Reviewerly/exHarmony huggingface dataset:
<i>The tamarind forest</i>: an augmented virtuality experience

The tamarind forest is a virtual reality (VR) installation that explores shifts of perception through a somatic experience in a virtual space. As part of a larger show (kimosphere no.4) that explored immersion and an entwinement of real and virtual spaces, The Tamarind forest experiments with augmented virtuality and invites the audience to pay attention to their physical body while taking a journey through a virtual forest. This paper describes the piece, the technology and the design process as well as the main takeaways and reflections.
Online Discrepancy Minimization via Persistent Self-Balancing Walks

We study the online discrepancy minimization problem for vectors in $\mathbb{R}^d$ in the oblivious setting where an adversary is allowed fix the vectors $x_1, x_2, \ldots, x_n$ in arbitrary order ahead of time. We give an algorithm that maintains $O(\sqrt{\log(nd/\delta)})$ discrepancy with probability $1-\delta$, matching the lower bound given in [Bansal et al. 2020] up to an $O(\sqrt{\log \log n})$ factor in the high-probability regime. We also provide results for the weighted and multi-color versions of the problem.
static Global Names

When we define a variable or a function inside the file/global scope, they have external linkage by default. They can be referred to from other .c files/translation units. The static keyword in front of variables and functions in a global scope marks them visible only to the current source file/translation unit, the unit in which they are declared/defined. We say the static specifier makes them have internal linkage. So, globals we do not want to share with other .c files should be marked as static. Here, both globals globalx and globalfn() are defined inside the source.c file and can be referred to from other .c source files as well:
Refinement of UAV dynamics model through online identification: a model-data hybrid approach

This paper investigates how to precisely and efficiently obtain the dynamics model of Unmanned Aerial Vehicle (UAV) which can used in single or swarm control. The UAVs are homogeneous in the mechanical structure, yet differ in a variety of unmodeled uncertainties, which cannot be ignored in high-fidelity simulation systems. To solve this, we propose a model-data hybrid approach that consists of two steps: 1) establishing a baseline model by first-principle; and 2) refining the model through online identification. More specifically, the baseline model is a linearized state equation with pending parameters. The parameters are adaptively determined through a least-squares identification algorithm based on real-time flight data. Finally, we design a comprehensive flight mission with 5 common modes (climbing, level flighting, turning, descending, and circling) for verification. The results of simulation experiment present a good consistency between the outputs of the refined dynamics model and the real flight data.
Batch Inverse-Variance Weighting: Deep Heteroscedastic Regression

Heteroscedastic regression is the task of supervised learning where each label is subject to noise from a different distribution. This noise can be caused by the labelling process, and impacts negatively the performance of the learning algorithm as it violates the i.i.d. assumptions. In many situations however, the labelling process is able to estimate the variance of such distribution for each label, which can be used as an additional information to mitigate this impact. We adapt an inverse-variance weighted mean square error, based on the Gauss-Markov theorem, for parameter optimization on neural networks. We introduce Batch Inverse-Variance, a loss function which is robust to near-ground truth samples, and allows to control the effective learning rate. Our experimental results show that BIV improves significantly the performance of the networks on two noisy datasets, compared to L2 loss, inverse-variance weighting, as well as a filtering-based baseline.
Cascaded Structural Representation for Face Alignment

This paper presents a highly intuitionistic, very efficient and accurate approach for face alignment. We directly gives an insight into the relationship between the feature space and the coordinate space. We first define a nonlinear function from local features extracted around landmarks to the deviation between estimated and ground truth landmarks. Then Taylor Expansion is used to approximate this nonlinear function. As this approximation works well in a short range of argument, we use a cascaded way to progressively refine the results. This is finally induced to a representation problem. Furthermore, our approach clearly demonstrates the structure and role of each cascaded module. A structured Schatten p-norm is defined to generate the Cascaded Structural Representation problem. Three kinds of structures are defined in this framework in the cascaded process to simulate the ideal situation for updating landmarks. Our approach achieves the state-of-the-art results when tested on current challenging benchmarks with fast speed on a desktop satisfying real-time performance.
Spoiler Alert: Using Natural Language Processing to Detect Spoilers in Book Reviews

This paper presents an NLP (Natural Language Processing) approach to detecting spoilers in book reviews, using the University of California San Diego (UCSD) Goodreads Spoiler dataset. We explored the use of LSTM, BERT, and RoBERTa language models to perform spoiler detection at the sentence-level. This was contrasted with a UCSD paper which performed the same task, but using handcrafted features in its data preparation. Despite eschewing the use of handcrafted features, our results from the LSTM model were able to slightly exceed the UCSD team's performance in spoiler detection.
Error Term Checking: Towards Chosen Ciphertext Security without Re-encryption.

