Reviewerly/exHarmony huggingface dataset:
A Novel Hybrid Grey Wolf Optimizer and Its Application to Feature Selection

As one of important methods to reduce the dimensions of high-dimensional data, feature selection aims to search the most effective feature subset from the original dataset. Grey wolf optimizer (GWO) is a effective swarm intelligence optimization algorithm, which has been applied to feature selection. But it may fall into local optima during the iterations. To this end, a novel feature selection algorithm based on hybrid grey wolf optimizer called HIGWO is proposed. Chaos theory is introduced into HIGWO to generate a higher quality initial population. In addition, a nonlinear parameter adjustment strategy is used to update the control parameter so as to achieve a better balance between exploration and exploitation. In order to vividly reflect the social dominant hierarchy of the pack, the adaptive decision weight coefficients are added into the position updating strategy. Salp algorithm is cooperated GWO to separately update the position of Alpha wolf to improve the influence of the dominant wolf. Finally, the greedy selection mechanism is adopted to ensure that a better solution would be obtained after the current iteration. The experimental results on the real datasets of UCI prove that HIGWO can effectively reduce the data dimension and obtain better classification performance.
Evaluation of the Design Exploration of a Binarized Neural Network for FPGA using HDLRuby

This paper presents an example of design exploration using the hardware description language HDLRuby of a binarized neural network (BNN) targeting a FPGA. It took 21 hours for describing and testing down to the FPGA mapping a BNN circuit that is customized directly from the result of offline software training. The description in HDLRuby has a total of 714 lines of code for any possible BNN architecture, while the same hardware reimplemented in Verilog HDL code requires 1107 lines of code for a single BNN architecture. Mapped on a FPGA, the BNN with an optimal structure of one inner layer with 256 neurons succeeded in computing one result per cycle, for a power consumption lower than 6.5W.
Adversarial Detector with Robust Classifier

Deep neural network (DNN) models are wellknown to easily misclassify prediction results by using input images with small perturbations, called adversarial examples. In this paper, we propose a novel adversarial detector, which consists of a robust classifier and a plain one, to highly detect adversarial examples. The proposed adversarial detector is carried out in accordance with the logits of plain and robust classifiers. In an experiment, the proposed detector is demonstrated to outperform a state-of-the-art detector without any robust classifier.
Tracking and Planning with Spatial World Models

We introduce a method for real-time navigation and tracking with differentiably rendered world models. Learning models for control has led to impressive results in robotics and computer games, but this success has yet to be extended to vision-based navigation. To address this, we transfer advances in the emergent field of differentiable rendering to model-based control. We do this by planning in a learned 3D spatial world model, combined with a pose estimation algorithm previously used in the context of TSDF fusion, but now tailored to our setting and improved to incorporate agent dynamics. We evaluate over six simulated environments based on complex human-designed floor plans and provide quantitative results. We achieve up to 92% navigation success rate at a frequency of 15 Hz using only image and depth observations under stochastic, continuous dynamics.
Realizable Learning is All You Need

The equivalence of realizable and agnostic learnability is a fundamental phenomenon in learning theory. With variants ranging from classical settings like PAC learning and regression to recent trends such as adversarially robust learning, it's surprising that we still lack a unified theory; traditional proofs of the equivalence tend to be disparate, and rely on strong model-specific assumptions like uniform convergence and sample compression. In this work, we give the first model-independent framework explaining the equivalence of realizable and agnostic learnability: a three-line blackbox reduction that simplifies, unifies, and extends our understanding across a wide variety of settings. This includes models with no known characterization of learnability such as learning with arbitrary distributional assumptions and more general loss functions, as well as a host of other popular settings such as robust learning, partial learning, fair learning, and the statistical query model. More generally, we argue that the equivalence of realizable and agnostic learning is actually a special case of a broader phenomenon we call property generalization: any desirable property of a learning algorithm (e.g. noise tolerance, privacy, stability) that can be satisfied over finite hypothesis classes extends (possibly in some variation) to any learnable hypothesis class.
Cyclotomic and abelian points in backward orbits of rational functions

