Reviewerly/exHarmony huggingface dataset:
MuRCL: Multi-Instance Reinforcement Contrastive Learning for Whole Slide Image Classification

Multi-instance learning (MIL) is widely adop- ted for automatic whole slide image (WSI) analysis and it usually consists of two stages, i.e., instance feature extraction and feature aggregation. However, due to the "weak supervision" of slide-level labels, the feature aggregation stage would suffer from severe over-fitting in training an effective MIL model. In this case, mining more information from limited slide-level data is pivotal to WSI analysis. Different from previous works on improving instance feature extraction, this paper investigates how to exploit the latent relationship of different instances (patches) to combat overfitting in MIL for more generalizable WSI classification. In particular, we propose a novel Multi-instance Rein- forcement Contrastive Learning framework (MuRCL) to deeply mine the inherent semantic relationships of different patches to advance WSI classification. Specifically, the proposed framework is first trained in a self-supervised manner and then finetuned with WSI slide-level labels. We formulate the first stage as a contrastive learning (CL) process, where positive/negative discriminative feature sets are constructed from the same patch-level feature bags of WSIs. To facilitate the CL training, we design a novel reinforcement learning-based agent to progressively update the selection of discriminative feature sets according to an online reward for slide-level feature aggregation. Then, we further update the model with labeled WSI data to regularize the learned features for the final WSI classification. Experimental results on three public WSI classification datasets (Camelyon16, TCGA-Lung and TCGA-Kidney) demonstrate that the proposed MuRCL outperforms state-of-the-art MIL models. In addition, MuRCL can achieve comparable performance to other state-of-the-art MIL models on TCGA-Esca dataset.
The Hydrophobicity Class Identification of Silicone-Rubber Samples using Deep Learning Algorithms

This paper presents an approach to classify the hydrophobicity characteristic of silicone rubber (SiR) samples using deep learning algorithms. By deforming the hydrophobicity property of SiR samples using corona discharges, images of water droplets placed on the sample surface were acquired. From the images, the contact angles of the droplets were determined to find the hydrophobicity classes. The generated water droplet image dataset was trained, validated, and tested utilizing AlexNet, VGGNet, and ResNet. The result shows that the modified AlexNet model with an accuracy of 99.36% is a reliable diagnostic method to identify the hydrophobicity qualification of the SiR samples.
Organization of distance learning of students in biology lessons

The article deals with the organization of distance education in biology classes, which is today the only way for students to obtain the necessary knowledge. The organization of the educational process by means of distance education is based on the use of both the best traditional teaching methods and new information and telecommunication technologies, as well as on the principles of independent learning of students. Online learning at school offers students a wide selection of educational videos, created both in the format of traditional lessons and in the form of small popular science films. Such tools significantly expand the possibilities of teaching biology, offer a fundamentally new approach to acquiring knowledge in general. To date, distance learning has become an integral part of the learning process in institutions of general secondary education. Of course, distance learning cannot completely replace the process of traditional learning in school classes, but distance technologies are quite capable of playing the role of convenient and effective tools for teaching biology at school.&#x0D; The article describes the services that helped diversify biology lessons during distance learning. The model of students' competence formation in biology lessons using distance learning technologies is also described. When building the model, the most essential properties and interrelationships of the learning process of students in biology classes in institutions of general secondary education with the help of distance learning technologies were taken into account. We constructed a model for training schoolchildren using distance learning technologies based on the following components: purpose, principles, content blocks, practical blocks, components of readiness and their corresponding criterion indicators, levels of students' competence development, and the result. the proposed model is based on the idea of a comprehensive approach to the process of teaching students in biology classes in remote mode and is implemented through a rational combination of the existing pedagogical experience of the teacher with innovative and information technologies, which are aimed at mastering the competencies of students in the process of educational activities in institutions of general secondary education.
Comparison of commercial and original methods for denoising electrical waveforms with constant or linearly variable magnitudes

Acquired electrical waveforms can be affected by white noise. The 1-st part of the paper analysis deals with the denoising of multi-period steady signals by using 3 methods: mean signal method, an original method relying on wavelet packet trees and the method implemented by the wavelet-based Matlab function wden. The signal length influence over the mean signal method’s accuracy is studied. The results yielded by the other 2 methods are also analyzed considering signals with 7 periods. Afterward the wavelet-based methods are used to denoise segments of 7 periods with linearly variable magnitudes (ascending or descending) for 3 different slopes. Artificial test signals, with rich harmonic content, were used. They were polluted by sets of 10 white noises with different powers. Maximum absolute deviations and mean square root deviations were computed considering the original signals, before pollution, versus the corresponding denoised signal. The metrics were computed relative to the maximum absolute value of the noise and allowed to determine the most accurate method.
ASSESSMENT OF TEMPORAL VARIATION OF HEAVY METALS IN SEDIMENTS OF WARRI RIVER, SOUTHWESTERN NIGERIA

