Reviewerly/exHarmony huggingface dataset:
Fundamentals of the ETS

This chapter presents some definitions related to the Evaluation of Text Summaries (ETS). These definitions help the reader understand the key concepts and ideas in general. In addition, we outline the general process of automatic evaluation, describing ROUGE as one of the most used methods in the state-of-the-art. Finally, we present the Genetic Algorithm (GA) to introduce some concepts and ideas behind linear optimization.
Experimenting with Linear Optimization of Metrics for Multi-document Summarization Evaluation

This chapter presents experiments and results on the linear optimization of content metrics for evaluating multi-document summaries. Through the Genetic Algorithm (GA), we propose combining and optimizing the degree of relevance of 27 individual metrics derived from ROUGE-C. LSA, and SIMetrix, whose output metric (called SECO-SEVA) seeks to best approximate human judgments. For this, we conducted experiments in DUC01 and DUC02 datasets, obtaining competitive results to state-of-the-art methods.
Validation/Evaluation

Central to the human-centred design process is the reflection on intermittent design proposals and collecting feedback throughout the design process. In this chapter we consider methods for evaluating design proposals in different stages of the design process, both in the early and later stages. Methods for conducting evaluations are discussed, both for conducting expert reviews and user evaluations.
Case Study: Does Following Best Practices Increase LibGuides Usage?

This case study describes the work of a library task force led by the author to implement best practices for the design of web-based library guides using the LibGuides platform. The task force’s goal was to increase usage of guides. The task force learned that students were primarily finding library guides through searching on the open web, which has significant implications for guide design. It was hoped that incorporating usability research and search engine optimization (SEO) techniques into guide design would drive more traffic to the guides. Statistics available from the LibGuides platform were compared between the 2020 and 2021 academic year and the 2021–2022 academic year but no improvement in guide usage was found. In fact, usage for most guides was lower in 2021–2022.
Natural Substitution of Emoji on Our Daily Life: Emoji Makes Us More Specific to Express Our Motion

Emoji is the graphical representation of emotions, thoughts and ideas. It has empathized on human’s motion and its fundamental condition. This paper represents how emojis reflect on our culture, language, religion and economical condition. And it also includes Sigmund Freud’s “Human’s Psychological Theory”; conscious and subconscious s mind, Karl Marx’s “Base-Super Structure” and the “Para-linguistic Theory on Emoji”. In this paper, we found how emojis have helped to find one fundamental language within world’s 7111 different languages. an exploratory and descriptive study in nature. In this paper we used semi-structure interview method and focus group discussion, as our research method. for collecting data from my respondents. It has I will also use the purposive sampling different perceptual sites where we could understand mental and social condition of someone and human’s basic condition of specific time. Emojis, by its extraordinary features glorify religious secularism also. But misspelling emojis are representing the c ause of frustration, insecurities on relationship, deducting social status and so on. In the meantime, if we learn right way of using emojis, it will help us to being beneficial one.
Empowerment of Fishermen Communities through the Utilization of Spinning Machine Technology in Weru Village, Paciran District

The problem faced by the Slap Strap Making Business Group is that the process of making slap ropes is still manual using a hand drill. This business group experienced many problems when using manual methods, such as the results of the twisting of the slapping rope were not uniform and many were loose. The second obstacle faced is that manual labor requires a lot of manpower for the slap rope spinning process. The last obstacle faced is when using a spinning machine there is a concern that the electricity supply will not be able to meet because the electricity supply in the area around the coast still uses 450 watts of electricity. So that if you use a spinning machine, it is hoped that the machine can be energy efficient and easy to use. The right solution is offered to solve the problem of the need for a spinning machine that is energy efficient and easy to use, namely the manufacture of a slap rope spinning machine with a chain shaft system. The machine includes various components, among others; frames, shafts, bearings, gears, dimmers, belts and pulleys. The propulsion system uses an electric motor. By using the slap rope spinning machine, it is be able to increase the efficiency and effectiveness of the slap rope spinning process.
Interdisciplinary knowledge with an individual development trajectory

The authors consider the technology of training highly professional specialists capable of managing the innovative development of industries. A new method of organizing a human-centered educational process is proposed, in which actual curricula are created directly during the academic semester with the help of additive technologies — the method of additive technologies in education.
Satisfaction of Search Can Be Ameliorated by Perceptual Learning: A Proof-of-Principle Study

