Reviewerly/exHarmony huggingface dataset:
Feature-Adaptive Interactive Thresholding of Large 3D Volumes

Thresholding is the most widely used segmentation method in volumetric image processing, and its pointwise nature makes it attractive for the fast handling of large three-dimensional samples. However, global thresholds often do not properly extract components in the presence of artifacts, measurement noise or grayscale value fluctuations. This paper introduces Feature-Adaptive Interactive Thresholding (FAITH), a thresholding technique that incorporates (geometric) features, local processing and interactive user input to overcome these limitations. Given a global threshold suitable for most regions, FAITH uses interactively selected seed voxels to identify critical regions in which that threshold will be adapted locally on the basis of features computed from local environments around these voxels. The combination of domain expert knowledge and a rigorous mathematical model thus enables a very exible way of local thresholding with intuitive user interaction. A qualitative analysis shows that the proposed model is able to overcome limitations typically occuring in plain thresholding while staying efficient enough to also allow the segmentation of big volumes.
Contrastive Psudo-supervised Classification for Intra-Pulse Modulation of Radar Emitter Signals Using data augmentation

The automatic classification of radar waveform is a fundamental technique in electronic countermeasures (ECM).Recent supervised deep learning-based methods have achieved great success in a such classification task.However, those methods require enough labeled samples to work properly and in many circumstances, it is not available.To tackle this problem, in this paper, we propose a three-stages deep radar waveform clustering(DRSC) technique to automatically group the received signal samples without labels.Firstly, a pretext model is trained in a self-supervised way with the help of several data augmentation techniques to extract the class-dependent features.Next,the pseudo-supervised contrastive training is involved to further promote the separation between the extracted class-dependent features.And finally, the unsupervised problem is converted to a semi-supervised classification problem via pseudo label generation. The simulation results show that the proposed algorithm can effectively extract class-dependent features, outperforming several unsupervised clustering methods, even reaching performance on par with the supervised deep learning-based methods.
Rebalanced Zero-shot Learning

Zero-shot learning (ZSL) aims to identify unseen classes with zero samples during training. Broadly speaking, present ZSL methods usually adopt class-level semantic labels and compare them with instance-level semantic predictions to infer unseen classes. However, we find that such existing models mostly produce imbalanced semantic predictions, i.e. these models could perform precisely for some semantics, but may not for others. To address the drawback, we aim to introduce an imbalanced learning framework into ZSL. However, we find that imbalanced ZSL has two unique challenges: (1) Its imbalanced predictions are highly correlated with the value of semantic labels rather than the number of samples as typically considered in the traditional imbalanced learning; (2) Different semantics follow quite different error distributions between classes. To mitigate these issues, we first formalize ZSL as an imbalanced regression problem which offers empirical evidences to interpret how semantic labels lead to imbalanced semantic predictions. We then propose a re-weighted loss termed Re-balanced Mean-Squared Error (ReMSE), which tracks the mean and variance of error distributions, thus ensuring rebalanced learning across classes. As a major contribution, we conduct a series of analyses showing that ReMSE is theoretically well established. Extensive experiments demonstrate that the proposed method effectively alleviates the imbalance in semantic prediction and outperforms many state-of-the-art ZSL methods. Our code is available at https://github.com/FouriYe/ReZSL-TIP23.
Experimenting with Selected Automated Approaches for Bias Analysis

This work first presents our attempts to establish an automated model using state-of-the-art approaches for analysing bias in search results of Bing and Google. Experimental results indicate that the current class-wise F1-scores of our best model are not sufficient to establish an automated model for bias analysis. Thus, we decided not to continue with this approach.
"Quantum supremacy" revisited: Low-complexity, deterministic solutions of the original Deutsch-Jozsa problem in classical physical systems

The original Deutsch-Jozsa (oDJ) problem is for an oracle (realized here as a database) of size N, where, according to their claim, the deterministic solution of the problem on a classical Turing computer requires O(N) computational complexity. They produced the famous Deutsch-Jozsa quantum algorithm that offered an exponential speedup over the classical computer, namely O[log(N)] complexity for the solution in a quantum computer. In this paper, the problem is implemented on an instantaneous noise-based logic processor. It is shown that, similarly to the quantum algorithm, the oDJ problem can deterministically be solved with O[log(N)] complexity. The implication is that by adding a truly random coin to a classical Turing machine and using this classical-physical algorithm can also speed up the deterministic solution of the Deutsch-Jozsa problem exponentially, similarly to the quantum algorithm. Then it is realized that the same database and the solution of the Deutsch-Jozsa problem can also be realized by using an identical algorithmic structure in a simpler way, even without noise/random coin. The only lost function in this new system, as compared to noise-based logic, is the ability to do generic parallel logic operations over the whole database. As the latter feature is not needed for the oDJ problem, it is concluded that the problem can be solved on a classical computer with O[log(N)] complexity even without a random coin. Therefore, while the oDJ algorithm is historically important step in the developments of quantum computers, it is insufficient to prove quantum supremacy. Note, there is also simplified Deutsch-Jozsa problem proposed later, which is more popular in the field, however it is irrelevant for the present paper.
CORL: Research-oriented Deep Offline Reinforcement Learning Library

