article examines and analyzes the penetration tests in laboratory conditions, as well as their practical implementation.
The Process of Teaching Students in the Deep Neural Network Laboratory Work

The various software availability for deep neural network implementation enables students easy and accessible implementation of different tasks in a wide area range. The ready for use applications using, for many of the deep neural network training stages is not related to a deeper understanding of the deep neural network theory. For this reason, the authors of this paper have developed an exercise with students' participation as many of the stages of the deep neural network training as possible. This paper presents one solution for laboratory exercise process organization in distance learning in deep neural network area. Each stage of the student's work is examined in detail. The specifics of its distance learning implementation are also discussed.
Dimensionality Reduction to Dynamically Reduce Data

We perform experiments using dynamic data reduction on datasets of moderate complexity, with focus on classification of a Micro-PCB image dataset. As deep learning models increase in complexity, the data that they use increases at a rate we can't keep up with. The result of this is often slight improvements to the model's accuracy, at the improportional cost of computational runtime, which increases the electricity used, and ultimately carbon emissions. By using data reduction techniques, we attempt to identify the least critical data to be excluded from training, which in turn cuts the environmental cost. We show the effect of data reduction techniques on moderately complex image data, including PCB images, to reduce runtime by 2% and improve the accuracy by 0.013%.
An Architecture for Creating Universal Interpersonal Communication Applications (UICAs)

This paper <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> This publication has emanated from research conducted with the financial support of the Bulgarian National Science Fund (BNSF) under the Grant No. KP-06-IP-CHINA/I (K∏-06- HIT - KHT AN/1). describes a generic architecture for the creation of universal interpersonal communication applications (UICAs) capable of selecting the ‘best’ service for communication with particular person(s), depending on the current context, based on the calculated service ratings considering different evaluation criteria. A suitable service categorization, serving the UICAs, is proposed. The relevance of the UICA approach to a user-centric realization of the Always Best Connected and best Served (ABC&S) communication paradigm is highlighted.
Weighted singular value decomposition basis of Szegő kernel and its applications to signal reconstruction and denoising

For any circular analytic signal f+(eit)∈H2(T), if the sequence {ak}k=0∞ in the unit disc D satisfies the hyperbolic non-separability condition ∑k=0∞(1−|ak|)=∞, it is known that the first N+1 term approximation function based on the Takenaka–Malmquist system (SN+1f+)(eit)=∑k=0N〈f+(eit),Bk(eit)〉Bk(eit),can approximate the analytic signal f+(eit) in the L2-norm sense, where Bk(eit)=1−|ak|21−ak¯eit∏l=0k−1eit−al1−al¯eit, k∈N is the Takenaka–Malmquist system which can be seen as a generalization of Fourier series. We find that (SN+1f+)(eit) can also be represented by the standard basis, Lagrange basis and weighted SVD basis. Different bases have different merits. The standard basis is simple. The Lagrange basis has a neat and compact form, which is very convenient for theoretical analysis. With the Takenaka–Malmquist system, the first N+1 term approximation function (SN+1f+)(eit) is able to write in an iterative form. The Takenaka–Malmquist system has the advantage of inheritance, and the point ak∈D can be easily selected step by step through the greedy principle. In this paper, we focus our attention on the weighted SVD basis. We give some orthogonal properties of the weighted SVD basis. It demonstrates that any real signal f(eit)∈L2(T) can be precisely reconstructed within the given error range by the weighted SVD basis decomposition. The experimental results reveal that the algorithm based on weighted SVD basis is more stable and accurate. Furthermore, compared with classical Fourier decomposition and adaptive Fourier decomposition algorithms, the experimental results show that the weighted SVD basis decomposition is more insensitive to noise. This will provide a new method for signal denoising.
Modifications of SHAP for Local Explanation of Function-Valued Predictions Using the Divergence Measures

Modifications of the well-known SHAP method for the local explanation of black-box classification models are proposed. It is supposed that the classifier prediction is represented in the form of a class probability distribution. A key idea behind the modifications is to replace the difference of characteristic functions in the framework of Shapley values with non-symmetric divergence measures between the predicted class probability distribution with a feature and without it. Such measures are used for estimating the feature contribution, for example, the Kullback-Leibler divergence and Chi-squared-divergence. For comparison purposes, we also study the symmetric Hellinger distance measure. A lot of numerical experiments on synthetic and real datasets illustrating the proposed modifications are provided and analyzed.
Using GIS to Determine Tsunami Shelter Location in South Coast Pangandaran District, Pangandaran Regency, Indonesia

