Reviewerly/exHarmony huggingface dataset:
Data-Driven Adaptive Simultaneous Machine Translation

In simultaneous translation (SimulMT), the most widely used strategy is the wait-k policy thanks to its simplicity and effectiveness in balancing translation quality and latency. However, wait-k suffers from two major limitations: (a) it is a fixed policy that can not adaptively adjust latency given context, and (b) its training is much slower than full-sentence translation. To alleviate these issues, we propose a novel and efficient training scheme for adaptive SimulMT by augmenting the training corpus with adaptive prefix-to-prefix pairs, while the training complexity remains the same as that of training full-sentence translation models. Experiments on two language pairs show that our method outperforms all strong baselines in terms of translation quality and latency.
Randomness of Shapes and Statistical Inference on Shapes via the Smooth Euler Characteristic Transform

In this article, we establish the mathematical foundations for modeling the randomness of shapes and conducting statistical inference on shapes using the smooth Euler characteristic transform. Based on these foundations, we propose two chi-squared statistic-based algorithms for testing hypotheses on random shapes. Simulation studies are presented to validate our mathematical derivations and to compare our algorithms with state-of-the-art methods to demonstrate the utility of our proposed framework. As real applications, we analyze a data set of mandibular molars from four genera of primates and show that our algorithms have the power to detect significant shape differences that recapitulate known morphological variation across suborders. Altogether, our discussions bridge the following fields: algebraic and computational topology, probability theory and stochastic processes, Sobolev spaces and functional analysis, analysis of variance for functional data, and geometric morphometrics.
Few-Shot Head Swapping in the Wild

The head swapping task aims at flawlessly placing a source head onto a target body, which is of great importance to various entertainment scenarios. While face swapping has drawn much attention, the task of head swapping has rarely been explored, particularly under the few-shot setting. It is inherently challenging due to its unique needs in head modeling and background blending. In this paper, we present the Head Swapper (HeSer), which achieves few-shot head swapping in the wild through two delicately designed modules. Firstly, a Head2Head Aligner is devised to holistically migrate pose and expression information from the target to the source head by examining multi-scale information. Secondly, to tackle the challenges of skin color variations and head-background mismatches in the swapping procedure, a Head2Scene Blender is introduced to simultaneously modify facial skin color and fill mismatched gaps in the background around the head. Particularly, seamless blending is achieved with the help of a Semantic-Guided Color Reference Creation procedure and a Blending UNet. Extensive experiments demonstrate that the proposed method produces superior head swapping results in a variety of scenes.
A Bayesian Approach To Graph Partitioning

A new algorithm based on bayesian inference for learning local graph conductance based on Gaussian Process(GP) is given that uses advanced MCMC convergence ideas to create a scalable and fast algorithm for convergence to stationary distribution which is provided to learn the bahavior of conductance when traversing the indirected weighted graph. First metric embedding is used to represent the vertices of the graph. Then, uniform induced conductance is calculated for training points. Finally, in the learning step, a gaussian process is used to approximate the uniform induced conductance. MCMC is used to measure uncertainty of estimated hyper-parameters.
Cupolets in a Chaotic Neuron Model

This paper reports the first finding of cupolets in a chaotic Hindmarsh-Rose neural model. Cupolets (chaotic, unstable, periodic, orbit-lets) are unstable periodic orbits that have been stabilized through a particular control scheme applying a binary control sequence. We demonstrate different neural dynamics (periodic or chaotic) of the Hindmarsh-Rose model through a bifurcation diagram where the external input current, $I$, is the bifurcation parameter. We select a region in the chaotic parameter space and provide the results of numerical simulations. In this chosen parameter space, a control scheme is applied when the trajectory intersects with either of the two control planes. The size of the control is determined by a bit in a binary control sequence. The control is either a small microcontrol (0) or a large macrocontrol (1) that adjusts the future dynamics of the trajectory . We report the discovery of many cupolets with corresponding control sequences and comment on the differences with previously reported cupolets in the double scroll system. We provide some examples of the generated cupolets and conclude by discussing potential implications for biological neurons.
Minimum Displacement Motion Planning for Movable Obstacles

This paper presents a minimum displacement motion planning problem wherein obstacles are displaced by a minimum amount to find a feasible path. We define a metric for robot-obstacle intersection that measures the extent of the intersection and use this to penalize robot-obstacle overlaps. Employing the actual robot dynamics, the planner first finds a path through the obstacles that minimizes the robot-obstacle intersections. The metric is then used to iteratively displace the obstacles to achieve a feasible path. Several examples are provided that successfully demonstrates the proposed problem.
MemFHE: End-to-End Computing with Fully Homomorphic Encryption in Memory

