Reviewerly/exHarmony huggingface dataset:
Expanded Edge Penalty Loss for Salient Object Detection

As an indispensable preprocessing technique for image understanding, salient object detection aims to extract interesting regions from an image for subsequent processing, which has attracted much attention since its wide range of applications. Recently, with the rapid development of artificial intelligence and machine learning, deep neural network especially deep convolutional neural network based methods have achieved competitive performances because of their strong feature representation capability. However, most of these methods often suffer from coarse boundaries. The main reason is that equal penalty factor is applied to each pixel in the image to optimize the network, but there are huge distinctions in prediction complexity among different ones actually. Specifically, pixels closer to the boundaries are increasingly difficult because of their huge gaps between structural information and semantic label. In these cases, we present an Expanded Edge Penalty Loss (E <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> PL) for salient object detection. E <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> PL gives bigger penalty factors to pixels distributed in boundary and near-boundary regions, and further dynamically adjusts their contributions to the model optimization. In addition, the experimental results on five public and challenging datasets have validated the superiority and effectiveness of the proposed method.
Majority Voting with Bidirectional Pre-translation For Bitext Retrieval

Obtaining high-quality parallel corpora is of paramount importance for training NMT systems. However, as many language pairs lack adequate gold-standard training data, a popular approach has been to mine so-called “pseudo-parallel” sentences from paired documents in two languages. In this paper, we outline some drawbacks with current methods that rely on an embedding similarity threshold, and propose a heuristic method in its place. Our method involves translating both halves of a paired corpus before mining, and then performing a majority vote on sentence pairs mined in three ways: after translating documents in language x to language y, after translating language y to x, and using the original documents in languages x and y. We demonstrate success with this novel approach on the Tatoeba similarity search benchmark in 64 low-resource languages, and on NMT in Kazakh and Gujarati. We also uncover the effect of resource-related factors (i.e. how much monolingual/bilingual data is available for a given language) on the optimal choice of bitext mining method, demonstrating that there is currently no one-size-fits-all approach for this task. We make the code and data used in our experiments publicly available.
Three-Dimensional Reconstruction Method of High-Resolution Image Based on Independently Adjustable Sparse Coefficient

Image 3D reconstruction is widely used in reverse engineering, artificial intelligence and other fields. In order to better guarantee the quality of 3D image reconstruction, this research proposes a 3D reconstruction method for high-resolution images based on independently adjustable sparsity coefficients. A single image is used to reconstruct a three-dimensional model, and then combined with the sparse coefficients, the local features of the single image three-dimensional reconstruction are collected and analyzed, and local denoising is performed. On this basis, the image information is corrected and reconstructed. By comparing the experimental results, it can be known that this method has high practicability and can effectively meet the design requirements.
Exploratory Analysis on Topic Modelling for Video Subtitles

In this paper, we explore different models available to perform topic modelling on subtitles files. Subtitle files are sourced from movies and represent the dialogue being spoken. Applying this to topic modelling would mean trying to obtain the topics regarding the video from only the subtitles. Our novel idea is to test whether it would be feasible to use topic modelling on subtitles to get topics of a movie. While topic modelling as an idea has been used previously in bio-informatics,patent indexing and much more, has not seen any application in this sphere. We extensively search for datasets, preprocess the subtitles files and try Latent Dirichlet Allocation, Hierarchical Dirichlet Processes and Latent Semantic Indexing methods of topic modelling on these documents. These are the top three prominent topic modelling models that are used today. Our results entail what model would work best for subtitle files
Sheaves as a Framework for Understanding and Interpreting Model Fit

As data grows in size and complexity, finding frameworks which aid in interpretation and analysis has become critical. This is particularly true when data comes from complex systems where extensive structure is available, but must be drawn from peripheral sources. In this paper we argue that in such situations, sheaves can provide a natural framework to analyze how well a statistical model fits at the local level (that is, on subsets of related datapoints) vs the global level (on all the data). The sheaf-based approach that we propose is suitably general enough to be useful in a range of applications, from analyzing sensor networks to understanding the feature space of a deep learning model.
DragonFly: Drone-Assisted High-Rise Monitoring for Fire Safety