We prove several results on backward orbits of rational functions over number fields. First, we show that if $K$ is a number field, $\phi\in K(x)$ and $\alpha\in K$ then the extension of $K$ generated by the abelian points in the backward orbit of $\alpha$ is ramified only at finitely many primes. This has the immediate strong consequence that if all points in the backward orbit of $\alpha$ are abelian then $\phi$ is post-critically finite. We use this result to prove two facts: on the one hand, if $\phi\in \mathbb Q(x)$ is a quadratic rational function not conjugate over $\mathbb Q^{\text{ab}}$ to a power or a Chebyshev map and all preimages of $\alpha$ are abelian, we show that $\phi$ is $\mathbb Q$-conjugate to one of two specific quadratic functions, in the spirit of a recent conjecture of Andrews and Petsche. On the other hand we provide conditions on a quadratic rational function in $K(x)$ for the backward orbit of a point $\alpha$ to only contain finitely many cyclotomic preimages, extending previous results of the second author. Finally, we give necessary and sufficient conditions for a triple $(\phi,K,\alpha)$, where $\phi$ is a Latt\`es map over a number field $K$ and $\alpha\in K$ for the whole backward orbit of $\alpha$ to only contain abelian points.
MDMMT-2: Multidomain Multimodal Transformer for Video Retrieval, One More Step Towards Generalization

In this work we present a new State-of-The-Art on the text-to-video retrieval task on MSR-VTT, LSMDC, MSVD, YouCook2 and TGIF obtained by a single model. Three different data sources are combined: weakly-supervised videos, crowd-labeled text-image pairs and text-video pairs. A careful analysis of available pre-trained networks helps to choose the best prior-knowledge ones. We introduce three-stage training procedure that provides high transfer knowledge efficiency and allows to use noisy datasets during training without prior knowledge degradation. Additionally, double positional encoding is used for better fusion of different modalities and a simple method for non-square inputs processing is suggested.
Leveraging pre-trained language models for conversational information seeking from text

Recent advances in Natural Language Processing, and in particular on the construction of very large pre-trained language representation models, is opening up new perspectives on the construction of conversational information seeking (CIS) systems. In this paper we investigate the usage of in-context learning and pre-trained language representation models to address the problem of information extraction from process description documents, in an incremental question and answering oriented fashion. In particular we investigate the usage of the native GPT-3 (Generative Pre-trained Transformer 3) model, together with two in-context learning customizations that inject conceptual definitions and a limited number of samples in a few shot-learning fashion. The results highlight the potential of the approach and the usefulness of the in-context learning customizations, which can substantially contribute to address the "training data challenge" of deep learning based NLP techniques the BPM field. It also highlight the challenge posed by control flow relations for which further training needs to be devised.
From Procedures, Objects, Actors, Components, Services, to Agents -- A  Comparative Analysis of the History and Evolution of Programming Abstractions

The objective of this chapter is to propose some retrospective analysis of the evolution of programming abstractions, from {\em procedures}, {\em objects}, {\em actors}, {\em components}, {\em services}, up to {\em agents}, %have some compare concepts of software component and of agent (and multi-agent system), %The method chosen is to by replacing them within a general historical perspective. Some common referential with three axes/dimensions is chosen: {\em action selection} at the level of one entity, {\em coupling flexibility} between entities, and {\em abstraction level}. We indeed may observe some continuous quest for higher flexibility (through notions such as {\em late binding}, or {\em reification} of {\em connections}) and higher level of {\em abstraction}. Concepts of components, services and agents have some common objectives (notably, {\em software modularity and reconfigurability}), with multi-agent systems raising further concepts of {\em autonomy} and {\em coordination}. notably through the notion of {\em auto-organization} and the use of {\em knowledge}. We hope that this analysis helps at highlighting some of the basic forces motivating the progress of programming abstractions and therefore that it may provide some seeds for the reflection about future programming abstractions.
Curriculum optimization for low-resource speech recognition

Modern end-to-end speech recognition models show astonishing results in transcribing audio signals into written text. However, conventional data feeding pipelines may be sub-optimal for low-resource speech recognition, which still remains a challenging task. We propose an automated curriculum learning approach to optimize the sequence of training examples based on both the progress of the model while training and prior knowledge about the difficulty of the training examples. We introduce a new difficulty measure called compression ratio that can be used as a scoring function for raw audio in various noise conditions. The proposed method improves speech recognition Word Error Rate performance by up to 33% relative over the baseline system
End-to-end Learnable Diversity-aware News Recommendation

Diversity is an important factor in providing high-quality personalized news recommendations. However, most existing news recommendation methods only aim to optimize recommendation accuracy while ignoring diversity. Reranking is a widely used post-processing technique to promote the diversity of top recommendation results. However, the recommendation model is not perfect and errors may be propagated and amplified in a cascaded recommendation algorithm. In addition, the recommendation model itself is not diversity-aware, making it difficult to achieve a good tradeoff between recommendation accuracy and diversity. In this paper, we propose a news recommendation approach named LeaDivRec, which is a fully learnable model that can generate diversity-aware news recommendations in an end-to-end manner. Different from existing news recommendation methods that are usually based on point- or pair-wise ranking, in LeaDivRec we propose a more effective list-wise news recommendation model. More specifically, we propose a permutation Transformer to consider the relatedness between candidate news and meanwhile can learn different representations for similar candidate news to help improve recommendation diversity. We also propose an effective list-wise training method to learn accurate ranking models. In addition, we propose a diversity-aware regularization method to further encourage the model to make controllable diversity-aware recommendations. Extensive experiments on two real-world datasets validate the effectiveness of our approach in balancing recommendation accuracy and diversity.
Framework for Optimal Reliability Design Considering System Usage Patterns Based on Big Data

