Reviewerly/exHarmony huggingface dataset:
Macro SOStream: An Evolving Algorithm to Self Organizing Density-Based Clustering with Micro and Macroclusters

This paper proposes a new evolving algorithm named Macro SOStream with entirely online learning and based on self-organizing density for data stream clustering. The Macro SOStream is based on the SOStream algorithm, but we incorporate macroclusters composed of microclusters. While microclusters have spherical shapes, macroclusters can have arbitrary shapes. Moreover, the Macro SOStream has the macrocluster merge functionality specially designed to improve its performance under data drift contexts. The Macro SOStream’s performance is compared to SOStream and DenStream algorithms’ performance using four synthetic datasets and the ARI performance metric to validate our proposal. Furthermore, we carry out an exhaustive analysis on the influence of adequate hyperparameter setup on these algorithms’ performance. As a result, the Macro SOStream presents good performance mainly in the context of data drift and for demands of non-spherical clusters.
The Development of Cognitive and Noncognitive Skills in Students in the Autonomous Province of Trento

The analysis of what human capital (HC) is has a long history and culminates in the acknowledgment that HC and its growth are very important for both cognitive education (cognitive skills (CSs)) and personal life (noncognitive skills (NCSs)) and that CSs and NCSs have a strong reciprocal relationship, as studies by Heckman demonstrated. The present contribution (following Heckman’s approach) analyzed the relationship between CSs and NCSs in a sample of middle school students in the Autonomous Province of Trento. The second goal of the research was to verify whether educational teaching behaviors improved students’ personalities. Aside from the use of administrative data (INVALSI data, 2015 and 2018), one survey was administered in the 2018–2019 schooling year to verify the relationship between NCSs and CSs. Moreover, we sought to determine whether education teaching behavior improved the students’ personalities (1522 students in 25 schools) and whether programs could enhance NCSs. Methodological tools for the analysis involved the generalized least squares approach to answer the first question and a difference-in-differences model for the second. The main results showed that the levels of NCSs affected the ability to learn and improve CSs; a challenging teaching approach, especially if accompanied by programs improving its quality, had positive results. Finally, the research suggested that a wider, national-based survey following students from primary to secondary school would allow for a greater understanding of the dynamics of CSs and NCSs.
The Bayesian learning classifier system

Learning Classifier Systems (LCSs) are a family of versatile rule-based machine learning algorithms. Despite their long research history, the foundations of most LCSs are still informal due to them having been developed in an ad-hoc manner. An exception to this is the fully Bayesian LCS described by Drugowitsch in his 2008 book. In the present paper we shortly reiterate the central points of that system and then showcase our Python implementation of it, reporting on our attempt at replicating Drugowitsch's empirical results as well as on the results of a preliminary comparison study with the well-known LCS XCSF on the same learning tasks. We are able to replicate parts of Drugowitsch's results and explain the remaining differences. The comparison results make us conclude that the system may be competitive and exhibits unique features. Finally, we identify its current greatest shortcomings and based on those the next steps towards making it more universally usable.
Addressing tactic volatility in self-adaptive systems using evolved recurrent neural networks and uncertainty reduction tactics

Self-adaptive systems frequently use tactics to perform adaptations. Tactic examples include the implementation of additional security measures when an intrusion is detected, or activating a cooling mechanism when temperature thresholds are surpassed. Tactic volatility occurs in real-world systems and is defined as variable behavior in the attributes of a tactic, such as its latency or cost. A system's inability to effectively account for tactic volatility adversely impacts its efficiency and resiliency against the dynamics of real-world environments. To enable systems' efficiency against tactic volatility, we propose a Tactic Volatility Aware (TVA-E) process utilizing evolved Recurrent Neural Networks (eRNN) to provide accurate tactic predictions. TVA-E is also the first known process to take advantage of uncertainty reduction tactics to provide additional information to the decision-making process and reduce uncertainty. TVA-E easily integrates into popular adaptation processes enabling it to immediately benefit a large number of existing self-adaptive systems. Simulations using 52,106 tactic records demonstrate that: I) eRNN is an effective prediction mechanism, II) TVA-E represents an improvement over existing state-of-the-art processes in accounting for tactic volatility, and III) Uncertainty reduction tactics are beneficial in accounting for tactic volatility. The developed dataset and tool can be found at https://tacticvolatility.github.io/
TAGA