When searching a visual image that contains multiple target objects of interest, human subjects often show a satisfaction of search (SOS) effect, whereby if the subjects find one target, they are less likely to find additional targets in the image. Reducing SOS or, equivalently, subsequent search miss (SSM), is of great significance in many real-world situations where it is of paramount importance to find all targets in a given image, not just one. However, studies have shown that even highly trained and experienced subjects, such as expert radiologists, are subject to SOS. Here, using the detection of camouflaged objects (or camouflage-breaking) as an illustrative case, we demonstrate that when naïve subjects are trained to detect camouflaged objects more effectively, it has the side effect of reducing subjects' SOS. We tested subjects in the SOS task before and after they were trained in camouflage-breaking. During SOS testing, subjects viewed naturalistic scenes that contained zero, one, or two targets, depending on the image. As expected, before camouflage-training, subjects showed a strong SOS effect, whereby if they had found a target with relatively high visual saliency in a given image, they were less likely to have also found a lower-saliency target when one existed in the image. Subjects were then trained in the camouflage-breaking task to criterion using non-SOS images, i.e., camouflage images that contained zero or one target. Surprisingly, the trained subjects no longer showed significant levels of SOS. This reduction was specific to the particular background texture in which the subjects received camouflage training; subjects continued to show significant SOS when tested using a different background texture in which they did not receive camouflage training. A separate experiment showed that the reduction in SOS was not attributable to non-specific exposure or practice effects. Together, our results demonstrate that perceptual expertise can, in principle, reduce SOS, even when the perceptual training does not specifically target SOS reduction.
A Universal Decoupled Training Framework for Human Parsing

Human parsing is an important technology in human-robot interaction systems. At present, the distribution of multi-category human parsing datasets is unbalanced, and the samples present a long-tailed distribution, which directly affects the performance of human parsing. Meanwhile, the similarity between different categories leads the model to predict false parsing results. To solve the above problems, a general decoupled training framework called Decoupled Training framework based on Pixel Resampling (DTPR) was proposed to solve the long-tailed distribution, and a new sampling method named Pixel Resampling based on Accuracy distribution (PRA) for semantic segmentation was also proposed and applied to this decoupled training framework. The framework divides the training process into two phases, the first phase is to improve the model feature extraction ability, and the second phase is to improve the performance of the model on tail categories. The training framework was evaluated in MHPv2.0 and LIP datasets, and tested in both high-precision and real-time SOTA models. The MPA metric of model trained by DTPR in above two datasets increased by more than 6%, and the mIoU metric increased by more than 1% without changing the model structure.
Flexible Acceptance Condition of Generics from a Probabilistic Viewpoint: Towards Formalization of the Semantics of Generics

Formalization of the semantics of generics has been considered extremely challenging for their inherent vagueness and context-dependence that hinder a single fixed truth condition. The present study suggests a way to formalize the semantics of generics by constructing flexible acceptance conditions with comparative probabilities. Findings from our in-depth psycholinguistic experiment show that two comparative probabilities-cue validity and prevalence-indeed construct the flexible acceptance conditions for generics in a systematic manner that can be applied to a diverse types of generics: Acceptability of IS_A relational generics is mostly determined by prevalence without interaction with cue validity; feature-describing generics are endorsed acceptable with high cue validity, albeit mediated by prevalence; and acceptability of feature-describing generics with low cue validity is mostly determined by prevalence irrespective of cue validity. Such systematic patterns indicate a great potential for the formalization of the semantics of generics.
Introduction

Touchless Artificial Intelligence (AI), robotic, and digital technology have emerged as a critical support systems for educational, therapeutic, and emotional needs during the COVID-19 pandemic. While it is important to highlight the benefits of AI and digital technology for areas of social life, it is of interest to highlight those areas, where humans are not yet replaceable by AI. This book volume titled "Managing Social Robotics and Socio-Cultural Business Norms – Parallel Worlds of Emerging AI and Human Virtues" comprises three sections. Section I—Social Robotics encompasses chapters capturing the complex world of social robotics and AI. Chapters 1–4 (under Section I) focus on the emerging world of AI and Social Robotics. Section II—Digital Technology addresses the complex details of digital technology, AI, and social media roles in business applications. Chapters 5–7 (under Section II) highlight digital media and technology and its impacts during the current turbulent times. Finally, in Section III—Parallel Worlds: Human-Focused Socio-Cultural Norms and Negotiations—an AI-Free Research Domain, the authors highlight human actions embedded within socio-cultural business norms (e.g., international negotiations, work culture, and religious diversity) that are not yet replaceable by AI. Chapters 8–10 (under Section III) highlight the parallel world of human values and socio-cultural norms that is AI-free and human-focused.
Artificial Intelligence (AI) in Marketing: How AI Supports Marketers Throughout the Consumer Journey

