Reviewerly/exHarmony huggingface dataset:
ANALISIS KEMAMPUAN LITERASI MEMBACA PADA CERITA LEGENDA SISWA KELAS 5 DI SDN CIPONDOH 8 KOTA TANGERANG

ABSTRACT Reading literacy is a core part of learning is schools that cannot be separated. This ability is the most basic foundation for achieving success in learning. The purpose of this study was to describe the literacy skills in reading comprehension of legend stories for fifth grade students at SDN Cipondoh 8, Tangerang City. The focus of this study was to determine the extent of literacy skills in reading comprehension of ledend stories for fifth grade students at SDN Cipondoh 8 Tangerang City. This research is a descriptive qualitative study with a teacher as the subject and 26 fifth grade students. The data collection techniques used were tests, interview, observation, and documentation. The research method used is descriptive method and data analysis uses data triangulation which consists of three activities namely, data reduction, data presentation and conclusions. Of the 26 subjects who were given a test to answer story questions, 2 people got grades in the very good category, then 10 people got grades in the good category, and 14 people got scores in the fair category. The results of the study showed that almost all students reading comprehension literacy skills were quite good, based on students comprehension abilities on indicators of understanding the meaning of words and the meaning of the stories they read.
Generative models in computer vision and biometrics

With the developments in the field of generative modeling and with the appearance of powerful model architectures, such as Generative Adversarial Networks (GAN), a wide range of new techniques and inventive algorithms has emerged recently to solve diverse computer vision problems, including many problems in the area of biometrics. In this talk, I will first provide a short overview of recent advances in generative modeling and describe some of our research efforts that focus explicitly on generative models. Next, I will present examples of our recent work utilizing generative models and talk about: (i) face image editing with our GAN inversion based MaskFaceGAN technique that allows for photo realistic image manipulation and explicitly addresses the problem of attribute entanglement seen with many latent-space based editing solutions, (ii) computer vision for fashion and virtual try-on with our context-driven C-VTON model and (ii) bimodal ocular image generation (and annotation) with our Dual-Branch StlyGAN2 (DB-StyleGAN) model. Finally, I will elaborate on some of the existing challenges with generative models and highlight future research directions.
Sarcasm Detection on Social Media using Machine Learning Approach

With sarcastic language, the speaker can say the exact opposite of what they mean, usually in a provocative, arrogant, and spiteful tone. Sarcasm can also be used to express annoyance or disappointment. Sarcasm is difficult to detect on social media due to the absence of several clues, such as tone and gestures. This paper seeks to find a powerful implementation, in order to detect sarcasm on social media. In this context, we propose a Machine Learning approach to predict if a statement is sarcastic or not. The sarcasm detection has garnered interest from researchers in several fields, including Artificial Intelligence and especially in Natural Language Processing (NLP). However, this subject is little addressed by researchers. In this paper, we will study the theoretical part of sarcasm detection, followed by the methodology adapted to the dataset, to finally present the results. The results obtained are promising, we were able to achieve an accuracy of 84% using RNN and 81% using SVM.
Uncertainty-Aware Dual-Evidential Learning for Weakly-Supervised Temporal Action Localization

Weakly-supervised temporal action localization (WTAL) aims to localize the action instances and recognize their categories with only video-level labels. Despite great progress, existing methods suffer from severe action-background ambiguity, which mainly arises from background noise and neglect of non-salient action snippets. To address this issue, we propose a generalized evidential deep learning (EDL) framework for WTAL, called Uncertainty-aware Dual-Evidential Learning (UDEL), which extends the traditional paradigm of EDL to adapt to the weakly-supervised multi-label classification goal with the guidance of epistemic and aleatoric uncertainties, of which the former comes from models lacking knowledge, while the latter comes from the inherent properties of samples themselves. Specifically, targeting excluding the undesirable background snippets, we fuse the video-level epistemic and aleatoric uncertainties to measure the interference of background noise to video-level prediction. Then, the snippet-level aleatoric uncertainty is further deduced for progressive mutual learning, which gradually focuses on the entire action instances in an “easy-to-hard” manner and encourages the snippet-level epistemic uncertainty to be complementary with the foreground attention scores. Extensive experiments show that UDEL achieves state-of-the-art performance on four public benchmarks. Our code is available in <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">github/mengyuanchen2021/UDEL</uri> .
Performance Evaluation of Partially Homomorphic Encryption Algorithms

