Reviewerly/exHarmony huggingface dataset:
Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style Transfer

Adversarial attacks and backdoor attacks are two common security threats that hang over deep learning. Both of them harness task-irrelevant features of data in their implementation. Text style is a feature that is naturally irrelevant to most NLP tasks, and thus suitable for adversarial and backdoor attacks. In this paper, we make the first attempt to conduct adversarial and backdoor attacks based on text style transfer, which is aimed at altering the style of a sentence while preserving its meaning. We design an adversarial attack method and a backdoor attack method, and conduct extensive experiments to evaluate them. Experimental results show that popular NLP models are vulnerable to both adversarial and backdoor attacks based on text style transfer -- the attack success rates can exceed 90% without much effort. It reflects the limited ability of NLP models to handle the feature of text style that has not been widely realized. In addition, the style transfer-based adversarial and backdoor attack methods show superiority to baselines in many aspects. All the code and data of this paper can be obtained at https://github.com/thunlp/StyleAttack.
Report on the International Master Degree in Research in Humanities and Educational Sciences at the Universidad Mayor de San Andrés (Bolivia): : a case of success during the pandemic

The present report describes the case of the Internacional Master Degree in Research in Humanities, the process that brought to its creation and its peculiar structure. The work shortly analyzes some of the program’s characteristics such as its goals, the curricular model, which is a key part of its success; it also explain the ability of being able to achieve the master degree program development during the pandemic. The combination of its modular structure, of its different and adaptable study plan, as well as its simple but effective tools show that, without the need of the highest technological levels, nor a very expensive infrastructure, it is still possible to achieve an example of success in postgraduate studies and in particular in research.
Acceleration of Strengthening Digital Literacy in the Era of Society 5.0

The geographical location of schools in coastal areas is certainly very influential on the development of digital literacy in the learning process. This is caused by various obstacles, both material constraints, human resources, and social constraints, which then have an impact on the inhibition of the digital-based learning process. The purpose of this study has three objectives, as follows: To determine the extent to which the use of digital literacy has implications for learning, to find out the obstacles and challenges in the application of digital literacy, and to optimize the application of digital literacy in learning activities. Qualitative analysis was used in this study. The average result of implementing digital literacy in coastal areas has only reached 45% in the learning process. There are also obstacles in its implementation due to limited Human Resources (HR) for both educators and students, lack of facilities and infrastructure, the location of school demographics, management of digital literacy applications that have not been maximized by schools and teachers who drive digital literacy. It can be concluded that the application of digital literacy is still not optimal.
PHYSICS AND METAPHYSICS OF FICTION REFLECTED IN FILM: A CASE OF DECRYPTION

The article deals with religious, materialistic, and mixed interpretations of the thing and the personality as generic entities in the story «Oh, whistle, and I'll come to you, my lad» by M. R. James and its screen versions. The differences found in the versions concern petty and significant deviations from the original story, which influence the initial message. The method used to achieve the results represented in the article combines the ideas of Philosophical Hermeneutics and those of the medieval exegetical method of allegorese applied to deal with obscure passages in sacred texts. The original story treated within the Protestant ideological paradigm gives way to materialistic views that are subject to refutation, reconsideration, and combination with philosophical issues in the screen versions – transponents. The thing as an inanimate object is endowed with personal qualities of a living being; on the contrary, an individual is viewed as a thing with no mind.
Subjective Probability and Stochastic Independence

This paper studies decision-making under uncertainty and introduces a new preference axiom called Subjective Independence . The latter requires some consistency between two forms of stochastic independence that can be inferred from choice behavior. Yet it can also be understood as a purely subjective version of the classical Independence axiom commonly used under risk. The main result presented in this paper uncovers the role that Subjective Independence plays in the axiomatic characterization of Subjective Expected Utility preferences.
The Hidden Lattice Problem

We consider the problem of revealing a small hidden lattice from the knowledge of a low-rank sublattice modulo a given sufficiently large integer -- the {\em Hidden Lattice Problem}. A central motivation of study for this problem is the Hidden Subset Sum Problem, whose hardness is essentially determined by that of the hidden lattice problem. We describe and compare two algorithms for the hidden lattice problem: we first adapt the algorithm by Nguyen and Stern for the hidden subset sum problem, based on orthogonal lattices, and propose a new variant, which we explain to be related by duality in lattice theory. Following heuristic, rigorous and practical analyses, we find that our new algorithm brings some advantages as well as a competitive alternative for algorithms for problems with cryptographic interest, such as Approximate Common Divisor Problems, and the Hidden Subset Sum Problem. Finally, we study variations of the problem and highlight its relevance to cryptanalysis.
Related Work on Image Quality Assessment

