Reviewerly/exHarmony huggingface dataset:
MIPD: An Adaptive Gradient Sparsification Framework for Distributed DNNs Training

Asynchronous training based on the parameter server architecture is widely used for scaling up the DNN training over large datasets and DNN models. Communication has been identified as the major bottleneck when deploying the DNN training over the large-scale distributed deep learning systems. Recent studies try to reduce the communication traffic through gradient sparsification and quantization approaches. We identify three limitations in previous studies. First, the fundamental guideline for gradient sparsification of their work is the magnitude of the gradient. However, the gradients&#x2019; magnitude represents the current optimization direction while it cannot indicate the significance of the parameters, which potentially results in delayed updating for the significant parameters. Second, their gradient quantization methods based on the entire model often lead to error accumulation for gradients aggregation since the gradients from different layers of the DNN model follow different distributions. Third, previous quantization approaches are CPU intensive, which generates strong overhead for the server. We propose <i>MIPD</i>, an adaptive and layer-wise gradient sparsification framework that compresses the gradients based on model interpretability and probability distribution of gradients. MIPD compresses the gradients according to the corresponding significance of its parameters, which is defined by model interpretability. An Exponential Smoothing method is also proposed to compensate for the dropped gradients on the server to reduce the gradients error. MIPD proposes to update half of the parameters for each training step to reduce the CPU overhead of the server. It encodes the gradients based on their probability distribution, thereby minimizing the approximated errors. Extensive experimental results generated on the GPU cluster indicate that the proposed framework effectively improves the training performance of DNNs by up to 36.2&#x0025;, which ensures high accuracy as compared to state-of-art solutions. Accordingly, the CPU and network usage of the server dropped by up to 42.0&#x0025; and 32.7&#x0025; respectively.
Multilevel Thresholding for Image Segmentation Using Mean Gradient

Image binarization and segmentation have been one of the most important operations in digital image processing and related fields. In spite of the enormous number of research studies in this field over the years, huge challenges still exist hampering the usability of some existing algorithms. Some of these challenges include high computational cost, insufficient performance, lack of generalization and flexibility, lack of capacity to capture various image degradations, and many more. These challenges present difficulties in the choice of the algorithm to use, and sometimes, it is practically impossible to implement these algorithms in a low-capacity hardware application where computational power and memory utilization are of great concern. In this study, a simple yet effective and noniterative global and bilevel thresholding technique is proposed. It uses the concept of image gradient vector to binarize or segment the image into three clusters. In addition, a parametric preprocessing approach is also proposed that can be used in image restoration applications. Evidences from the experiments from both visual and standard evaluation metrics show that the proposed methods perform exceptionally well. The proposed global thresholding outperforms the formidable Otsu thresholding technique.
A Machine Teaching Framework for Scalable Recognition

We consider the scalable recognition problem in the fine-grained expert domain where large-scale data collection is easy whereas annotation is difficult. Existing solutions are typically based on semi-supervised or self-supervised learning. We propose an alternative new framework, MEMORABLE, based on machine teaching and online crowd-sourcing platforms. A small amount of data is first labeled by experts and then used to teach online annotators for the classes of interest, who finally label the entire dataset. Preliminary studies show that the accuracy of classifiers trained on the final dataset is a function of the accuracy of the student annotators. A new machine teaching algorithm, CMaxGrad, is then proposed to enhance this accuracy by introducing explanations in a state-of-the-art machine teaching algorithm. For this, CMaxGrad leverages counterfactual explanations, which take into account student predictions, thereby proving feedback that is student-specific, explicitly addresses the causes of student confusion, and adapts to the level of competence of the student. Experiments show that both MEMORABLE and CMaxGrad outperform existing solutions to their respective problems.
THE SENSE/REFERENCE DISTINCTION IN CONSTRUCTIVE SEMANTICS

Editorial Note This lecture was given by Per Martin-Löf at Leiden University on August 25, 2001 at the invitation by Göran Sundholm to address the topic mentioned in the title and to reflect on Dummett’s earlier effort of almost a decade before (published in this journal). The lecture was part of a three-day conference on Gottlob Frege. Sundholm arranged for the lecture to be recorded and commissioned Bjørn Jespersen to make a transcript. The information in footnote 1, which Sundholm provided, has been independently confirmed by Thomas Ricketts in an email to the author. The present version has been edited by Ansten Klev. Following the displayed text (Int-id) there is a lacuna in the original transcript corresponding to a pause in the recording when the tape was changed. The continuous text of the present version is the result of a few additions to the original transcript suggested by Klev and agreed to by the author.
A Target Deblurring Method for Frequency Diverse Array Imaging