Homomorphic encryption allows the computation of encrypted data without decryption or key exchanges. In this work, we focus on the performance characteristics of three additively homomorphic algorithms, Paillier, Damgård-Jurik and ElGamal. Our main contribution is the run-time analysis and comparison of these algorithms under variable key lengths during encryption (including key generation) and homomorphic addition operations. There is extensive theoretical coverage of the cryptographic strength of these algorithms but limited performance benchmarking in the literature. By implementing and exercising these algorithms under various loads, we observe how the theoretical underpinnings of each algorithm's strength affect their speed. Our findings align with the expected performance penalties of complex key generation and encryption processes while demonstrating promising performance speeds on the homomorphic operations. The latter is of particular importance given that the computationally expensive key generation and encryption would be performed once or very few times in a production environment. At the same time, the homomorphic operations would be performed numerous times and with high frequency.
Reusing Your Favourite Analysis Framework to Handle Workflows of Product Line Models

Model management frameworks support a wide array of analyses, transformations, and workflows, but lack native support for handling product lines of models. Yet the ubiquity of domains that heavily use model-driven techniques and are built using product lines, such as automotive, require adaptation, or lifting, of model management frameworks to be variability-aware. Lifting might introduce new implementation and validation costs, especially in safety-critical contexts. To facilitate the implementation and validation of variability-aware model management workflows, this paper provides a novel taxonomy of lifting methods. We compare the lifting methods in their capacity to reuse existing components and validation results. We then define a general framework for lifting and validating model management workflows, and report on an experience of lifting and validating modeling tasks and workflows in an existing Eclipse-based model management framework.
Geometric analysis of gaits and optimal control for three-link kinematic swimmers

Many robotic systems locomote using gaits - periodic changes of internal shape, whose mechanical interaction with the robot's environment generate characteristic net displacements. Prominent examples with two shape variables are the low Reynolds number 3-link "Purcell swimmer" with inputs of 2 joint angles and the "ideal fluid" swimmer. Gait analysis of these systems allows for intelligent decisions to be made about the swimmer's locomotive properties, increasing the potential for robotic autonomy. In this work, we present comparative analysis of gait optimization using two different methods. The first method is variational approach of "Pontryagin's maximum principle" (PMP) from optimal control theory. We apply PMP for several variants of 3-link swimmers, with and without incorporation of bounds on joint angles. The second method is differential-geometric analysis of the gaits based on curvature (total Lie bracket) of the local connection for 3-link swimmers. Using optimized body-motion coordinates, contour plots of the curvature in shape space give visualization that enables identifying distance-optimal gaits as zero level sets. Combining and comparing results of the two methods enables better understanding of changes in existence, shape and topology of distance-optimal gait trajectories, depending on the swimmers' parameters.
The Polynomial Method is Universal for Distribution-Free Correlational SQ Learning

We consider the problem of distribution-free learning for Boolean function classes in the PAC and agnostic models. Generalizing a beautiful work of Malach and Shalev-Shwartz (2022) that gave tight correlational SQ (CSQ) lower bounds for learning DNF formulas, we give new proofs that lower bounds on the threshold or approximate degree of any function class directly imply CSQ lower bounds for PAC or agnostic learning respectively. While such bounds implicitly follow by combining prior results by Feldman (2008, 2012) and Sherstov (2008, 2011), to our knowledge the precise statements we give had not appeared in this form before. Moreover, our proofs are simple and largely self-contained. These lower bounds match corresponding positive results using upper bounds on the threshold or approximate degree in the SQ model for PAC or agnostic learning, and in this sense these results show that the polynomial method is a universal, best-possible approach for distribution-free CSQ learning.
Eight-input optical programmable logic array enabled by parallel spectrum modulation

Despite over 40 years' development of optical logic computing, the studies have been still struggling to support more than four operands, since the high parallelism of light has not been fully leveraged blocked by the optical nonlinearity and redundant input modulation in existing methods. Here, we propose a scalable multi-input optical programmable logic array (PLA) with minimal logical input, enabled by parallel spectrum modulation. By making full use of the wavelength resource, an eight-input PLA is experimentally demonstrated, and there are 2^256 possible combinations of generated logic gates. Various complex logic fuctions, such as 8-256 decoder, 4-bit comparator, adder and multiplier are experimentally demonstrated via leveraging the PLA. The scale of PLA can be further extended by fully using the dimensions of wavelength and space. As an example, a nine-input PLA is implemented to realize the two-dimensional optical cellular automaton for the first time and perform Conway's Game of Life to simulate the evolutionary process of cells. Our work significantly alleviates the challenge of extensibility of optical logic devices, opening up new avenues for future large-scale, high-speed and energy-efficient optical digital computing.
Trustworthy Representation Learning Across Domains