In this paper, we propose DragonFly, a drone-based data collection framework to enhance real-time situational awareness in high-rise buildings, focusing specifically on mission-critical high-rise fire scenarios. The goal of our proposed solution is to use multiple drones with visual sensors to collect reliable and timely data for monitoring the exterior of a high-rise building. Drones are especially useful in obtaining data from hard-to-access regions in high-rise fires that are used to monitor fire/smoke that might have propagated to higher floors, detect the presence of humans requiring assistance near windows, and determine window open/close states which can have a significant impact on the speed and direction of fire spread. Given a dynamically evolving set of events and multiple drones, the core challenge addressed is to develop a plan for multiple drones to gather a set of observations that can improve both the coverage (identify more events) and accuracy (obtain fine-grained for improved event detection). We develop a solution for the Multi-drone Waypoint scheduling problem (NP-hard) in two steps: 1) allocation of monitoring tasks (AMT) to individual drones and 2) dynamic waypoint scheduling (DWS) that determines the waypoint sequence for each drone to visit. We evaluate our proposed approach using a simulated high-rise fire scenario with a realistic fire spread model and study the applicability and efficiency of the proposed algorithms compared to baseline techniques. The simulation results demonstrate the superior performance of the proposed AMT-DWS algorithms. DragonFly achieve 33&#x0025; fewer missing events and up to 39 times gain in accuracy, captured as the minimum weighted AUC (Area Under Curve) as compared to baseline algorithms. DragonFly delivers over 85&#x0025; missing events and about 1.3 times the minimum weighted AUC in comparison to current approaches.
Integration of Information Technology and PE Teaching Process

Abstract Since entering the new era, with the rapid development of information technology and the acceleration of the process of global economic integration, many aspects of my country’s social life and economic fields are undergoing profound historical changes. The integration of information technology and physical education is becoming a hot topic in my country’s current information technology education and even the entire education informatization process. The purpose of this article is to study the integration of information technology and physical education process. This article uses modern information technology to integrate with physical education. The teaching effect produced by it is significantly better than the traditional teaching model. It can more effectively solve the difficulties in the textbook, and can help students establish movement representations and understand movements faster. Essentials to stimulate students’ interest in learning. This research systematically elaborates the connotation and significance of information technology and the impact of information technology on middle school physical education, and scientifically explores the physical education curriculum, analyzes the basis and conditions of the integration of physical education and information technology education, and studies the information technology and middle school through teaching experiments. The mode and effect of physical education integration. This article takes the university’s physical education major curriculum integration as the research object, and conducts research and analysis through literature method, questionnaire survey method and mathematical statistics method to define the level of information technology and physical education major curriculum integration. Investigation and research show that more than 30% of physical education humanities teachers can often find a suitable resource bank and obtain relevant teaching materials, but the lack of targeted physical education software largely affects teachers’ access to resources.
The Quantum Internet: a Communication Engineering Perspective

Internet just turned 50: five decades that shaped the world we live in. Indeed, Internet itself evolved astonishingly since the beginning, from a network prototype consisting of a few static nodes in the early days to a leviathan interconnecting with billions of devices half of the world’s population. Yet the fundamental assumption underlying Internet’s design – i.e., transmitting messages that can be encoded in a sequence of classical bits – remained unchanged during these five decades. But the dawn of the engineering phase of quantum technologies is challenging Internet’s fundamental assumption. Quantum devices demand for communication primitives – namely, the ability to distribute entangled states and to transmit quantum information – governed by the laws of quantum mechanics. Hence, principles and phenomena with no counterpart in classical networks require a major network-paradigm shift to harness the quantum mechanics specificities. This presentation aims at shedding light on the challenges and the open problems arising with the design of a protocol stack for the Quantum Internet.
An introduction on distinguish tracking frameworks: methods and experiments

Visual object tracking aims to capture the given target in the whole sequence, where the target will suffer challenges such as occlusion, motion blur, distractors, and scale variation. Several popular frameworks are proposed for improving accuracy and speed. This paper aims to review the existing tracking frameworks, such as correlation filter, Siamese network, and multi-domain network and their variants. We also give a brief comparison of the OTB benchmark. Our review can give an in-depth introduction to visual object tracking tasks and help beginners recognize this area efficiently.
Dynamic task allocation algorithm based on D-NSGA3

