Reviewerly/exHarmony huggingface dataset:
An Automatic Assessment Method of Cyber Threat Intelligence Combined with ATT&amp;CK Matrix

With the occurrence of cyber security incidents, the value of threat intelligence is coming to the fore. Timely extracting Indicator of Compromise (IOC) from cyber threat intelligence can quickly respond to threats. However, the sparse text in public threat intelligence scatters useful information, which makes it challenging to assess unstructured threat intelligence. In this paper, we proposed Cyber Threat Intelligence Automated Assessment Model (TIAM), a method to automatically assess highly sparse threat intelligence from multiple dimensions. TIAM implemented automatic classification of threat intelligence based on feature extraction, defined assessment criteria to quantify the value of threat intelligence, and combined ATT&amp;CK to identify attack techniques related to IOC. Finally, we associated the identified IOCs, ATT&amp;CK techniques, and intelligence quantification results. The experimental results shown that TIAM could better assess threat intelligence and help security managers to obtain valuable cyber threat intelligence.
Algorithm Model Design of the Aging Transformation Scheme of Computer Intelligent Aided Technology under the Background of Rural Revitalization

The study is to improve the living environment of the elderly in rural areas, improve the quality of life and well-being of the elderly, and ensure the living environment of the elderly while promoting rural revitalization and development under the policy background of rural revitalization, this study optimizes the aging transformation scheme by integrating computer intelligent auxiliary technology, and analyzes and compares the algorithm model design of the aging transformation scheme through the algorithms of the traditional transformation scheme and computer intelligent auxiliary technology. The results show that compared with the transformation scheme of the traditional algorithm, the transformation scheme after using computer intelligent auxiliary technology has higher accuracy and coupling for the aging transformation scheme, and has a better effect on improving rural development, beautiful rural construction, and improving the life of rural residents.
Integration graph attention network and multi‐centre constrained loss for cross‐modality person re‐identification

Cross-modality person re-identification is a challenging task due to the large visual appearance difference between RGB and infrared images. Existing studies mainly focus on learning local features and ignore the correlation between local features. In this paper, the Integration Graph Attention Network is proposed to learn the completed correlation between local features via the graph structure. To this end, the authors learn the coarse-fine attention weights to aggregate the local features by considering local detail and global information. Furthermore, the Multi-Centre Constrained Loss is proposed to optimise the feature similarity by constraining the centres of modality and identity. It simultaneously utilises three kinds of centre constraints, that is intra-identity centre constraint, modality centre constraint, and inter-identity centre constraint, in order to reduce the influence of modality information explicitly. The proposed method is evaluated on two standard benchmark datasets, that is SYSU-MM01 and RegDB, and the results demonstrate that the authors’ method achieves better performance than the state-of-the-art methods, for example, surpassing NFS by 4.8% and 6.0% mAP on the single-shot setting in All-search and Indoor-search modes, respectively.
Stylometric similarity in literary corpora: Non-authorship clustering and<i>Deutscher Novellenschatz</i>

Abstract A distant-reading task in literary corpus analysis is to group stylometrically similar texts. Since there are many ways to define writing style, the result not only depends on the clustering method but even more so on the measure of similarity. With authorship attribution, the predominant application of stylometry, as its benchmark much research has addressed the utility of methods for measuring similarity. We use a corpus of German-language novellas to demonstrate that one may be interested in very different meaningful groups of texts simultaneously, and that these can be recovered from stylometric clustering if the measure is chosen accordingly. As can be expected, different measures do better at recovering groups associated with, for instance, subgenre, author gender, or narrative perspective. As a consequence, it is suggested that corpus analyses should not be based on what is currently considered the most refined measure of stylometric similarity, but rather break down the decisions that yield a specific measure and provide substantively justified arguments for them.
Green City Landscape Design Based on GIS System

In order to solve the problem of green urban garden landscape design, a green urban garden landscape design method based on GIS system is proposed. This method introduces component GIS and compares three AO-based GIS secondary development modes. Based on the developed solution, comprehensively considering the index evaluation of urban landscaping and its influencing factors, the functional structure of the GIS-based landscaping management information system is designed, and the system requirements are analyzed in detail. It solves the problem that the cost of traditional green urban garden landscape design is too high, and the experiment proves that improving the landscape environment of urban public space has practical value for the research of urban landscape system. Combined with practical application, it is found that the intervention of rainwater landscape can effectively improve the aesthetics and artistry of urban rainwater public facilities. At the same time, the development of GIS and its related technologies (such as database technology and network technology) has also promoted the construction and development of urban landscaping system, which has a great effect on the beautification of the environment, which verifies the validity of the experiment.
Extraction of English Keyword Information Based on CAD Mesh Model

