Reviewerly/exHarmony huggingface dataset:
Enriching Verbal Feedback from Usability Testing: Automatic Linking of Thinking-Aloud Recordings and Stimulus using Eye Tracking and Mouse Data

The think aloud method is an important and commonly used tool for usability optimization. However, analyzing think aloud data could be time consuming. In this paper, we put forth an automatic analysis of verbal protocols and test the link between spoken feedback and the stimulus using eye tracking and mouse tracking. The gained data - user feedback linked to a specific area of the stimulus - could be used to let an expert review the feedback on specific web page elements or to visualize on which parts of the web page the feedback was given. Specifically, we test if participants fixate on or point with the mouse to the content of the webpage that they are verbalizing. During the testing, participants were shown three websites and asked to verbally give their opinion. The verbal responses, along with the eye and cursor movements were recorded. We compared the hit rate, defined as the percentage of verbally mentioned areas of interest (AOIs) that were fixated with gaze or pointed to with the mouse. The results revealed a significantly higher hit rate for the gaze compared to the mouse data. Further investigation revealed that, while the mouse was mostly used passively to scroll, the gaze was often directed towards relevant AOIs, thus establishing a strong association between spoken words and stimuli. Therefore, eye tracking data possibly provides more detailed information and more valuable insights about the verbalizations compared to the mouse data.
Approximate and ensemble local entanglement transformations for multipartite states

Understanding multipartite entanglement is a key goal in quantum information. Entanglement in pure states can be characterised by considering transformations under Local Operations assisted by Classical Communication (LOCC). However, it has been shown that, for $n\ge5$ parties, multipartite pure states are generically isolated, i.e., they can neither be reached nor transformed under LOCC. Nonetheless, in any real lab, one never deterministically transforms a pure initial state exactly to a pure target state. Instead, one transforms a mixed state near the initial state to an ensemble that is on average close to the target state. This motivates studying approximate LOCC transformations. After reviewing in detail the known results in the bipartite case, we present the gaps that remain open in the multipartite case. While the analysis of the multipartite setting is much more technically involved due to the existence of different SLOCC classes, certain features simplify in the approximate setting. In particular, we show that it is sufficient to consider pure initial states, that it is sufficient to consider LOCC protocols with finitely-many rounds of communication and that approximate transformations can be approximated by ensemble transformations within an SLOCC class. Then, we formally define a hierarchy of different forms of approximate transformations that are relevant from a physical point of view. Whereas this hierarchy collapses in the bipartite case, we show that this is not the case for the multipartite setting, which is fundamentally richer. To wit, we show that optimal multipartite approximate transformations are not generally deterministic, that ensemble transformations within an SLOCC class can achieve a higher fidelity than deterministic transformations within an SLOCC class, and that there are approximate transformations with no deterministic transformations nearby.
Score Function Gradient Estimation to Widen the Applicability of Decision-Focused Learning

Many real-world optimization problems contain unknown parameters that must be predicted prior to solving. To train the predictive machine learning (ML) models involved, the commonly adopted approach focuses on maximizing predictive accuracy. However, this approach does not always lead to the minimization of the downstream task loss. Decision-focused learning (DFL) is a recently proposed paradigm whose goal is to train the ML model by directly minimizing the task loss. However, state-of-the-art DFL methods are limited by the assumptions they make about the structure of the optimization problem (e.g., that the problem is linear) and by the fact that can only predict parameters that appear in the objective function. In this work, we address these limitations by instead predicting \textit{distributions} over parameters and adopting score function gradient estimation (SFGE) to compute decision-focused updates to the predictive model, thereby widening the applicability of DFL. Our experiments show that by using SFGE we can: (1) deal with predictions that occur both in the objective function and in the constraints; and (2) effectively tackle two-stage stochastic optimization problems.
Best practices for quantum error mitigation with digital zero-noise extrapolation

Digital zero-noise extrapolation (dZNE) has emerged as a common approach for quantum error mitigation (QEM) due to its conceptual simplicity, accessibility, and resource efficiency. In practice, however, properly applying dZNE to extend the computational reach of noisy quantum processors is rife with subtleties. Here, based on literature review and original experiments on noisy simulators and real quantum hardware, we define best practices for QEM with dZNE for each step of the workflow, including noise amplification, execution on the quantum device, extrapolation to the zero-noise limit, and composition with other QEM methods. We anticipate that this effort to establish best practices for dZNE will be extended to other QEM methods, leading to more reproducible and rigorous calculations on noisy quantum hardware.
Probabilistic Operational Correspondence (Technical Report)