Task allocation is a key part of unmanned aerial vehicle (UAV) swarm. Although a large number of solving algorithms have been developed, there are few technologies that support task allocation algorithms in dynamic environments. Obviously, this is not in accord with the actual situation. The battlefield is changing rapidly, which may lead to the failure of the allocated tasks and the inability to allocate new tasks. In order to deal with this problem, this paper improves the original D-NSGA3 algorithm to adapt to the dynamic environment. The experimental results show that, compared with the original static algorithm, the proposed algorithm has better effect in solving the task allocation problem of high-dimensional multi-objective agent based on maximizing the number of successfully allocated tasks, maximizing the benefits of executing tasks, minimizing the consumption cost, and minimizing the time cost.
Construction of Indoor Environment Spatial Layout Planning Model Based on 3D Virtual Simulation Technology

In view of the low accuracy of spatial quantitative layout in the existing layout planning model, the planning attribute nodes of the spatial layout planning model deviate from the standard attribute points, and the planning effect of the planning model is poor. To solve this problem, the indoor environment spatial layout planning model based on 3D virtual simulation technology is constructed. The 3D virtual simulation technology is used to mark the space planning points, and the genetic algorithm is used to solve the multi-level layout of indoor space features, control the layout accuracy, quantify the spatial structure of indoor environment, and set the layout accuracy parameters to complete the construction of the layout planning model. After setting the simulation scene, the simulation scene layout planning points are calibrated, and the planning model based on the spatial geometric characteristics, the planning model based on the network element layout optimization algorithm and the designed planning model are selected for testing. The results show that the designed planning model divides the attribute nodes evenly around the standard attribute points, and the planning effect is the best.
APIA: An Architecture for Policy-Aware Intentional Agents.

This paper introduces the APIA architecture for policy-aware intentional agents. These agents, acting in changing environments, are driven by intentions and yet abide by domain-relevant policies. This work leverages the AIA architecture for intention-driven intelligent agents by Blount, Gelfond, and Balduccini. It expands AIA with notions of policy compliance for authorization and obligation policies specified in the language AOPL by Gelfond and Lobo. APIA introduces various agent behavior modes, corresponding to different levels of adherence to policies. APIA reasoning tasks are reduced to computing answer sets using the Clingo solver and its Python API.
Criteria and Indices Substantiation of the Stream Cryptoconversion Efficiency

The efficiency of the functioning of cryptographic data conversion schemes must be estimated from the point of view of their immunity to various methods of information disclosure, as well from the point of view of imposed restrictions on the cryptoconversion velocity and on the using particularity. This section discusses the efficiency indices and criteria of stream ciphers, which characterize cryptographic strength, performance, versatility, etc.
In The Era of Big Data, Training the Practical Innovation Ability of Applied Talents with the Carrier of Mathematical Modeling Competition

In recent years, with the gradual development of innovative technology, innovative technology has become the main driving force for the development of mathematics major in China. With China gradually stepping into the era of big data, it cultivates a large number of mathematics professionals for China through the use of innovative technology. This paper aims to train a large number of outstanding talents for Our country by adopting mathematics competition mode, and improve students' data processing ability under the background of big data, so as to comprehensively improve students' mathematical innovation ability. This paper mainly takes data modeling contest as the carrier and conducts analysis and research from three aspects: competition platform, teacher formulation and student management, so as to cultivate innovative talents in mathematics. This paper also builds a mathematical innovation practice platform by cultivating students' innovative thinking ability and practical ability, so as to improve students' mathematical innovation ability and cultivate high-quality mathematical talents.
Ermis: a middleware for bridging data collection and data processing in IoT streaming applications

