Reviewerly/exHarmony huggingface dataset:
How effective is expressive? Consumer perceptions of new vernacular features in Facebook webcare responses

We investigate how intensifying (e.g. deliberate repetition of punctuation symbols) and non-intensifying (e.g. emoji or chatspeak abbreviations) new vernacular features in responses to consumer complaints on corporate Facebook pages influence perceptions of conversational human voice, interactional justice and corporate credibility, and whether company type and new vernacular usage in the complaint itself moderate these effects. We carried out a 3 (new vernacular features in response: absent, intensifying, non-intensifying) × 2 (new vernacular features in complaint: absent, present) × 2 (company type: progressive, traditional) between-subjects experiment with 718 Flemish consumers. Findings show that organisational responses containing non-intensifying features generate equally positive ratings as those without new vernacular. This points towards an extended norm of appropriate language use in webcare, i.e. adherence to (somewhat informal) Standard Dutch, but with some room for non-intensifying new vernacular. Intensifying expressive compensation strategies, however, negatively impacted conversational human voice, interactional justice and corporate credibility perceptions. The effect on the latter two is mediated to a certain extent by the perceived appropriateness of these features, highlighting the importance of Language Expectancy Theory and Role Theory as underlying frameworks in this field. No clear moderation of company type or new vernacular usage in the consumer complaint was observed.
An angle measurement algorithm for high-voltage switch aided by clustering based on ghost reduction PBAS

The high-voltage switch is an important equipment for transmission in the power system. In autonomous inspection, a major task is to detect the state of the switches. Aimed at state determination, an angle measurement algorithm that estimates the angle between two switch arms is presented in this paper. The GR-PBAS algorithm is proposed to obtain accurate foreground switch segmentation results, eliminating the ghost at the same time. The line segments are extracted by introducing PPHT for representation. A line assignment algorithm is then put forward to map the line segments to the two arms of a switch by K-means, after which the angle can finally be calculated without much effort. The experiment and validation on real-scene data demonstrate the effectiveness of the proposed algorithm.
Topic discovery from short reviews based on data enhancement

With the rapid development of social media and mobile Internet, short reviews, such as Weibo and Twitter, have exploded online. Discovering topics from short reviews is significant for many practical applications. It can effectively not only identify users’ attitudes and emotions but also enhance customer satisfaction and shopping experience. Because reviews are relatively short, the sparsity of reviews considerably restricts the quality of topic discovery. To improve the efficiency of topic discovery, we introduce the concept of data enhancement and strengthen the data in sentences and words in short reviews based on the weight of importance. We then propose a topic model for reviews to topic discovery based on data enhancement (shorted as DE-LDA). We verify the rationality and feasibility of DE-LDA on real datasets. Results show that the proposed method outperforms benchmarks in topic discovery and also has better clustering effects.
Entity relation extraction model based on probabilistic threshold decay

Omission of entities in the extraction process is caused by the model that performs joint extraction of entity relations by estimating the initial and last positions of entities. The probability threshold for predicting entity positions is dynamically altered in this work by modifying the probability threshold for judging entity positions. This reduces the loss of subject entities in the prediction process based on assessing entity positions using the proximity principle. Furthermore, different weights are provided to distinct entity relations to optimize the model's effect on entity relation extraction with few samples and raise extraction accuracy. Experiments show that, when compared to the existing algorithm, the algorithm can reduce the probability of the model missing subject entities during the extraction process, and the model's accuracy improves by nearly 0.2% to 0.5% in a small number of samples extracted using random sampling. The accuracy of model extraction can be improved by altering the weight assignment function, and the experimental results have improved by approximately 1%.
Supporting Remote Experimentation Through Computer-Mediated Communication

With the emergence of COVID-19 and the resultant reduction in direct personal contact, we were faced with a novel challenge of how to conduct studies that involved both semi-structured elicitation of detailed cognitive information and the evaluation of software interfaces and algorithms. We leveraged distributed communication technologies to support data collection across three separate human-computer interaction studies. We describe these studies in terms of specific computer-mediated communication methods and features and how we used them. We also present lessons learned, potential risks and detriments of using such approaches, (e.g., connectivity dependence, reduced ability to observe non-verbal cues, and diminished interpersonal interaction), along with how the research community can compensate for the shortcomings of distributed data collection in human-centered research.
Learner, Assignment, and Domain: Contextualizing Search for Comprehension