Purpose: This study presents a framework for the robust reliability design of a multi-functional system by considering various usage patterns of an existing system.<BR>Methods: The framework consists of four stages: 1) identifying the critical component in a multi-functional system, 2) collecting and analyzing data from users, 3) designing for optimal system reliability, and 4) validating system reliability in various operating environments. Moreover, the framework includes failure mode and effects analysis (FMEA), big data on system usage, the redundancy allocation problem (RAP), and the genetic algorithm (GA).<BR>Conclusion: By using a framework that considers the usage pattern of a system, it was possible to achieve target reliability and a robust design for various usage patterns. Compared to the usage patterns experienced by many users in the existing structure, it was confirmed that most users experienced few (or no) failures. In addition, the framework was robust during the verification stage.
Method for Generating Randomly Perturbed Density Operators Subject to Different Sets of Constraints

This paper presents a general method for producing randomly perturbed density operators subject to different sets of constraints. The perturbed density operators are a specified "distance" away from the state described by the original density operator. This approach is applied to a bipartite system of qubits and used to examine the sensitivity of various entanglement measures on the perturbation magnitude. The constraint sets used include constant energy, constant entropy, and both constant energy and entropy. The method is then applied to produce perturbed random quantum states that correspond with those obtained experimentally for Bell states on the IBM quantum device ibmq_manila. The results show that the methodology can be used to simulate the outcome of real quantum devices where noise, which is important both in theory and simulation, is present.
THE USE OF QR-CODES IN PROFESSIONAL TRAINING OF STUDENTS OF ENVIRONMENTAL AND EDUCATIONAL INDUSTRIES

The article emphasizes the significance of the information and digital competence of teachers, which includes the ability to navigate in the information and digital space, the search and critical analysis of information, the use of educational electronic resources and creation of new ones, the use of digital technologies in the educational process. It notes that Digital Competence of Educators the European framework identifies six main areas of application: professional involvement; digital resources; teaching and learning; evaluation; promoting students' digital competence.&#x0D; The article reveals the importance of the m-learning implementation. It is the use of mobile technology in the educational process, the use of a wide range of digital portable mobile devices and applications. The best pedagogical experience in the implementation of mobile learning on the example of the use of QR codes is summarized. The main resources for creating and reading QR codes, examples of their use in educational activities are highlighted.&#x0D; Our own experience of using QR codes in the professional training of future biologists and teachers of natural sciences has been revealed. In particular, it is used in the Museum of Nature of Zhytomyr Ivan Franko State University to expand the museum's capabilities, the application of its exposition for educational excursions, independent work of students, to organize quests and educational games and more. The stages of projects using QR codes are described: the digital guide "Dendrological Secrets of Chaudoir Park" (the mayor grant for gifted youth) and "Digital Dendrological Guide of Zhytomyr" (the project of social action "Strengthening Intersectoral Cooperation for Social Cohesion", co-financed by the European Union and the British Council in Ukraine). Their implementation contributes to the creation of information space for Biology lessons, open-air Ecology, classes of ecological and naturalistic circles, botanical, biogeographical, ecological excursions, development of hiking tourism, etc.
Improving English Writing Performance for High School Students through the Think-Pair-Share Technique

Writing is an important aspect of language instruction since it is a necessary tool for students to develop their understanding of linguistic elements in practice. Nonetheless, when the teacher asked students to compose an English paragraph, several students struggled to put their thoughts into words appropriately. Not only do they struggle with syntax and vocabulary, but they also have trouble identifying and developing ideas. One of the weaknesses of certain students' writing ability is that the execution of concepts is not clear and consistent, and they still rely significantly on sample essays. Accordingly, in this research work, the researcher's purpose is to use the Think-Pair-Share technique to improve high school students' English writing performance. The main objective is to build a procedure for creating and evaluating the Think-Pair-Share (TPS) Technique, to assess the impact of the TPS on students' writing performance, and to collect feedback from them on the benefits and drawbacks of the TPS technique, as well as possible solutions to promote the use of TPS technique. The experimental research design was utilized to begin the study, along with many questionnaires and expert approaches. A total of 40 students were chosen at random, half of them were assigned to the experimental group (TPS technique) and the remainder to the control group (no TPS technique). All of the students were required to complete a writing pre-and post-test, but only the experimental group were required to complete the post-questionnaire. The findings show that using the TPS technique in the classroom can result in considerable improvements in writing performance. In addition, all of the students gave excellent feedback. Furthermore, the study offers some recommendations for eliminating the drawbacks of applying TPS in order to enhance the technique's potential and make it significantly more fascinating and useful.
Reconstructing Training Data from Diverse ML Models by Ensemble Inversion