Contaminated sediment poses a serious environmental problem, therefore knowledge of the concentration of heavy metals in stream sediments is very important because of their significance to aquatic life and human health. The concentration of heavy metals (Cd, Pb, Cr, As, Ni, Zn, Fe and Cu), in stream sediments of some parts of Warri River, Southwestern Nigeria were examined during the rainy and dry seasons using Atomic Absorption Spectrophotometer, in order to assess their contamination levels using the environmental contamination indexes; Geoaccumulation Index, Enrichment Factor (EF), and Contamination Factor (CF). For both the rainy and dry seasons, the results revealed average heavy metal concentrations obtained in all samples in the study area in the order As&gt;Cd&gt;Pb&gt;Ni&gt;Cr&gt;Zn&gt;Cu&gt;Fe. The trend in mean Igeo values was as follows: As (-0.66) &gt; Zn (-4.93) &gt; Cu (-5.8) &gt; Fe (-5.31) &gt; Cd (5.76) &gt; Pb (-6.35) &gt; Ni (11.25), showing that River Warri’s sediments were not polluted by heavy metals. Cu= Zn (0.08), Cd (0.34), Pb (0.02), As (0.01), and Ni (0.00) were the mean CF values across all measured locations, indicating a low contamination factor. The average EFs for the sediments were As (1.09) &gt; Cd (1.10) &gt; Cu (1.46) &gt; Zn (1.61) &gt; Pb (1.09) &gt; Ni (1.09) &gt; Fe (0.83), showing low enrichment (&lt;2). Evaluation of enrichment factor, geo-accumulation index and contamination factor values showed that the stream sediments of the study area were unpolluted. Heavy metals in the sediments were found to be linked to anthropogenic activities in the study region, according to the results of the hierarchical cluster analysis (HCA). The metal concentrations in the sediment were found to be elevated at some sampled points, it is therefore recommended that annual monitoring of the stream sediments of the study area is undertaken. Also, sustainable practices should be employed in order to conserve the resources of the Warri River.
News Recommendation Algorithm Based on Multiple Perspectives

Today, with the rapid development of the Internet, the channels for people to obtain news are more and more diversified, and the number of online news is also increasing rapidly. However, it is more and more difficult for users to find the news that they are interested in. Therefore, personalized news the recommendation system is of great significance. In view of the fact that the current news recommendation system fails to fully consider the semantic information of news and the singleness of the modeling factors of news text, it cannot fully consider the semantic information of news and cannot accurately vectorize the information of news content, we propose News Recommendation Algorithm Based on Multiple Perspectives (BTEC). The model using the Bert model and attention to vectorize the body, title, event in the news respectively, and the dense layer is used to vectorize the news categories. then according to this four perspective are fused to process the candidate news and user browsing news data, respectively to obtain the corresponding candidate news vectorization and user vectorization, and dot multiplication them to obtain the probability of users clicking on the candidate news. that is, the news recommendation result. Experimental data show that BTEC improves the F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> index by about 8% compared with the other news recommendation algorithm.
Multi-robot Cooperative Patrol Path Planning Method under Road Network Constraints

Multi-robot systems have great advantages and wide applications in the fields of ground reconnaissance, environmental monitoring, and key area patrols. However, in the field of multi-robot collaboration, it is difficult to take into account the patrol efficiency and path privacy simultaneously, especially when any intelligent intruders happen. To conquer the above difficulty, path randomness is proposed as a criterion to establish a multi-robot cooperative path planning model based on idle time dispersion, so as the path information privacy and security in the patrol process can be protected. In order to get the solution of the model, the index of idle time dispersion is introduced to get a tailored ant colony algorithm. Finally, patrol paths of multi mobile robots with high security are obtained. Several simulations in the simple road network and complex road network are conducted to verify the feasibility and effectiveness of the proposed method.
<i>EdgeMatrix</i>: A Resource-Redefined Scheduling Framework for SLA-Guaranteed Multi-Tier Edge-Cloud Computing Systems