The increasing amount of data and the growing complexity of problems has resulted in an ever-growing reliance on cloud computing. However, many applications, most notably in healthcare, finance or defense, demand security and privacy which today's solutions cannot fully address. Fully homomorphic encryption (FHE) elevates the bar of today's solutions by adding confidentiality of data during processing. It allows computation on fully encrypted data without the need for decryption, thus fully preserving privacy. To enable processing encrypted data at usable levels of classic security, e.g., 128-bit, the encryption procedure introduces noticeable data size expansion - the ciphertext is much bigger than the native aggregate of native data types. In this paper, we present MemFHE which is the first accelerator of both client and server for the latest Ring-GSW (Gentry, Sahai, and Waters) based homomorphic encryption schemes using Processing In Memory (PIM). PIM alleviates the data movement issues with large FHE encrypted data, while providing in-situ execution and extensive parallelism needed for FHE's polynomial operations. While the client-PIM can homomorphically encrypt and decrypt data, the server-PIM can process homomorphically encrypted data without decryption. MemFHE's server-PIM is pipelined and is designed to provide flexible bootstrapping, allowing two encryption techniques and various FHE security-levels based on the application requirements. We evaluate MemFHE for various security-levels and compare it with state-of-the-art CPU implementations for Ring-GSW based FHE. MemFHE is up to 20kx (265x) faster than CPU (GPU) for FHE arithmetic operations and provides on average 2007x higher throughput than the state-of-the-art while implementing neural networks with FHE.
Cognitive Visual Tracking of Hand Gestures in Real-Time RGB Videos

Real-time visual hand tracking is quite different from commonly tracked objects in RGB videos. Because the hand is a biological object and hence suffers from both physical and behavioral variations during its movement. Furthermore, the hand acquires a very small area in the image frame, and due to its erratic pattern of movement, the quality of images in the video is affected considerably, if recorded from a simple RGB camera. In this chapter, we propose a hybrid framework to track the hand movement in RGB video sequences. The framework integrates the unique features of the Faster Region-based Convolutional Neural Network (Faster R-CNN) built on Residual Network and Scale-Invariant Feature Transform (SIFT) algorithm. This combination is enriched with the discriminative learning power of deep neural networks and the fast detection capability of hand-crafted features SIFT. Thus, our method online adapts the variations occurring in real-time hand movement and exhibits high efficiency in cognitive recognition of hand trajectory. The empirical results shown in the chapter demonstrate that the approach can withstand the intrinsic as well as extrinsic challenges associated with visual tracking of hand gestures in RGB videos.
Grasping the Arrow of Time from the Singularity: Decoding Micromotion in Low-dimensional Latent Spaces from StyleGAN

The disentanglement of StyleGAN latent space has paved the way for realistic and controllable image editing, but does StyleGAN know anything about temporal motion, as it was only trained on static images? To study the motion features in the latent space of StyleGAN, in this paper, we hypothesize and demonstrate that a series of meaningful, natural, and versatile small, local movements (referred to as "micromotion", such as expression, head movement, and aging effect) can be represented in low-rank spaces extracted from the latent space of a conventionally pre-trained StyleGAN-v2 model for face generation, with the guidance of proper "anchors" in the form of either short text or video clips. Starting from one target face image, with the editing direction decoded from the low-rank space, its micromotion features can be represented as simple as an affine transformation over its latent feature. Perhaps more surprisingly, such micromotion subspace, even learned from just single target face, can be painlessly transferred to other unseen face images, even those from vastly different domains (such as oil painting, cartoon, and sculpture faces). It demonstrates that the local feature geometry corresponding to one type of micromotion is aligned across different face subjects, and hence that StyleGAN-v2 is indeed "secretly" aware of the subject-disentangled feature variations caused by that micromotion. We present various successful examples of applying our low-dimensional micromotion subspace technique to directly and effortlessly manipulate faces, showing high robustness, low computational overhead, and impressive domain transferability. Our codes are available at https://github.com/wuqiuche/micromotion-StyleGAN.
Generative 3D Animation Pipelines: Automating Facial Retargeting Workflows

Design tools in the 3D industry, while powerful, are still tedious and labor-intensive when it comes to bringing a creative idea for a visual effect to life. In this position paper, we discussed how an infamous generative synthetic media, deepfakes, could be of use and embedded into common sophisticated 3D workflows to reduce user workloads in areas such as 3D model editing, material design, and character animation. As a case discussion, we also prototyped a tool to address the retargeting problem in character animation. Although deepfakes themselves have received a negative public image, the results of our interviews with field experts are unexpectedly positive in regard to our tool that utilizes deepfake algorithms. Lastly, we also discussed our experience and observed design practices to put deepfakes to good use, including how we could avoid potential misuses directly by design, how this design changes user interactions, and subsequent open issues.
Embracing AWKWARD! Real-time Adjustment of Reactive Plans Using Social Norms

