Reviewerly/exHarmony huggingface dataset:
FedPOIRec: Privacy Preserving Federated POI Recommendation with Social Influence

With the growing number of Location-Based Social Networks, privacy preserving location prediction has become a primary task for helping users discover new points-of-interest (POIs). Traditional systems consider a centralized approach that requires the transmission and collection of users' private data. In this work, we present FedPOIRec, a privacy preserving federated learning approach enhanced with features from users' social circles for top-$N$ POI recommendations. First, the FedPOIRec framework is built on the principle that local data never leave the owner's device, while the local updates are blindly aggregated by a parameter server. Second, the local recommenders get personalized by allowing users to exchange their learned parameters, enabling knowledge transfer among friends. To this end, we propose a privacy preserving protocol for integrating the preferences of a user's friends after the federated computation, by exploiting the properties of the CKKS fully homomorphic encryption scheme. To evaluate FedPOIRec, we apply our approach into five real-world datasets using two recommendation models. Extensive experiments demonstrate that FedPOIRec achieves comparable recommendation quality to centralized approaches, while the social integration protocol incurs low computation and communication overhead on the user side.
On the Statistical Benefits of Curriculum Learning

Curriculum learning (CL) is a commonly used machine learning training strategy. However, we still lack a clear theoretical understanding of CL's benefits. In this paper, we study the benefits of CL in the multitask linear regression problem under both structured and unstructured settings. For both settings, we derive the minimax rates for CL with the oracle that provides the optimal curriculum and without the oracle, where the agent has to adaptively learn a good curriculum. Our results reveal that adaptive learning can be fundamentally harder than the oracle learning in the unstructured setting, but it merely introduces a small extra term in the structured setting. To connect theory with practice, we provide justification for a popular empirical method that selects tasks with highest local prediction gain by comparing its guarantees with the minimax rates mentioned above.
False membership rate control in mixture models

The clustering task consists in partitioning elements of a sample into homogeneous groups. Most datasets contain individuals that are ambiguous and intrinsically difficult to attribute to one or another cluster. However, in practical applications, misclassifying individuals is potentially disastrous and should be avoided. To keep the misclassification rate small, one can decide to classify only a part of the sample. In the supervised setting, this approach is well known and referred to as classification with an abstention option. In this paper the approach is revisited in an unsupervised mixture model framework and the purpose is to develop a method that comes with the guarantee that the false membership rate (FMR) does not exceed a pre-defined nominal level $\alpha$. A plug-in procedure is proposed, for which a theoretical analysis is provided, by quantifying the FMR deviation with respect to the target level $\alpha$ with explicit remainder terms. Bootstrap versions of the procedure are shown to improve the performance in numerical experiments.
A Bayesian based Intelligent Troubleshooting System

Troubleshooting systems can bring different benefits in assets management, particularly for service operations, facilitating the diagnosis of problems and faulty components identification. However, these systems are commonly based on rigid computation logic unable to handle uncertainties. In this work, a knowledge-based system exploiting the Bayesian theorem was developed and applied in a troubleshooting tool that relies on human-machine interaction. The required knowledge and the algorithm were analyzed and tested to ensure robustness and self-learning capabilities. Subsequently, the system was implemented in an industrial environment, specifically from a crane manufacturing company. The algorithm is robust to errors and provides the possibility of not answering some questions. However, the system performance is highly dependent on the questions, both in terms of quantity (adequate number compared to possible failures) and quality (effective to discriminate among failures). Indeed, this work shows how the system knowledge enhancement by introducing additional questions can significantly improve the troubleshooting performance. Future developments may involve user-friendliness enhancement and self-learning implementation to add and update questions over time.
Face Detection in Extreme Conditions: A Machine-learning Approach

Face detection in unrestricted conditions has been a trouble for years due to various expressions, brightness, and coloration fringing. Recent studies show that deep learning knowledge of strategies can acquire spectacular performance inside the identification of different gadgets and patterns. This face detection in unconstrained surroundings is difficult due to various poses, illuminations, and occlusions. Figuring out someone with a picture has been popularized through the mass media. However, it's miles less sturdy to fingerprint or retina scanning. The latest research shows that deep mastering techniques can gain mind-blowing performance on those two responsibilities. In this paper, I recommend a deep cascaded multi-venture framework that exploits the inherent correlation among them to boost up their performance. In particular, my framework adopts a cascaded shape with 3 layers of cautiously designed deep convolutional networks that expect face and landmark region in a coarse-to-fine way. Besides, within the gaining knowledge of the procedure, I propose a new online tough sample mining method that can enhance the performance robotically without manual pattern choice.
The Effectiveness of the Three-Dimensional (3D) Design Animation Programme in the Institutes of Higher Learning Malaysia