CORL is an open-source library that provides thoroughly benchmarked single-file implementations of both deep offline and offline-to-online reinforcement learning algorithms. It emphasizes a simple developing experience with a straightforward codebase and a modern analysis tracking tool. In CORL, we isolate methods implementation into separate single files, making performance-relevant details easier to recognize. Additionally, an experiment tracking feature is available to help log metrics, hyperparameters, dependencies, and more to the cloud. Finally, we have ensured the reliability of the implementations by benchmarking commonly employed D4RL datasets providing a transparent source of results that can be reused for robust evaluation tools such as performance profiles, probability of improvement, or expected online performance.
MAPL: Parameter-Efficient Adaptation of Unimodal Pre-Trained Models for Vision-Language Few-Shot Prompting

Large pre-trained models have proved to be remarkable zero- and (prompt-based) few-shot learners in unimodal vision and language tasks. We propose MAPL, a simple and parameter-efficient method that reuses frozen pre-trained unimodal models and leverages their strong generalization capabilities in multimodal vision-language (VL) settings. MAPL learns a lightweight mapping between the representation spaces of unimodal models using aligned image-text data, and can generalize to unseen VL tasks from just a few in-context examples. The small number of trainable parameters makes MAPL effective at low-data and in-domain learning. Moreover, MAPL's modularity enables easy extension to other pre-trained models. Extensive experiments on several visual question answering and image captioning benchmarks show that MAPL achieves superior or competitive performance compared to similar methods while training orders of magnitude fewer parameters. MAPL can be trained in just a few hours using modest computational resources and public datasets. We release our code and pre-trained model weights at https://github.com/mair-lab/mapl.
Language Model Decoding as Likelihood-Utility Alignment

A critical component of a successful language generation pipeline is the decoding algorithm. However, the general principles that should guide the choice of a decoding algorithm remain unclear. Previous works only compare decoding algorithms in narrow scenarios, and their findings do not generalize across tasks. We argue that the misalignment between the model's likelihood and the task-specific notion of utility is the key factor to understanding the effectiveness of decoding algorithms. To structure the discussion, we introduce a taxonomy of misalignment mitigation strategies (MMSs), providing a unifying view of decoding as a tool for alignment. The MMS taxonomy groups decoding algorithms based on their implicit assumptions about likelihood--utility misalignment, yielding general statements about their applicability across tasks. Specifically, by analyzing the correlation between the likelihood and the utility of predictions across a diverse set of tasks, we provide empirical evidence supporting the proposed taxonomy and a set of principles to structure reasoning when choosing a decoding algorithm. Crucially, our analysis is the first to relate likelihood-based decoding algorithms with algorithms that rely on external information, such as value-guided methods and prompting, and covers the most diverse set of tasks to date. Code, data, and models are available at https://github.com/epfl-dlab/understanding-decoding.
Flash Flood Disaster Mitigation Through Environmental Education

Flash flood disasters often hit many areas in Indonesia and can generate various losses. These conditions are exacerbated by people’s low knowledge and interest in their environment. Therefore, if people’s environmental education increases, especially their flood disaster mitigation knowledge, they can be prepared and better protect themselves from such disasters. People’s environmental education must start from determining the reason of the flood disaster in their environment to discovering how to avoid the disaster. This research design is a one-group post-test design. The collected data in this research is a written test result about knowledge in flash flood disaster mitigation materials as an implementation in environmental education in the community. The research data results were analyzed by means of simple regression, logistics regression, and correlation. Based on this research, environmental education with disaster mitigation materials has a high understanding level. In the disaster step, the highest understanding is before it happens. The correlation between educational strata and gender on environmental education and disaster mitigation materials is low. Finally, the role of women in disaster mitigation needs to improve because they have a higher understanding than males of disaster mitigation materials.
Extended Missing Data Imputation via GANs for Ranking Applications