Deep learning has been widely adopted in many real-world applications, especially in image classification. However, researches have shown that minor distortions imperceptible to humans may mislead classifiers. One way to improve the robustness is using adversarial attacks to obtain adversarial examples and re-training the classifier with those images. However, the connections between attacks and application scenarios are rarely discussed. This paper proposes a novel black-box adversarial attack that is specifically designed for real-world application scenarios: The transfer-based black-box adversarial attack with genetic algorithms (TAGA). TAGA adopts a genetic algorithm to generate the adversarial examples and reduces the ensuing query costs with a surrogate model based on the transferability of adversarial attacks. Empirical results show that perturbing embeddings in the latent space helps the attack algorithm quickly obtain adversarial examples and that the surrogate fitness function reduces the number of function evaluations. Compared with several state-of-the-art attacks, TAGA improves the classifiers more under the application scenario in terms of the summation of natural and defense accuracy.
Environment driven dynamic decomposition for cooperative coevolution of multi-agent systems

Cooperative co-evolutionary approaches have shown the ability to evolve specialised agent behaviours in multi-agent systems. A key aspect of applying cooperative co-evolution as an optimisation technique is the decomposition of the overall problem into smaller, interacting sub-components. Typically, problem decomposition occurs a priori, with a common approach being to decompose by agent, where each agent's behaviour corresponds to a separate evolving individual. Such approaches have inherent scalability limitations as the number of agents increases. In this work, we demonstrate a novel dynamic decomposition scheme based on the hierarchal subdivision of the environment, rather than by agent. In our approach, decomposition is performed simultaneously with the evolution of behaviour, starting with behaviour for the whole environment, followed by environment sub-division and behaviour specialisation. The approach was evaluated by evolving the behaviour of a set of cooperating combat unit agents in a real-time strategy game. The game was set in an environment composed of areas, each requiring a different behaviour for optimal gameplay. Using the behaviour evolved by our approach, the agents consistently defeated a numerically superior opponent and demonstrated location-appropriate behaviour. This approach may benefit other multi-agent systems where distinct environment sections call for different specialisation.
Evolving transferable neural pruning functions

Structural design of neural networks is crucial for the success of deep learning. While most prior works in evolutionary learning aim at directly searching the structure of a network, few attempts have been made on another promising track, channel pruning, which recently has made major headway in designing efficient deep learning models. In fact, prior pruning methods adopt human-made pruning functions to score a channel's importance for channel pruning, which requires domain knowledge and could be sub-optimal. To this end, we pioneer the use of genetic programming (GP) to discover strong pruning metrics automatically. Specifically, we craft a novel design space to express high-quality and transferable pruning functions, which ensures an end-to-end evolution process where no manual modification is needed on the evolved functions for their transferability after evolution. Unlike prior methods, our approach can provide both compact pruned networks for efficient inference and novel closed-form pruning metrics which are mathematically explainable and thus generalizable to different pruning tasks. While the evolution is conducted on small datasets, our functions shows promising results when applied to more challenging datasets, different from those used in the evolution process. For example, on ILSVRC-2012, an evolved function achieves state-of-the-art pruning results.
EvoIsland

We present EvoIsland, a scalable interactive evolutionary user interface framework inspired by the spatially isolated land masses seen on Earth. Our generalizable interaction system encourages creators to spatially explore a wide range of design possibilities through the combination, separation, and rearrangement of hexagonal tiles on a grid. As these tiles are grouped into islandlike clusters, localized populations of designs form through an underlying evolutionary system. The interactions that take place within EvoIsland provide content creators with new ways to shape, display and assess populations in evolutionary systems that produce a wide range of solutions with visual phenotype outputs.
Latent graph learning with dual-channel attention for relation extraction

As a building block of information retrieval, relation extraction aims at predicting the relation type between two given entities in a piece of text. This task becomes challenging when it is confronted with long text that contains many task-unrelated tokens. Recent attempts to solve this problem have resorted to learning the relatedness among tokens. However, how to obtain appropriate graph for better relatedness representation still remains outstanding, while existing methods have room to improve. In this paper, we propose a novel latent graph learning method to enhance the expressivity of contextual information for the entities of interest. In particular, we design a dual-channel attention mechanism for multi-view graph learning and pool the learned multi-views to sift unrelated tokens for latent graph. This process can be repeated many times for refining the latent structure. We show that our method achieves superior performance on several benchmark datasets, compared to strong baseline models and prior multi-view graph learning approach.
An embedded method: Improve the relevance of text and face image with enhanced face attributes

