Reviewerly/exHarmony huggingface dataset:
An Application that Combines Learning and Games to Make Learners Keep Interested in Learning

Learning a new language and a new culture is always fun and interesting at the beginning. However, when learners get deeper into the learning process, they find that whether it is memorizing vocabulary, learning dif erent types of grammar, or learning a new culture that is completely dif erent from the learner's country, it becomes very dif icult and learners gradually lose their enthusiasm for learning. Also, some of the textbooks right now are published a long time ago. Teaching some 20 or 30 years ago’s culture won't help the learner and may make them misunderstand the modern culture of that country. So, why can't we give the learner more accurate information and keep the learning process as fun as ever? This paper designs a software that combines the games and learning together. By setting up dif erent scenarios, building a variety of representative buildings, adding various characters to communicate with, designing dif erent culturally significant objects, and conceiving an interesting story plot to let people who are learning a new language or culture can learn in a unique and more fun way. After applying the app to language schools, Japanese language learner communities, and the Internet, the results showed that the app makes learners learn in a more relaxed atmosphere and makes them want to learn more.
The construction practice and prospects of global scientific data repository platform

As an essential infrastructure, scientific data repositories play an important role in promoting the practice of open research data. As a bridge between polices and researchers, it makes the data sharing possible. However, it is a challenge to build a repository to be trustworthy and in compliant with FAIR principles. More and more guidelines on how to select a repository and what a trustworthy repository should be like have been brought up. The research elaborates several popular principles and extracts their common requirements. Upon those, the paper analyzes the repositories registered on the website of re3data and summarizes their current development. Moreover, the research focuses on the featured practices in some typical repositories, including domain-specific and generalist data repositories. At last, the research analyzes the development trend of international scientific data repositories in terms of trustworthiness, openness, and ecologization, which can be used as an instructive reference for the construction and development of similar platforms in China.
Report from the 4th Int. Workshop on Education through Advanced Software Engineering and Artificial Intelligence (EASEAI '22)

The growth of digital technologies has drastically altered daily life. Both the general public and specialized groups must continuously acquire new knowledge and skills. The EASEAI workshop series addresses this issue by examining the fields of software engineering, education, and AI research to discover ways they can be integrated. This workshop gathers researchers, educators, and practitioners who apply advanced software engineering and AI in education, and allows for a cross-generational and interdisciplinary exchange of ideas among students, to examine current practices and establish new future goals. More details at https://easeai.github.io.
The model libraries for creative economy

The article provides an overview of the possibilities for the model libraries to get involved in the creative economy. The role of science fiction literature and fairy tales in formation of creative thinking is emphasized. The author analyzes the potential of libraries in teaching readers creative thinking through TIPS technology (Theory of Inventive Problem Solving). The features of library creative space are examined.
Optimizing Payroll Accounting Information System at West Serang Tax Service Office: A Case Study

This study aims to determine whether the employee payroll accounting information system at the West Serang Pratama Tax Service Office. The research method used is descriptive method. The types of data used are primary data and secondary data. Data collection methods used are observation methods, interview methods, and literature methods. The employee payroll accounting information system at the West Serang Primary Tax Service Office is in accordance with the Standard Operational Procedure issued by the Directorate General of Taxes which is under the auspices of the Ministry of Finance of the Republic of Indonesia as a whole almost in accordance with the Standard Operational Procedure, this shows that the employee payroll accounting information system at The West Serang Pratama Tax Service Office has been implemented properly.
Simplicity and learning to distinguish arguments from modifiers

We present a learnability analysis of the argument-modifier distinction, asking whether there is information in the distribution of English constituents that could allow learners to identify which constituents are arguments and which are modifiers. We first develop a general description of some of the ways in which arguments and modifiers differ in distribution. We then identify two models from the literature that can capture these differences, which we call the argument-only model and the argument-modifier model. We employ these models using a common learning framework based on two simplicity biases which tradeoff against one another. The first bias favors a small lexicon with highly reusable lexical items, and the second, opposing, bias favors simple derivations of individual forms – those using small numbers of lexical items. Our first empirical study shows that the argument-modifier model is able to recover the argument-modifier status of many individual constituents when evaluated against a gold standard. This provides evidence in favor of our general account of the distributional differences between arguments and modifiers. It also suggests a kind of lower bound on the amount of information that a suitably equipped learner could use to identify which phrases are arguments or modifiers. We then present a series of analyses investigating how and why the argument-modifier model is able to recover the argument-modifier status of some constituents. In particular, we show that the argumentmodifier model is able to provide a simpler description of the input corpus than the argument-only model, both in terms of lexicon size, and in terms of the complexity of individual derivations. Intuitively, the argument-modifier model is able to do this because it is able to ignore spurious modifier structure when learning the lexicon. These analyses further support our general account of the differences between arguments and modifiers, as well as our simplicity-based approach to learning.
Utilizing Neural Networks for Semantic Segmentation on RGB/LiDAR Fused Data for Off-road Autonomous Military Vehicle Perception