Encodings are the main way to compare process calculi. By applying quality criteria to encodings we analyse their quality and rule out trivial or meaningless encodings. Thereby, operational correspondence is one of the most common and most important quality criteria. It ensures that processes and their translations have the same abstract behaviour. We analyse probabilistic versions of operational correspondence to enable such a verification for probabilistic systems. Concretely, we present three versions of probabilistic operational correspondence: weak, middle, and strong. We show the relevance of the weaker version using an encoding from a sublanguage of probabilistic CCS into the probabilistic pi-calculus. Moreover, we map this version of probabilistic operational correspondence onto a probabilistic behavioural relation that directly relates source and target terms. Then we can analyse the quality of the criterion by analysing the relation it induces between a source term and its translation. For the second version of probabilistic operational correspondence we proceed in the opposite direction. We start with a standard simulation relation for probabilistic systems and map it onto a probabilistic operational correspondence criterion. This technical report contains the proofs to the lemmata and theorems of [8] as well as some additional material.
Generative Pretraining in Multimodality

We present Emu, a Transformer-based multimodal foundation model, which can seamlessly generate images and texts in multimodal context. This omnivore model can take in any single-modality or multimodal data input indiscriminately (e.g., interleaved image, text and video) through a one-model-for-all autoregressive training process. First, visual signals are encoded into embeddings, and together with text tokens form an interleaved input sequence. Emu is then end-to-end trained with a unified objective of classifying the next text token or regressing the next visual embedding in the multimodal sequence. This versatile multimodality empowers the exploration of diverse pretraining data sources at scale, such as videos with interleaved frames and text, webpages with interleaved images and text, as well as web-scale image-text pairs and video-text pairs. Emu can serve as a generalist multimodal interface for both image-to-text and text-to-image tasks, and supports in-context image and text generation. Across a broad range of zero-shot/few-shot tasks including image captioning, visual question answering, video question answering and text-to-image generation, Emu demonstrates superb performance compared to state-of-the-art large multimodal models. Extended capabilities such as multimodal assistants via instruction tuning are also demonstrated with impressive performance.
MAP- and MLE-Based Teaching

Imagine a learner L who tries to infer a hidden concept from a collection of observations. Building on the work [4] of Ferri et al., we assume the learner to be parameterized by priors P(c) and by c-conditional likelihoods P(z|c) where c ranges over all concepts in a given class C and z ranges over all observations in an observation set Z. L is called a MAP-learner (resp. an MLE-learner) if it thinks of a collection S of observations as a random sample and returns the concept with the maximum a-posteriori probability (resp. the concept which maximizes the c-conditional likelihood of S). Depending on whether L assumes that S is obtained from ordered or unordered sampling resp. from sampling with or without replacement, we can distinguish four different sampling modes. Given a target concept c in C, a teacher for a MAP-learner L aims at finding a smallest collection of observations that causes L to return c. This approach leads in a natural manner to various notions of a MAP- or MLE-teaching dimension of a concept class C. Our main results are: We show that this teaching model has some desirable monotonicity properties. We clarify how the four sampling modes are related to each other. As for the (important!) special case, where concepts are subsets of a domain and observations are 0,1-labeled examples, we obtain some additional results. First of all, we characterize the MAP- and MLE-teaching dimension associated with an optimally parameterized MAP-learner graph-theoretically. From this central result, some other ones are easy to derive. It is shown, for instance, that the MLE-teaching dimension is either equal to the MAP-teaching dimension or exceeds the latter by 1. It is shown furthermore that these dimensions can be bounded from above by the so-called antichain number, the VC-dimension and related combinatorial parameters. Moreover they can be computed in polynomial time.
Unmasking the giant: A comprehensive evaluation of ChatGPT's proficiency in coding algorithms and data structures

The transformative influence of Large Language Models (LLMs) is profoundly reshaping the Artificial Intelligence (AI) technology domain. Notably, ChatGPT distinguishes itself within these models, demonstrating remarkable performance in multi-turn conversations and exhibiting code proficiency across an array of languages. In this paper, we carry out a comprehensive evaluation of ChatGPT's coding capabilities based on what is to date the largest catalog of coding challenges. Our focus is on the python programming language and problems centered on data structures and algorithms, two topics at the very foundations of Computer Science. We evaluate ChatGPT for its ability to generate correct solutions to the problems fed to it, its code quality, and nature of run-time errors thrown by its code. Where ChatGPT code successfully executes, but fails to solve the problem at hand, we look into patterns in the test cases passed in order to gain some insights into how wrong ChatGPT code is in these kinds of situations. To infer whether ChatGPT might have directly memorized some of the data that was used to train it, we methodically design an experiment to investigate this phenomena. Making comparisons with human performance whenever feasible, we investigate all the above questions from the context of both its underlying learning models (GPT-3.5 and GPT-4), on a vast array sub-topics within the main topics, and on problems having varying degrees of difficulty.
BLUEX: A benchmark based on Brazilian Leading Universities Entrance eXams

