Reviewerly/exHarmony huggingface dataset:
Cross-Species 3D Face Morphing via Alignment-Aware Controller

We address cross-species 3D face morphing (i.e., 3D face morphing from human to animal), a novel problem with promising applications in social media and movie industry. It remains challenging how to preserve target structural information and source ﬁne-grained facial details simultaneously. To this end, we propose an Alignment-aware 3D Face Morphing (AFM) framework, which builds semantic-adaptive correspondence between source and target faces across species, via an alignment-aware controller mesh (Explicit Controller, EC) with explicit source/target mesh binding. Based on EC, we introduce Controller-Based Mapping (CBM), which builds semantic consistency between source and target faces according to the semantic importance of different face regions. Additionally, an inference-stage coarse-to-ﬁne strategy is exploited to produce ﬁne-grained meshes with rich facial details from rough meshes. Extensive experimental results in multiple people and animals demonstrate that our method produces high-quality deformation results.
On the Practical Robustness of the Nesterov’s Accelerated Quasi-Newton Method

This study focuses on the Nesterov's accelerated quasi-Newton (NAQ) method in the context of deep neural networks (DNN) and its applications. The thesis objective is to confirm the robustness and efficiency of Nesterov's acceleration to quasi-Netwon (QN) methods by developing practical algorithms for different fields of optimization problems.
Meaning of Adverbial in Nias Language

The goal of this study was to look into the meaning of adverbial in Nias. This study used a qualitative research method and a descriptive approach. In addition, the data was analyzed using the Quirk et al. (1985) theory. The findings of this study revealed that the meanings found in Nias language referred to Nias language adverbial types. The meaning of adverbial of place expressing position, direction, goal, and source was discovered. Meanwhile, the adverbial of time can be used to express a specific point in time, duration, or frequency. Furthermore, the adverbial of manner has the ability to express manner, means, instrument, and agentive. It was discovered that the adverbial of degree has the ability to express measure, frequency, and duration. Other adverbials in Nias language have other meanings, such as adverbial of reason, which can express why, adverbial of condition, which can express the condition itself, and adverbial of purpose, which can express a goal or purpose.&#x0D; Keywords: adverbial, meaning, Nias language
A Correlation Study among Reading Habit, Intrapersonal Intelligence and Reading Comprehension Ability

The purpose of this research was to know the correlation between: (1) reading habit (X1) and reading comprehension ability (Y); (2) intrapersonal intelligence (X2) and reading comprehension ability (Y); and reading habit (X1), intrapersonal intelligence (X2) and reading comprehension ability (Y). This research used quantitative method to collect the data with descriptive correlational design. The population of this research was the fourth semester students of the English Department of IAIN Kerinci which consisted of 44 students as the sample were collected through total sampling technique. In analyzing the data, the researcher used Pearson Product Moment and Multiple Linear Regression technique by using SPSS (Statistical Program for Social Science) 20.0 for windows. It showed that: (1) the coefficient correlation between students’ reading habit and their reading comprehension ability (X1+Y) was 0,867 with a significance value of 0,000 which smaller than 0,05. It indicates that there was very strong positive significant correlation between students’ reading habit and their reading comprehension ability; (2) the coefficient correlation between students’ intrapersonal intelligence and their reading comprehension ability (X2+Y) was 0,576 with a significance value of 0,000 which smaller than 0,05. It means that there was enough positive significant correlation between students’ intrapersonal intelligence and their reading comprehension ability; and (3) the value of significant correlation between students’ reading habit, intrapersonal intelligence and their reading comprehension ability (X1+X2+Y) was 0.000 which smaller than 0.05 (Sig &lt; 0.05). Moreover, the value of simultant test was Fcalculate = 62,472 &gt; Ftable = 2,83. It implies that H3 was accepted and H0 was rejected. In other word, there was significant correlation between students’ reading habit, intrapersonal intelligence and their reading comprehension.
Adaptive Pairwise Weights for Temporal Credit Assignment

How much credit (or blame) should an action taken in a state get for a future reward? This is the fundamental temporal credit assignment problem in Reinforcement Learning (RL). One of the earliest and still most widely used heuristics is to assign this credit based on a scalar coefficient, lambda (treated as a hyperparameter), raised to the power of the time interval between the state-action and the reward. In this empirical paper, we explore heuristics based on more general pairwise weightings that are functions of the state in which the action was taken, the state at the time of the reward, as well as the time interval between the two. Of course it isn't clear what these pairwise weight functions should be, and because they are too complex to be treated as hyperparameters we develop a metagradient procedure for learning these weight functions during the usual RL training of a policy. Our empirical work shows that it is often possible to learn these pairwise weight functions during learning of the policy to achieve better performance than competing approaches.
Image Compression Schemes Incorporating Modified Contrast Sensitivities of Human Visual System