Chosen ciphertext security for lattice based encryption schemes is generally achieved through a generic transformation such as the Fujisaki-Okamoto transformation. This method requires full re-encryption of the plaintext during decapsulation, which typically dominates the cost of the latter procedure. In this work we show that it is possible to develop alternative transformations specifically designed for lattice based encryption schemes. We propose two novel chosen ciphertext transformations, ETC1 and ETC2, in which re-encryption is replaced by checking the error term of the input ciphertext. We show that our new ciphertext validity check can be securely applied to lattice based encryption schemes under specific conditions. For the NIST post-quantum standardization candidate Threebears we show a speed-up for decapsulation of up to 37.4%. Moreover, as our method only changes the validation check during decapsulation, it is fully backwards compatible with existing implementations of the Fujisaki-Okamoto transformation.
Analisis Pengaruh Marketplace, Sosial Media dan Website terhadap Minat Bertransaksi Secara Online pada Mahasiswa FEB UMS

This study aims to analyze the Marketplace, Social Media and Websites on Online Transaction Interest in FEB UMS Students. The sample in this study amounted to 100 UMS Student respondents using a questionnaire for data collection. The sampling technique used non-probability sampling method. Data analysis techniques using multiple linear regression analysis. The results of this study show that: Marketplace, Social Media and Websites on Online Transaction Interests at FEB UMS Students. The results of this study indicate that Marketplace variables significantly influence Online Transaction Interests, accepted. Social Media Variable has a significant effect on Online Transaction Interests, accepted. Website Variables significantly influence Online Transaction Interest, accepted
Learning without Forgetting: Task Aware Multitask Learning for Multi-Modality Tasks

Existing joint learning strategies like multi-task or meta-learning focus more on shared learning and have little to no scope for task-specific learning. This creates the need for a distinct shared pretraining phase and a task-specific finetuning phase. The fine-tuning phase creates separate systems for each task, where improving the performance of a particular task necessitates forgetting some of the knowledge garnered in other tasks. Humans, on the other hand, perform task-specific learning in synergy with general domain-based learning. Inspired by these learning patterns in humans, we suggest a simple yet generic task aware framework to incorporate into existing joint learning strategies. The proposed framework computes task-specific representations to modulate model parameters during joint learning. Hence, it performs both shared and task-specific learning in a single-phase resulting in a single model for all the tasks. The single model itself achieves significant performance gains over the existing joint learning strategies. For example, we train a model on Speech Translation (ST), Automatic Speech Recognition (ASR), and Machine Translation (MT) tasks using the proposed task aware joint learning approach. This single model achieves a performance of 28.64 BLEU score on ST MuST-C English-German, WER of 11.61 on ASR TEDLium v3, and BLEU score of 23.35 on MT WMT14 English-German tasks. This sets a new state-of-the-art performance (SOTA) on the ST task while outperforming the existing end-to-end ASR systems with a competitive performance on the MT task.
Machine translation into morphologically rich low-resource languages

Machine translation is an important natural language processing application, enabling widened access to information, cultural interchange, and business opportunities in a multilingual world. Driven by research into deep neural networks, machine translation has recently made rapid advances, particularly in the fluency of the translation output. As the methods tend to be data-hungry,high-resource languages have benefited more than low-resource ones. In this work, the aim is to improve machine translation into low-resource morphologically rich languages. Rich morphology leads to a combinatorial explosion in the number of word forms,resulting in very large vocabularies, containing many poorly modeled rare words. This thesis addresses these challenges with multiple approaches. The focus is on methods for segmenting words into subwords, to get more frequent and thus easier learned representations, and to increase the symmetry between languages. It is important to exploit additional resources from related tasks,such as parallel data from related high-resource language pairs and monolingual data from both low- and high-resource languages. Useful auxiliary data sets for multimodal translation can befound from captioning and text-only translation tasks. The methods for exploiting this auxiliary data include cross-lingual learning and data augmentation e.g. using denoising sequence autoen-coders and subword regularization. Learning setups used in the thesis include using unsupervised and language-independent methods, using active learning to guide an annotation effort to produce more informative data, and using scheduled multi-task learning to improve cross-lingual transfer. Contributions of the thesis include five novel segmentation methods: Morfessor FlatCat, Omorfi-restricted Morfessor, Cognate Morfessor, Morfessor EM+Prune, and a semi-supervised neural method. An active learning strategy for Morfessor FlatCat is presented. Evaluation of segmentation quality is performed using both intrinsic and extrinsic automatic methods. Morfessor EM+Prunefinds models with both lower cost and better quality in unsupervised segmentation than Morfessor Baseline. Active learning is superior to random selection for collecting annotations. The best performance in semi-supervised segmentation is achieved when using Morfessor FlatCat segmentations as features in a conditional random field. Contributions to machine translation include a target-side multi-task learning scheme, and scheduled multi-task learning with a denoising sequence autoencoder. LeBLEU, an evaluation measure suitable for morphologically rich languages is presented. Evaluation of translation quality is performed using both automatic and human evaluation. When resources are scarce, the most important auxiliary data comes from related languages. Other types of auxiliary data, such as monolingual corpora, are also beneficial and the gains are partly cumulative.; Konekaannos on tarkea luonnollisten kielten kasittelyn sovellus, joka mahdollistaa entista laajemman paasyn tietoon…
Artificial Intelligence and Copyright: A Comparative Analysis

