Reviewerly/exHarmony huggingface dataset:
Concert hall reputations versus reality over time, for better or worse?

The acoustical reputations of many concert halls provide a curious study of human nature as illuminating as their acoustical attributes. This paper reveals how the reputations of 7 prominent halls that opened between 1880 and 1973 have morphed and offers conjecture as to the reasons. Discussed briefly, are Troy Music Hall (1875), Carnegie Hall (1891), Boston Symphony Hall (1900), Philharmonic Hall (1962), and Phoenix Symphony Hall (1973) among others. Each has undergone renovations and met with varying public reaction.
A Python Library for Matrix Algebra on GPU and Multicore Architectures

Despite C/C++ and Python both being very popular programming languages, each tool possesses unique advantages and disadvantages. Notably, computers can run C/C++ code very quickly, but C/C++ code has to first be compiled and the syntax can be difficult for new programmers to understand. Python code, however, sacrifices speed for an easy-to-understand syntax and can be run interactively. Thankfully, it is possible to combine the benefits of Python and C/C++. For example, NumPy is a popular package of linear algebra operations written in C but used with Python. Such a combination allows programmers to not only utilize the fast speeds of C code but also Python's simple syntax. NumPy's potential, however, is limited by its inability to run on graphics processing units (GPUs), processors specialized for handling computations. On the other hand, a linear algebra library known as Matrix Algebra on GPU and Multicore Architectures (MAGMA) is suited for running its code on GPUs. Coupled with the fact that its code is written in C/C++, MAGMA offers extremely fast computations. To combine MAGMA's speed with Python's easy-to-understand syntax, we researched how to use C++ code with Python. By researching a tool known as Simplified Wrapper and Interface Generator (SWIG), we created PyMAGMA - a library of chosen MAGMA functions which can be imported in Python 3.9 for use.
Optimal Selection of Dropout Probability for Stochastic Training of Deep Residual Network

Considering the problem of low efficiency in traditional stochastic training of residual network by empirically parameters tuning for dropout probability selection, this paper proposes the relationship between the stochastic training model of deep residual network and stochastic optimal control theory, and then the expression of the optimal dropout probability is obtained by theoretical derivation. First the stochastic training model of deep residual network obtained by derivation can be approximated as a stochastic dynamic system, which means that it can provide the guidelines of stochastic learning strategies from the perspective of the stochastic dynamic system. Moreover, the stochastic training and learning process of deep residual network can be approximated as a stochastic optimal control problem. Finally, the optimal solution can be solved by dynamic programming or the minimum principle, thus the optimal selection method of the adaptive hyperparameter namely dropout probability is obtained. Experimental results are provided to illustrate the effectiveness of the optimal selection of dropout probability for stochastic training of deep residual network.
A Negative Survey based Method for Preserving Topology Privacy in Social Networks

Currently, the rapid popularity of social network platforms makes the real social relations in social networks face the potential risk of disclosure. Therefore, most users may refuse to provide their social relations for the purpose of privacy preservation, which is not conducive to the development of social networks. The negative survey is an emerging sensitive information collection method, which can collect sensitive information while preserving personal privacy. In this paper, based on negative survey, we propose an algorithm called NetNS to preserve the privacy of social relations, namely the topology privacy of social networks. The proposed NetNS is simple and efficient, which only randomly divides the nodes in social networks into different groups, and disturbs the topology of each group by a dedicated negative survey model. Experiments are carried out on three real networks of different sizes. The experimental results show that compared to three algorithms tailored for topology privacy of social networks, the proposed NetNS can against graph structure attack while realizing better utility of social networks.
Mobile Robot Navigation in Dynamic Environments Using Reinforcement Learning