The frequency diversity array (FDA) is subject to its own property, which will cause the problem of target blurring in imaging. The existing FDA deblurring target imaging method generally adopts the design of nonlinear frequency increments, but it also brings new problems associated with sidelobe increases. Aiming at the problem of frequency diversity array image deblurring, this letter proposes an FDA deblurring target imaging method. Starting from the mechanism through which the blur is caused by the mutual influence of frequency increments, a mathematical model of image domain blur is established. A deconvolution method is proposed to achieve high-quality imaging without blur under arbitrary frequency increments of FDA. What`s more, a low-error imaging method based on ripple transfer is proposed to deal with the problem of error accumulation in deblurring. Finally, the effectiveness of the algorithm is verified by experimental simulation.
View Synthesis Tool for VR Immersive Video

This chapter addresses the view synthesis of natural scenes in virtual reality (VR) using depth image-based rendering (DIBR). This method reaches photorealistic results as it directly warps photos to obtain the output, avoiding the need to photograph every possible viewpoint or to make a 3D reconstruction of a scene followed by a ray-tracing rendering. An overview of the DIBR approach and frequently encountered challenges (disocclusion and ghosting artifacts, multi-view blending, handling of non-Lambertian objects) are described. Such technology finds applications in VR immersive displays and holography. Finally, a comprehensive manual of the Reference View Synthesis software (RVS), an open-source tool tested on open datasets and recognized by the MPEG-I standardization activities (where “I” refers to “immersive”) is described for hands-on practicing.
Let’s See Clearly: Contaminant Artifact Removal for Moving Cameras

Contaminants such as dust, dirt and moisture adhering to the camera lens can greatly affect the quality and clarity of the resulting image or video. In this paper, we propose a video restoration method to automatically remove these contaminants and produce a clean video. Our approach first seeks to detect attention maps that indicate the regions that need to be restored. In order to leverage the corresponding clean pixels from adjacent frames, we propose a flow completion module to hallucinate the flow of the background scene to the attention regions degraded by the contaminants. Guided by the attention maps and completed flows, we propose a recurrent technique to restore the input frame by fetching clean pixels from adjacent frames. Finally, a multi-frame processing stage is used to further process the entire video sequence in order to enforce temporal consistency. The entire network is trained on a synthetic dataset that approximates the physical lighting properties of contaminant artifacts. This new dataset and our novel framework lead to our method that is able to address different contaminants and outperforms competitive restoration approaches both qualitatively and quantitatively.
Coarsely-labeled Data for Better Few-shot Transfer

Few-shot learning is based on the premise that labels are expensive, especially when they are fine-grained and require expertise. But coarse labels might be easy to acquire and thus abundant. We present a representation learning approach - PAS that allows few-shot learners to leverage coarsely-labeled data available before evaluation. Inspired by self-training, we label the additional data using a teacher trained on the base dataset and filter the teacher’s prediction based on the coarse labels; a new student representation is then trained on the base dataset and the pseudo-labeled dataset. PAS is able to produce a representation that consistently and significantly outperforms the baselines in 3 different datasets. Code is available at https://github.com/cpphoo/PAS
Central Bank Digital Currencies in a World with Negative Nominal Interest Rates

Both academics and central banks have recently started to analyze merits and dangers of introducing central bank digital currencies (CBDC), i.e., some form of central bank moneyCentral bankCentral bank money handled through electronic means and accessible to the broad public.
Measurement in the Age of Information

Information is the resolution of uncertainty and manifests itself as patterns. Although complex, most observable phenomena are not random and instead are associated with deterministic, chaotic systems. The underlying patterns and symmetries expressed from these phenomena determine their information content and compressibility. While some patterns, such as the existence of Fourier modes, are easy to extract, advances in machine learning have enabled more comprehensive methods in feature extraction, most notably in their ability to elicit non-linear relationships. Herein we review methods concerned with the encoding and reconstruction of natural signals and how they might inform the discovery of useful transform bases. Additionally, we illustrate the efficacy of data-driven bases over generic ones in encoding information whilst discussing these developments in the context of “fourth paradigm” metrology. Toward this end, we propose that existing metrological standards and norms may need to be redefined within the context of a data-rich world.
Karting racing: A revisit to PPO and SAC algorithm