With an increase in the volume of consumer-curated data, marketers, and advertisers rely increasingly on Artificial Intelligence (AI) to transform this data into valuable consumer insights. AI’s key building blocks, such as Natural Language Process (NLP), image recognition, speech recognition, problem solving and reasoning, and machine learning, provide a gateway for marketers to understand and predict the needs and demands of consumers as well as reach them at all levels of their consumer journey. The purpose of this research is to understand deep linkages and ties between AI applications and subsequent AI relationships with consumers through digital marketing interfaces of various organizations. Three case studies are presented in this chapter to examine the proposed framework through AI Applications—Relationships lenses and implications for researchers, marketers, and policy makers are offered.
Human Behavior Recognition Method Based on Edge Intelligence

The increasingly intelligent video surveillance system is the certain result of the gradual maturity of information technology. Human behavior recognition is one of the important tasks in the area of intelligent security monitoring. This paper proposes a human behavior recognition mechanism that uses edge-cloud collaborative computing. Firstly, at the edge node <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msub> <mrow> <mi>N</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </math> , the video is preprocessed to remove similar frames and the extracted skeleton sequence is expressed in multiple levels. Then the cloud trains the spatial-temporal graph ConvNet model and deploys it to the edge nodes <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <msub> <mrow> <mi>N</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> <mo>∼</mo> <msub> <mrow> <mi>N</mi> </mrow> <mrow> <mi>m</mi> </mrow> </msub> </math> . The edge uses the trained model to complete behavior recognition tasks and uploads the results to the cloud for fusion to obtain the final behavior category. The experimental results prove that the advantages of edge-cloud collaboration have made the model recognition accuracy rate steadily increase by more than 2.2%.
Engineering Safety Management System Based on Robot Intelligent Monitoring

In order to improve the effect of engineering safety management, this paper combines intelligent monitoring technology to construct a monitoring robot system. Moreover, this paper analyzes the characteristics of the digital impulse signal and analyzes the least square filtering and infinite impulse response filtering. At the same time, this paper detects the sample material on the detection test platform, obtains the digital pulse signal, and selects different filtering algorithms and filtering parameters. In addition, this paper uses a signal analysis and processing platform to establish a gamma energy spectrum and analyze the resolution of hydrogen peaks in the gamma energy spectrum. Finally, this paper compares and verifies the filtering algorithms and constructs an engineering safety management system based on robot intelligent monitoring. From the simulation monitoring test results, it can be seen that the engineering safety management system based on robot intelligent detection proposed in this paper has a good effect.
Attribute coordinate system‐based representation and application method of affective cognition

The traditional fuzzy Petri nets (FPNs) rarely contain the affective features when making reasoning decisions, primarily because the existing affective computing model is not suitable to be described directly in FPNs. In order to solve this problem, this paper introduces an attribute coordinate system and puts forward the affective representation and computing model based on the extraction and integration relationship between the attributes of things, which describes the affective change curve according to the cognition of the thinking subject to the attributes of the decision object. With this new model of affective computing, it is more convenient to utilize a qualitative mapping model and fuzzy qualitative judgment rules to express affective decisions and conveniently describe affective attributes in FPNs. A reasoning decision case illustrates the proposed method's feasibility and advantages.
Light field occlusion removal network via foreground location and background recovery

Occlusion removal in an image can aid in facilitating the robustness of numerous computer vision tasks, e . g ., detection and tracking in surveillance. However, the invisible property of contents behind the occlusions limits occlusion removal from the single view. Recently, the emerging light field (LF) data, which contains rich multi-view perception of the scene, provides potential solution for this challenge. To better exploit the capability of occlusion location and occluded contents recovery from LF data, in this paper, we propose a LF occlusion removal network (LFORNet), which consists of three key sub-networks: the foreground occlusion location (FOL) sub-network, the background content recovery (BCR) sub-network, and the refinement sub-network. Specifically, both FOL sub-network and BCR sub-network explore the multi-view information of LF data, and thus they are constructed with the same network structure to estimate the occlusion mask and the coarse occluded contents map, respectively. The refinement sub-network aggregates the above two outputs to obtain the refined occlusion removal. Meanwhile, we use multi-angle view stacks as the input of the network, which can make full use of the inherent information among the LF views. Experimental results show that our method is suitable for different sizes of occlusions, and surpasses the state-of-the-art approaches in both synthetic and real-world scenes. • A light field de-occlusion network was proposed. • The network breaks the main task into several sub-tasks. • This network can extract occlusion mask while removing occlusion task. • Experimental results show that our method achieves superior performances. counterparts.
Guidelines for the Application of Data Mining to the Problem of School Dropout