The navigation system is one of the most important and crucial concerns in the research of mobile robots. Perception, cognition, action, human-robot interaction, and control systems are among the difficulties that have been resolved. Each navigation system must handle the aforementioned common designs to ensure that all duties may be completed. The navigation system is built on learning techniques that provide the ability to reason in the face of environmental uncertainty. However, the design will be difficult to build due to a number of factors, including inherent uncertainties in the unorganized environment. A more expensive design cost, computational resources, and larger memory are all required in this case. Navigating an autonomous robot in an uncontrolled environment is difficult because it necessitates the cooperation of a number of subsystems. Mobile robots must be intelligent in order to adapt to navigation in unfamiliar environments, such as environmental cognition, behavioral decisions, and learning. The robot will then navigate around these obstacles without collapsing and arrive at a specific destination point. Combining two processes, such as environmental mapping and robot behaviors, can result in behavior-based navigation. Obstacle avoidance, wall following, corridor following, and target seeking are some examples. If only one of the two processes is used, the system should be used in two ways. When this approach is used, two major issues are bound to arise: I the combination of two simple behaviors to form a complex one, and (ii) the integration of more than two behaviors. Behavior induced by multiple concurrent goals can be smoothly blended into a dynamic sequence of control action. This study is concerned with the automatic navigation of a mobile robot from its starting point to its destination point. To solve a few sub-problems associated with automatic navigation in an uncontrolled environment. Monte Carlo simulation is used to evaluate the algorithm’s performance and show under what conditions the algorithm performs better and worse. Obtaining position mapping to optimize action on mobile robots using a reinforcement learning framework. Reinforcement learning necessitates a large number of training samples, making it difficult to apply directly to real-world mobile robot navigation scenarios. To address this issue, the robot is trained in a Gazebo platform middleware Robot Operating System (ROS) simulation environment, followed by Q-Learning training on mobile robots.
BA-GAN: Bidirectional Attention Generation Adversarial Network for Text-to-Image Synthesis

It is difficult for the generated image to maintain semantic consistency with the text descriptions of natural language, which is a challenge of text-to-image generation. A bidirectional attention generation adversarial network (BA-GAN) is proposed in this paper. The network achieves bidirectional attention multi-modal similarity model, which establishes the one-to-one correspondence between text and image through mutual learning. The mutual learning involves the relationship between sentences and images, and between words in the sentences and sub-regions in images. Meanwhile, a deep attention fusion structure is constructed to generate a more real and reliable image. The structure uses multi branch to obtain the fused deep features and improves the generator’s ability to extract text semantic features. A large number of experiments show that the performance of our model has been significantly improved.
Efficient Reinforcement Learning for 3D LiDAR Navigation of Mobile Robot

Developing an efficient automatic navigation system for mobile robots is challenging in the strange scenarios where robots can only observe the environment of the surrounding limited area. While other distributed automatic navigation systems exist, they often require extracting semantic information to calculate navigation action, which requires extra modules to provide perceptual information and is not robust. We propose an end-to-end automatic navigation system based on the reinforcement learning technology. In particular, the raw 3D LiDAR data is used to directly map an efficient navigation policy. We design a novel dense reward function to handle the reward sparsity issue and provide a graphical representation method to enable the efficient feature learning from the raw 3D LiDAR data in our navigation system. In addition, an imitation learning based policy initialization is introduced before the subsequent reinforcement learning, which increases the learning efficiency and, in the meantime, still encouraging the robot to explore all the potential states to achieve advanced performance than the imitated experts. Our navigation model is trained in the Webots environment and the experimental results show that our model has efficient and flexible navigation performance in complex environments. More importantly, trained model can be easily extended to unfamiliar environments.
Robust Stereo Matching with an Unfixed and Adaptive Disparity Search Range

Stereo matching is an essential basis for various applications in computer vision, but currently, most stereo matching methods have poor generalization performance and require a fixed disparity search range. In this work, we propose to adopt the operation "sub-pixel three-around-maximum", which supports an unfixed disparity search range, to replace the currently popular operation soft argmax. We also propose to directly supervise the feature extractor by three loss functions. Just depending on the feature extractor, we can obtain an accurate semi-dense disparity map before cost aggregation, which can help to remove adaptively the redundancy part of the predefined disparity search range. An adaptive disparity search range for each stereo pair can save much time and memory. The proposed architecture achieves state-of-the-art cross-domain generalization performance in the datasets KITTI2012&2015, and experimental results demonstrate that our method supports unfixed and adaptive disparity search range.
Additional professional education in the field of physical culture and sports: features of content and organization

Relevance. Additional professional education, focused on the implementation of professional standards, will reach a new level. DPO involves purposeful work with students and employers, ensuring the continuity of educational and professional standards. Purpose. To determine the features of additional professional education in terms of content and organization of the learning process. Methodology and organization of the research . In the context of the realization of the concept of lifelong learning, the practice-oriented activity of students becomes relevant. The results of the research . Procedural knowledge allows us to take into account the specifics of the professional activities of SFKiS specialists, systematize their knowledge, skills and already acquired experience, and project a holistic view of the prospects of self-realization in the profession. Conclusions. Constant work on oneself within the framework of the collection program provides the subject with dynamism in cognition and a strategic perspective in mastering the profession and self-realization in professional activity. Following the concept of teaching throughout life (but no longer blindly!) gives confidence in professional compliance during the transition from one professional activity to another in the FKiS industry.
Research on Traffic Accident Detection Based on Vehicle Perspective