This paper deals with the importance of working on image compression, which is reduce the number of bits needed to represent an image while maintaining a desirable quality. This is done by eliminating data that are visually unnecessary and taking advantage of redundancy that is inherent in most images. Image compression plays a critical role in telemetric applications. Image compression involves reducing the typically massive amount of data to represent an image. This is done by eliminating data that are visually unnecessary and taking advantage of redundancy that is inherent in most images. It is desired that either single image or sequences of images be transmitted over computer networks at large distances so as that they could be used for a multitude of purposes. Image compression is essential for applications such as transmission and storage in databases. It aims to reduce the bit rate for transmission or storage while maintaining an acceptable fidelity or a good image quality.
Fast Payoff Matrix Sparsification Techniques for Structured Extensive-Form Games

The practical scalability of many optimization algorithms for large extensive-form games is often limited by the games' huge payoff matrices. To ameliorate the issue, Zhang and Sandholm recently proposed a sparsification technique that factorizes the payoff matrix A into a sparser object A = Â + UVᵀ, where the total combined number of nonzeros of Â, U, and V, is significantly smaller. Such a factorization can be used in place of the original payoff matrix in many optimization algorithm, such as interior-point and second-order methods, thus increasing the size of games that can be handled. Their technique significantly sparsifies poker (end)games, standard benchmarks used in computational game theory, AI, and more broadly. We show that the existence of extremely sparse factorizations in poker games can be tied to their particular Kronecker-product structure. We clarify how such structure arises and introduce the connection between that structure and sparsification. By leveraging such structure, we give two ways of computing strong sparsifications of poker games (as well as any other game with a similar structure) that are i) orders of magnitude faster to compute, ii) more numerically stable, and iii) produce a dramatically smaller number of nonzeros than the prior technique. Our techniques enable—for the first time—effective computation of high-precision Nash equilibria and strategies subject to constraints on the amount of allowed randomization. Furthermore, they significantly speed up parallel first-order game-solving algorithms; we show state-of-the-art speed on a GPU.
Learning V1 Simple Cells with Vector Representation of Local Content and Matrix Representation of Local Motion

This paper proposes a representational model for image pairs such as consecutive video frames that are related by local pixel displacements, in the hope that the model may shed light on motion perception in primary visual cortex (V1). The model couples the following two components: (1) the vector representations of local contents of images and (2) the matrix representations of local pixel displacements caused by the relative motions between the agent and the objects in the 3D scene. When the image frame undergoes changes due to local pixel displacements, the vectors are multiplied by the matrices that represent the local displacements. Thus the vector representation is equivariant as it varies according to the local displacements. Our experiments show that our model can learn Gabor-like filter pairs of quadrature phases. The profiles of the learned filters match those of simple cells in Macaque V1. Moreover, we demonstrate that the model can learn to infer local motions in either a supervised or unsupervised manner. With such a simple model, we achieve competitive results on optical flow estimation.
Analysis on Compressed Domain: A Multi-Task Learning Approach

Image compression approaches based on deep learning have achieved remarkable success. Existing studies mainly focus on human vision and machine analysis tasks taking reconstructed images as input. However, those methods need images to be decoded before performing downstream visual tasks, which motivates us to explore how to directly conduct visual analysis using the compressed data without decoding. The overview of our proposed model is shown as Fig. 1(a). Specifically, a task-agnostic learning-based compression model is proposed, which effectively supports various compressed domain-based analytical tasks meanwhile reserves outstanding re-constructed perceptual quality compared with traditional and learning-based codecs. To obtain the extremely compacted data representation with essential semantic infor-mation, we take the help of the generative model on decoder part. Then, we propose a multi-task learning model which can directly obtain semantic information from the compressed visual data. The pipeline of the proposed model is detailedly illus-trated in Fig. 1(b). In addition, joint optimization strategy is adopted to achieve the best balance point among compression efficiency, reconstructed image quality, and the downstream visual tasks&#x0027; performance. Experimental results verify that our proposed compressed domain-based multi-task analysis model outperforms the reconstructed image-based method on transmission efficiency, saving more than ten times of bit-rate consumption while preserving comparable visual analysis precision (i.e., classification and segmentation tasks) when compared with RGB image input models, which is evaluated on the CelebA-HO dataset.
ECOLOGICAL LAMENTATION AND ADVOCACY IN EKA BUDIANTA’S SELECTED POEMS