Modern search systems are largely designed and optimized for simple navigational or fact-finding tasks, with little support for complex tasks involving comprehension and learning. In response, the search-as-learning research community has undertaken a wide range of research questions focused on understanding how various types of learning outcomes are affected by searcher characteristics, the search task, and the search system. Typically, these views embed learning within a search system. In this paper we take a different view, embedding search within a framework for an end-to-end learning system designed to support learning in a formal educational context. Our central goal is to motivate research questions aligned to advance progress on techniques for active support of comprehension and formal learning. Thus we intentionally set aside goals for informal and surface learning. We argue that to be effective, such a search-centric learning system must model four key components: individual students (searcher factors), the educational domain (topic factors), academic assignments (task factors), and progress toward learning goals (the objective function of the end-to-end system). In modeling these components, our hypothetical system makes inferences about students’ learning histories, knowledge states, comprehension, and the utilities of different types of information resources. We present examples of possible techniques and data sources for each model. We also introduce the novel concept of leveraging school assignments as rich task context. Our intention is not to propose a functional system, but to frame search-as-learning in the context of comprehension and to inspire research questions arising from an end-to-end view of this important research domain.
Integrated Multi-task Agent Architecture with Affect-Like Guided Behavior

Inspired by how people’s cognitive and affective systems work together, this work proposes a reinforcement learning agent framework to support adaptive learning by dynamically adjusting the agent’s goals, focus of attention on states, and available actions. The agent framework includes subsystems for modeling affective states, sub-goal selection, attention, and action affordance, which are all implemented as individual reinforcement learning systems. The agent’s affective states moderate its other subsystems. Results show that agents using this architecture outperform vanilla Q-Learning agents in a mini-Go game.
Orthogonal Defect Classification-based Ontology Construction and Application of Software-hardware Integrated Error Pattern of Software-intensive Systems

Orthogonal defect classification (ODC) is a multi-dimensional measurement system with both qualitative and quantitative characteristics. And it is currently widely used in the software industry. However, its high level of abstraction leads to limited semantic information. Therefore, it seems to have a limited role in the process of software engineering of software-intensive systems (SISs). To solve this problem, this paper first analyzes software error lifetime from the perspective of knowledge-based software engineering and proposes an error generation model. Then, the paper proposes the concepts of software error pattern (SEP) and software requirements error pattern (SREP) based on the ODC. Then, according to an error generation mechanism, four types of software-hardware integrated error pattern (SHIEP) in the requirement stage, which is a sub-category of SREP, and corresponding ontology representation are given, focusing on “scenario”, “error manifestation” and “solution”. Finally, this paper takes a certain type of airborne radar software system as an example, uses protégé to edit the SHIEPs and instances, and further introduces the application of software FMEA based on the above work. The results show that the prior information based on the SHIEPs is helpful to discover potential failures and failure modes that may adversely affect the function or performance of SISs. Therefore, the proposed SHIEP is of great significance for improving the quality of software development and verification.
Research on development method of an artificial intelligent decision-making system

An artificial intelligent decision-making system based on Deep Q Network is developed according to the characteristic of the optoelectronic countermeasures for defense. In view of the high complexity of the input state variables of the system, simulation method is used to sift the state variables so as to reduce the input dimension of the network. In addition, simulation method is used to generate enough samples for the network training. Aiming at the adaptability evaluation of the system, evolutionary evaluation index is designed and simulation method is used to evaluate the adaptability of online learning ability of the system.
BWPT: Binary weight partial‐sum table for BNN/BWN acceleration

This letter proposes a novel architecture used in processing elements (PEs) to accelerate binary neural networks (BNNs) and binary weight networks (BWNs). We use the partial-sum value of weights with matching input activation to form a lookup table, named binary weight partial-sum table (BWPT). Following that, the constructed BWPT PE improves the computing efficiency in a variety of data flows. The proposed design has a 3× lower power delay product than the baseline PE and reduces the power consumption by 11.2%, 7.1% and 5.4% compared to 3-bit activation lookup table-based PE (LBPE3), 3-bit reduction tree PE and baseline PE, respectively.
Contemporary Urban Space Philosophy in China Using Lightweight Deep Learning Model-Under Ecological Ethics

It aims to improve the construction of ecological civilization and promote the common development of urban and ecology. Firstly, contemporary ecological ethics is explored, and its principles and characteristics are summarized. Then, the technique of convolutional neural network (CNN) image in a deep learning model is analyzed. Finally, deep convolutional neural networks (DCNN) are used to analyze and model the spatial characteristics of contemporary cities based on ecological ethics. According to the investigations, rural residential areas are more consistent with ecological ethics than urban residential areas when compared with the ecological characteristics of farmland and forest, and the highest ecological eigenvalues of the two areas are about 8 and 6. In the analysis of urban space, the maximum value of ecological eigenvalues of an airport is 9, and that of a stadium is 8. However, the scope of their construction that is consistent with ecological ethics is very small. Moreover, the eigenvalues of ecological ethics in the urban business circle of casinos are not only very low (the highest values are about 5 and 3), but also consistent with the construction norms of ecological ethics. The work of urban spatial philosophy is optimized based on the adoption of the DCNN model of deep learning in ecological ethics, which not only provides the reference for future ecological urban planning but also contributes to the common development of urban and ecology.
Correction to: Spike Sorting of Non-Stationary Data in Successive Intervals Based on Dirichlet Process Mixtures