Due to the existence of quality degradations introduced in various stages of visual signal acquisition, compression, transmission and display, image quality assessment (IQA) plays a vital role in image-based applications. According to whether the reference image is complete and available, image quality evaluation can be divided into three categories: Full-Reference(FR), Reduced- Reference(RR), and Non- Reference(NR). This article will review the state-of-the-art image quality assessment algorithms.
Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP

The principle of independent causal mechanisms (ICM) states that generative processes of real world data consist of independent modules which do not influence or inform each other. While this idea has led to fruitful developments in the field of causal inference, it is not widely-known in the NLP community. In this work, we argue that the causal direction of the data collection process bears nontrivial implications that can explain a number of published NLP findings, such as differences in semi-supervised learning (SSL) and domain adaptation (DA) performance across different settings. We categorize common NLP tasks according to their causal direction and empirically assay the validity of the ICM principle for text data using minimum description length. We conduct an extensive meta-analysis of over 100 published SSL and 30 DA studies, and find that the results are consistent with our expectations based on causal insights. This work presents the first attempt to analyze the ICM principle in NLP, and provides constructive suggestions for future modeling choices. Code available at https://github.com/zhijing-jin/icm4nlp
Analisis Efisiensi dan Kehilangan Air pada Jaringan Irigasi Daerah Irigasi Sekampung Bunut

Considering the bad impact of water loss which has an impact on its efficiency, the authors are interested in analyzing the efficiency and water loss in irrigation networks in the Sekampung Bunut Irrigation Area (DI). Efficiency and water losses were analyzed using the in-discharge discharge method. The data used in this analysis consist of primary data in the form of flow velocity data with current meters, irrigation channel cross-sections, temperature data, humidity data, then secondary data, namely data on the length of solar radiation and wind speed data. Loss of water in the secondary irrigation channel KR Irrigation Area (DI) of Sekampung Bunut lost an average of 17.15%. Meanwhile, the efficiency of the secondary channel in the Irrigation Area (DI) of Sekampung Bunut was 82.85%. The theoretical value of the average efficiency in the secondary channel KR is 90%. This shows that the amount of efficiency is still below the requirements of theoretical efficiency. One of the factors that cause water loss in the secondary channel in the Sekampung Bunut Irrigation Area is the evaporation factor that occurs along the irrigation channel. Keywords: Channel efficiency, water loss, water discharge.
Language Resource Efficient Learning for Captioning

Due to complex cognitive and inferential efforts involved in the manual generation of one caption per image/video input, the human annotation resources are very limited for captioning tasks. We define language resource efficient as reaching the same performance with fewer annotated captions per input. We first study the performance degradation of caption models in different language resource settings. Our analysis of caption models with SC loss shows that the performance degradation is caused by the increasingly noisy estimation of reward and baseline with fewer language resources. To mitigate this issue, we propose to reduce the variance of noise in the baseline by generalizing the single pairwise comparison in SC loss and using multiple generalized pairwise comparisons. The generalized pairwise comparison (GPC) measures the difference between the evaluation scores of two captions with respect to an input. Empirically, we show that the model trained with the proposed GPC loss is efficient on language resource and achieves similar performance with the state-of-the-art models on MSCOCO by using only half of the language resources. Furthermore, our model significantly outperforms the state-of-the-art models on a video caption dataset that has only one labeled caption per input in the training set.
Designing an augmented reality experience to increase awareness of Indigenous histories and knowledges

This article provides an overview of a design for a proposed project to increase awareness of Indigenous histories and knowledges through an Augmented Reality (AR) experience designed for Swinburne...
Term distance, frequency and collocations

In this paper I study two co-occurrence measures, local to a particular corpus, for constructing collocations or relevance relations between words or terms. One is a distance measure, while the other uses different co-occurrence windows, one contained in the other. Both are discussed with respect to the common method of comparing co-occurrence measures within a particular corpus to those of a reference corpus. A practical consequence of these measures is that they may relieve the burden of computing a reference statistic, which may incur a high computational cost. We also believe that distance, as a measure in itself, has a theoretical interest. Being different from frequency, it may add something new to collocation analysis.
Voice of Amazon's Alexa reportedly revealed