With more people owning cars, traffic accidents happen more frequently and result in many more fatalities each year. It is challenging for current roadside traffic incident detection equipment to have many blind spots, especially after an accident, which results in secondary injuries from traffic events not being discovered in time. As a result, a technique for detecting traffic incidents is presented for use on the vehicle side and is based on video captured by the car camera. The backbone of the proposed enhanced YOLOv5l model for the detection model is the EfficientNet structure with the SE layer eliminated, which drastically decreases the number of model parameters and computing work. To increase model detection accuracy, the fused attention technique is implemented at the prediction side. When compared to the YOLOv5s model, the upgraded model's mAP improves by 13% to 87.7%; when compared to the YOLOv5l model, our model's detection speed reduces by 7ms to 22.7ms, allowing it to be placed at the vehicle side for real-time detection of road traffic incidents.
Programming Models for Determining Optimal R&amp;D Arrangement in Mobile Application Development Process

The R&D (Research & Development) process of mobile application can be regarded as a complex system engineering that needs to take into account a variety of influencing elements, such as customer’s demands, engineering measures and business goals etc., as well as their interplay. A novel decision support platform is proposed to assist project managers in determining the optimal technical measures with implementation degrees and resource allocation scheme. Specially, the HDQ (House of Development Quality) is designed to collect essential data and information as well as reflect correlations among elements, which can effectively connect isolated information islands. Through the transform of HDQ, a multi-objective programming model is constructed to meet customers’ overall demands and specific requirements. A combination of specified technical measures and resource allocation can be obtained under ideal conditions. Additionally, the extended model considering resource substitution is designed to realize further improvement by effectively utilizing the surplus resource in the above optimal condition, demonstrating the advantage of proper managerial arrangement and resource deployment. Finally, the R&D case of family financial management application is conducted to illustrate the effectiveness of the suggested method, which offers a new perspective on mobile application R&D project management.
MuCAAT: Multilingual Contextualized Authorship Anonymization of Texts from social networks

Social networks are a source of data that can be useful to researchers in a variety of fields. However, an important limitation to the applicability of this kind of data is the presence of private and sensitive information in it. Therefore, it is important to anonymize it before using it in research to ensure that it is ethical and does not lead to the leak of private information. Most anonymization methods involve removing named entities and identifiers from text. However, in most cases, this level of anonymization is not enough since the authorship of texts can be determined by the style of writing. We propose a new text generation method that takes into account the features of social networks and allows effective anonymization of text style. We evaluate the effectiveness of the proposed model on social media texts, including the Sentiment 140 dataset. The result shows that our model outperforms the state-of-the-art model for authorship obfuscation and semantics preservation.
Information Extraction and Analysis of Chinese Traffic News

With the development of society, road accidents have been one of the leading causes of accidental death. Analyzing the factors affecting road traffic accident occurrences is vital to develop safety. However, traffic accident data is limited for us due to policy and privacy. This paper proposes an information extraction model to extract traffic accident key information from real Chinese news reports. The model fuses lexicon and character information for vector representation, which makes up for the deficiency of a single character. We test the proposed model on a real news dataset, and the experimental result shows that the model has significantly improved. Compared to existing methods, the model obtains an average of 5.18% development on precision and 7.03% development on F1-score. Then, we utilized the traffic accidents key information extracted by the model to capture association rule by apriori algorithm. The result shows that potential traffic safety problems can be mined from the extracted information.
Having the Right Tool

After dealing with everything important Feedback Arc Set in the first chapter of the book, and after presenting a number of algorithms, giving both breadth and depth, in the second chapter, in this chapter one will find a miniature guide for algorithmically solving Feedback Arc Set. This will be achieved by focusing on both special, and more general cases. Algorithms suggested are disparate in efficiency, approximability, and practical relevance.
MLTK01: A Prototyping Toolkit for Tangible Learning Things

This work illustrates and reflects on the design process of MLTK01, an open-source toolkit for fast prototyping tangible learning things, built on top of Arduino and ml5js. The toolkit was developed as a response to the current lack of fast and easy to use tools for tangible experiments with machine learning. Learning from insights gained through previous projects, we defined a set of basic building blocks necessary to enable such experiments and engaged in an iterative process of sketching, prototyping and preliminary testing of the toolkit. MLTK01 includes a custom PCB, a software library and accessories. Together with a descriptive account of the design process we also discuss possible applications of the toolkit and its implications for a design process of tangible learning things.
Trump’s hateful rhetoric and First Amendment failures: Re-envisioning incitement, true threats, and hate speech