[This corrects the article DOI: 10.1007/s11571-022-09781-7.].
Tangible and Full-Body Interfaces in Learning

This chapter builds on growing trends such as the maker movement, programmable children’s toys like LEGO Mindstorms, and gaming consoles with movement sensors like the Nintendo Wii and the Microsoft Kinect. These technologies include fabrication and construction technologies such as 3D printers, laser cutters, and milling machines; embedded computing where small computational devices – such as motion sensors or LED lights – are inserted into physical artifacts; novel materials such as conductive threads or materials with shape memory; optics and tracking with cameras and GPS position sensing. In recent years, computing technologies have increased in power, decreased in cost, and become dramatically smaller. These features enable wearable devices that can be integrated with children’s crafts, tools, and playgrounds. The chapter reviews technologies including virtual reality, body augmentation, programmable toys like LEGO Mindstorms, and manipulatives that are responsive to body movement.
360-degree Video Technology with Potential Use in Educational Applications

360-degree video technology has the potential to be a useful tool in a variety of applications. This new type of video can be shot in an omni-directional format, allowing users to view the video from any angle as it is playing. Due to the development of lower-cost technologies and the massive expansion in online video content, this technology&#x0027;s capabilities and advantages have been shown. This paper discusses some characteristics of this technology, as well as some case studies in education, which could be useful also in pandemic scenarios.
Von Mises–Fisher Elliptical Distribution

Modern probabilistic learning systems mainly assume symmetric distributions, however, real-world data typically obey skewed distributions and are thus not adequately modeled through symmetric distributions. To address this issue, a generalization of symmetric distributions called elliptical distributions are increasingly used, together with further improvements based on skewed elliptical distributions. However, existing approaches are either hard to estimate or have complicated and abstract representations. To this end, we propose a novel approach based on the von-Mises-Fisher (vMF) distribution to obtain an explicit and simple probability representation of skewed elliptical distributions. The analysis shows that this not only allows us to design and implement nonsymmetric learning systems but also provides a physically meaningful and intuitive way of generalizing skewed distributions. For rigor, the proposed framework is proven to share important and desirable properties with its symmetric counterpart. The proposed vMF distribution is demonstrated to be easy to generate and stable to estimate, both theoretically and through examples.
Photonic dot-product engine for optical signal and information processing

Abstract In this paper, a novel 2 × 2 Mach–Zehnder-interferometer (MZI)-based dot-product calculator is proposed and analyzed in the silicon-on-insulator (SOI) platform. To calculate the dot product, a phase-shifted Bragg grating (PSBG) modulator is placed in each arm of the MZI for the phase modulation at the resonant wavelength, followed by a 3 dB 2 × 2 directional coupler (DC) as the output to convert the phase difference into the intensity distribution across bar and cross ports. Moreover, an electro-absorption modulator (EAM) is implemented between the PSBG and the DC to change the intensity of the passing light in both arms. Theoretical modeling shows that by adjusting the phase difference and absorption strength individually, multiplication of two input values can be achieved using the proposed design. Numerical analysis over 10 000 dot-product operations with 7 bit precision for input values reveals a mean squared error (MSE) of 2.67 × 10 −5 . By cascading multiple proposed designs operated at different wavelengths, vector-by-vector multiplication can be realized in parallel, leveraging the wavelength-division multiplexing (WDM) scheme and the Bragg reflection mechanism, with results superior to the current and prior MZI-based processors with coherent light sources. This design paves the way for large-scale optical information processing systems with high throughput.
Quadcopter control comparison between PD and RL approach

This paper contains four major concepts about the quadcopter. Firstly, I drive dynamics analysis of the quadcopter. Mainly, apply force analysis with Newton and Euler’s method on the quadcopter. Then, applying first order ODE and second order ODE conduct formulas to obtain velocity and location expression. This part also helps to construct the computer simulation. Secondly, this paper introduces a commonly used quadcopter control technique-PD control. PD control stands for proportional-derivate control. It’s a control method that measured the desired position and actual position then outputs control commands. Third, the paper illustrates how machine learning algorithms can be applied to the control of the quadcopter. The machine learning algorithm used in this paper is the deep deterministic policy gradient (DDPG). Finally, I designed three tasks for experimental purpose. These tasks include flying directly upward from the ground, flying in x-direction, and flying in all three directions at the same time. Then, evaluating how does each control method works through these experiments.
Cloud Security Issues and Log-based Proactive Strategy