animation is a new field that has piqued the interest of many students at the university, vocational, and school levels.No comprehensive study was conducted on the framework of effectiveness with regard to reaction, learning, attitude, and overall consequence of the 3D animation programme in the institutes of higher learning.The elements were assessed from the perspective of the stakeholders, who included final-year students, working graduates, academics, and industrial representatives.In this study, a qualitative method was used using the purposive sampling technique via the interview approach.This study included Malaysian public and private institutes of higher learning that offer animation programmes.Reaction, course subjects, duration, and academic expertise were the main concerns expressed by the four groups of respondents.Meanwhile, there was a dearth of understanding and knowledge in this subject in terms of the learning component.The respondents' main topics of discussion were attitude, selfability, skills, and knowledge.The implications of this study contribute to the development of a framework for a guideline on the legislation and implementation of the 3D animation programme in institutions of higher learning in order to improve the programme.Stakeholder input should be considered when assessing the programme's effectiveness to ensure its direction in meeting current needs.
Towards Safe Reinforcement Learning with a Safety Editor Policy

We consider the safe reinforcement learning (RL) problem of maximizing utility with extremely low constraint violation rates. Assuming no prior knowledge or pre-training of the environment safety model given a task, an agent has to learn, via exploration, which states and actions are safe. A popular approach in this line of research is to combine a model-free RL algorithm with the Lagrangian method to adjust the weight of the constraint reward relative to the utility reward dynamically. It relies on a single policy to handle the conflict between utility and constraint rewards, which is often challenging. We present SEditor, a two-policy approach that learns a safety editor policy transforming potentially unsafe actions proposed by a utility maximizer policy into safe ones. The safety editor is trained to maximize the constraint reward while minimizing a hinge loss of the utility state-action values before and after an action is edited. SEditor extends existing safety layer designs that assume simplified safety models, to general safe RL scenarios where the safety model can in theory be arbitrarily complex. As a first-order method, it is easy to implement and efficient for both inference and training. On 12 Safety Gym tasks and 2 safe racing tasks, SEditor obtains much a higher overall safety-weighted-utility (SWU) score than the baselines, and demonstrates outstanding utility performance with constraint violation rates as low as once per 2k time steps, even in obstacle-dense environments. On some tasks, this low violation rate is up to 200 times lower than that of an unconstrained RL method with similar utility performance. Code is available at https://github.com/hnyu/seditor.
Learning Representations with Contrastive Self-Supervised Learning for Histopathology Applications

Unsupervised learning has made substantial progress over the last few years, especially by means of contrastive self-supervised learning. The dominating dataset for benchmarking self-supervised learning has been ImageNet, for which recent methods are approaching the performance achieved by fully supervised training. The ImageNet dataset is however largely object-centric, and it is not clear yet what potential those methods have on widely different datasets and tasks that are not object-centric, such as in digital pathology. While self-supervised learning has started to be explored within this area with encouraging results, there is reason to look closer at how this setting differs from natural images and ImageNet. In this paper we make an in-depth analysis of contrastive learning for histopathology, pin-pointing how the contrastive objective will behave differently due to the characteristics of histopathology data. We bring forward a number of considerations, such as view generation for the contrastive objective and hyper-parameter tuning. In a large battery of experiments, we analyze how the downstream performance in tissue classification will be affected by these considerations. The results point to how contrastive learning can reduce the annotation effort within digital pathology, but that the specific dataset characteristics need to be considered. To take full advantage of the contrastive learning objective, different calibrations of view generation and hyper-parameters are required. Our results pave the way for realizing the full potential of self-supervised learning for histopathology applications.
Error Bounds for a Matrix-Vector Product Approximation with Deep ReLU  Neural Networks

Among the several paradigms of artificial intelligence (AI) or machine learning (ML), a remarkably successful paradigm is deep learning. Deep learning's phenomenal success has been hoped to be interpreted via fundamental research on the theory of deep learning. Accordingly, applied research on deep learning has spurred the theory of deep learning-oriented depth and breadth of developments. Inspired by such developments, we pose these fundamental questions: can we accurately approximate an arbitrary matrix-vector product using deep rectified linear unit (ReLU) feedforward neural networks (FNNs)? If so, can we bound the resulting approximation error? In light of these questions, we derive error bounds in Lebesgue and Sobolev norms that comprise our developed deep approximation theory. Guided by this theory, we have successfully trained deep ReLU FNNs whose test results justify our developed theory. The developed theory is also applicable for guiding and easing the training of teacher deep ReLU FNNs in view of the emerging teacher-student AI or ML paradigms that are essential for solving several AI or ML problems in wireless communications and signal processing; network science and graph signal processing; and network neuroscience and brain physics.
Regularization by Misclassification in ReLU Neural Networks