This paper presents the AWKWARD architecture for the development of hybrid agents in Multi-Agent Systems. AWKWARD agents can have their plans re-configured in real time to align with social role requirements under changing environmental and social circumstances. The proposed hybrid architecture makes use of Behaviour Oriented Design (BOD) to develop agents with reactive planning and of the well-established OperA framework to provide organisational, social, and interaction definitions in order to validate and adjust agents' behaviours. Together, OperA and BOD can achieve real-time adjustment of agent plans for evolving social roles, while providing the additional benefit of transparency into the interactions that drive this behavioural change in individual agents. We present this architecture to motivate the bridging between traditional symbolic- and behaviour-based AI communities, where such combined solutions can help MAS researchers in their pursuit of building stronger, more robust intelligent agent teams. We use DOTA2, a game where success is heavily dependent on social interactions, as a medium to demonstrate a sample implementation of our proposed hybrid architecture.
Variational Learning for Unsupervised Knowledge Grounded Dialogs

Recent methods for knowledge grounded dialogs generate responses by incorporating information from an external textual document. These methods do not require the exact document to be known during training and rely on the use of a retrieval system to fetch relevant documents from a large index. The documents used to generate the responses are modeled as latent variables whose prior probabilities need to be estimated. Models such as RAG and REALM, marginalize the document probabilities over the documents retrieved from the index to define the log likelihood loss function which is optimized end-to-end. In this paper, we develop a variational approach to the above technique wherein, we instead maximize the Evidence Lower bound (ELBO). Using a collection of three publicly available open-conversation datasets, we demonstrate how the posterior distribution, that has information from the ground-truth response, allows for a better approximation of the objective function during training. To overcome the challenges associated with sampling over a large knowledge collection, we develop an efficient approach to approximate the ELBO. To the best of our knowledge we are the first to apply variational training for open-scale unsupervised knowledge grounded dialog systems.
We're Not Gonna Break It! Consistency-Preserving Operators for Efficient Product Line Configuration

When configuring a software product line, finding a good trade-off between multiple orthogonal quality concerns is a challenging multi-objective optimisation problem. State-of-the-art solutions based on search-based techniques create invalid configurations in intermediate steps, requiring additional repair actions that reduce the efficiency of the search. In this work, we introduce consistency-preserving configuration operators (CPCOs)--genetic operators that maintain valid configurations throughout the entire search. CPCOs bundle coherent sets of changes: the activation or deactivation of a particular feature together with other (de)activations that are needed to preserve validity. In our evaluation, our instantiation of the IBEA algorithm with CPCOs outperforms two state-of-the-art tools for optimal product line configuration in terms of both speed and solution quality. The improvements are especially pronounced in large product lines with thousands of features.
Examining the User Interface Development Stage in the Software Development Process

For the software development world, the simplest definition for interface design is designing websites, software, mobile applications or other informatics tools to create an interactive experience with the user for facilitated use.The purpose here is to meet the needs of the user, design the properties that enable the user to reach his/her targets and create a simple and visual representation of the data.Steve Job's expression "The design is not just what it looks like and feels like.The design is how it works."highlights that design must not be limited only to being aesthetically pleasing and created the bestseller products of today with various design awards.In this study, analyzes about user interface design were made throughout the software development process and tips were given for a successful design.
Effects of Empowerment Education Program on Happiness, Interpersonal Competence and Confidence of Elderly People

This study is aimed to develop an empowerment education program for the elderly in a senior welfare center and identify its effect on the subjects' happiness, interpersonal competence and confidence.This research was conducted following the nonequivalent control group pretest-posttest design.Excluding omitted responses and extreme outliers, there were 47 elderly participants (experimental group:23, control group:24) from a senior welfare center in K city with voluntary participation and permission from the elderly.The empowerment program was provided for 3 weeks (6 sessions).The general characteristics, happiness, interpersonal competence and confidence were analyzed using the χ2 and t-test with SPSS/WIN 24 program.The results of the study suggest that the program significantly increased the happiness scores of the experimental group (t=4.73,p<.001), the interpersonal competence scores of the experimental group (t=3.79,p<.001) and the confidence scores of the experimental group (t=3.42,p<.001) compared to control group.The empowerment education program for the elderly on a senior welfare center was validated by the results of improving happiness, interpersonal competence and confidence in experimental group compared to control group.Therefore, application of the effectiveness of empowerment education program was significant positive effects on interventional strategy for improving happiness, interpersonal competence and confidence.The results of this study are intended to provide basic data for the possibility of the development and application of the empowerment education program.
Transparency of Financial Reports of Companies in The Republic of Kosovo

