Reviewerly/exHarmony huggingface dataset:
Trading Off Privacy, Utility, and Efficiency in Federated Learning

Federated learning (FL) enables participating parties to collaboratively build a global model with boosted utility without disclosing private data information. Appropriate protection mechanisms have to be adopted to fulfill the opposing requirements in preserving privacy and maintaining high model utility . In addition, it is a mandate for a federated learning system to achieve high efficiency in order to enable large-scale model training and deployment. We propose a unified federated learning framework that reconciles horizontal and vertical federated learning. Based on this framework, we formulate and quantify the trade-offs between privacy leakage, utility loss, and efficiency reduction, which leads us to the No-Free-Lunch (NFL) theorem for the federated learning system. NFL indicates that it is unrealistic to expect an FL algorithm to simultaneously provide excellent privacy, utility, and efficiency in certain scenarios. We then analyze the lower bounds for the privacy leakage, utility loss, and efficiency reduction for several widely-adopted protection mechanisms, including Randomization , Homomorphic Encryption , Secret Sharing, and Compression . Our analysis could serve as a guide for selecting protection parameters to meet particular requirements.
Dialog Act Guided Contextual Adapter for Personalized Speech Recognition

Personalization in multi-turn dialogs has been a long standing challenge for end-to-end automatic speech recognition (E2E ASR) models. Recent work on contextual adapters has tackled rare word recognition using user catalogs. This adaptation, however, does not incorporate an important cue, the dialog act, which is available in a multi-turn dialog scenario. In this work, we propose a dialog act guided contextual adapter network. Specifically, it leverages dialog acts to select the most relevant user catalogs and creates queries based on both – the audio as well as the semantic relationship between the carrier phrase and user catalogs to better guide the contextual biasing. On industrial voice assistant datasets, our model outperforms both the baselines - dialog act encoder-only model, and the contextual adaptation, leading to the most improvement over the no-context model: 58% average relative word error rate reduction (WERR) in the multi-turn dialog scenario, in comparison to the prior-art contextual adapter, which has achieved 39% WERR over the no-context model.
YOLOv5-SFE: An algorithm fusing spatio-temporal features for detecting and recognizing workers' operating behaviors

The occurrence of production accidents can be effectively reduced by monitoring workers' operating behaviors in real time. However, most of the monitoring tasks are currently performed by the monitoring personnel, which takes up a lot of manpower and material resources. To solve this problem, a YOLOv5-SFE algorithm is proposed in this paper for real-time detection and recognition of workers' operating behaviors. The YOLOv5-SFE algorithm makes the following contributions: (1) During data preprocessing, a hash sampling algorithm is used to extract frames with low similarity. (2) A feature enhancement module is designed and integrated into YOLOv5 to distinguish between valid and invalid information. (3) A convolution-based spatio-temporal feature fusion module is designed and is inserted after the extraction of spatial features to extract the temporal features between multiple frames. The videos of workers' operating behaviors are from factories’ industrial scene. The improved algorithm in this paper was trained and tested on the dataset. Compared with the original algorithm, the accuracy of the algorithm improves from 89.3% to 94.7%, the recall improves from 81.5% to 90.8%, and the mean average precision(mAP) improves from 88.2% to 92%. The results show that the improved algorithm is able to accurately detect and recognize workers' operating behaviors in real time, thereby improving the safety of the production process.
Enhanced FPGA implementation of Echo State Networks for chaotic time series prediction

The Echo State Network (ESN) is one of the most used machine learning methods for predicting chaotic time series. The topology of an ESN comprises three layers, namely: input, hidden, and output layer. In this work, different ESN topologies are analyzed by modifying the structure and number of internal connections in the hidden layer, which neurons are connected randomly. The ESN topologies are analyzed to have 2%, 6%, 20%, 40%,…, and up to 100% of internal connections. The prediction error is associated to the Mean Squared Error (MSE), which results have similar values for all ESN topologies, as long as the Echo state property is guaranteed. It is highlighted that the prediction horizon obtained with 2% of internal connections, is more significant than that obtained with 100%, providing a prediction of 80 and 60 steps-ahead, respectively. The prediction results significantly impact the implementation in a field-programmable gate array (FPGA), so that an enhanced hardware realization is introduced herein by reducing the use of multipliers by more than 90% compared with a fully connected topology, without significantly affecting the measure of the prediction horizon and MSE. Another contribution is the FPGA implementation of the activation function that is an hyperbolic tangent function for which different alternatives are shown to minimize the error of the approximated function without significantly increasing the implementation cost or latency.
Graph-based extractive text summarization method for Hausa text