We study the implicit bias of ReLU neural networks trained by a variant of SGD where at each step, the label is changed with probability $p$ to a random label (label smoothing being a close variant of this procedure). Our experiments demonstrate that label noise propels the network to a sparse solution in the following sense: for a typical input, a small fraction of neurons are active, and the firing pattern of the hidden layers is sparser. In fact, for some instances, an appropriate amount of label noise does not only sparsify the network but further reduces the test error. We then turn to the theoretical analysis of such sparsification mechanisms, focusing on the extremal case of $p=1$. We show that in this case, the network withers as anticipated from experiments, but surprisingly, in different ways that depend on the learning rate and the presence of bias, with either weights vanishing or neurons ceasing to fire.
Properties of determinantal polynomials of subspaces of matrices over a  finite field

Let K be a field and let M_n(K) denote the space of n x n matrices with entries in K. Let M be a subspace of M_n(K) of dimension d with the property that there are elements in M with non-zero determinant. Given a basis of M, we define the determinantal polynomial P_{M} of M with respect to the basis. It is a homogeneous polynomial of degree n in d indeterminates that gives the determinant of any element of M by evaluation in K^d. This paper investigates the interrelationship of M and P_{M}. We confine ourselves to finite fields K, where we can obtain useful information by applying the Lang-Weil theorem on the number of zeros of absolutely irreducible polynomials. A combination of Chevalley's theorem on the zeros of polynomials in several variables and the Lang-Weil theorem leads to theorems about the characteristic polynomials of elements of M when n is a prime. We also draw attention to cases when the elements of M with non-zero determinant are a proper subspace, and provide non-trivial examples of this phenomenon.
DB-BERT: a Database Tuning Tool that "Reads the Manual"

DB-BERT is a database tuning tool that exploits information gained via natural language analysis of manuals and other relevant text documents. It uses text to identify database system parameters to tune as well as recommended parameter values. DB-BERT applies large, pre-trained language models (specifically, the BERT model) for text analysis. During an initial training phase, it fine-tunes model weights in order to translate natural language hints into recommended settings. At run time, DB-BERT learns to aggregate, adapt, and prioritize hints to achieve optimal performance for a specific database system and benchmark. Both phases are iterative and use reinforcement learning to guide the selection of tuning settings to evaluate (penalizing settings that the database system rejects while rewarding settings that improve performance). In our experiments, we leverage hundreds of text documents about database tuning as input for DB-BERT. We compare DB-BERT against various baselines, considering different benchmarks (TPC-C and TPC-H), metrics (throughput and run time), as well as database systems (Postgres and MySQL). In all cases, DB-BERT finds the best parameter settings among all compared methods. The code of DB-BERT is available online at https://itrummer.github.io/dbbert/.
Fast Object Extraction and Euler Number on Block Represented Images

The identification of objects in binary images is a fundamental task in image analysis and pattern recognition tasks.The Euler number of a binary image is an important topological measure which is used as a feature in image analysis.In this paper, a very fast algorithm for the detection and localization of the objects and the computation of the Euler number of a binary image is proposed.The proposed algorithm operates in one scan of the image and is based on the Image Block Representation (IBR) scheme.The proposed algorithm is more efficient than conventional pixel based algorithms in terms of execution speed and representation of the extracted information.
Using Conversational Artificial Intelligence to Support Children's Search in the Classroom

We present pathways of investigation regarding conversational user interfaces (CUIs) for children in the classroom. We highlight anticipated challenges to be addressed in order to advance knowledge on CUIs for children. Further, we discuss preliminary ideas on strategies for evaluation.
Scene Graph Generation: A Comprehensive Survey

Deep learning techniques have led to remarkable breakthroughs in the field of generic object detection and have spawned a lot of scene-understanding tasks in recent years. Scene graph has been the focus of research because of its powerful semantic representation and applications to scene understanding. Scene Graph Generation (SGG) refers to the task of automatically mapping an image into a semantic structural scene graph, which requires the correct labeling of detected objects and their relationships. Although this is a challenging task, the community has proposed a lot of SGG approaches and achieved good results. In this paper, we provide a comprehensive survey of recent achievements in this field brought about by deep learning techniques. We review 138 representative works that cover different input modalities, and systematically summarize existing methods of image-based SGG from the perspective of feature extraction and fusion. We attempt to connect and systematize the existing visual relationship detection methods, to summarize, and interpret the mechanisms and the strategies of SGG in a comprehensive way. Finally, we finish this survey with deep discussions about current existing problems and future research directions. This survey will help readers to develop a better understanding of the current research status and ideas.
Data Containers