The voice behind Amazon's cloud-based artificial intelligence service “Alexa” may have just been identified.
Noether's Learning Dynamics: Role of Symmetry Breaking in Neural Networks

In nature, symmetry governs regularities, while symmetry breaking brings texture. In artificial neural networks, symmetry has been a central design principle to efficiently capture regularities in the world, but the role of symmetry breaking is not well understood. Here, we develop a theoretical framework to study the "geometry of learning dynamics" in neural networks, and reveal a key mechanism of explicit symmetry breaking behind the efficiency and stability of modern neural networks. To build this understanding, we model the discrete learning dynamics of gradient descent using a continuous-time Lagrangian formulation, in which the learning rule corresponds to the kinetic energy and the loss function corresponds to the potential energy. Then, we identify "kinetic symmetry breaking" (KSB), the condition when the kinetic energy explicitly breaks the symmetry of the potential function. We generalize Noether's theorem known in physics to take into account KSB and derive the resulting motion of the Noether charge: "Noether's Learning Dynamics" (NLD). Finally, we apply NLD to neural networks with normalization layers and reveal how KSB introduces a mechanism of "implicit adaptive optimization", establishing an analogy between learning dynamics induced by normalization layers and RMSProp. Overall, through the lens of Lagrangian mechanics, we have established a theoretical foundation to discover geometric design principles for the learning dynamics of neural networks.
Data security management of logistics network based on blockchain technology

The logistics network is composed of lines and nodes. The traditional logistics is too centralized, so it is necessary to try “decentralization” to reduce the management difficulty and operational risk. This paper proposes a security data management method based on blockchain to manage distributed logistics network system. Through IOT technology, the warehouse will be connected by tagging individual items and operating hardware to improve the transparency and localization of all assets. The client of IOT node encrypts the logistics transaction data and uploads it to the blockchain to ensure the privacy and unforgeability of the logistics information, so that the encrypted data can be decrypted by the system nodes and uploaded to the blockchain through the logistics network. According to the experiment of school enterprise cooperation enterprise, this is a sharing framework with high security and high availability.
Factual Consistency Evaluation for Text Summarization via Counterfactual Estimation

Despite significant progress has been achieved in text summarization, factual inconsistency in generated summaries still severely limits its practical applications. Among the key factors to ensure factual consistency, a reliable automatic evaluation metric is the first and the most crucial one. However, existing metrics either neglect the intrinsic cause of the factual inconsistency or rely on auxiliary tasks, leading to an unsatisfied correlation with human judgments or increasing the inconvenience of usage in practice. In light of these challenges, we propose a novel metric to evaluate the factual consistency in text summarization via counterfactual estimation, which formulates the causal relationship among the source document, the generated summary, and the language prior. We remove the effect of language prior, which can cause factual inconsistency, from the total causal effect on the generated summary, and provides a simple yet effective way to evaluate consistency without relying on other auxiliary tasks. We conduct a series of experiments on three public abstractive text summarization datasets, and demonstrate the advantages of the proposed metric in both improving the correlation with human judgments and the convenience of usage. The source code is available at https://github.com/xieyxclack/factual_coco.
Development of E-Book Teaching Materials Based on The Problem Based Instruction Course of Instruction Management Courses in Indonesian Literature Students UNIMED

Model for Performance Management Courses for Indonesian Literature Students Unimed.(2) To describe the product usage of E-Book Teaching Materials Based on Problem Based Instruction Model for Performance Management Courses for Indonesian Literature Students Unimed.(3) To test the effectiveness of the product of E-Book Teaching Materials Based on Problem Based Instruction Models for Performance Management Courses for Indonesian Literature Students Unimed.The research method uses research and development (R&D) methods from the ADDIE R&D model (Analysis, Design, Development, Implementation, and Evaluation).The results of research conducted in the development of E-Book Teaching Materials Product Development Based on Problem Based Instruction Models for the Performance Management Course received a percentage of feasibility/validity as many as 92% of material experts, 90.6% of media experts, and 93.3% of management learning experts show.Product Development Effectiveness of E-Book Teaching Materials Based on Problem Based Instruction Models for Performance Management Courses based on Pre-Test, Post-Test, and t-test.The results of the Pre-Test showed that 9 students or as many as 45% completed, with an average score of 63.5.The results of the Post-Test test of student completeness increased from before, namely 17 students who got a complete score or as much as 85%, with an average score of 80.There is a significant difference between student learning outcomes before and after using E-Book Teaching Materials Based on Problem Based Instruction Models for Performance Management Courses.Thus, it can be concluded that the product of E-Book Teaching Materials Based on the Problem Based Instruction Model for the Performance Management Course is feasible, practical, and effective to be used for Unimed Indonesian Literature Students.
Modern Approaches to the Study of Music of the Twentieth Century