Writing about environmental issues before and after climate change and other human-made ecological damages, Eka Budianta has continually taken up environmental topics in his oeuvre. The study presented in the present article aims to scrutinize 6 (six) selected poems by Eka Budianta to see how the poet has dealt with ecological debate throughout the years. The study draws insights from criticisms within Environmental Humanities framework and uses a qualitative-interpretative method. The six poems comprise four poems written in 1984, i.e., “River Notes”, “The Yearning of the Wind”, “Song for Tiom”, and “Song of a Townsman”; a poem written in 2012 titled “Setelah Sudaraku Tenggelam”; and the most recent one, written in 2020, “Sungai Sejati”. The lines and stanzas of each poem are read and interpreted according to their respective themes, poetic devices, and contents to see if they demonstrate the principles of Ecopoetry. The study results in the following findings. First the lamentation for the loss of nature is present in “River Notes”, “The Yearning of the Wind”, and “Song of a Townsman”. Second, “Song for Tiom” and “Setelah Saudaraku Tenggelam” are elegies for, respectively, ecological destruction in Papua and the Situ Gintung Lake tragedy. Third, optimistic tone is palpable in Eka Budianta’s newest poem “Sungai Sejati”. Fourth, the inclusion of non-human agency like landscape, plants, and animals helps reinforce the green messages the poet seeks to express. This study concurs that literature can partake in exposing global climate change as well as advocating sustainable living in a way often ignored in ecological praxis that only celebrates concrete results.
Implementation of Fuzzy Logic Using Mamdani Method to Determine The Quantity of Bag Production (Case Study In Roman Indah Padang Bag Factory)

Roman Indah bag Factory is a bag manufacturing company that receives orders from customers every month. Fuzzy Logic is used to define the production known so far, so that the preferred result can be used as a basis for manager's decision. In the calculation process, Fuzzy Logic Mamdani requires maximum and minimum production data, maximum and minimum demand data, and maximum and minimum inventory data. Fuzzy logic is able to map an input into an output without factor factors. Fuzzy logic is used to create a model of a system that is able to determine the quantity of production. the factors that affect the quantity of production. Fuzzy logic is called the old new logic because the science of modern fuzzy logic and methodological was discovered only a few years ago, in fact the concept of fuzzy logic itself has been in us for a long time. Mamdani method is the most common method when it comes to fuzzy methodology. Mamdani method uses a set of IF-THEN rules derived from experienced operators/experts. The Mamdani model is often known as the Max-Min model. By using Mamdani method in Roman Indah handbag factory can assist in the efficiency of time and labour, because using Mamdani method can calculate the amount of production in the next month, so from the results can be derived consideration material decision by the manager, whether in determining raw materials, promotion, bag model, consumer, HR, etc. so that more company profits.
Assessing the Impact of COVID-19 on Digital Payments in Azerbaijan

The COVID19 pandemic, which has lasted for more than a year, has had a significant impact on the economies of the world's countries and economic relations between them. One of the most specific features of the pandemic is the minimization of physical contact and the establishment of relationships at a distance. In such circumstances, we considered it expedient to analyze the impact of the pandemic on digital payments in the Republic of Azerbaijan. As it is known, digital payments are mainly carried out remotely in electronic form, in this regard, the pandemic creates favorable conditions for expanding the use of digital payments, but on the one hand leads to the contraction of the economy. That is why the article first analyzes the current situation and infrastructure opportunities for digital payments in Azerbaijan, and examines the situation in the ecosystem in this regard. Also, payment card transactions before and after the pandemic period, noncash payments, e-commerce transactions, transactions through exchange systems and e-banking services, and key macroeconomic indicators were compared. However, it was defined whether the pandemic will affect digital payments. At the same time, the impact of the pandemic on digital payments in international practice has been studied and the situation in several countries has been studied. In the end, the results obtained on the basis of the analysis are presented in the conclusion section of the article.
Reasoning in Non-normal Modal Description Logics

Non-normal modal logics, interpreted on neighbourhood models which generalise the usual relational semantics, have found application in several areas, such as epistemic, deontic, and coalitional reasoning. We present here preliminary results on reasoning in a family of modal description logics obtained by combining ALC with non-normal modal operators. First, we provide a framework of terminating, correct, and complete tableau algorithms to check satisfiability of formulas in such logics with the semantics based on varying domains. We then investigate the satisfiability problems in fragments of these languages obtained by restricting the application of modal operators to formulas only, and interpreted on models with constant domains, providing tight complexity results.
Native Speaker Interaction with Young Learners in English Class - Hybrid Learning Models