Abstract The geographical condition of the Pangandaran Regency affects the coastal area, periodically suffering hydrological hazards. Based on the Regional Disaster Management Agency of Pangandaran, Pangandaran Regency experienced Tsunami with a run-up height of 8 to 15.7 meters in 2006. The Tsunami caused 664 people to die, many to be injured, and caused social environment disruption. Therefore, this research aims to support disaster mitigation by identifying additional shelter locations and generating evacuation routes sequentially. This research uses the network analyst method of Geographic Information System (GIS). The identification of evacuation shelters utilizes several parameters, e.g., tsunami hazard catchment area, land slope levels, land cover, and street network. In addition, the spatial analysis considers average travel time as the impedance to resemble the actual evacuation process. The result indicates that 11 evacuation shelters scattered over the study area were dominantly located in Pangandaran Village. Moreover, this research generates 24 evacuation routes from the incident location to each shelter. The closest shelters accessed 8 minutes from incidents 4 and 18 are shelters 3 and 9, respectively. On the contrary, the farthest shelter is shelter 9, with a maximum impedance of 22 minutes from incidents 14 and 15. Furthermore, the most appropriate shelters are shelters 2 and 8, considering the elevation height of 10 and 7 in the open space.
LiDAR DTM Application for Flood Inundation Modeling and Visualization (Case Study: Batu City, East Java)

Abstract LiDAR DTM has been widely used for simulating flood inundation from river overflows. This flood inundation simulation needs to be done as an alternative step in the flood control system and flood disaster mitigation to determine the estimated area affected by the flood. Flood simulation is a form of unsteady flow that is simulated with the HEC-RAS program that allows the user to perform one-dimensional steady flow, one and two-dimensional unsteady flow calculations, sediment transport/mobile bed computations, and water temperature/water quality modeling, and visualizes flooded areas and flood-affected areas using a geographic information system approach. Hydrological data is obtained from daily rainfall processing at the time of station rain. Hydrograph analysis was performed using the SCS CN model on the HEC-HMS software to estimate the flow rate and peak discharge. Watershed area, curve number, Impervious value, Initial Abstraction, and time lag are parameters that affect hydrograph analysis. From the results of the hydrograph analysis, the peak discharge of the model is 119.992 m2/s. Meanwhile, hydraulic analysis using HEC-RAS resulted in an inundation of 364 m2 which inundated three villages, namely Bulukerto, Bumiaji and Sidomulyo villages. The depth of the inundated area ranged from 0-15 m with an average depth of 4.534 m. The Sidomulyo area affected by the flood inundation scenario is 4,924 meters with an area of 0,02 ha. Bumiaji village was flooded with an area of 0,007 ha with a scenario of inundation depth of 4,049 m. Bulukerto village which was affected by the flood has an area of 20,972,832 m2 with a scenario of inundation depth reaching 4,343 m. Then some land cover such as residential land that is inundated by this flood simulation is 0,002 ha. Buildings from residential areas that were flooded reached 209 buildings. Then the area of agricultural land affected is 0,02 ha. In addition, there are also green open spaces affected by this simulation with an inundation area of 0,003 ha.
Creative ideas, but elementary mistakes: A reply to Brandner and colleagues to promote best practices in error management theory research

In their commentary on our paper, Brandner et al. commit an elementary statistical mistake that leads to entirely erroneous conclusions. When this statistical error is corrected, the effects described in our original paper appear exactly as reported. In principle, we could end our reply there. However, ending our reply there would be a lost opportunity for promoting best practices in Error Management Theory (EMT) research. The commentators make several other missteps that present the opportunity to draw attention to important principles in EMT research and offer clarifications that we hope assist in the operationalization, design, and interpretation of EMT-inspired studies in the future. We discuss these points and provide several EMT research scenarios to help to illustrate a key principle. We hope this reply highlights some of the key elements of best practices in EMT research and sheds light on pitfalls that researchers must make sure to avoid.
Cross-Domain Object Detection by Dual Adaptive Branch

The object detection task usually assumes that the training and test samples obey the same distribution, and this assumption is not valid in reality, therefore the study of cross-domain object detection is proposed. Compared with image classification, the cross-domain object detection task presents the greater challenge, which requires both accurate classification and localization of samples in the target domain. The teacher-student framework (the student model is supervised by pseudo-labels from the teacher model) has produced a large accuracy improvement in cross-domain object detection. Feature-level adversarial training is used in the student model, which allows features in the source and target domains to share a similar distribution. However, the direction and gradient of the weights can be divided into domain-specific and domain-invariant features, and the purpose of domain adaptive is to focus on the domain-invariant features while eliminating interference from the domain-specific features. Inspired by this, we propose a teacher-student framework named dual adaptive branch (DAB), which uses domain adversarial learning to address the domain distribution. Specifically, we ensure that the student model aligns domain-invariant features and suppresses domain-specific features in this process. We further validate our method based on multiple domains. The experimental results demonstrate that our proposed method significantly improves the performance of cross-domain object detection and achieves the competitive experimental results on common benchmarks.
Sensor Fusion with Asynchronous Decentralized Processing for 3D Target Tracking with a Wireless Camera Network

We present a method to acquire 3D position measurements for decentralized target tracking with an asynchronous camera network. Cameras with known poses have fields of view with overlapping projections on the ground and 3D volumes above a reference ground plane. The purpose is to track targets in 3D space without constraining motion to a reference ground plane. Cameras exchange line-of-sight vectors and respective time tags asynchronously. From stereoscopy, we obtain the fused 3D measurement at the local frame capture instant. We use local decentralized Kalman information filtering and particle filtering for target state estimation to test our approach with only local estimation. Monte Carlo simulation includes communication losses due to frame processing delays. We measure performance with the average root mean square error of 3D position estimates projected on the image planes of the cameras. We then compare only local estimation to exchanging additional asynchronous communications using the Batch Asynchronous Filter and the Sequential Asynchronous Particle Filter for further fusion of information pairs' estimates and fused 3D position measurements, respectively. Similar performance occurs in spite of the additional communication load relative to our local estimation approach, which exchanges just line-of-sight vectors.
Partition-function estimation: Quantum and quantum-inspired algorithms