With the development of networking technology, the computing system has evolved towards the multi-tier paradigm gradually. However, challenges, such as multi-resource heterogeneity of devices, resource competition of services, and networked system dynamics, make it difficult to guarantee service-level agreement (SLA) for the applications. In this paper, we propose a multi-tier edge-cloud computing framework, EdgeMatrix, to maximize the throughput of the system while guaranteeing different SLA priorities. First, in order to reduce the impact of physical resource heterogeneity, EdgeMatrix introduces the Networked Multi-agent Actor-Critic (NMAC) algorithm to re-define physical resources with the same quality of service as logically isolated resource units and combinations, i.e., cells and channels. In addition, a multi-task mechanism is designed in EdgeMatrix to solve the problem of Joint Service Orchestration and Request Dispatch (JSORD) for matching the requests and services, which can significantly reduce the optimization runtime. For integrating above two algorithms, EdgeMatrix is designed with two time-scales, i.e., coordinating services and resources at the larger time-scale, and dispatching requests at the smaller time-scale. Realistic trace-based experiments proves that the overall throughput of EdgeMatrix is 36.7% better than that of the closest baseline, while the SLA priorities are guaranteed still.
Tendency Analysis of Python Programming Classes for Junior and Senior High School Students

In this study, we analyze Python program codes and questionnaire results collected in the programming classes for 23 senior high school students on December 19, 2020, and for 48 junior high school students on August 11 and 12, 2021. Each student entered and executed the 168 Python programs described in the text on Google Colaboratory using each personal computer. Students’ programs were collected by sharing in Google Drive. In the analysis, the similarity matrix is obtained from the correct / incorrect relationship of the students’ programs and hierarchical clustering is performed by Ward's method and visualized by the dendrogram. As a result, the cluster is confirmed from the correct/incorrect relationship. For the questionnaire, we use conjoint analysis for the relationship with the number of correct answers, and Quantification Method of the Third Type for the relationship with the questionnaire category. As a result, we confirm that the tendency for the senior high school students is the same as or different from that for the junior high school students.
Towards Overcoming the Undercutting Problem

Mining processes of Bitcoin and similar cryptocurrencies are currently incentivized with voluntary transaction fees and fixed block rewards which will halve gradually to zero. In the setting where optional and arbitrary transaction fee becomes the prominent/remaining incentive, Carlsten et al. [CCS 2016] find that an undercutting attack can become the equilibrium strategy for miners. In undercutting, the attacker deliberately forks an existing chain by leaving wealthy transactions unclaimed to attract petty complaint miners to its fork. We observe that two simplifying assumptions in [CCS 2016] of fees arriving at fixed rates and miners collecting all accumulated fees regardless of block size limit are often infeasible in practice and find that they are inaccurately inflating the profitability of undercutting. Studying Bitcoin and Monero blockchain data, we find that the fees deliberately left out by an undercutter may not be attractive to other miners (hence to the attacker itself): the deliberately left out transactions may not fit into a new block without “squeezing out” some other to-be transactions, and thus claimable fees in the next round cannot be raised arbitrarily. This work views undercutting and shifting among chains rationally as mining strategies of rational miners. We model profitability of undercutting strategy with block size limit present, which bounds the claimable fees in a round and gives rise to a pending (cushion) transaction set. In the proposed model, we first identify the conditions necessary to make undercutting profitable. We then present an easy-to-deploy defense against undercutting by selectively assembling transactions into the new block to invalidate the identified conditions. Indeed, under a typical setting with undercutters present, applying this avoidance technique is a Nash Equilibrium. Finally, we complement the above analytical results with an experimental analysis using both artificial data of normally distributed fee rates and actual transactions in Bitcoin and Monero.
Simple Knowledge Organization System (SKOS)

SKOS (Simple Knowledge Organization System) is a recommendation from the World Wide Web Consortium (W3C) for representing controlled vocabularies, taxonomies, thesauri, classifications, and similar systems for organizing and indexing information as linked data elements in the Semantic Web, using the Resource Description Framework (RDF). The SKOS data model is centered on “concepts”, which can have preferred and alternate labels in any language as well as other metadata, and which are identified by addresses on the World Wide Web (URIs). Concepts are grouped into hierarchies through “broader” and “narrower” relations, with “top concepts” at the broadest conceptual level. Concepts are also organized into “concept schemes”, also identified by URIs. Other relations, mappings, and groupings are also supported. This article discusses the history of the development of SKOS and provides notes on adoption, uses, and limitations.
A Blockchain-Based Architecture for Access Control Management of IoT Applications

Internet of Things (IoT) integrates the physical world with the Internet to facilitate sharing data among entities. These IoT applications bring security and management challenges. In this paper, using blockchain technology, an end-to-end secure architecture is proposed for an auditable tamper-proof log of sensors' data and events. A Role Based Access Control policy is enforced using smart contracts that controls access to both sensors' data and executing commands on actuators. For confidentiality, the data is kept encrypted in transit and at rest. In addition, the data is stored off-the-chain in a distributed content-based addressable network, while its address and access records are stored at the blockchain. The proposed design is suitable for real-time and mission-critical IoT applications. Finally, a proof-of-concept implementation shows the efficiency and scalability of the proposed architecture.
GhostLeg: Selective Memory Coalescing for Secure GPU Architecture