Recently, given the current situation the world is undergoing with the COVID-19 pandemic, a lot of companies encourage remote working from home. As a result, the use of the cloud has increased, but as a consequence, cyberattacks on the cloud systems have escalated. However, organizations and companies still have concerns about the use of the cloud as most of them remain unaware of the security threats against cloud system. This work researches through major concerns of the organizations and companies for cloud security and tries to explain why it is important for the organization to be aware of the threats that the cloud is facing. It also proposes a three-step strategy to help companies and organizations to secure their cloud system.
Data Analysis and Application of English Learners’ Learning Input Based on Big Data Processing Technology

The study of the impact of student engagement on academic performance has become one of the most significant research subjects in the world of foreign language education quality evaluation. This article refines and summarizes the implementation steps and design strategies of language classroom education via the level of the learners’ English learning investment and influencing factors under the mixed learning mode with the latest technology, and offers an important aspect for curriculum teachers to fully utilize the various constituent elements of classroom environment (teachers and students, technology, resources and environment, etc.) via particular instance usage and influence investigation.
Automated surface mapping via unsupervised learning and classification of Mercury Visible–Near-Infrared reflectance spectra

In this work we apply unsupervised learning techniques for dimensionality reduction and clustering to remote sensing hyperspectral Visible-Near Infrared (VNIR) reflectance spectra datasets of the planet Mercury obtained by the MErcury Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER) mission. This approach produces cluster maps, which group different regions of the surface based on the properties of their spectra as inferred during the learning process. While results depend on the choice of model parameters and available data, comparison to expert-generated geologic maps shows that some clusters correspond to expert-mapped classes such as smooth plains on Mercury. These automatically generated maps can serve as a starting point or comparison for traditional methods of creating geologic maps based on spectral patterns. The code and data used in this work is available as Python Jupyter Notebook on the github public repository MESSENGER-Mercury-Surface-Cassification-Unsupervised_DLR1 funded by the European Union's Horizon 2020 grant No 871149.
MAPPER: Managing Application Performance via Parallel Efficiency Regulation

State-of-the-art systems, whether in servers or desktops, provide ample computational and storage resources to allow multiple simultaneously executing potentially parallel applications. However, performance tends to be unpredictable, being a function of algorithmic design, resource allocation choices, and hardware resource limitations. In this article, we introduce MAPPER, a manager of application performance via parallel efficiency regulation. MAPPER uses a privileged daemon to monitor (using hardware performance counters) and coordinate all participating applications by making two coupled decisions: the degree of parallelism to allow each application to improve system efficiency while guaranteeing quality of service (QoS), and which specific CPU cores to schedule applications on. The QoS metric may be chosen by the application and could be in terms of execution time, throughput, or tail latency, relative to the maximum performance achievable on the machine. We demonstrate that using a normalized parallel efficiency metric allows comparison across and cooperation among applications to guarantee their required QoS. While MAPPER may be used without application or runtime modification, use of a simple interface to communicate application-level knowledge improves MAPPER’s efficacy. Using a QoS guarantee of 85% of the IPC achieved with a fair share of resources on the machine, MAPPER achieves up to 3.3 \( \times \) speedup relative to unmodified Linux and runtime systems, with an average improvement of 17% in our test cases. At the same time, MAPPER violates QoS for only 2% of the applications (compared to 23% for Linux), while placing much tighter bounds on the worst case. MAPPER relieves hardware bottlenecks via task-to-CPU placement and allocates more CPU contexts to applications that exhibit higher parallel efficiency while guaranteeing QoS, resulting in both individual application performance predictability and overall system efficiency.
Research on recognition of interference signal based on deep learning

In response to the fact that most traditional communication interference recognition algorithms stay at a shallow learning level and cannot provide a detailed portrayal of the feature information inside the data, this paper proposes a deep convolutional neural network (CNN) based communication interference signal classification and recognition method to achieve the classification and recognition of five types of interference signals. This paper firstly introduces the network structure of CNN, the role of each layer, the convolution principle and common pooling operations, and then, describes the process of CNN-based communication interference signal classification and recognition, and verifies that the CNNbased communication interference signal classification and recognition method has better interference signal recognition rate and robustness through simulation analysis.