Modern streaming analytics platforms, such as Apache Flink, can receive, process, and act on real-time streaming data from various sources to take immediate action as events occur in the real world. Combined with the emergence of the Internet of Things (IoT), these platforms have revolutionized many application domains by deriving strategic insights from incoming sensor data. For instance, manufacturers can retrofit production lines with sensors, enabling them to rapidly detect and correct problems by analyzing incoming sensor data in real-time. However, when sensors generate data too slowly, the application produces incorrect results due to insufficient information. Conversely, generating data too fast rapidly decreases sensor battery lifetime and wastes storage resources. Consequently, the data generation rate of the IoT infrastructure must match the data processing rate of the application(s) at all times, which is a challenging task due to the several facets of IoT dynamism: sensor mobility, sensor failure, changing application data requirements, varying message inter-generation and transmission delay. Currently, application developers are required to manually configure sensor sampling periods to match these rates; a time-consuming process that is inefficient, if not ineffective, due to the dynamism of the IoT infrastructure. In this paper, we tackle this problem by introducing Ermis, a novel middleware that runs on the gateway and automatically adapts sensor sampling periods at runtime to match application requirements, without relying on any previous knowledge about the underlying IoT infrastructure. To the best of our knowledge, this is the first solution that can ensure the presence of sufficient data for the IoT applications under various sources of dynamism, while minimizing unnecessary transmissions to extend sensor battery lifetime. Extensive evaluation on real-time data from a physical testbed demonstrated a ~34% increase in the expected battery lifetime, while consistently ensuring data sufficiency, compared to state-of-the-art.
Multiple Image Encryption Algorithm Based on Random Chaotic System and Bit Permutation

A new random chaotic system demonstrating good chaotic natures is constructed. A multiple image encryption algorithm based on the proposed random chaotic system and bit permutation is proposed. Firstly, two original images are synthesized into one plain-image, chaotic sequence related to plain-image and generated by random chaotic system is then applied to shuffle pixels of the plain-image. Bit permutation is also performed to permute the binary image yielded by the scrambled image. Finally, the binary image is transformed into a 8-bit gray image and a diffusion process is carried out to obtain the cipher-image. Experimental results and security analysis show that the encryption algorithm has high security and can resist various classical attacks.
Risk assessment of computer sabotage in management automation systems

The relevance of the study is due to the increasing number of cyber-attacks on management automation systems, among which computer sabotage occupies a special place. In this class of cybercrime, risk dynamics as the probability of occurrence of damages of a certain value are studied. Risk dynamics is tracked over the past five years for the most developed countries and globally. It is shown that both the frequency and the severity of computer sabotage, despite the pandemic, are steadily increasing. This calls for international and national action. The findings of this paper prove that computer crime of the "computer sabotage" type should be seen as a separate type of computer crime, with a tendency to grow. Today it has become a cross-border phenomenon, and combating it only within national territories will not be as effective as it should be.
Reinforcement Learning Algorithm for Partially Observable Environment Considering State Estimation Error Prediction

Watabe, Yuuya Shibuya, TakeshiReinforcement learning is a machine learning technique that attempts to learn a policy based on a reward criterion through trial and error in the target environment. In this paper, we investigate a reinforcement learning algorithm for a partially observable environment without prior knowledge about the state transition and observation likelihood functions. When only noisy or incomplete measurement is available due to inexpensive sensors, we can use a state estimator in combination with a policy trained via reinforcement learning to recover the performance of the policy. In such cases, performance may degrade due to state estimation error. To solve this problem, we propose a planning method that considers the prediction of state estimation error. The proposed algorithm indirectly predicts the state estimation error and avoids to get into areas where large state estimation error may occur using prediction. We tested the proposed method in a simple inverted pendulum task, where the agent only receives noisy and drifted measurements. Empirical result demonstrates that performance of policy in a partially observable environment is improved by the proposed method.
Origin of higher education: a personal-value paradigm

The development of the personal-value paradigm in the Russian higher education during reforms and counter-reforms is considered. The role of the scientific community in the process of developing and implementing a personal-value concept is revealed. Special attention is paid to the organization of the personalistic educational process in autonomous universities. According to the author, the personality-value model of higher education is the most effective in the humanitarian higher education. The personalist axiology of education determined the goals, forms and methods and ways of learning scientific truth, with the active involvement of students in the process of research work, which contributes to the development of their creative personalities. Constant updating of the didactic complex: goals, content, forms and methods of teaching implies the need to build an educational process adequate to the advanced level of achievements in the evolution of science. Society as a customer forms a university focused on obtaining the socio-cultural skills necessary for the progress of science and education. The personality-alienated model levels the axiology of science and higher education and focuses on the criteria of usefulness and competence, which significantly reduces the level of qualification of graduates. There is a conflict of ambitendent innovation processes: the personality-alienated model leads to an authoritarian trend in education, while at the same time, the super-valuable technologization of the educational process aimed at the formation of narrow specialists is carried out in higher education.