Automatic text summarization is one of the most promising solutions to the ever-growing challenges of textual data as it produces a shorter version of the original document with fewer bytes, but the same information as the original document. Despite the advancement s in automatic text summarization research, research involving the development of automatic text summarization methods for documents written in Hausa, a Chadic language widely spoken in West Africa by approximately 150,000,000 people as either their first or second language, is still in early stages of development. This study proposes a novel graph-based extractive single-document summarization method for Hausa text by modifying the existing PageRank algorithm using the normalized common bigrams count between adjacent sentences as the initial vertex score. The proposed method is evaluated using a primarily collected Hausa summarization evaluation dataset comprising of 113 Hausa news articles on ROUGE evaluation toolkits. The proposed approach outperformed the standard methods using the same datasets. It outperformed the TextRank method by 2.1%, LexRank by 12.3%, centroid-based method by 19.5%, and BM25 method by 17.4%.
Small Data Fusion Algorithm for Personalized Library Recommendations

In order to better grasp the needs of library users and provide them with more accurate knowledge services, combining the characteristics of university libraries, this article applies library small data to personalized recommendation and proposes a small data fusion algorithm model for library personalized recommendation. This model combines the characteristics of small data and realizes multi-dimensional small data fusion by using fully connected neural network to capture the potential collaborative filtering information between users and projects, better grasp the needs of readers and users, and provide valuable assistance for subsequent personalized recommendation research. The effectiveness of the proposed method in personalized recommendation of library resources is verified by comparing several groups of experiments on public and self-built data sets.
Word Data Research and Prediction Based on Wordle Game

As a very popular game in recent years, Wordle contains many interesting rules.This paper hopes to get a mathematical model that can measure the difficulty of words by analyzing the words that appear in Wordle, and use this model to predict the difficulty of the word "EEIRE".First of all, we process the data of response times.The data with 1-6 times of answer is used as negative indicator, and the data with more than 6 times of answer is used as positive indicator.Then, we using the comprehensive rank-sum ratio evaluation method, the RSR values of each sample are calculated, and then sorted and divided into three difficulty levels: 1, 2 and 3, where "1" means simple and "3" means difficult.Finally, we code the words and train them with CNN.Finally, we predict the word EEIRE to be of medium difficulty.
Mind the Gap: Achieving a Super-Grover Quantum Speedup by Jumping to the End

We present a quantum algorithm that has rigorous runtime guarantees for several families of binary optimization problems, including Quadratic Unconstrained Binary Optimization (QUBO), Ising spin glasses (p-spin model), and k-local constraint satisfaction problems (k-CSP). We show that either (a) the algorithm finds the optimal solution in time O*(2(0.5−c)n) for an n-independent constant c, a 2cn advantage over Grover's algorithm; or (b) there are sufficiently many low-cost solutions such that classical random guessing produces a (1−η) approximation to the optimal cost value in sub-exponential time for arbitrarily small choice of η. Additionally, we show that for a large fraction of random instances from the k-spin model and for any fully satisfiable or slightly frustrated k-CSP formula, statement (a) is the case. The algorithm and its analysis are largely inspired by Hastings' short-path algorithm.
Explanation Ontology: A general-purpose, semantic representation for supporting user-centered explanations