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Image segmentation has historically been a technique for analyzing terrain for military autonomous vehicles. One of the weaknesses of image segmentation from camera data is that it lacks depth information, and it can be affected by environment lighting. Light detection and ranging (LiDAR) is an emerging technology in image segmentation that is able to estimate distances to the objects it detects. One advantage of LiDAR is the ability to gather accurate distances regardless of day, night, shadows, or glare. This study examines LiDAR and camera image segmentation fusion to improve an advanced driver-assistance systems (ADAS) algorithm for off-road autonomous military vehicles. The volume of points generated by LiDAR provides the vehicle with distance and spatial data surrounding the vehicle. Processing these point clouds with semantic segmentation is a computationally intensive process requiring fusion of camera and LiDAR data so that the neural network can process depth and image data simultaneously. We create fused depth images by using a projection method from the LiDAR onto the images to create depth images (RGB-Depth). A neural network is trained to segment the fused data from RELLIS-3D, which is a multi-modal data set for off road robotics. This data set contains both LiDAR point clouds and corresponding RGB images for training the neural network. The labels from the data set are grouped as objects, traversable terrain, non-traversable terrain, and sky to balance underrepresented classes. Results on a modified version of DeepLabv3+ with a ResNet-18 backbone achieves an overall accuracy of 93.989 percent.&lt;/div&gt;&lt;/div&gt;
Quantum repeater using two-mode squeezed states and atomic noiseless amplifiers

We perform a theoretical investigation into how a two-mode squeezed vacuum state, which has undergone photon loss, can be stored and purified using noiseless amplification with a collection of solid-state qubits. The proposed method may be used to probabilistically increase the entanglement between the two parties sharing the state. The proposed amplification step is similar in structure to a set of quantum scissors. However, in this work the amplification step is realized by a state transfer from an optical mode to a set of solid-state qubits, which act as a quantum memory. We explore two different applications, the generation of entangled many-qubit registers and the construction of quantum repeaters for long-distance quantum key distribution.
Hybrid Dark Channel Prior for Image Dehazing Based on Transmittance Estimation by Variant Genetic Algorithm

Image dehazing has always been one of the main areas of research in image processing. The traditional dark channel prior algorithm (DCP) has some shortcomings, such as incomplete fog removal and excessively dark images. In order to obtain haze-free images with high quality, a hybrid dark channel prior (HDCP) algorithm is proposed in this paper. HDCP first employs Retinex to remove the interference of the illumination component. The variant genetic algorithm (VGA) is then used to obtain the guidance image required by the guided filter to optimize the atmospheric transmittance. Finally, the modified dark channel prior algorithm is used to obtain the dehazed image. Compared with three other modified DCP algorithms, HDCP has the best subjective visual effects of haze removal and color fidelity. HDCP also shows superior objective indexes in the mean square error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and information entropy (E) for different haze degrees.
PLATFORM VISIONS AND INVASIONS: SPATIAL (RE)IMAGINATIONS IN BIG TECH DISCOURSE

This panel analyzes the textual and audiovisual discourses in which big tech companies envision social spaces and their platforms’ roles in relation to those spaces. The panel asks: What are the visions of human life and technology that big tech companies narrate in relation to public and social spaces? And how do these tech-generated visions compare to current insights in and critiques of how these companies intervene in and disrupt social spaces? Situated within the field of critical platform studies, the panel’s premise is that the discourses produced by tech companies form an integral part of these companies’ interventions in people’s relations with themselves, others, and their environments. In order to develop a critical understanding of the platform society, it is crucial to examine those discourses through which tech companies present themselves almost as state-like powers that can be trusted with “public” services. Employing methods of textual and visual analysis, this panel offers such perspective. In its analysis of big tech’s spatial visions, the panel moves between different scales of spaces (domestic, educational, urban, extraterrestrial). In relation to these spaces, the panel articulates a discrepancy between the visions of life produced by tech companies and big tech’s invasive, perhaps even extractivist and colonial logic.
EXPLORING THE MULTIPLICITY OF COLONIALITY: A FOCUS ON THE AGENCY OF INTERNET PROSUMERS IN THE GLOBAL SOUTH IN THE COLONIALITY PROJECT.