We propose Conditional Imputation GAN, an extended missing data imputation method based on Generative Adversarial Networks (GANs). The motivating use case is learning-to-rank, the cornerstone of modern search, recommendation system, and information retrieval applications. Empirical ranking datasets do not always follow standard Gaussian distributions or Missing Completely At Random (MCAR) mechanism, which are standard assumptions of classic missing data imputation methods. Our methodology provides a simple solution that offers compatible imputation guarantees while relaxing assumptions for missing mechanisms and sidesteps approximating intractable distributions to improve imputation quality. We prove that the optimal GAN imputation is achieved for Extended Missing At Random (EMAR) and Extended Always Missing At Random (EAMAR) mechanisms, beyond the naive MCAR. Our method demonstrates the highest imputation quality on the open-source Microsoft Research Ranking (MSR) Dataset and a synthetic ranking dataset compared to state-of-the-art benchmarks and across various feature distributions. Using a proprietary Amazon Search ranking dataset, we also demonstrate comparable ranking quality metrics for ranking models trained on GAN-imputed data compared to ground-truth data.
Hiding Data Hiding

Data hiding is the art of hiding secret data into a cover object such as digital image for covert communication. In this paper, we make the first step towards hiding ``data hiding'', which is totally different from many conventional works that directly embed secret data in a given cover object. In detail, we propose a novel method to disguise data hiding tools, including a data embedding tool and a data extraction tool, as a deep neural network (DNN) with an ordinary task (i.e., style transfer). After training the DNN for both style transfer and data hiding, while the DNN can transfer the style of an image to the target one, it can also hide secret data into a cover image or extract secret data from a stego image. In other words, the tools of data hiding are hidden to avoid arousing suspicion. Experimental results and analysis have shown the feasibility, applicability and superiority of the proposed method.
Training very large scale nonlinear SVMs using Alternating Direction Method of Multipliers coupled with the Hierarchically Semi-Separable kernel approximations

Typically, nonlinear Support Vector Machines (SVMs) produce significantly higher classification quality when compared to linear ones but, at the same time, their computational complexity is prohibitive for large-scale datasets: this drawback is essentially related to the necessity to store and manipulate large, dense and unstructured kernel matrices. Despite the fact that at the core of training a SVM there is a \textit{simple} convex optimization problem, the presence of kernel matrices is responsible for dramatic performance reduction, making SVMs unworkably slow for large problems. Aiming to an efficient solution of large-scale nonlinear SVM problems, we propose the use of the \textit{Alternating Direction Method of Multipliers} coupled with \textit{Hierarchically Semi-Separable} (HSS) kernel approximations. As shown in this work, the detailed analysis of the interaction among their algorithmic components unveils a particularly efficient framework and indeed, the presented experimental results demonstrate a significant speed-up when compared to the \textit{state-of-the-art} nonlinear SVM libraries (without significantly affecting the classification accuracy).
See Clicks Differently: Modeling User Clicking Alternatively with Multi Classifiers for CTR Prediction

Many recommender systems optimize click through rates (CTRs) as one of their core goals, and it further breaks down to predicting each item's click probability for a user (user-item click probability) and recommending the top ones to this particular user. User-item click probability is then estimated as a single term, and the basic assumption is that the user has different preferences over items. This is presumably true, but from real-world data, we observe that some people are naturally more active in clicking on items while some are not. This intrinsic tendency contributes to their user-item click probabilities. Besides this, when a user sees a particular item she likes, the click probability for this item increases due to this user-item preference.
<scp>FeReD:</scp> Federated Reinforcement Learning in the DBMS

Federated learning enables clients to enrich their locally trained models via updates performed by a coordination server based on aggregates of local models. There are multiple advances in methods and applications of federated learning, in particular in cross-device federation, where clients having limited data and computational resources collaborate in a joint learning problem. Given the constraint of limited resources in cross-device federation, we study the potential benefits of embedded in-DBMS learning, illustrated in a federated reinforcement learning problem. We demonstrate FeReD, a system that contrasts the performance of cross-device federation using Q-learning, a popular reinforcement learning algorithm. FeReD offers step-by-step guidance for in-DBMS SQLite implementation challenges for both horizontal and vertical data partitioning. FeReD also allows to contrast the Q-learning implementations in SQLite vs a standard Python implementation, by highlighting their learning performance, computational efficiency, succinctness and expressiveness. A video of FeReD is available at https://www.youtube.com/watch?v=2kRIu_C5RZA and its open source code at https://github.com/sotostzam/FeReD.
Study on the Challenges faced by Women Working Educational Managers