Can AI-created works be protected by copyright? A comparative analysis of Swiss, European, UK, and American Law.
Learning Semi-Supervised Representation Enrichment Using Longitudinal Imaging-Genetic Data

Alzheimer's Disease (AD) is a progressive memory disorder that causes irreversible cognitive decline. Recently, many statistical learning methods have been presented to predict cognitive declines by using longitudinal imaging data. However, missing records that broadly exist in the longitudinal neuroimaging data have posed a critical challenge for effectively using these data in machine learning models. To tackle this difficulty, in this paper we propose a novel approach to integrate longitudinal (dynamic) phenotypic data and static genetic data to learn a fixed-length biomarker representation using the enrichment learned from the temporal data in multiple imaging modalities. Armed with this enriched biomarker representation, as a fixed-length vector per participant, conventional machine learning models can be used to predict clinical outcomes associated with AD. We have applied our new method on the Alzheimer's Disease Neruoimaging Initiative (ADNI) cohort and achieved promising experimental results that validate its effectiveness.
Exploring the Implications of School Curriculum Design Based on the Analysis of Linkage Between IB MYP Curriculum and Backward Design in Aspects of Goal Setting and Assessment

The present study aimed to analyze the link between Backward Design and IB curriculum, which is frequently mentioned and recommended as an alternative in overcoming the problems of our existing education. We also explored the implications of school curriculum design. This study has the following two major research lines. The first was to analyze the link between the Backward Design and IB curriculum. The results were derived through literature research and feedback from seven experts. The second was to explore the implications of such a relevance analysis for the Goals (Objectives) and assessments of school curriculum design. Several scholars, as well as the Daegu and Jeju education offices, acknowledge the advantages of IB education and strive to introduce it into public education. With the prospective increase of the number of IB-certified schools in Korea, such analysis and exploration of implications are expected to have a positive impact on our education in the future.
The Main Issues of the State Policy in Education in 2019

In 2019, the implementation of the “Education” national project began in the education sector. If in 2012–2018 the state policy’s main objective was to raise wages of teaching employees, now within the next six years it is necessary to carry out ten federal projects – included in the specified national project – which set the lines of the long-term development of this sector.
A New End-to-end Modulation Recognition Algorithm Based on Deep Learning

To solve the problem of difficult modulation recognition of emitter signals at low signal-to-noise and low recognition rate caused by improper feature extraction, a new end-to-end modulation recognition algorithm based on deep learning is proposed. The algorithm can effectively recognize LFM, Costas, Frank code, and P1-P4 code. Firstly, an end-to-end modulation recognition network based on bi-directional long-short term memory is proposed to extract the time features of emitter signals for recognition. Then, to extract the space features of emitter signals, it explores a one-dimensional convolutional neural network structure. Finally, one-dimensional convolutional neural network and bi-directional long-short term memory are combined with pre-training and fine-tuning to accelerate the convergence speed and improve the recognition rate. The simulation results show that the recognition rate of the algorithm can reach 75.23% at the -6dB signal-to-noise ratio. It can also be used for modulation recognition in the mechanical field.
School Literacy Movement in the Industrial Revolution Era 4.0 in Building Speed Reading Skills

The low reading interest in Indonesia is a reason why the School Literacy Movement (GLS) is urgent to face of the industrial revolution 4.0 which is an all-digital era. Read quickly ability give effect in competition at the global level so GLS is needed in order to increasing of student’s reading speed ability. This research is aimed to get knows the effect of GLS on students' reading speed at SDN Karapyak 1. This research was conducted in semester 1 started from July to December academic year 2018/2019. Research approval is a descriptive qualitative discussion. The sample of the research is 74 students of grade IV. Data collection by KPM test (speed reading per minute). Based on the first KPM test, only 4 students (5.4%) gets grades above the KPM standard, after treats by the GLS program in the second test, around 54 students (73%) gets grades above the KPM standard, as allocated 67, 6%. So that the School Literacy Movement Program can improve reading skills of fourth grade students in elementary schools Karapyak 1
Recommending More Efficient Workflows to Software Developers