One common trend in recent studies of language models (LMs) is the use of standardized tests for evaluation. However, despite being the fifth most spoken language worldwide, few such evaluations have been conducted in Portuguese. This is mainly due to the lack of high-quality datasets available to the community for carrying out evaluations in Portuguese. To address this gap, we introduce the Brazilian Leading Universities Entrance eXams (BLUEX), a dataset of entrance exams from the two leading universities in Brazil: UNICAMP and USP. The dataset includes annotated metadata for evaluating the performance of NLP models on a variety of subjects. Furthermore, BLUEX includes a collection of recently administered exams that are unlikely to be included in the training data of many popular LMs as of 2023. The dataset is also annotated to indicate the position of images in each question, providing a valuable resource for advancing the state-of-the-art in multimodal language understanding and reasoning. We describe the creation and characteristics of BLUEX and establish a benchmark through experiments with state-of-the-art LMs, demonstrating its potential for advancing the state-of-the-art in natural language understanding and reasoning in Portuguese. The data and relevant code can be found at https://github.com/Portuguese-Benchmark-Datasets/BLUEX
On the hull and complementarity of one generator quasi-cyclic codes and four-circulant codes

We study one generator quasi-cyclic codes and four-circulant codes, which are also quasi-cyclic but have two generators. We state the hull dimensions for both classes of codes in terms of the polynomials in their generating elements. We prove results such as the hull dimension of a four-circulant code is even and one-dimensional hull for double-circulant codes, which are special one generator codes, is not possible when the alphabet size $q$ is congruent to 3 mod 4. We also characterize linear complementary pairs among both classes of codes. Computational results on the code families in consideration are provided as well.
Fisher-Weighted Merge of Contrastive Learning Models in Sequential Recommendation

Along with the exponential growth of online platforms and services, recommendation systems have become essential for identifying relevant items based on user preferences. The domain of sequential recommendation aims to capture evolving user preferences over time. To address dynamic preference, various contrastive learning methods have been proposed to target data sparsity, a challenge in recommendation systems due to the limited user-item interactions. In this paper, we are the first to apply the Fisher-Merging method to Sequential Recommendation, addressing and resolving practical challenges associated with it. This approach ensures robust fine-tuning by merging the parameters of multiple models, resulting in improved overall performance. Through extensive experiments, we demonstrate the effectiveness of our proposed methods, highlighting their potential to advance the state-of-the-art in sequential learning and recommendation systems.
Joint Perceptual Learning for Enhancement and Object Detection in Underwater Scenarios

Underwater degraded images greatly challenge existing algorithms to detect objects of interest. Recently, researchers attempt to adopt attention mechanisms or composite connections for improving the feature representation of detectors. However, this solution does \textit{not} eliminate the impact of degradation on image content such as color and texture, achieving minimal improvements. Another feasible solution for underwater object detection is to develop sophisticated deep architectures in order to enhance image quality or features. Nevertheless, the visually appealing output of these enhancement modules do \textit{not} necessarily generate high accuracy for deep detectors. More recently, some multi-task learning methods jointly learn underwater detection and image enhancement, accessing promising improvements. Typically, these methods invoke huge architecture and expensive computations, rendering inefficient inference. Definitely, underwater object detection and image enhancement are two interrelated tasks. Leveraging information coming from the two tasks can benefit each task. Based on these factual opinions, we propose a bilevel optimization formulation for jointly learning underwater object detection and image enhancement, and then unroll to a dual perception network (DPNet) for the two tasks. DPNet with one shared module and two task subnets learns from the two different tasks, seeking a shared representation. The shared representation provides more structural details for image enhancement and rich content information for object detection. Finally, we derive a cooperative training strategy to optimize parameters for DPNet. Extensive experiments on real-world and synthetic underwater datasets demonstrate that our method outputs visually favoring images and higher detection accuracy.
The matrix permanent and determinant from a spin system

In contrast to the determinant, no algorithm is known for the exact determination of the permanent of a square matrix that runs in time polynomial in its dimension. Consequently, non interacting fermions are classically efficiently simulatable while non-interacting bosons are not, underpinning quantum supremacy arguments for sampling the output distribution of photon interferometer arrays. This work introduces a graph-theoretic framework that bridges both the determinant and permanent. The only non-zero eigenvalues of a sparse non-Hermitian operator $\breve{M}$ for $n$ spin-$1/2$ particles are the $n$th roots of the permanent or determinant of an $n\times n$ matrix $M$, interpreting basis states as bosonic or fermionic occupation states, respectively. This operator can be used to design a simple and straightforward method for the classical determination of the permanent that matches the efficiency of the best-known algorithm. Gauss-Jordan elimination for the determinant of $M$ is then equivalent to the successive removal of the generalized zero eigenspace of the fermionic $\breve{M}$, equivalent to the deletion of some nodes and reweighting of the remaining edges in the graph such that only $n$ nodes survive after the last step. In the bosonic case, the successive removal of generalized zero eigenspaces for $\breve{M}$ is also equivalent to node deletion, but new edges are added during this process, which gives rise to the higher complexity of computing the permanent. Our analysis may point the way to new strategies for classical and quantum evaluation of the permanent.
Quality assessment for microscopic parasite images