The education sector in Pakistan is facing various challenges, one of which is the under-representation of women in positions of educational management. Underrepresentation of women in positions of educational leadership has been all around as perceived in the writing of the United States and Europe (Lopez, 2008) just as in Asian settings (Shamsodin, Saeed &amp; Shahla, 2012). This socio-culturalcation level. Further settings his study also explored the internal and external barriers faced by women within their cultures and environments. The objectives of the study were to: examine the role of gender in shaping female perception for pursuing educational management position, explore the nature of household responsibilities being performed by women working in educational management, identify the influence of work environment on the performance of female managers, inspect the influence of socioeconomic background and socio cultural beliefs to women pursuing educational management related posts. This study utilized a mixed method approach. All the females on managerial position in Islamabad (International Islamic University Islamabad) and Rawalpindi (Fatima Jinnah Women University Rawalpindi) was identified as population /sample size and universal sampling technique was used. In this study, the researcher administered self-developed questionnaire and semi structured interview guide for collecting data from the participants. Researcher personally visited all the selected universities for data collection. Frequency and percentages was used to analyse the data collected from the questionnaire. Qualitative data were analysed through thematic analysis. It is concluded that need-based regular training program for improvement of women leadership.
Sequence-Driven Analytics and Prediction

Existing algorithms for sequential patterns do not address data heterogeneity. These algorithms can mine unusual patterns in the database and use them for the prediction. Traditional mining methods ignore pattern usefulness concerning heterogeneity and uncertainty. Some patterns may often exist in the database but may not add to its overall usefulness. Due to the changing database, this field can undertake more effective mining and prediction. Data security has become a priority with more data and user privacy problems. Laws and regulations help develop a safe data society, but performing pattern analysis without sharing data presents a new problem. Now, legally mandated data protection rules create issues with new data regulations and laws that require more attention. First, we have investigated conventional sequential mining methods that can improve effectiveness and efficiency in a heterogeneous environment. Second, we investigate learning and evolution methods for privacy protection. In the following, we briefly describe the insights we gained by publishing the defined research.
Using blended learning to help EFL students to reach B1 level

This research studies the implementation of blended learning in the case of a third semester group of students of English Language Teaching Bachelor Degree. The study was held in the State University of Chiapas, specifically in the Language School of Tapachula. These students were invited to participate in this project in order to determine the efficacy of the blended learning in the preparation for the PET. The methodology used in this research was an experimental method applied from a quantitative-descriptive approach. The subjects of the study were BA in ELT students of the Language School of Tapachula. Two PET exams were applied, one before the study and another at the end of the study. In the middle of the research, the students received both, face to face and online sessions along with questionnaires, surveys and interviews. This research affirms that blended learning offers several benefits for those students who show commitment with their learning goals, however, some students did not show any progress in most of the cases due to their lack of dedication.
Influence of Information Sharing on Performance of Manufacturing Firms in Kenya

Information has emerged as a key resource that improves manufacturing companies' performance and helps a company efficiently satisfy consumers' expectations. The goal of the study was to ascertain how information sharing affected the performance of manufacturing companies. The study was backed by the stakeholder theory and the systems theory. The explanatory research design was used in the study. The sample size was 264 procurement managers from Kenyan manufacturing enterprises, with 766 procurement managers as the target population. Respondents were chosen using stratified, simple random, and purposive sampling methods. The primary data collected using questionnaires. Data analysis was done using descriptive statistics (means, standard deviations) and inferential analysis (linear regression analysis) with the aid of SPSS program. The regression model showed that (R2 =0.276), indicating that the information sharing predicted 27.6% of the firm performance. The information sharing (β=0.417, P=0.000) had positive and significant effect on performance of manufacturing firms. The study concludes that information sharing has significant effect on performance of manufacturing companies in Kenya. It has been determined that the implementation of information sharing, the quality of the shared information, the kind of shared information, and the technology used to communicate this information all affect how much information businesses exchange throughout supply chains. According to the report, industry participants in the manufacturing sector should collaborate closely in order to improve the quality of the information that they share with other providers.
Extracting, Computing and Exploring the Parameters of Statistical Models using R

The recent growth of data science is partly fueled by the ever-growing amount of data and the joint important developments in statistical modeling, with new and powerful models and frameworks becoming accessible to users. Although there exist some generic functions to obtain model summaries and parameters, many package specific modeling functions do not provide such methods to allow users to access such valuable information.