Traditional methods only consider topic information in English vocabulary information extraction, lose the statistical feature information of the keywords themselves, and easily ignore the semantic information of the words. In order to improve the extraction efficiency of English keyword information, based on the CAD mesh model, this paper adds constraint factors such as vertex neighborhood flatness, vertex degree, side length, and flatness on both sides of the side on the basis of the original QEM quadratic error simplification algorithm, and it incorporates a smoothing effect into the edge folding cost function. Moreover, based on the proposed normal vector-based QEM mesh simplification algorithm, the point selection after the edge folding operation is fixed as the vertices of the original edge, and it is applied to the mesh parameterization. In addition, the algorithm solves the local parameterization problem of partially deleted vertices after the simplification operation of each layer is completed. After the model is constructed, the performance of the model is verified through experiments. The research shows that the English keyword information extraction model constructed in this paper is effective.
Introduction to Interaction Design

In this chapter, the field of interaction design is introduced, and the different disciplines involved in interaction design are considered. Main concepts such as usability and user experience are defined, and design objectives for usability and user experience are considered.
Design Process

In this chapter, we take a closer look at the design process. We summarise design processes from different fields of engineering and conclude that the human-centred design process provides the best structure to ensure that knowledge about and feedback from prospective users is integrated in the design process. Finally, we consider more recent developments, leading to the insight that design is not just a matter of trying to satisfy user needs, but that considerations regarding users and a design vision should be integrated in a balanced process.
Why quantifiers float: A response to Kim (2013)

The aim of this discussion article is twofold. First, we critically review Jong-Bok Kim’s (2013) proposal [Floated numeral classifiers in Korean: A non-derivational, functional account. Lingua 133, 189–212], articulating the strengths and weaknesses of his approach. Second, after identifying some crucial weaknesses of Kim’s proposal, we offer an alternative analysis of floated quantifiers (FQs), while maintaining the same goal set by Kim (2013). In doing so, we point out several challenges Kim’s analysis faces, primarily the adoption of the theme-rheme partition. We demonstrate that the theme-rheme division is orthogonal to the focus property FQs exhibit. Ultimately, we show that our criticism strengthens Kim’s analysis, as opposed to contradicting it.
MonoPoly: A practical monocular 3D object detector

3D object detection plays a pivotal role in driver assistance systems and has practical requirements for small storage and fast inference. Monocular 3D detection alternatives abandon the complexity of LiDAR setup and pursues the effectiveness and efficiency of the vision scheme. In this work, we propose a set of anchor-free monocular 3D detectors called MonoPoly based on the keypoint paradigm. Specifically, we design a polynomial feature aggregation sampling module to extract multi-scale context features for auxiliary training and alleviate classification and localization misalignment through an attention-aware loss. Extensive experiments show that the proposed MonoPoly series achieves an excellent trade-off between performance and model size while maintaining real-time efficiency on KITTI and nuScenes datasets.
The Effects of Cooperative and Competitive Situations on Statistical Learning

Devising cooperative or competitive situations is an important teaching strategy in educational practices. Nevertheless, there is still controversy regarding which situation is better for learning. This study was conducted to explore the effects of cooperative and competitive situations on statistical learning, through the alternating serial reaction time (ASRT) task. Individual cooperative and competitive situations were devised in this study, in which individual situation served as the control condition. Ninety recruited participants were randomly assigned to a cooperative, competitive, or individual group to perform the ASRT task. For general learning, cooperative and competitive situations could indeed make learners respond faster, and there was no significant difference in the RT between the cooperative and competitive groups. Moreover, statistical learning was observed in all three groups. An additional analysis of the early stage of the experiment showed that the learning effect of the competitive group was greater than those of the cooperative and individual groups, in terms of statistical learning. However, the final learning effect was not significantly different among the three groups. Overall, the cooperative and competitive situations had a positive impact on learning and enabled the students to acquire approximately the same learning effect in a shorter time period, compared with the individual situation. Specifically, the competitive situation accelerated the statistical learning process but not the general learning process.
Parallel-FST: A feature selection library for multicore clusters