Proximal Policy Optimization (PPO) is a classical algorithm in reinforcement learning, which has been tested in a collection of benchmark tasks. In this paper, we test PPO in Unity environment to train Karting racing agent, with different parameters and different training settings. In our experiments, the improved PPO algorithm has a better performance in convergence rate and practical results (the average speed of agent) than baseline and other algorithms, such as Soft Actor-Critic (SAC). Visually, the agent trained by PPO appears more stable, which is able to cope with challenging tracks and has better generalization ability when we turned our trained model into a new environment. Our improved algorithm enables several applications, such as auto-pilot and UAV navigation.
Groundwater Vulnerability Zones Mapping Using DRASTIC and GOD Methods in Krendowahono Village, Karanganyar Regency

Abstract Krendowahono Village is located in Karanganyar Regency, Central Java, Indonesia. During the dry season, several hamlets in Krendowahono Village experience a shortage of clean water. So an attempt must be made to maintain the quality and protect groundwater sources in areas of Krendowahono Village with fresh groundwater supplies. Analysing the vulnerability of groundwater is one of the attempts that can be made. The DRASTIC and GOD methods were used to conduct a vulnerability analysis of groundwater. The DRASTIC and GOD vulnerability assessments were then validated using water samples taken from resident wells. Groundwater vulnerability assessment utilizing the DRASTIC method resulted in the identification of two vulnerability classes: low vulnerability and medium vulnerability. While the GOD method generates three vulnerability classes for groundwater, they are low vulnerability class, medium vulnerability class, and high vulnerability class. The maximum correlation value between the results of the GOD method and the test of water samples with parameters of free ammonia, total dissolved solids (TDS), and salinity was obtained when the results of the GOD method and the test of water samples were validated using Spearman’s correlation. The correlation value associated with the pH parameter was the highest in the DRASTIC method. Likewise, the GOD method produced favourable performance in regional studies. This is demonstrated by the significant correlation between the GOD susceptibility test findings and the water sample test results.
Capability Boehm: challenges and opportunities for garbage collection with capability hardware

The Boehm-Demers-Weiser Garbage Collector (BDWGC) is a widely used, production-quality memory management framework for C and C++ applications. In this work, we describe our experiences in adapting BDWGC for modern capability hardware, in particular the CHERI system, which provides guarantees about memory safety due to runtime enforcement of fine-grained pointer bounds and permissions. Although many libraries and applications have been ported to CHERI already, to the best of our knowledge this is the first analysis of the complexities of transferring a garbage collector to CHERI. We describe various challenges presented by the CHERI micro-architectural constraints, along with some significant opportunities for runtime optimization. Since we do not yet have access to capability hardware, we present a limited study of software event counts on emulated micro-benchmarks. This experience report should be helpful to other systems implementors as they attempt to support the ongoing CHERI initiative.
Integration of semantic patterns and fuzzy concepts to reduce the boundary region in three-way decision-making

A three-way decision framework based on fuzzy concepts can better understand uncertain decision boundaries by automatically dividing opinions into three decision regions: positive, negative, and boundary regions. However, when the fuzzy values of the opinions are very close, the boundary regions tend to be large. The difficult problem is how to significantly reduce the boundary regions while maintaining high classification accuracy. The most useful opinion features include fuzzy concepts and semantic features (or patterns). Unlike fuzzy concepts, semantic features are represented by using semantic patterns that frequently appear in opinions. We also observe a low statistical correlation between semantic patterns and fuzzy concepts. Therefore, this paper proposes a novel opinion classification method that integrates semantic patterns with fuzzy concepts in a three-way decision framework. The new method can increase the discriminative power of classifying opinions using features. Experimental results verify that the integration of fuzzy concepts and semantic patterns has better classification performance than using fuzzy concepts alone. This also shows that fusing multiple features is an effective solution to represent opinions more meaningfully, thereby effectively improving classification accuracy and reducing uncertain boundaries in three-way opinion classification.
Temporal Action Detection with Multi-level Supervision

Training temporal action detection in videos requires large amounts of labeled data, yet such annotation is expensive to collect. Incorporating unlabeled or weakly-labeled data to train action detection model could help reduce annotation cost. In this work, we first introduce the Semi-supervised Action Detection (SSAD) task with a mixture of labeled and unlabeled data and analyze different types of errors in the proposed SSAD baselines which are directly adapted from the semi-supervised classification literature. Identifying that the main source of error is action incompleteness (i.e., missing parts of actions), we alleviate it by designing an unsupervised foreground attention (UFA) module utilizing the conditional independence between foreground and background motion. Then we incorporate weakly-labeled data into SSAD and propose Omni-supervised Action Detection (OSAD) with three levels of supervision. To overcome the accompanying action-context confusion problem in OSAD baselines, an information bottleneck (IB) is designed to suppress the scene information in non-action frames while preserving the action information. We extensively benchmark against the baselines for SSAD and OSAD on our created data splits in THUMOS14 and ActivityNet1.2, and demonstrate the effectiveness of the proposed UFA and IB methods. Lastly, the benefit of our full OSAD-IB model under limited annotation budgets is shown by exploring the optimal annotation strategy for labeled, unlabeled and weakly-labeled data. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Study on Missing Values and Outlier Detection in Concurrence with Data Quality Enhancement for Efficient Data Processing