To solve the problem of low quality and lack of specific attributes in the text-to-face synthesis task, this paper proposes EFA, a general embedding method for strengthening face attributes in the text-to-image synthesis models. First, we re-encode the irregular word-level descriptions scattered in sentences to form word encoding. Then, we design the embedded local feature extraction layer for discriminators of different models to learn more specific information related to face attributes. Next, we associate the word encoding with the extracted face image feature regions to obtain face attribute domain classification loss of the real image and the generated image. Finally, in the training process, we adopt the loss function to constrain the generator and discriminator to improve their performance. This method can improve the quality of text-to-face synthesis and enhance the semantic correlation between the generated image and text description. A large number of experimental results on the newly released Multi-Modal CelebA-HQ dataset verify the validity of our method, and the experimental results are competitive compared with state of the art. Especially, our approach boosts the FID by 47.75% over AttnGAN, by 33.68% over ControlGAN, by 10.05% over DM-GAN, and by 12.52% over DF-GAN. Code is available at https://github.com/cookie-ke/EFA . • Text-to-face image synthesis filed has strong application prospects. • The proposed embedded method EFA strengths the model's learning of salient features. • EFA can be embedded into different models to improve their performance. • Verify the effectiveness and versatile of EFA by using the face dataset on 4 models.
image2data: An R package to turn images in data sets

Data visualization is an essential and powerful tool to generate hypotheses, uncover patterns, and disseminate findings.It is crucial that introductory statistics courses train students to become critical authors and consumers of data visualization.Ludic data sets might help teaching statistics to students by making graphics more enjoyable, using images as instantaneous feedback, encouraging to discover hidden patterns, and reducing their focus on traditional hypothesis testing.Those data sets that have hidden images can be difficult to come by for teachers.These considerations have led to the development of a package that could easily create data sets from images for educational purposes.The purpose of this study is to present image2data, an R package that generates data sets from images.In this study, we show how to install the package, explain the basic arguments, and show three examples on how it can be used for teaching.Future studies could evaluate the effectiveness and motivation generated by using hidden images in data sets.Our hope is that by using hidden image in data sets, students will be inspired to decrypt data sets and discover the unexpected.
Symmetrical irregular local features for fine-grained visual classification

Fine-grained visual classification (FGVC) has small inter-class variations and large intra-class variations, therefore, recognizing sub-classes belonging to the same meta-class is a difficult task. Recent studies have primarily addressed this problem by locating the most discriminative image regions, and the extracted image regions have been used to improve the ability to capture subtle differences. Most of these studies used regular anchors to extract local features. However, the local features of the target are mostly irregular geometric shapes. These methods cannot fully extract the features and inevitably include a large amount of irrelevant information, resulting in reduced credibility of the evaluation results. However, the spatial relationship between the features is easily overlooked. This study proposes a novel local feature extraction anchor generator (LFEAG) to simulate the shapes of irregular features. Thus, discriminative features can be fully included in the extracted features. In addition, an effective symmetrized local feature extraction module (SLFEM) based on an attention mechanism is proposed to fully use the spatial relationship between the extracted local features and highlight discriminative features. Experiments on six popular fine-grained benchmark datasets: CUB-200-2011, Stanford Dogs, Food-101, Oxford-IIIT Pets, Aircraft and NA-Birds, are conducted to demonstrate the advantages of our proposed method.
A study on the Chinese poetry and national enlightenment consciousness of Sofa(小坡) Oh Hyo-won(吳孝媛)during the Japanese colonial period

This thesis studies the modern enlightenment consciousness that appeared in the Chinese poem of Sofa Oh Hyo-won, a female poet who lived in Japanese colonial era. He is a female poet who was born as a woman at the end of the Joseon Dynasty and lived a strange life with a strange fate, leaving 474 poems. Most of his poems express Japanese colonial era's feminine daily life with affection, so if you look at the trajectory of life along his poems, Japanese colonial era is vividly depicted and revealed.&#x0D; His poems reveal an enlightenment consciousness and advanced worldview to emphasize the need for modern women's education and establish a women's school by enlightening the Confucian feudal society of the Southern Journey to Korea. In particular, he looked back at Japanese colonial era Japan and was able to see the perception of women's education and modern civilization in poems. In addition, he moved to China, looked at the process of modernization of China, and returned after recognizing the East Asian modern era during the transition period through exchanges with Chinese celebrities. His poems written at this time have a small amount of poetry, but some poems reveal the characteristics of resistance literature, which implies nationalist literary tendencies and patriotic fighting spirit during the Japanese colonial period.&#x0D; The consciousness of modern enlightenment that appears in his poems contains the awakening of the ego toward our people. The theme consciousness of his poems reveals anti-feudal, ethnic, and popular movement tendencies, and contains a spirit of desperate patriotic enlightenment aimed at excitement and education. It was only at this time that I could see the buds of true national literature and realism literature. Oh Hyo-won's Chinese poem is the Unique style of Joseon Women's History.
New Technologies, Human Rights, and COVID-19: Evaluating the South Korean Response