Feature selection is a subfield of machine learning focused on reducing the dimensionality of datasets by performing a computationally intensive process. This work presents Parallel-FST, a publicly available parallel library for feature selection that includes seven methods which follow a hybrid MPI/multithreaded approach to reduce their runtime when executed on high performance computing systems. Performance tests were carried out on a 256-core cluster, where Parallel-FST obtained speedups of up to 229x for representative datasets and it was able to analyze a 512 GB dataset, which was not previously possible with a sequential counterpart library due to memory constraints.
A Reappraisal of Dependency Length Minimization as a Linguistic Universal

Dependency length minimization is widely regarded as a cross-linguistic universal reflecting syntactic complexity in natural languages. A typical way to operationalize dependency length in corpus-based studies has been to count the number of words between syntactically related words. However, such a formulation ignores the syntactic nature of the linguistic material that intervenes a dependency. In this work, we investigate if the number of syntactic heads (rather than the number of words) that intervene a dependency better captures the syntactic complexity across languages. We demonstrate that the dependency length minimization constraint in terms of the number of words could arise as a consequence of constraints on the intervening heads and the tree properties such as node arity. The current study highlights the importance of syntactic heads as central regions of structure building during processing. The results show that when syntactically related words are nonadjacent, increased structure building in the intervening region is avoided.
A no-reference perceptual image quality assessment database for learned image codecs

The drastic growth of research in image compression, especially deep learning-based image compression techniques, poses new challenges to objective image quality assessment (IQA). Typical artifacts encountered in the emerging image codecs are significantly different from that produced by traditional block-based codecs, leading to inapplicability of the existing objective IQA algorithms. Towards advancing the development of objective IQA algorithms for recent compression artifacts, we built a learning-based compressed image quality assessment (LCIQA) database involving traditional block-based image codecs, hybrid neural network based image codecs, convolutional neural network based and generative adversarial network (GAN) based end-to-end optimized image coding approaches. Our study confirms the statistical difference and human perception difference between reconstructions of learned compression and traditional block-based compression. We propose a two-step deep learning model for learning-based compressed image quality assessment. Extensive experiments on LCIQA database demonstrate that our proposed model performs better than other counterparts on learning-based compressed images, especially on GAN compressed images, and achieves competitive performance to the state-of-the-art IQA metrics on traditional compressed images. • An image quality database for learning-based compressed images is established. • Statistical difference and human perception difference between reconstructions of learned and block-based compression are proved. • Pre-training an image codec type identification network contributes to the quality–relevant features extraction for GAN compressed images.
Research on hierarchical pedestrian detection based on SVM classifier with improved kernel function

The research of pedestrian target detection in complex scenes is still of great significance. Aiming at the problem of high missed detection rate and poor timeliness of pedestrian target detection in complex scenes. This paper proposes an improved classification method. First, Haar features were extracted from the images to be detected, and the candidate areas of pedestrians were determined by Adaboost classifier. Then, the traditional SVM classifier was improved by using the combined kernel function instead of the single kernel function, and the optimal proportion of each function in the combined kernel function was found by using the adaptive particle swarm optimization algorithm. Finally, the improved SVM classifier was combined with the fusion feature to further detect the candidate area to accurately locate the pedestrian’s position. Experimental results show that compared with the traditional detection framework, the proposed method can effectively improve the detection speed and the detection accuracy. This method has certain practical significance for pedestrian target detection in complex scenes.
Chinese humanities scholars' reading of academic <scp>E‐books</scp>: A survey on reading strategies and tasks

Abstract Academic books are essential to humanities scholars. With the rapid development of digital publishing, digital reading has become mainstream, and electronic academic books are gradually being absorbed by humanities scholars. In this case, it is important to understand their use of reading tasks and strategies when reading electronic‐academic books, and to summarize the characteristics of their e‐book reading behaviour. This study therefore outlined the academic reading characteristics of humanities scholars, as well as their e‐book reading strategies and tasks. Subsequently, a user reading strategy questionnaire was compiled, and a survey conducted. Finally, descriptive statistics and sample paired t ‐tests were adopted to obtain the usage of e‐book reading strategies of Chinese humanities scholars. Results showed that prior knowledge was the most used strategy, followed by understanding and decision‐making, self‐regulation, and self‐monitoring strategies. Significant differences in the frequency and preference of reading strategies in different e‐book reading tasks were also identified. This paper further discusses the characteristics of e‐book reading behaviour of humanities scholars, and the future direction of electronic academic book construction.
Comparing the Effects of Cookbook and Non-Cookbook Based Lab Activities In a Calculus-Based Introductory Physics Course