the subject of research – modern academic music of the 20th century is a special kind of art, the effective development of which by students of art departments and universities requires appropriate methods. The purpose of the work is to consider problematic teaching methods that ensure the effective mastery of complex artistic material by university students who do not have sympathy for 20th century music. To verify the effectiveness of the application of problem learning techniques when students mastered avantgarde music, a pedagogical experiment was conducted to introduce problem problems, the case method and the design method into the process of mastering musical and historical disciplines. The study carried out a theoretical justification and introduced into creative practice the methods considered, which made it possible to involve the student in the development of discipline in the process of creative activity, which significantly increases the level of cognitive activity and contributes to the formation of students; to solve the problem of modern musical skill, students of the musical and pedagogical profile of education were presented with problem problems, and the task requiring brainstorming – to practically “guess” the alleged image of the author – had the necessary didactic effect. Methodological recommendations related to the use of problematic methods in music education are formulated.
THE ROLE OF ELLIPTICAL STRUCTURES IN THE STRUCTURE OF A LITERARY TEXT (BASED ON THE ITALIAN LANGUAGE)

This article touches on the topic of elliptical structures in the structure of literary text. They constitute the means of conveying indirect expression. And they can convey emotional, logical and objective-evaluative information. The use of ellipsis in fiction emphasizes the character and style of the author. Elliptical constructions have the specificity of the absence of a verb predicate, a verb that was not mentioned in the context, i.e. for the transmission of the message is not necessary in a semantic sense. Due to the absence of a verbal predicate, they can contain the meaning represented by the context.
Deep Learning with Label Differential Privacy

The Randomized Response (RR) algorithm is a classical technique to improve robustness in survey aggregation, and has been widely adopted in applications with differential privacy guarantees. We propose a novel algorithm, Randomized Response with Prior (RRWithPrior), which can provide more accurate results while maintaining the same level of privacy guaranteed by RR. We then apply RRWithPrior to learn neural networks with label differential privacy (LabelDP), and show that when only the label needs to be protected, the model performance can be significantly improved over the previous state-of-the-art private baselines. Moreover, we study different ways to obtain priors, which when used with RRWithPrior can additionally improve the model performance, further reducing the accuracy gap between private and non-private models. We complement the empirical results with theoretical analysis showing that LabelDP is provably easier than protecting both the inputs and labels.
Using APL to Present on Screens

In Chapter 9 , we converted the Botmock diagram into an Alexa skill and configured the VUI. However, the primary purpose of our 90-year calendar skill is to show you your 90-year calendar. To do this, we will need to check if the device has a screen, and if it does, we can create a 90-year calendar presentation with Alexa Presentation Language (APL).
Content and Requirements for the Development of Environmental Competence in Future Teachers

Personal development in the education system is ensured by the formation of universal educational actions as the basis of the educational and upbringing process. The concept of universal learning activities takes into account the experience of the competence-based approach, i.e. using the acquired knowledge and skills in practice.
Development and Comparative Analysis of Geospatial Feature Automatic Extraction System in Open-source Environment

The aim of this study is to develop a system for geospatial feature extraction from images to be obtained from CAS500-1/2 satellites currently being developed by the Ministry of Land, Infrastructure, and Transport, Republic of Korea.The feasibility of automatic geospatial feature extraction is verified by applying the relative radiometric normalization technique to KOMPSAT-3A satellite images, which are expected to have similar specifications to CAS500 images.Furthermore, the developed system is compared with commercial software to verify its classification accuracy.In this study, two KOMPSAT-3A satellite images were collected and relative radiometric normalization was performed.Identical parameters and threshold values were applied to both the commercial software and the developed system to extract geospatial features by feature class and analyze the classification accuracy using an error matrix.Image segmentation and image classification were performed for grassland, ground, roads, buildings, and water bodies.The results indicated a classification accuracy of 90% or higher, which was set as the accuracy goal.The difference in the classification accuracy of the two systems was less than approximately 1%, implying comparable performances for the two systems.Using the geospatial feature extraction system developed by us, it is expected that basic data will be generated for monitoring national territory such as forests and urban areas.