Donald Trump’s hate-filled rhetoric while in office emphasizes long-existing shortcomings of free speech doctrine dealing with various forms of threatening speech. More so, because of his elected position during those speech moments, his speech elevates those concerns to a new level. His disregard for civil discourse has opened the door for a new speech behavior in national politics, one where inciteful, threatening or hateful speech is used more frequently and, as such, is becoming more socially acceptable. Each of those speech moments invokes claims of First Amendment protection. All of them were unimaginable topics 50 years ago and yet, from a legal perspective, all were or would be judged today using principles and legal doctrines crystalized in the 1950s and 1960s. Clearly, these are different times but what, if anything, does that mean for claims to free speech protections? This paper maintains that the answer to the problems inherent in current First Amendment treatment of hate-filled political speech involves two actions. First, the pre-existing doctrines of incitement and true threats need to be updated to make them applicable to current communication formats. Second, the courts must consider the addition of a constitutional category of hate speech.
Cryptographic Data Formats

Two universal cryptographic data formats have been described so far: OpenPGP in chapter 16 and PKCS#7/CMS in chapter 17 . This chapter deals with two more recent formats, which are gaining importance: XML and JSON, with their respective cryptographic constructs.
Performance Analysis of Smartwatch Text Input Using Different Hand Postures and Models

As society develops, the use of smartwatches is becoming more common, and so too is the demand for more complex functions, including text input. Despite it being known that hand posture affects text input performance, most of the previous research has overlooked the influence of hand posture on smartwatch text input performance. Most of the current input methods have employed language and spatial models to improve text input performance. However, there is still a lack of research on the effect of hand posture on the input method after adding these models. Therefore, this study focused on the effect of the language model and spatial model when different hand postures (index finger support, no finger support, thumb and middle finger support, and thumb finger support) and different movement situations (walking and standing) were used.
Research on short text entity disambiguation based on entity type classification

Entity disambiguation is an effective method to solve the problem of multi-meaning words in language. Due to the length of the short text, most models can not accurately understand the contextual semantic information of ambiguous words. In view of the above difficulties, this paper constructed the entity disambiguation model based on BERT (Bidirectional Encoder Representations from Transformers), and proposed a semantic enhancement method to solve the problem of insufficient semantics after feature extraction. The starting and ending position vectors of linked text type values were extracted from the output results of BERT model and input to the full connection layer. In order to optimize the effect of existing short-text entity disambiguation model, an auxiliary entity disambiguation model method was proposed. After obtaining the results of entity disambiguation model, The Entity Type Classification (ETC) model is introduced to integrate with the original Entity disambiguation model to further proofread the results of Entity disambiguation and correct some entities incorrectly linked by Entity disambiguation model. Experimental results show that compared with the original model, the proposed method improves the F1 values of CCKS2019 and CCKS2020 by 2.16% and 1.07%, respectively, which verifies the effectiveness of the proposed method.
Performance-Power Tradeoff in Heterogeneous SaaS Clouds With Trustworthiness Guarantee

Software-as-a-service (SaaS) clouds grow dramatically due to cost-effectiveness, availability, and flexibility. Quality of service (QoS) and power, which represent performance and cost, respectively, are conflicting yet critical issues in the service scheduling of SaaS clouds, and some researchers have investigated the tradeoff between them. However, existing works do not involve QoS attacks in which untrusted service providers provide fake QoS values to absorb service requests, resulting in lower user experience and system profits. In this paper, we jointly consider the QoS performance, queue congestion, and energy consumption to formulate the performance-power tradeoff while considering QoS attacks. To address this NP scheduling problem, we propose a Lyapunov-based decomposition strategy that converts the original problem into three equivalent subproblems. By aggregating the solving strategies for the three subproblems, we develop the online service selection and trustworthiness management algorithm that optimizes the performance–power tradeoff while resisting QoS attacks. In addition, a light-weighted trustworthiness management strategy is designed to update trustworthiness values without storing large amounts of past information. Mathematical analyses and simulations demonstrate that our proposed control framework realizes detection and resistance of QoS attacks and a <inline-formula><tex-math notation="LaTeX">$[O(1 / V), O(V)]$</tex-math></inline-formula> tradeoff between performance and power with a performance-power tradeoff parameter V.