The financial reporting of any business entity wherever it operates is essential for the efficient management of that entity. Financial reporting in accordance with IFRS, international best practices and EU directives helps to create a more transparent business environment and enhances the credibility of other stakeholders. The financial reporting of companies in the Republic of Kosovo is important for a well-functioning market economy and provides some of the basic elements for a proper and sound financial system in Kosovo. The purpose of this research is to analyze whether the financial statements of companies in Kosovo are transparent, and each interested party can find their financial statements and other necessary information, as well as how they are prepared in accordance with IFRS/IAS requirements. Providing quality financial reporting helps users of that information to make important economic decisions. Financial information published and available to stakeholders in most cases to companies in Kosovo is considered not very reliable and not very transparent, as well as incomplete. The results of this research reflect the lack of transparency of financial information from companies in Kosovo, which is easily verified through the qualifications of the opinion of the auditor's report, clicks on the website, etc.
PENDAMPINGAN DALAM MENINGKATKAN KEMAMPUAN STATISTIK MAHASISWA DENGAN SOFTWARE R

This article aims to describe the process and results of community service activities that seek to improve the knowledge and skills of IAIQ (Institute of Islamic Religion and Quran) al-Ittifaqiah Indralaya students in processing statistical data with R software. The service activity was carried out for two days. The first day adopted the tutorial lecture method, which included discussion, questions, and answers. While the second day focused on a data processing practicum with R, The results of the pre-test showed that the average knowledge of the target group on statistical data processing with R was only 10.41 points. After the training, the average score of the participants increased to 21.85 points. This service activity was statistically proven to increase the knowledge and skills of the participants; t (40) = 15.2719, p 0.001. The existence of this community is expected to become an arena for learning and sharing information about R. In addition, Sriwijaya University needs to continue financing the introduction of R software to the younger generation in the Ogan Ilir District, especially among students. Because mastering R is a form of digital competence for the younger generation of Indonesia who will work in the Revolution 5.0 era.
EVI: Multilingual Spoken Dialogue Tasks and Dataset for Knowledge-Based Enrolment, Verification, and Identification

Knowledge-based authentication is crucial for task-oriented spoken dialogue systems that offer personalised and privacy-focused services. Such systems should be able to enrol (E), verify (V), and identify (I) new and recurring users based on their personal information, e.g. postcode, name, and date of birth. In this work, we formalise the three authentication tasks and their evaluation protocols, and we present EVI, a challenging spoken multilingual dataset with 5,506 dialogues in English, Polish, and French. Our proposed models set the first competitive benchmarks, explore the challenges of multilingual natural language processing of spoken dialogue, and set directions for future research.
Set Operations in Python for Translational Medicine

This is the world’s first tutorial article on Python programing on set operations for beginners and practitioners in translational medicine or medicine in general. This tutorial will allow researchers to demonstrate and showcase their tools on PyPI packages around the world. Via the PyPI packaging, a Python application with a single source code can run on Windows, MacOS, and Linux operating systems. In addition to the PyPI packaging, the reproducibility and quality of the source code must be guaranteed. This paper shows how to publish the Python application in Code Ocean after the PyPI packaging. Code Ocean is used in IEEE, Springer, and Elsevier for software reproducibility validation. First, programmers must understand how to scrape a dataset over the Internet. Second, the dataset files must be read in Python. Third, a program must be built to compute the target values using set operations. Fourth, the Python program must be converted to the PyPI package. Finally, the PyPI package is uploaded. Code Ocean plays a key role in publishing validation for software reproducibility. This paper depicts a vaers executable package as an example for calculating the number of deaths due to COVID-19 vaccines. Calculations were based on gender (male and female), age group, and vaccine group (Moderna, Pfizer, and Novartis), respectively.
Geochemistry Applied to the Exploration of Mineral Deposits

Geochemistry can be applied to the exploration of mineral deposits, for which it is necessary to understand the fundamentals of geochemical prospecting, the geochemical dispersion of elements based on their chemical properties. This chapter presents the basics of geochemical prospecting including: element mobility depending on ionic potential, pH, and Eh, with examples of Cu mobility during supergenic alteration of a primary sulfide deposit, a brief overview of sampling/geochemical prospecting methods, as well as a case study of the geochemical prospecting study carried out in the vanadium (V), uranium (U), and zinc (Zn) sedimentary mineral deposit of Puyango, Ecuador, in which anomalous and subanomalous values were detected in rock samples of various pathfinder elements of V and U.