Model Inversion (MI), in which an adversary abuses access to a trained Machine Learning (ML) model attempting to infer sensitive information about its original training data, has attracted increasing research attention. During MI, the trained model under attack (MUA) is usually frozen and used to guide the training of a generator, such as a Generative Adversarial Network (GAN), to reconstruct the distribution of the original training data of that model. This might cause leakage of original training samples, and if successful, the privacy of dataset subjects will be at risk if the training data contains Personally Identifiable Information (PII). Therefore, an in-depth investigation of the potentials of MI techniques is crucial for the development of corresponding defense techniques. High-quality reconstruction of training data based on a single model is challenging. However, existing MI literature does not explore targeting multiple models jointly, which may provide additional information and diverse perspectives to the adversary. We propose the ensemble inversion technique that estimates the distribution of original training data by training a generator constrained by an ensemble (or set) of trained models with shared subjects or entities. This technique leads to noticeable improvements of the quality of the generated samples with distinguishable features of the dataset entities compared to MI of a single ML model. We achieve high quality results without any dataset and show how utilizing an auxiliary dataset that's similar to the presumed training data improves the results. The impact of model diversity in the ensemble is thoroughly investigated and additional constraints are utilized to encourage sharp predictions and high activations for the reconstructed samples, leading to more accurate reconstruction of training images.
A Comparative Study on Language Models for Task-Oriented Dialogue Systems

The recent development of language models has shown promising results by achieving state-of-the-art performance on various natural language tasks by fine-tuning pretrained models. In task-oriented dialogue (ToD) systems, language models can be used for end-to-end training without relying on dialogue state tracking to track the dialogue history but allowing the language models to generate responses according to the context given as input. This paper conducts a comparative study to show the effectiveness and strength of using recent pretrained models for fine-tuning, such as BART and T5, on endto-end ToD systems. The experimental results show substantial performance improvements after language model fine-tuning. The models produce more fluent responses after adding knowledge to the context that guides the model to avoid hallucination and generate accurate entities in the generated responses. Furthermore, we found that BART and T5 outperform GPT-based models in BLEU and F1 scores and achieve state-of-the-art performance in a ToD system.
Deep Hierarchical Semantic Segmentation

Humans are able to recognize structured relations in observation, allowing us to decompose complex scenes into simpler parts and abstract the visual world in multiple levels. However, such hierarchical reasoning ability of human perception remains largely unexplored in current literature of semantic segmentation. Existing work is often aware of flatten labels and predicts target classes exclusively for each pixel. In this paper, we instead address hierarchical semantic segmentation (HSS), which aims at structured, pixel-wise description of visual observation in terms of a class hierarchy. We devise HSSN, a general HSS framework that tackles two critical issues in this task: i) how to efficiently adapt existing hierarchy-agnostic segmentation networks to the HSS setting, and ii) how to leverage the hierarchy information to regularize HSS network learning. To address i), HSSN directly casts HSS as a pixel-wise multi-label classification task, only bringing minimal architecture change to current segmentation models. To solve ii), HSSN first explores inherent properties of the hierarchy as a training objective, which enforces segmentation predictions to obey the hierarchy structure. Further, with hierarchy-induced margin constraints, HSSN reshapes the pixel embedding space, so as to generate well-structured pixel representations and improve segmentation eventually. We conduct experiments on four semantic segmentation datasets (i.e., Mapillary Vistas 2.0, Cityscapes, LIP, and PASCAL-Person-Part), with different class hierarchies, segmentation network architectures and backbones, showing the generalization and superiority of HSSN.
InterpretTime: a new approach for the systematic evaluation of neural-network interpretability in time series classification

We present a novel approach to evaluate the performance of interpretability methods for time series classification, and propose a new strategy to assess the similarity between domain experts and machine data interpretation. The novel approach leverages a new family of synthetic datasets and introduces new interpretability evaluation metrics. The approach addresses several common issues encountered in the literature, and clearly depicts how well an interpretability method is capturing neural network's data usage, providing a systematic interpretability evaluation framework. The new methodology highlights the superiority of Shapley Value Sampling and Integrated Gradients for interpretability in time-series classification tasks.