We present two algorithms, one quantum and one classical, for estimating partition functions of quantum spin Hamiltonians. The former is a DQC1 (Deterministic quantum computation with one clean qubit) algorithm, and the first such for complex temperatures. The latter, for real temperatures, achieves performance comparable to a state-of-the-art DQC1 algorithm [Chowdhury et al. Phys. Rev. A 103, 032422 (2021)]. Both our algorithms take as input the Hamiltonian decomposed as a linear combination Pauli operators. We show this decomposition to be DQC1-hard for a given Hamiltonian, providing new insight into the hardness of estimating partition functions.
Truncated Normal Mixture Prior Based Deep Latent Model for Color Normalization of Histology Images

The variation in color appearance among the Hematoxylin and Eosin (H&E) stained histological images is one of the major problems, as the color disagreement may affect the computer aided diagnosis of histology slides. In this regard, the paper introduces a new deep generative model to reduce the color variation present among the histological images. The proposed model assumes that the latent color appearance information, extracted through a color appearance encoder, and stain bound information, extracted via stain density encoder, are independent of each other. In order to capture the disentangled color appearance and stain bound information, a generative module as well as a reconstructive module are considered in the proposed model to formulate the corresponding objective functions. The discriminator is modeled to discriminate between not only the image samples, but also the joint distributions corresponding to image samples, color appearance information and stain bound information, which are sampled individually from different source distributions. To deal with the overlapping nature of histochemical reagents, the proposed model assumes that the latent color appearance code is sampled from a mixture model. As the outer tails of a mixture model do not contribute adequately in handling overlapping information, rather are prone to outliers, a mixture of truncated normal distributions is used to deal with the overlapping nature of histochemical stains. The performance of the proposed model, along with a comparison with state-of-the-art approaches, is demonstrated on several publicly available data sets containing H&E stained histological images. An important finding is that the proposed model outperforms state-of-the-art methods in 91.67% and 69.05% cases, with respect to stain separation and color normalization, respectively.
High Throughput VMs Placement With Constrained Communication Overhead and Provable Guarantees

Placement of VMs in the cloud is one of the most fundamental problems in systems research. Traditionally, placement algorithms assume that the schedulers have complete information about the currently available resources at each host. However, this assumption is in many cases unrealistic, as gathering fresh status information from each of the thousands of hosts in a large data center incurs excessive communication overhead, which results in long queueing delays. Efforts to resolve this problem by employing several parallel schedulers typically exhibit collisions when several schedulers are simultaneously trying to place VMs on the same host. Our work analyzes the performance of various placement algorithms and provides empirical evidence that using multiple randomized schedulers obtains high throughput, while significantly decreasing both the communication overhead, and the number of collisions between schedulers. We, therefore, introduce Adaptive Partial State Random (APSR) – an efficient parallel random resource management algorithm that samples only from a small number of hosts and dynamically adjusts the degree of parallelism to provide provable guarantees on the probability of collisions between distinct schedulers. We formally analyze APSR, evaluate it on real workloads, and integrate it into the popular OpenStack cloud management platform. Our evaluation shows that APSR matches the throughput provided by other parallel schedulers, while achieving up to 13x lower decline ratio and a reduction of over 85% in communication overheads.
A Comparative Study of Information Literacy Study Trends Between Korea and Other Countries Using Keyword Networks

Abstract This study aims to examine and compare the trends of information literacy research in Korea and in some English-speaking other countries in the last two decades from 2001 to 2020 through keyword network analysis. With regard to English-speaking countries, author keywords were collected from articles retrieved from the Scopus database using the search term “information literacy.” For the Korean LIS field, the author keywords were collected from articles retrieved through a Korean academic database called “KCI” using the search term “information literacy.” For other countries, a total of 1163 articles and 2454 author keywords were collected for this study. Among these author keywords, the top 10 that appeared most were “academic libraries,” “library education,” “assessment,” “collaboration,” “higher education,” “undergraduate students,” “e-learning,” “information literacy instruction,” “instruction,” and “instructional design.” By comparison, a total of 120 articles and 352 author keywords were collected for this study in Korea. Among the keywords, eight that appeared most frequently were “information literacy instruction,” “school libraries,” “school librarians,” “library education,” “academic libraries,” “information services,” “user education,” and “public libraries.” Based on the study results, some recommendations for Korea’s IL research can be made as follows: First, particular attention needs to be given to the subjects of “assessment” and “collaboration” related to IL research. Second, the research related to the subject of IL has been carried out by a few researchers majoring in school libraries. Therefore, in addition to researchers in the LIS field, those from various other fields need to have an interest in the subject.