In the past decade, trustworthy Artificial Intelligence (AI) has emerged as a focus for the AI community to ensure better adoption of AI models, and explainable AI is a cornerstone in this area. Over the years, the focus has shifted from building transparent AI methods to making recommendations on how to make black-box or opaque machine learning models and their results more understandable by experts and non-expert users. In our previous work, to address the goal of supporting user-centered explanations that make model recommendations more explainable, we developed an Explanation Ontology (EO). The EO is a general-purpose representation that was designed to help system designers connect explanations to their underlying data and knowledge. This paper addresses the apparent need for improved interoperability to support a wider range of use cases. We expand the EO, mainly in the system attributes contributing to explanations, by introducing new classes and properties to support a broader range of state-of-the-art explainer models. We present the expanded ontology model, highlighting the classes and properties that are important to model a larger set of fifteen literature-backed explanation types that are supported within the expanded EO. We build on these explanation type descriptions to show how to utilize the EO model to represent explanations in five use cases spanning the domains of finance, food, and healthcare. We include competency questions that evaluate the EO’s capabilities to provide guidance for system designers on how to apply our ontology to their own use cases. This guidance includes allowing system designers to query the EO directly and providing them exemplar queries to explore content in the EO represented use cases. We have released this significantly expanded version of the Explanation Ontology at https://purl.org/heals/eo and updated our resource website, https://tetherless-world.github.io/explanation-ontology, with supporting documentation. Overall, through the EO model, we aim to help system designers be better informed about explanations and support these explanations that can be composed, given their systems’ outputs from various AI models, including a mix of machine learning, logical and explainer models, and different types of data and knowledge available to their systems.
Initial-state-dependent quantum speed limit for dissipative state preparation: Framework and optimization

Dissipation has traditionally been considered a hindrance to quantum information processing, but recent studies have shown that it can be harnessed to generate desired quantum states. To be useful for practical applications, the ability to speed up the dissipative evolution is crucial. In this study, we focus on a Markovian dissipative-state-preparation scheme in which the prepared state is one of the energy eigenstates. We derive an initial-state-dependent quantum speed limit (QSL) that offers a more refined measure of the actual evolution time compared to the commonly used initial-state-independent relaxation time. This allows for passive optimization of dissipative evolution across different initial states. By minimizing the dissipated heat during the preparation process, conditioned on the minimization of evolution time using the QSL, we find that the preferred initial state has a specific permutation of diagonal elements with respect to an ordered energy eigenbasis of increasing eigenvalues. In this configuration, the population on the prepared state is the largest, and the remaining diagonal elements are sorted in an order resembling that of a passive state in the same ordered energy eigenbasis. We demonstrate the effectiveness of our strategy in a dissipative Rydberg-atom system for preparing the Bell state. Our work provides insights into the optimization of dissipative-state-preparation processes and could have significant implications for practical quantum technologies.
M3GAT: A Multi-modal, Multi-task Interactive Graph Attention Network for Conversational Sentiment Analysis and Emotion Recognition

Sentiment and emotion, which correspond to long-term and short-lived human feelings, are closely linked to each other, leading to the fact that sentiment analysis and emotion recognition are also two interdependent tasks in natural language processing (NLP). One task often leverages the shared knowledge from another task and performs better when solved in a joint learning paradigm. Conversational context dependency, multi-modal interaction, and multi-task correlation are three key factors that contribute to this joint paradigm. However, none of the recent approaches have considered them in a unified framework. To fill this gap, we propose a multi-modal, multi-task interactive graph attention network, termed M3GAT, to simultaneously solve the three problems. At the heart of the model is a proposed interactive conversation graph layer containing three core sub-modules, which are: (1) local-global context connection for modeling both local and global conversational context, (2) cross-modal connection for learning multi-modal complementary and (3) cross-task connection for capturing the correlation across two tasks. Comprehensive experiments on three benchmarking datasets, MELD, MEISD, and MSED, show the effectiveness of M3GAT over state-of-the-art baselines with the margin of 1.88%, 5.37%, and 0.19% for sentiment analysis, and 1.99%, 3.65%, and 0.13% for emotion recognition, respectively. In addition, we also show the superiority of multi-task learning over the single-task framework.
User-Defined Hand Gesture Interface to Improve User Experience of Learning American Sign Language

Sign language can make possible effective communication between hearing and deaf-mute people. Despite years of extensive pedagogical research, learning sign language remains a formidable task, with the majority of the current systems relying extensively on online learning resources, presuming that users would regularly access them; yet, this approach can feel monotonous and repetitious. Recently, gamification has been proposed as a solution to the problem, however, the research focus is on game design, rather than user experience design. In this work, we present a system for user-defined interaction for learning static American Sign Language (ASL), supporting gesture recognition for user experience design, and enabling users to actively learn through involvement with user-defined gestures, rather than just passively absorbing knowledge. Early findings from a questionnaire-based survey show that users are more motivated to learn static ASL through user-defined interactions.
GEMVS: a novel approach for automatic 3D reconstruction from uncalibrated multi-view Google Earth images using multi-view stereo and projective to metric 3D homography transformation