MATLAB and Python have many powerful data containers. MATLAB’s primary containers are the matrix, cell array, and struct, while Python mainstays are lists, dictionaries, and, for the intended audience of this book, NumPy arrays. A fourth key container in the Python landscape is the Pandas dataframe which corresponds most closely to a MATLAB table. Tables and dataframes are covered in Chapter 13 .
Image Denoising with Control over Deep Network Hallucination

Deep image denoisers achieve state-of-the-art results but with a hidden cost.As witnessed in recent literature, these deep networks are capable of overfitting their training distributions, causing inaccurate hallucinations to be added to the output and generalizing poorly to varying data.For better control and interpretability over a deep denoiser, we propose a novel framework exploiting a denoising network.We call it controllable confidence-based image denoising (CCID).In this framework, we exploit the outputs of a deep denoising network alongside an image convolved with a reliable filter.Such a filter can be a simple convolution kernel which does not risk adding hallucinated information.We propose to fuse the two components with a frequencydomain approach that takes into account the reliability of the deep network outputs.With our framework, the user can control the fusion of the two components in the frequency domain.We also provide a user-friendly map estimating spatially the confidence in the output that potentially contains network hallucination.Results show that our CCID not only provides more interpretability and control, but can even outperform both the quantitative performance of the deep denoiser and that of the reliable filter, especially when the test data diverge from the training data.
Investigating the Use of One-Class Support Vector Machine for Software Defect Prediction

Defect prediction aims at identifying software components that are likely to cause faults before a software is made available to the end-user. To date, this task has been modeled as a two-class classification problem, however its nature also allows it to be formulated as a one-class classification task. Previous studies show that One-Class Support Vector Machine (OCSVM) can outperform two-class classifiers for within-project defect prediction, however it is not effective when employed at a finer granularity (i.e., commit-level defect prediction). In this paper, we further investigate whether learning from one class only is sufficient to produce effective defect prediction model in two other different scenarios (i.e., granularity), namely cross-version and cross-project defect prediction models, as well as replicate the previous work at within-project granularity for completeness. Our empirical results confirm that OCSVM performance remain low at different granularity levels, that is, it is outperformed by the two-class Random Forest (RF) classifier for both cross-version and cross-project defect prediction. While, we cannot conclude that OCSVM is the best classifier, our results still show interesting findings. While OCSVM does not outperform RF, it still achieves performance superior to its two-class counterpart (i.e., SVM) as well as other two-class classifiers studied herein. We also observe that OCSVM is more suitable for both cross-version and cross-project defect prediction, rather than for within-project defect prediction, thus suggesting it performs better with heterogeneous data. We encourage further research on one-class classifiers for defect prediction as these techniques may serve as an alternative when data about defective modules is scarce or not available.
Archival training for employees of the Sungai Pinang Luar Village Office, Samarinda City

Apart from being an organizational asset, properly organized archives are also useful as material for making quick and accurate organizational decisions. The proper implementation of archives can reduce the number of management errors that the organization will make. Human resources, standard archive handling procedures, and archive storage systems are some of the archival issues at the Sungai Pinang Luar Village Office. This training resulted in the management of archives using a problem-based classification system and the recovery of archives using an alphabetical system.
Model-Value Inconsistency as a Signal for Epistemic Uncertainty

Using a model of the environment and a value function, an agent can construct many estimates of a state's value, by unrolling the model for different lengths and bootstrapping with its value function. Our key insight is that one can treat this set of value estimates as a type of ensemble, which we call an \emph{implicit value ensemble} (IVE). Consequently, the discrepancy between these estimates can be used as a proxy for the agent's epistemic uncertainty; we term this signal \emph{model-value inconsistency} or \emph{self-inconsistency} for short. Unlike prior work which estimates uncertainty by training an ensemble of many models and/or value functions, this approach requires only the single model and value function which are already being learned in most model-based reinforcement learning algorithms. We provide empirical evidence in both tabular and function approximation settings from pixels that self-inconsistency is useful (i) as a signal for exploration, (ii) for acting safely under distribution shifts, and (iii) for robustifying value-based planning with a model.