As AI systems have obtained significant performance to be deployed widely in our daily live and human society, people both enjoy the benefits brought by these technologies and suffer many social issues induced by these systems. To make AI systems good enough and trustworthy, plenty of researches have been done to build guidelines for trustworthy AI systems. Machine learning is one of the most important parts for AI systems and representation learning is the fundamental technology in machine learning. How to make the representation learning trustworthy in real-world application, e.g., cross domain scenarios, is very valuable and necessary for both machine learning and AI system fields. Inspired by the concepts in trustworthy AI, we proposed the first trustworthy representation learning across domains framework which includes four concepts, i.e, robustness, privacy, fairness, and explainability, to give a comprehensive literature review on this research direction. Specifically, we first introduce the details of the proposed trustworthy framework for representation learning across domains. Second, we provide basic notions and comprehensively summarize existing methods for the trustworthy framework from four concepts. Finally, we conclude this survey with insights and discussions on future research directions.
FedDAT: An Approach for Foundation Model Finetuning in Multi-Modal Heterogeneous Federated Learning

Recently, foundation models have exhibited remarkable advancements in multi-modal learning. These models, equipped with millions (or billions) of parameters, typically require a substantial amount of data for finetuning. However, collecting and centralizing training data from diverse sectors becomes challenging due to distinct privacy regulations. Federated Learning (FL) emerges as a promising solution, enabling multiple clients to collaboratively train neural networks without centralizing their local data. To alleviate client computation burdens and communication overheads, previous works have adapted Parameter-efficient Finetuning (PEFT) methods for FL. Hereby, only a small fraction of the model parameters are optimized and communicated during federated communications. Nevertheless, most previous works have focused on a single modality and neglected one common phenomenon, i.e., the presence of data heterogeneity across the clients. Therefore, in this work, we propose a finetuning framework tailored to heterogeneous multi-modal FL, called Federated Dual-Aadapter Teacher (FedDAT). Specifically, our approach leverages a Dual-Adapter Teacher (DAT) to address data heterogeneity by regularizing the client local updates and applying Mutual Knowledge Distillation (MKD) for an efficient knowledge transfer. FedDAT is the first approach that enables an efficient distributed finetuning of foundation models for a variety of heterogeneous Vision-Language tasks. To demonstrate its effectiveness, we conduct extensive experiments on four multi-modality FL benchmarks with different types of data heterogeneity, where FedDAT substantially outperforms the existing centralized PEFT methods adapted for FL.
On Sudden Cessation in Circular Motion

This short paper presents a simple analytical model for the abrupt termination of circular motion, as discussed in the "The Most Mind-Blowing Aspect of Circular Motion".
Fat Shattering, Joint Measurability, and PAC Learnability of POVM Hypothesis Classes

We characterize learnability for quantum measurement classes by establishing matching necessary and sufficient conditions for their PAC learnability, along with corresponding sample complexity bounds, in the setting where the learner is given access only to prepared quantum states. We first probe the results from previous works on this setting. We show that the empirical risk defined in previous works and matching the definition in the classical theory fails to satisfy the uniform convergence property enjoyed in the classical setting for some learnable classes. Moreover, we show that VC dimension generalization upper bounds in previous work are frequently infinite, even for finite-dimensional POVM classes. To surmount the failure of the standard ERM to satisfy uniform convergence, we define a new learning rule -- denoised ERM. We show this to be a universal learning rule for POVM and probabilistically observed concept classes, and the condition for it to satisfy uniform convergence is finite fat shattering dimension of the class. We give quantitative sample complexity upper and lower bounds for learnability in terms of finite fat-shattering dimension and a notion of approximate finite partitionability into approximately jointly measurable subsets, which allow for sample reuse. We then show that finite fat shattering dimension implies finite coverability by approximately jointly measurable subsets, leading to our matching conditions. We also show that every measurement class defined on a finite-dimensional Hilbert space is PAC learnable. We illustrate our results on several example POVM classes.
Inferring gender from name: a large scale performance evaluation study