I have compared the effects of Cookbook based lab activities with the impacts of doing lab inquiry based on a small introductory engineering physics course. Performance in lab activities that did and did not require the Cookbook procedure was compared using a final questionnaire from the participating students in that course. The population of students who did the lab in each scenario was the same because they enrolled in the same course. In the course, I planned five lab activities of three different types. The first type, which I call “Cookbook” activities (CB) were prepared and then completed by students. The second type of activities, which I call” Non- Cookbook” (NCB) were inquiry-based and orally guide. The final activity type was called “Both” and was a combination of two other activities. I investigated the activities in two dimensions: “scientific skills” and “general skills”. The results for scientific skills showed in NCB “Predicting” have improved. In CB, “Measuring the quantity” and “Sorting/classifying” have positive points of improving scientific skills. For general skills, in NCB, “Recalling concepts” and “Conceptual understanding” were improved. In CB, “Conceptual understanding” and “Scientific writing skills” and “Self-efficacy” were improved. “Writing the lab report” is a tough skill in NCB, while it is easy for CB.
Analysis of Malicious Node Identification Algorithm of Internet of Vehicles under Blockchain Technology: A Case Study of Intelligent Technology in Automotive Engineering

False messages sent by malicious or selfish vehicle nodes will reduce the operation efficiency of the Internet of Vehicles, and can even endanger drivers in serious cases. Therefore, it is very important to detect malicious vehicle nodes in the network in a timely manner. At present, the existing research on detecting malicious vehicle nodes in the Internet of Vehicles has some problems, such as difficulties with identification and a low detection efficiency. Blockchain technology cannot be tampered with or deleted and has open and transparent characteristics. Therefore, as a shared distributed ledger in decentralized networking, blockchain can promote collaboration between transactions, processing and interaction equipment, and help to establish a scalable, universal, private, secure and reliable car networking system. This paper puts forward a block-network-based malicious node detection mechanism. Using blockchain technology in a car network for malicious node identification algorithm could create a security scheme that can ensure smooth communication between network vehicles. A consensus on legal vehicle identification, message integrity verification, false message identification and malicious vehicle node identification form the four parts of the security scheme. Based on the public–private key mechanism and RSA encryption algorithm, combined with the malicious node identification algorithm in the Internet of Vehicles, the authenticity of the vehicle’s identity and message is determined to protect the vehicle’s security and privacy. First, a blockchain-based, malicious node detection architecture is constructed for the Internet of vehicles. We propose a malicious node identification algorithm based on the blockchain consensus mechanism. Combined the above detection architecture with the consensus mechanism, a comprehensive and accurate verification of vehicle identity and message authenticity is ensured, looking at the four aspects of vehicle identification, accounting node selection, verification of transmission message integrity and identification of the authenticity of transmission messages. Subsequently, the verification results will be globally broadcast in the Internet of Vehicles to suppress malicious behavior, further ensure that reliable event messages are provided for the driver, improve the VANET operation environment, and improve the operation efficiency of the Internet of Vehicles. Comparing the proposed detection mechanism using simulation software, the simulation results show that the proposed blockchain-based trust detection mechanism can effectively improve the accuracy of vehicle node authentication and identification of false messages, and improve network transmission performance in the Internet of Vehicles environment.
Human action recognition using a convolutional neural network based on skeleton heatmaps from two-stage pose estimation

Human action recognition based on skeleton information has been extensively used in various areas, such as human–computer interaction. In this paper, we extracted human skeleton data by constructing a two-stage human pose estimation model, which combined the improved single shot detector (SSD) algorithm with convolutional pose machines (CPM) to obtain human skeleton heatmaps. The backbone of the SSD algorithm was replaced with ResNet, which can characterize images effectively. In addition, we designed multiscale transformation rules for CPM to fuse the information of different scales and a convolutional neural network for the classification of the skeleton keypoints heatmaps to complete action recognition. Indoor and outdoor experiments were conducted on the Caster Moma mobile robot platform, and without an external remote control, the real-time movement of the robot was controlled by the leader through command actions.