This paper proposes a novel approach for automatic 3D surface reconstruction from uncalibrated and multi-view Google Earth images by using a multi-view stereo method and 3D projective to metric transformation. Without the Rational Polynomial Coefficients, it is impossible to obtain the metric reconstruction of the 3D surface from multi-view satellite images. We solve the uncalibrated multi-view satellite image problem by employing a multi-view stereo vision technique followed by a projective to metric transformation. The virtual pose parameters of the satellite images are obtained by using COLMAP, and the virtual 3D projective reconstruction is done by using EnSoft3D. For projective to metric transformation, we propose to employ 3D homography transformation. Eight 3D correspondence pairs on the viewing frustums between the virtual reference camera and the ideal nadir camera are used to derive a 3D homography matrix. Using the 3D homography matrix, we finally obtain the metric reconstruction of 3D surface up to an unknown height scale in a reference coordinate system of the Google Earth desktop software. Experiments are done in several world locations on Google Earth including building and vegetation areas. Reconstruction error analysis with the Data Fusion Contest 19 dataset is also presented. The average of MAE and RMSE of five tile regions in the dataset are 1.596 m and 2.083 m, respectively.
Differentiable Histogram Loss Functions for Intensity-based Image-to-Image Translation

We introduce the HueNet - a novel deep learning framework for a differentiable construction of intensity (1D) and joint (2D) histograms and present its applicability to paired and unpaired image-to-image translation problems. The key idea is an innovative technique for augmenting a generative neural network by histogram layers appended to the image generator. These histogram layers allow us to define two new histogram-based loss functions for constraining the structural appearance of the synthesized output image and its color distribution. Specifically, the color similarity loss is defined by the Earth Mover's Distance between the intensity histograms of the network output and a color reference image. The structural similarity loss is determined by the mutual information between the output and a content reference image based on their joint histogram. Although the HueNet can be applied to a variety of image-to-image translation problems, we chose to demonstrate its strength on the tasks of color transfer, exemplar-based image colorization, and edges <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\to$</tex-math></inline-formula> photo, where the colors of the output image are predefined. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/mor-avi-aharon-bgu/HueNet.git</uri>
Coherent category training enhances generalization in prototype-based categories.

A major question for the study of learning and memory is how to tailor learning experiences to promote knowledge that generalizes to new situations. In two experiments, we used category learning as a representative domain to test two factors thought to influence the acquisition of conceptual knowledge: the number of training examples (set size) and the similarity of training examples to the category average (set coherence). Across participants, size and coherence of category training sets were varied in a fully crossed design. After training, participants demonstrated the breadth of their category knowledge by categorizing novel examples varying in their distance from the category center. Results showed better generalization following more coherent training sets, even when categorizing items furthest from the category center. Training set size had limited effects on performance. We also tested the types of representations underlying categorization decisions by fitting formal prototype and exemplar models. Prototype models posit abstract category representations based on the category's central tendency, whereas exemplar models posit that categories are represented by individual category members. In Experiment 1, low coherence training led to fewer participants relying on prototype representations, except when training length was extended. In Experiment 2, low coherence training led to chance performance and no clear representational strategy for nearly half of the participants. The results indicate that highlighting commonalities among exemplars during training facilitates learning and generalization and may also affect the types of concept representations that individuals form. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Color-Unrelated Head-Shoulder Networks for Fine-Grained Person Re-identification

Person re-identification (re-id) attempts to match pedestrian images with the same identity across non-overlapping cameras. Existing methods usually study person re-id by learning discriminative features based on the clothing attributes (e.g., color, texture). However, the clothing appearance is not sufficient to distinguish different persons especially when they are in similar clothes, which is known as the fine-grained (FG) person re-id problem. By contrast, this paper proposes to exploit the color-unrelated feature along with the head-shoulder feature for FG person re-id. Specifically, a color-unrelated head-shoulder network (CUHS) is developed, which is featured in three aspects: (1) It consists of a lightweight head-shoulder segmentation layer for localizing the head-shoulder region and learning the corresponding feature. (2) It exploits instance normalization (IN) for learning color-unrelated features. (3) As IN inevitably reduces inter-class differences, we propose to explore richer visual cues for IN by an attention exploration mechanism to ensure high discrimination. We evaluate our model on the FG-reID, Market1501, and DukeMTMC-reID datasets, and the results show that CUHS surpasses previous methods on both the FG and conventional person re-id problems.