Data analytics is the process of analyzing raw data to make predictions and derive conclusions. This process involves collecting and organizing data to discover hidden patterns and draw insight into the data. The methods and approaches of data analytics are automated using various algorithms and mathematical formulas. Data analytics provides real-time and actionable perceptions on data that enable more accurate and prompter decision-making. During the data acquisition phase, missing values and outliers are encountered that affect the model's reliability. Missing values are the data missing in a dataset, more common on large datasets that arise due to information loss, dropout, or non-response of participants. Missing values affects the accuracy of the result and also may lead to biased results. Outliers are the abnormal values that happen to have deviated from the normal distribution pattern of data distribution. Outliers are extreme, unrealistic, extremely big, or small values in a dataset that arise due to manual errors like participant response errors and data entry errors. Outliers also affect the accuracy of the results and lead to over or underestimated resultant values. As missing values and outliers degrade the performance of the analytical data models, various research works have focused on finding and handling such values. This paper reviews the various aspects of missing values and outliers in the preprocessing phase of data analytics to enhance the accuracy of the data model.
Unconditional Scene Graph Generation

Despite recent advancements in single-domain or single-object image generation, it is still challenging to generate complex scenes containing diverse, multiple objects and their interactions. Scene graphs, composed of nodes as objects and directed-edges as relationships among objects, offer an alternative representation of a scene that is more semantically grounded than images. We hypothesize that a generative model for scene graphs might be able to learn the underlying semantic structure of real-world scenes more effectively than images, and hence, generate realistic novel scenes in the form of scene graphs. In this work, we explore a new task for the unconditional generation of semantic scene graphs. We develop a deep auto-regressive model called SceneGraphGen which can directly learn the probability distribution over labelled and directed graphs using a hierarchical recurrent architecture. The model takes a seed object as input and generates a scene graph in a sequence of steps, each step generating an object node, followed by a sequence of relationship edges connecting to the previous nodes. We show that the scene graphs generated by SceneGraphGen are diverse and follow the semantic patterns of real-world scenes. Additionally, we demonstrate the application of the generated graphs in image synthesis, anomaly detection and scene graph completion.
Data Services Librarians’ Responsibilities and Perspectives on Research Data Management

This study of data services librarians is part of a series of studies examining the current roles and perspectives on Research Data Management (RDM) services in higher education. Reviewing current best practices provides insights into the role-based responsibilities for RDM services that data services librarians perform, as well as ways to improve and create new services to meet the needs of their respective university communities. Objectives: The objectives of this article are to provide the context of research data services through a review of past studies, explain how they informed this qualitative study, and provide the methods and results of the current study. This study provides an in-depth overview of the overall job responsibilities of data services librarians and as well as their perspectives on RDM through job analyses. Methods: Job analysis interviews provide insight and context to the tasks employees do as described in their own words. Interviews with 10 data services librarians recruited from the top 10 public and top 10 private universities according to the 2020 Best National University Rankings in the US News and World Reports were asked 30 questions concerning their overall job tasks and perspectives on RDM. Five public and five private data services librarians were interviewed. The interviews were recorded and transcribed. The transcriptions were analyzed in NVivo using a grounded theory application of open, axial, and selective coding to generate categories and broad themes based on the responses using synonymous meanings. Results: The results presented here provide the typical job tasks of data services librarians that include locating secondary data, reviewing data management plans (DMPs), conducting outreach, collaborating, and offering RDM training. Fewer data services librarians assisted with data curation or manage an institutional repository. Discussion: The results indicate that there may be different types of data services librarians depending on the mix of responsibilities. Academic librarianship will benefit from further delineation of job titles using tasks while planning, advertising, hiring, and evaluating workers in this emerging area. There remain many other explorations needed to understand the challenges and opportunities for data services librarians related to RDM. Conclusions: This article concludes with a proposed matrix of job tasks that indicates different types of data services librarians to inform further study. Future job descriptions, training, and education will all benefit from differentiating between the many associated research data services roles and with increased focus on research data greater specializations will emerge.