Microscopy lens distortion will cause errors during parasites image acquisition for water sample inspection.Since water sample inspection is crucial for treated water monitoring, the quality of microscopic parasite images such as Giardia and Cryptosporidium need to be monitored as well to avoid errors in treated water inspection.In this work, the subjective and objective evaluation of parasite images were performed.The parasite species studied were Cryptosporidium and Giardia (oo)cysts.Parasite image database consisting of 20 reference images and 360 distorted images were used in the evaluation.The distorted images were generated from the reference images by applying distortion to the reference images with Gaussian White Noise and Motion Blur, at 9 levels of distortions.Twenty subjects were selected to assess the distorted images for the subjective evaluation.The scores obtained from the subjects were transformed into Mean Opinion Score (MOS).In the objective evaluation, six Full Reference-IQA (FR-IQA) metrics, namely MSSIM, SSIM, FSIM, IWSSIM, GMSD and VIF were used to evaluate the distorted images.The subjective MOS scores were used as the benchmark to determine the most suitable objective IQA to assess parasite images.The relationship between the subjective MOS and objective IQAs are examined using performance metrics namely PLCC and RMSE.It was found that MSSIM is the most suitable IQA to assess parasite images distorted with Gaussian White Noise and Motion Blur.
THE ANALYSIS OF LANGUAGE STYLE USED IN MERRY RIANA’S YOUTUBE CHANNEL ON THE PLAYLIST ‘SPOKEN WORD’

The aim of this study was to find out the types of language styles and identify the dominant style of language used by Merry Riana in her selected motivational speech videos as a motivator. The data of this study were taken from Merry Riana’s YouTube Channel and the researchers chose 3 motivational speech videos in the "SPOKEN WORD" playlist. In analyzing the data, the researchers used descriptive qualitative method. The researchers used the theory proposed by Martin Joss (1967) to identify the types of language styles. According to Martin Joss (1967), there are five language styles: frozen style, formal style, casual style, consultative style, and intimate style. The researchers found that in Merry Riana’s motivational speech used three language styles from five language styles proposed by Martin Joss. There were 64 formal styles, 42 casual styles, and 4 intimate styles from the 117 sentences uttered by the speaker in the selected video. The most dominant style of language found in this study was the formal style.
Pengaplikasian Media Audio Visual Dalam Pembelajaran Berbicara Bahasa Jerman (Sprechfertigkeiten)

In learning German, vocabulary mastery is mandatory. Especially in the digital era, challenges in language learning can be answered through the application of audio-visual media. Along with that, the production of audio-visual media in teaching students has not yet been optimal, so the purpose of this activity is to introduce the production of audio-visual media to teachers in language learning and see the effectiveness of using audio-visual media in the language learning process. The method used in this activity is to include various lectures accompanied by direct practice. The results of this activity are in the form of teachers' skills in making audio-visual media, especially in language learning. Prior to this activity, there was still a very small percentage of teachers who could make and use audio-visual media in learning. It can be seen that the enthusiasm of the teachers in this activity is that ≥ 90% of the teachers have been able to make and use animated media in language learning. This is very effective in keeping students enthusiastic about participating in learning, especially in language learning.
Image Retargeting in Cyber-Physical Systems with IoT and Network Security using Fractal Images

This research paper proposes a comprehensive framework for integrating fractal image retargeting techniques in cyber-physical systems (CPS) with the Internet of Things (IoT), addressing the challenges of device heterogeneity, content-aware resizing, and network security concerns.The framework incorporates fractal image retargeting algorithms and network security mechanisms to ensure the privacy, integrity, and authenticity of transmitted and processed images.By considering the unique requirements of CPS with IoT, the framework enables seamless image adaptation across different devices, optimizing image delivery and enhancing user experiences.The implementation and evaluation of the framework demonstrate its effectiveness in achieving content-aware resizing, preserving important visual content, and efficient resource utilization.Comparative analysis against existing techniques highlights the advantages of the proposed framework, including adaptability to CPS with IoT, network security considerations, and improved image quality.User feedback confirms the usability and visual quality of the retargeted images, reinforcing the practical applicability of the framework.This research contributes to the practical implementation of efficient and secure image adaptation in real-world CPS applications, benefiting domains such as medical imaging, multimedia systems, and remote sensing.The results emphasize the importance of optimal image delivery and resource utilization in diverse applications within CPS with IoT environments.