Existing recommendation systems can help developers improve their software development abilities by recommending new programming tools, such as a refactoring tool or a program navigation tool. However, simply recommending tools in isolation may not, in and of itself, allow developers to successfully complete their tasks. In this paper, I introduce a new recommendation system that recommends workflows, or sequences of tools, to developers. By learning more efficient workflows, the system could make software developers more efficient.
한·중 정치류 신문 사설의 ‘개입’ 대조 연구

This study aims to suggest the educational implication of Korean Engagement though contrasting the engagement reflected in Chinese and Korean editorials. In order to do this, 100 editorials (Korean editorials 50, Chinese editorials 50) were selected from The Chosun Ilbo, The JoongAng Ilbo, The Dong-A Daily News and People’s Daily, Guang Ming Daily which are the representative and authoritative newspapers in Korea and as well as in China. We extracted the engagement and did a t-text. As a result, the total of monogloss and heterogloss didn’t have a statistically significant differences in both Korean and Chinese editorials had used more heterogloss than monogloss. In the case of dialogic expansion, the use of entertain, attribution, acknowledge, distance and the total of dialogic expansion showed a statistically significant difference. Descriptive statistics showed that Korean newspaper editorials used more entertain, attribution, acknowledge, distance than Chinese newspaper editorials. In the case of dialogic contraction it was found that the use of disclaim, deny, counter, proclaim, pronounce and the total of dialogic contraction had a statistically significant difference but except the concur and endorsement. Descriptive statistics showed that the Korean editorials used more disclaim, deny, counter than Chinese editorials, however Chinese editorials used more proclaim, pronounce than Korean editorials. This is related to the genre characteristics of Chinese and Korean argumentative writings and that of the newspapers. Based on the above discussion, educational implications were presented in terms of subjects, textbooks, and educational content.(Dalian University of Foreign Languages)
Pendampingan Design Expert Formulasi Kosmetik Industri Kosmetika CV Nosin Indonesia, Gading Sari, Sembur, Tirtomartani, Kalasan, Sleman

In formula optimization to find the best formula, trial and error are often carried out which requires a lot of time and money. The use of Design-Expert software which is a product of State-Ease can simplify the experimental design process to the best formula, thereby reducing trial and error. CV Nosin Indonesia is one of the cosmetic industries that is developing in Yogyakarta. It is necessary to design products in the formulation domain so that the cosmetic products produced are of good quality with efficient production costs. Students from Bachelor degree of Pharmacy Study Program Universitas Ahmad Dahlan are involved in the community service to introduce the benefits of the program n the work field. The purpose of this community service is to introduce, train, and assist the use of program design experts. Community service methods are carried out to provide understanding and use of program design experts in the pharmaceutical and cosmetic industry. Also, a short training program was conducted on the application of an expert design program in optimizing formulas and the manufacturing process of drugs and cosmetics. Community service with extension methods and short training in the use of program design experts in the Pharmaceutical and Cosmetics Industry has proven to be more effective, can increase the ability of participants to understand the material, and provide useful value because it can be applied directly in the pharmaceutical and cosmetic industry.
Certyfikacja biegłości języka włoskiego a potrzeby studentów szkół wyższych

The present paper aims at presenting Italian language proficiency certificates offered by Italian universities and other institutions. A particular focus was put on such certification features which better address the university students’ needs.
VALUABLE ORIENTATIONS IN CULTURE FORMATION OF PEDAGOGICAL INTERACTION TEACHERS – STUDENTS

The essence of forming the culture of pedagogical interaction of subjects of educational process of higher education institutions in the system of professional and ethical values is revealed. The main definitions of the process of forming a culture of pedagogical interaction are defined: "subject-object interaction", "subject-subject interaction", "pedagogical interaction", "culture of pedagogical interaction". The pedagogical interaction is highlighted as a process of cooperation on the basis of mutual assistance and mutual understanding in order to effectively solve educational problems, emotions, attitudes, positions in the educational process. The culture of pedagogical interaction is outlined as a system of professional and ethical values, which have become internal beliefs of personality, personal qualities, which regulate social and pedagogical relations and are manifested in behavior consistent with the requirements of the professional and ethical code. It is the systemic integrity of interconnected components: axiological, technological, creative and personal. The axiological component represents the culture of pedagogical interaction as a set of pedagogical values. The technology component includes methods and techniques that ensure its effectiveness, as well as the skills and professional interaction and communication. The creative component is manifested in the creation and realization of professional and ethical values and is characterized by autonomy, flexibility and variability of thinking and actions, ability to predict the consequences of social and pedagogical actions, readiness for creative cooperation. The personal component reveals the manifestation of a culture of interaction as a specific way of realizing the essential qualities of the individual. The content and structure of pedagogical interaction, psychological and pedagogical factors and conditions for forming the culture of pedagogical interaction of the subjects of the educational process of the institution of higher education are revealed.