A person's gender is a crucial piece of information when performing research across a wide range of scientific disciplines, such as medicine, sociology, political science, and economics, to name a few. However, in increasing instances, especially given the proliferation of big data, gender information is not readily available. In such cases researchers need to infer gender from readily available information, primarily from persons' names. While inferring gender from name may raise some ethical questions, the lack of viable alternatives means that researchers have to resort to such approaches when the goal justifies the means - in the majority of such studies the goal is to examine patterns and determinants of gender disparities. The necessity of name-to-gender inference has generated an ever-growing domain of algorithmic approaches and software products. These approaches have been used throughout the world in academia, industry, governmental and non-governmental organizations. Nevertheless, the existing approaches have yet to be systematically evaluated and compared, making it challenging to determine the optimal approach for future research. In this work, we conducted a large scale performance evaluation of existing approaches for name-to-gender inference. Analysis are performed using a variety of large annotated datasets of names. We further propose two new hybrid approaches that achieve better performance than any single existing approach.
SafeAR: Towards Safer Algorithmic Recourse by Risk-Aware Policies

With the growing use of machine learning (ML) models in critical domains such as finance and healthcare, the need to offer recourse for those adversely affected by the decisions of ML models has become more important; individuals ought to be provided with recommendations on actions to take for improving their situation and thus receiving a favorable decision. Prior work on sequential algorithmic recourse -- which recommends a series of changes -- focuses on action feasibility and uses the proximity of feature changes to determine action costs. However, the uncertainties of feature changes and the risk of higher than average costs in recourse have not been considered. It is undesirable if a recourse could (with some probability) result in a worse situation from which recovery requires an extremely high cost. It is essential to incorporate risks when computing and evaluating recourse. We call the recourse computed with such risk considerations as Safe Algorithmic Recourse (SafeAR). The objective is to empower people to choose a recourse based on their risk tolerance. In this work, we discuss and show how existing recourse desiderata can fail to capture the risk of higher costs. We present a method to compute recourse policies that consider variability in cost and connect algorithmic recourse literature with risk-sensitive reinforcement learning. We also adopt measures "Value at Risk" and "Conditional Value at Risk" from the financial literature to summarize risk concisely. We apply our method to two real-world datasets and compare policies with different risk-aversion levels using risk measures and recourse desiderata (sparsity and proximity).
A Frame Stack Semantics for Sequential Core Erlang

We present a small-step, frame stack style, semantics for sequential Core Erlang, a dynamically typed, impure functional programming language. The semantics and the properties that we prove are machine-checked with the Coq proof assistant. We improve on previous work by including exceptions and exception handling, as well as built-in data types and functions. Based on the semantics, we define multiple concepts of program equivalence (contextual, CIU equivalence, and equivalence based on logical relations) and prove that the definitions are all equivalent. Using this we are able to give a correctness criterion for refactorings by means of contextually equivalent symbolic expression pairs, which is one of the main motivations of this work.
Characterising representation dynamics in recurrent neural networks for object recognition

Recurrent neural networks (RNNs) have yielded promising results for both recognizing objects in challenging conditions and modeling aspects of primate vision. However, the representational dynamics of recurrent computations remain poorly understood, especially in large-scale visual models. Here, we studied such dynamics in RNNs trained for object classification on MiniEcoset, a novel subset of ecoset. We report two main insights. First, upon inference, representations continued to evolve after correct classification, suggesting a lack of the notion of being ``done with classification''. Second, focusing on ``readout zones'' as a way to characterize the activation trajectories, we observe that misclassified representations exhibit activation patterns with lower L2 norm, and are positioned more peripherally in the readout zones. Such arrangements help the misclassified representations move into the correct zones as time progresses. Our findings generalize to networks with lateral and top-down connections, and include both additive and multiplicative interactions with the bottom-up sweep. The results therefore contribute to a general understanding of RNN dynamics in naturalistic tasks. We hope that the analysis framework will aid future investigations of other types of RNNs, including understanding of representational dynamics in primate vision.
Open-set Face Recognition with Neural Ensemble, Maximal Entropy Loss and Feature Augmentation

Open-set face recognition refers to a scenario in which biometric systems have incomplete knowledge of all existing subjects. Therefore, they are expected to prevent face samples of unregistered subjects from being identified as previously enrolled identities. This watchlist context adds an arduous requirement that calls for the dismissal of irrelevant faces by focusing mainly on subjects of interest. As a response, this work introduces a novel method that associates an ensemble of compact neural networks with a margin-based cost function that explores additional samples. Supplementary negative samples can be obtained from external databases or synthetically built at the representation level in training time with a new mix-up feature augmentation approach. Deep neural networks pre-trained on large face datasets serve as the preliminary feature extraction module. We carry out experiments on well-known LFW and IJB-C datasets where results show that the approach is able to boost closed and open-set identification rates.