The containment and management of the COVID-19 pandemic and its impacts have been a leading priority for nations since its onset in early 2020. South Korea is widely recognized as having one of the best responses to the pandemic without utilizing strict lockdowns or authoritarian measures to safeguard public health and the economy. To do this, Korea utilized new digital technologies at every stage of the process to ensure that infected individuals were tested, traced, and treated, also known as the Three T Strategy. However, the Korean case also shows how the use of technological forms of governance can aggravate preexisting human rights problems and how it can dynamically create new types of victims through enhanced capabilities that allow for the surveillance, categorization, and control of populations. This paper theorizes the Korean Three T Strategy and its constituent technologies as a datafication cycle and critically explores its human rights impacts. It finds that the use of new technologies to combat COVID-19 avoided some kinds of human rights violations associated with lockdowns in other countries, but also created the potential for human rights violations that were more obscure, complex, and difficult to measure. This finding suggests that while new technologies can help mitigate the trade-offs between economic growth, public health, and human rights, their usage alone does not provide a comprehensive solution.
A Sense-Based Semantic Similarity Measure Using a Shallow Neural Network

In this chapter, the authors have studied different knowledge-based semantic similarity measures that are used to calculate semantic similarity between two words. Further, finding semantic similarity between two words helps in calculating semantic similarity between two sentences. Existing semantic similarity measures do not give the importance to the word's sense or meaning. Therefore, they have proposed a shallow neural network-based semantic similarity measure through which they find its similarity using its sense. It produces word embedding of words in documents using Word2vec on the books of A Song of Ice and Fire.
“Serendipity Engineering for Seductive Hypermedia” and “User Analysis Using Socialnomics”

This chapter expends the application of the ecological cognition framework to serendipity engineering, seductive hypermedia, user analysis, and socialnomics. It updates the theory to better account for the advances in computational science computational intelligence. In terms of ‘serendipity engineering for seductive hypermedia', the chapter looks at how to design information systems to account for the pleasant occurrences that happen in offline environments studied by those in sales and marketing where beneficial outcomes often occur by chance encounters. In terms of ‘user analysis using socialnomics', it looks at how a parametric user model based on the ecological framework can be used to understand users of information systems. To do this, a number of socialnomics equations based on the parametric user model are explored, including to calculate probability of seduction and probability of serendipity in an information system. The parametric model presented has great applicability for information and communications technology solution providers.
Progression of E-Publishing Capacity Building in Nigeria

The variety of opportunities offered by the internet makes it attractive for publishing, especially for developing nations previously unable to distribute publications on a global scale. However, Nigerian publishers striving to seize the moment are grossly under-reported in literature, notwithstanding that their innovations could create opportunities globally. This study, therefore, describes the progression of e-publishing in Nigeria with emphasis on e-publishing capacity building, collaboration, and outsourcing. Data were collected from publishers and their websites by means of in-depth interviews, website observation, and survey, and findings indicate that publishers are building e-publishing capacity by launching websites, e-book clubs, online bookshops, and e-libraries in schools and by collaborating with foreign e-book distribution firms. This study, therefore, provides updated information on an emerging market with huge investment and collaboration potential.
Application of Blockchain in Educational Big Data

With the rapid development of big data technologies, their applications are growing rapidly. Conventional big data management is vulnerable. If any tampering occurs over stored database or sometimes untrusted data gathering occurs, then it causes serious issues with conventional data management. To resolve these problems, this chapter is focused on the development of blockchain technology for secure educational big data (EBD). Blockchain techniques divide the data blocks into secure data blocks using cryptography algorithms. The secure data blocks are connected in the chain with each other. This ensures security as well as flexibility. So, in this chapter, the proposed methodology had adopted blockchain technology and ensures its efficiency due to its decentralized and flexible approach.
Integration of Blockchain and IoT (BIoT)

The internet of technology (IoT) refers to the connectivity of smart devices with the internet to accumulate data and transfer it to smart devices. But with the lack of built-in security measures, IoT is vulnerable to privacy and security threats. Blockchain technology assists in the security needs of IoT devices. The main characteristics of blockchain technology like immutability, transparency, auditability, and encryption of data sort out the inadequacy of IoT devices. This chapter represents an inclusive survey on blockchain and IoT technologies with the integration of these technologies to overcome the shortfall. This chapter includes an overview of both technologies with their advantages and disadvantages. It also highlights the domains of their applications and represents the advantages of integration of blockchain and IoT technology to develop BIoT (blockchain-based IoT). Also, it analyzes the main challenges faced during the smooth integration of blockchain and IoT technologies.