Dropout is a complex phenomenon based on interrelated factors such as personal, institutional, structural, sociocultural, among other ones. It represents a waste of resources for students, their families, schools and society, and continues to be a challenge for educational institutions. In the last decade, the growing amount of data from educational institutions and the emergence of data science have led to data mining methodologies to explore this problem empirically. In this work, we map the literature on how data mining has been addressed face-to-face dropout. We synthesize different aspects, all of them related to steps of a generic data mining process. Our findings reveal a low level of formalism in theories, methodologies and pre-processing steps, with most papers making comparisons of different algorithms and features on the data available in the institution’s information system. Finally, we present some guidelines that can be used to improve the research on this topic.
High-Level Visual Encoding Model Framework with Hierarchical Ventral Stream-Optimized Neural Networks

Visual encoding models based on deep neural networks (DNN) show good performance in predicting brain activity in low-level visual areas. However, due to the amount of neural data limitation, DNN-based visual encoding models are difficult to fit for high-level visual areas, resulting in insufficient encoding performance. The ventral stream suggests that higher visual areas receive information from lower visual areas, which is not fully reflected in the current encoding models. In the present study, we propose a novel visual encoding model framework which uses the hierarchy of representations in the ventral stream to improve the model's performance in high-level visual areas. Under the framework, we propose two categories of hierarchical encoding models from the voxel and the feature perspectives to realize the hierarchical representations. From the voxel perspective, we first constructed an encoding model for the low-level visual area (V1 or V2) and extracted the voxel space predicted by the model. Then we use the extracted voxel space of the low-level visual area to predict the voxel space of the high-level visual area (V4 or LO) via constructing a voxel-to-voxel model. From the feature perspective, the feature space of the first model is extracted to predict the voxel space of the high-level visual area. The experimental results show that two categories of hierarchical encoding models effectively improve the encoding performance in V4 and LO. In addition, the proportion of the best-encoded voxels for different models in V4 and LO show that our proposed models have obvious advantages in prediction accuracy. We find that the hierarchy of representations in the ventral stream has a positive effect on improving the performance of the existing model in high-level visual areas.
A Novel Encryption Scheme in Ship Remote Control against Differential Fault Attack

Cyber security has drawn more attention in the research of intelligent and unmanned ships. The remote control command transmitted in time sequence has a high similarity. This makes the ships more vulnerable to cyber-attacks, especially when they are controlled remotely. Aiming at the defense of Differential Fault Attack (DFA), this paper improved the SM4 algorithm in the phase of the S-box generation and circular encryption. The Wasserstein GAN Gradient Penalty (WGAN-GP) is used to generate S-boxes dynamically to confuse differential distribution tables. After the round encryption, the combination transformation is further applied to prevent from DFA. The corresponding symmetric decryption algorithm is also developed. Simulation result shows that the generated S-box meets the cryptography criteria and the combined transformation effectively hides the sensitive information in output ciphertext and guards against the DFA.
HEFT: A History-Enhanced Feature Transfer framework for incremental event detection

Incremental event detection is a challenging subfield of event detection, which could be applied in various complex situations, especially for the real-time streaming data scenario where new event classes are continuously emerging. To handle the catastrophic forgetting problem, knowledge distillation has been proved to be a promising mechanism since it requires no extra tools or data storage. However, conventional knowledge distillation-based methods are not effective due to two challenging issues: (a) there is an inherent conflict between reserving history information from old tasks and adjusting to new tasks with shared model output features. (b) the historical category information is not fully utilized, making the model prone to forgetting knowledge on the observed tasks. In this work, we present a History-Enhanced Feature Transfer (HEFT) framework to solve the two challenges mentioned above. For the first challenge, we employ a feature transfer module to separate reserving and adjusting sub-functions by reconstructing the old task features. In this manner, the reconstructed features are responsible for preserving historical knowledge, and the feature extractor with followed classifier only requires to focus on new task classification. For the second issue, a history-enhanced question answering model is introduced to reinforce the ability to memorize historical information, which leverages the trained categories as clues to guide the reasoning process. The experimental results show HEFT outperforms the state-of-the-art model by 6.2% and 9.1% of the whole F1 score on ACE 2005 and TAC KBP 2017 benchmarks, respectively. The ablation study and case study also demonstrate that HEFT could overcome catastrophic forgetting of old class event triggers without heavily relying on historically preserved samples.