This paper provides a critique to the limitation of decoloniality discourses which take a unilateral approach in which hegemony is seen as originating from the Global North and resistance as located in the Global South. The article therefore aims to “voice” out the silent ways in which internet users in the Global South could be enhancing coloniality and thwarting attempts at decolonisation. Consequently, it seeks to contribute to the theorization of coloniality by privileging the concept of hybridity not as a logic of resistance but as a hegemonic site in which power inequalities and othering is perpetuated. To support the arguments made in this article, two political WhatsApp communities (East Asembo Development Forum and Kabula Forward) in Kenya imagined around the rural physical communities from which members originate, were purposively selected. The goal of both communities is to hold their political leaders accountable and agitate for development. Adapting netnography as a method which allows for researcher immersion in online spaces, data was collected through background listening, interviews and Focus Group Discussions. Data was analysed through a Discursive Material Analysis in which both the WhatsApp technology affordances/constraints and human actors’ actions, were read as text. Findings show that despite the plurality (hybridity) of the composition of the WhatsApp communities, participation is defined through hegemonic capitalism discourse, through which political participation is metaphorically substituted by materialism.
Caterina Fake

Flickr is a photo sharing and hosting platform, serving a community of photography enthusiasts. Founded in 2004 by Caterina Fake, Stewart Butterfield, and Jason Classon, Flickr permits users to upload chronological photostreams, tag photos, and rate their “interestingness .” Flickr was also a pioneer in implementing “tag clouds ,” whereby users can find photos based on keywords. In 2015, Flickr reported 112 million registered users and more than 3.5 million daily image uploads. The site hosts over 11 billion images and in 2007 was the 19th most visited website globally. Influential users include the White House official photographer and international museums and galleries. Just a year after launching, Flickr was acquired by Yahoo! for $25 million. In 2017, Verizon purchased Yahoo!, including Flickr, for $4.5 billion, and in 2018, Flickr was purchased by SmugMug for an undisclosed price. Fake has since served as Chairman of Etsy and on the board of the Sundance Institute and cofounded Yes VC.
Enhancing BERT for Short Text Classification with Latent Information

With explosive growth of short text, short text categorization has been attracted increasing attention. How to alleviate the sparsity of short texts is a research hotspot, and takes a enormous challenge for classical text categorization technique. In this paper, we focus on short text expansion based on multi-granularity and explore to construct an EBLI (Enhancing BERT with Latent Information) model by combining BERT and latent information for addressing short text classification task. Additionally, we establish a memory bank to store the whole document topic information that assists in the joint training of deep semantic features and topic features. Experimental results with five widely datasets show that our proposed model achieves better performance of short text classification as well as promote the generalization ability and strong competition ability for the classifier.
Coarse-to-Fine Open Information Extraction via Relation Oriented Reading Comprehension

Open information extraction (Open IE), aiming at distilling structured, machine-readable triples from natural language text, plays an important role in various applications, including natural language understanding, knowledge graph construction, etc. Previous supervised Open IE approaches are mostly tailored to extract predicate-argument triples, in which the predicate is usually limited to verb phrases, whereas, the semantic relations expressed within noun phrases are being neglected. However, identifying semantic relation between entities is no trivial task due to the implicit and complex relation expressions. To address the above issue, we present ReadOIE, a framework for coarse-to-fine Open IE via relation oriented reading comprehension, to extract relation-entity triples. In our framework, all entity pairs are extracted to generate structured questions and the input sentence is regarded as the context passage. Semantic relations that best answer the questions are then extracted by comprehending the given context. Moreover, in order to identify the non-existence relations between entities, we design a coarse-to-fine relation extraction approach consisted of an extensive detection module and an intensive extraction module. The extensive detection performs relation existence judgement on a coarse level and intensive extraction identifies the relation on a fine-grained level. Extensive experiments on benchmark datasets demonstrate that ReadOIE outperforms the state-of-the-art baselines.
Sparse Linear Capsules for Matrix Factorization-Based Collaborative Filtering