The purpose of this study is to examine a native speaker's interaction with young learners in an English class to determine (1) what types of sentences she used in instructing young learners and (2) what tenses she used in giving instruction and asking questions while teaching young learners in the classroom. The researcher used the qualitative descriptive technique in order to perform the investigation. The information was gathered via class observation, including recording three meetings' worth of teaching and learning activities. After that, the tape was transcribed to analyze the data. The participants were a native English speaker and young learners from grade two primary students. The study's results reveal that the native speaker used various phrase types in her instruction, including simple, compound, complex, and compound-complex sentences. However, the most type of sentence she used is a simple sentence followed by a compound-complex and complex sentence. A minor type of sentence she used is a compound sentence. She also used three distinct tenses during her teaching. They exist in the present, the past, and the future. The most type of tenses she used was present and future tense. She almost rarely used past tense.
Learning by Competition of Self-Interested Reinforcement Learning Agents

An artificial neural network can be trained by uniformly broadcasting a reward signal to units that implement a REINFORCE learning rule. Though this presents a biologically plausible alternative to backpropagation in training a network, the high variance associated with it renders it impractical to train deep networks. The high variance arises from the inefficient structural credit assignment since a single reward signal is used to evaluate the collective action of all units. To facilitate structural credit assignment, we propose replacing the reward signal to hidden units with the change in the L2 norm of the unit's outgoing weight. As such, each hidden unit in the network is trying to maximize the norm of its outgoing weight instead of the global reward, and thus we call this learning method Weight Maximization. We prove that Weight Maximization is approximately following the gradient of rewards in expectation. In contrast to backpropagation, Weight Maximization can be used to train both continuous-valued and discrete-valued units. Moreover, Weight Maximization solves several major issues of backpropagation relating to biological plausibility. Our experiments show that a network trained with Weight Maximization can learn significantly faster than REINFORCE and slightly slower than backpropagation. Weight Maximization illustrates an example of cooperative behavior automatically arising from a population of self-interested agents in a competitive game without any central coordination.
Fuzzy Employee Placement Suitability Assessment using Mamdani Fuzzy Method

For a company, employees are the spearhead for the company because they have a function as Customer Service, customer satisfaction is obtained after interacting with the company through the performance of its employees. Employees are expected to meet all customer needs with the applicable regulations in the company and become a source of income for the company. To get reliable employees, one way is to assess the suitability of their placement in the appropriate position. With the assessment of the suitability of positioning, companies can consider and determine the provision of rewards, promotions, punishments, training, coaching, counseling, or transfers for their employees. The choice of method used to assess the suitability of employee placement in a company is Fuzzy Mamdani. With the Fuzzy Mamdani method, a system that is completely measurable and human assessments that tend to be immeasurable can be bridged. Assessment of the suitability of employee placement using the resulting fuzzy method, is expected to be an alternative solution for companies to give positions to their employees.
Rethinking Optimization with Differentiable Simulation from a Global Perspective

Differentiable simulation is a promising toolkit for fast gradient-based policy optimization and system identification. However, existing approaches to differentiable simulation have largely tackled scenarios where obtaining smooth gradients has been relatively easy, such as systems with mostly smooth dynamics. In this work, we study the challenges that differentiable simulation presents when it is not feasible to expect that a single descent reaches a global optimum, which is often a problem in contact-rich scenarios. We analyze the optimization landscapes of diverse scenarios that contain both rigid bodies and deformable objects. In dynamic environments with highly deformable objects and fluids, differentiable simulators produce rugged landscapes with nonetheless useful gradients in some parts of the space. We propose a method that combines Bayesian optimization with semi-local 'leaps' to obtain a global search method that can use gradients effectively, while also maintaining robust performance in regions with noisy gradients. We show that our approach outperforms several gradient-based and gradient-free baselines on an extensive set of experiments in simulation, and also validate the method using experiments with a real robot and deformables. Videos and supplementary materials are available at https://tinyurl.com/globdiff
Research on a Sugeno Fuzzy Logic Controller Compared to a Mamdani-Based PI-Type Fuzzy Logic Inference Model

Fuzzy logic – based control schemes have been selected as a perfect replacement for traditional regulators e.g. PI and PID due to their undeniable advantages such as no need of exact model and only depending upon expert’s experiences. It should be prominent in use from these fuzzy logic structures the PI-like Mamdani and Sugeno – based inference models have been widely used in academic studies. This paper focuses on creating a theoretical analysis and comparison for both of them. A typical case study regarding the speed control of a hydropower plant against various load changes is taken into consideration in order to demonstrate the applicability of the proposed control strategy. This is considered to be one of the most crucial control strategies for operation and stability of a hydropower plant. It was found that a lot of promising simulation results obtained by using Matlab/Simulink software when applying the PI-like Sugeno fuzzy logic controller are superior to those of the existing counterparts such as conventional PID and Mamdani – based one in dealing with the generator’s speed control problem presented in this study