Architectural considerations for secure executions are getting more critical for GPU since popular security applications and libraries have been ported to a GPU domain to rely on GPU’s massively parallel computations. Recent studies disclosed the security attack models that exploit GPU’s architectural vulnerabilities to leak the secret keys of AES. The attack models exploit the high correlations between the execution time of a kernel and the number of memory requests generated from memory coalescing. Thus the prior architectural defenses provide secure executions by randomizing or restricting the memory coalescing from load warps. However, those defense approaches result in significant performance overhead since memory coalescing is an essential feature for improving the performance of GPU. In this paper, we propose GhostLeg, an efficient architectural defense approach against correlation-based GPU security attacks. GhostLeg selectively applies secure executions for load warps to minimize performance overhead induced by concealing memory coalescing behavior. Our analysis of AES reveals that only the load warps whose index addresses are influenced by secret keys are vulnerable to security attacks. In order to minimize the performance overhead by secure executions, GhostLeg pinpoints the load warps that require secure executions based on the class of a source register. The secure flag assigned to each register can be set by propagation from non-deterministic user data (GhostLeg-ND) or a specific directive marked by programmers (GhostLeg-Key). Our evaluation shows that GhostLeg guarantees secure executions against the correlation-based attacks and GhostLeg-ND exhibits 54.7% higher performance compared to the state-of-the-art GPU defense solution.
On a Balanced Delta Robot for Precise Aerial Manipulation: Implementation, Testing, and Lessons for Future Designs

Using a delta-manipulator for stabilisation of an end-effector to perform precise spatial positioning is a current area of interest in aerial manipulation. High speed precision movements of a manipulator can cause disturbances to the aerial platform, which hinders trajectory tracking and in some cases could be sufficient to cause a loss of control of the vehicle. In this paper, a statically balanced delta aerial manipulator is developed and evaluated. The system is balanced using three counter-masses to reduce the force imparted onto the base and thus reduce perturbations to the movement of the drone. The system is thoroughly tested following trajectories while mounted to a force sensor and while on-board an aerial vehicle. Results show that the forces transmitted to the base in all axes are reduced considerably, however improvements in overall flight accuracy are not observed in aerial settings. Design lessons to make a balanced delta-manipulator viable for practical implementation on an aerial vehicle are discussed in depth. A video summarising the flight testing results is available at https://youtu.be/fXKnosnVKCk.
The One Where They Reconstructed 3D Humans and Environments in TV Shows

TV shows depict a wide variety of human behaviors and have been studied extensively for their potential to be a rich source of data for many applications. However, the majority of the existing work focuses on 2D recognition tasks. In this paper, we make the observation that there is a certain persistence in TV shows, i.e., repetition of the environments and the humans, which makes possible the 3D reconstruction of this content. Building on this insight, we propose an automatic approach that operates on an entire season of a TV show and aggregates information in 3D; we build a 3D model of the environment, compute camera information, static 3D scene structure and body scale information. Then, we demonstrate how this information acts as rich 3D context that can guide and improve the recovery of 3D human pose and position in these environments. Moreover, we show that reasoning about humans and their environment in 3D enables a broad range of downstream applications: re-identification, gaze estimation, cinematography and image editing. We apply our approach on environments from seven iconic TV shows and perform an extensive evaluation of the proposed system.
HFGAN: A Heterogeneous Fusion Generative Adversarial Network for Sar-to-Optical Image Translation

Due to the influence of the imaging mechanism of SAR images, it is difficult to interpret ground information through SAR images without expert knowledge. On the contrary, optical images have rich spatial and color information, so it is necessary to conduct research on the translation of SAR to optical remote sensing images. In this end, we propose a heterogeneous fusion generative adversarial network (HFGAN) for SAR-to-optical image translation. There are two main improvements: (1) Complementary generation of global structure and texture information. A heterogeneous fusion generator and a multi-scale discriminator are proposed to ensure that the global and detailed features of the generated image are more accurate and rich. (2) Color fidelity. Chromatic aberration loss are introduced to reduce the color difference between the generated image and the real optical image. Through qualitative and quantitative experiments, it is proved that the proposed method not only obtains better visual effects, but also has certain progress in the evaluation metrics, which proves that the proposed method is superior to the previous advanced methods in SAR-to-optical image translation.