Collaborative filtering (CF) plays a key role in recommender systems, which consists of two basic disciplines: neighborhood methods and latent factor models. Neighborhood methods are most effective at capturing the very localized structure of a given rating matrix, while latent factor models are generally effective at capturing its global structure. However, two disciplines fail to capture these two structures simultaneously. Motivated by the sparse linear methods and the recently developed capsule networks, we propose a new matrix factorization model for collaborative filtering based on sparse linear capsule networks, which attempts to embed the neighborhood information into latent factors and finally get the very localized and global structure of a given rating matrix. Experiments on real-world datasets demonstrate that our model outperforms seven state-of-the-art matrix factorization-based CF methods in terms of rating prediction accuracy.
China’s internal and foreign AI use contrasts sharply

Significance They build on last year’s rules on ‘deep synthesis’ (or ‘deepfake’) content and long-established rules on online content management. While Chinese tech majors such as Baidu and Alibaba are trialling their AI tools, China is strictly regulating the domestic use of this technology. However, Beijing is using this technology very differently overseas to advance its foreign policy goals. Impacts EU regulation recognises the deepfake problem but US regulations lag far behind. AI firms will face liabilities for abuse of the technology they market. Western audiences are more resilient to disinformation than before but such content is becoming more realistic.
A study on the adaptive technology of intelligent learning system based on mobile terminal recognition

With the rapid development of the mobile internet technology, m-learning is gradually becoming a key modern remote learning way. With the combination of mobile technology and digitalized technology, a learning mode based on mobile terminals is created to provide a learning environment unlimited by the time and space for learners, thus meeting the real-time learning needs of learners. However, the current mobile terminal-oriented learning system is still in the exploratory stage, and how to build an intelligent learning system for mobile terminal access is a hot spot concerned by numerous researchers. Based on this, this paper explores the Intelligent Mobile Learning System (IMLS) solution from the two technical dimensions of user terminal recognition and learning content adaptability and describes its development environment and key technologies. Experimental results show that users’ convenience and access can be enhanced to some extent by the system, which will further enhance their learning quality.
INSTITUTIONS, CULTURE, AND ENTREPRENEURSHIP: DIRECT, TOTAL, AND COMPLIANCE BURDEN EFFECTS

Empirical research on the effects of culture on national entrepreneurship rates has been inconclusive, leading to contradicting theories to explain these mixed results. Results have also been sensitive to which covariates are in the empirical analysis. Given that culture might affect entrepreneurship directly and indirectly through institutions, we model the Direct and Total Effects of culture on entrepreneurship accounting for possible endogeneity effects. We use recent innovations in econometrics that are robust to model selection errors to estimate the direct and Total Effects of culture on entrepreneurship across countries. Using GLOBE’s nine dimensions of culture on an expanded sample size, we find that Future Orientation, Gender Egalitarianism, Assertiveness, and Institutional Collectivism have robust positive Total Effects on national entrepreneurship. In contrast, Uncertainty Avoidance has a robust negative Total Effect on entrepreneurship. We also find that the Total Effect is greater than the Direct Effect for Assertiveness, Institutional Collectivism, and Uncertainty Avoidance and smaller than the Total Effect for the remaining—Performance Orientation, Future Orientation, Humane Orientation, In-Group Collectivism, Gender Egalitarianism, Power Distance. This suggests strong institutions that serve as a catalyst that engenders entrepreneurship in high Assertiveness, high Institutional, and high Uncertainty Avoidance cultures. Conversely, in high-Performance Orientation, Future Orientation, Humane Orientation, In-Group Collectivism, Gender Egalitarianism, and Power Distance countries, strong institutions can sometimes impose significant compliance burdens that dampen the natural cultural proclivity that supports institutions and entrepreneurship.
Optimizing blocked-Davidson method based on adaptive algorithm using the new Sunway supercomputer

The Davidson method is a traditional large-scale iterative diagonalization method in computational quantum chemistry. The Blocked-Davidson method is a classical version of the Davidson method, which is an important part of VASP software, occupying 22.9% of the calculation time. When VASP is transplanted to the new Sunway supercomputer, it is incompatible with the many-core heterogeneous environment of Sunway processor. The Blocked-Davidson method does not use slave-core accelerators. This paper designs collaborative computing, double buffer, store variables in slave-core LDM, LDM level two access, and segmented transmission methods of Blocked-Davidson method based on Sunway many-core architecture. The test results showed that the function in the Blocked-Davidson method accelerated 30.65 times with a single core group in the silica test case. However, negative optimization of the Blocked-Davidson method happens in some test cases such as ZrTiAlVSiN5 and Hg50. Therefore, an adaptive algorithm of automatic selection of less time-consuming optimization paths is designed to solve the negative optimization. The maximum acceleration is 23.6 times compared with not using the adaptive algorithm.