Reviewerly/exHarmony huggingface dataset:
Fast MSE-Based Sampling of Bandlimited Graph Signals via Low-Pass Impulse Responses

Sampling is a fundamental problem in graph signal processing that selects a node subset to collect samples, so that data in the remaining nodes can be well recovered. Existing eigen-decomposition-free (ED-free) graph sampling schemes are not designed to minimize mean square error (MMSE) of reconstructed bandlimited graph signals, often resulting in sub-par MSE performance. In this paper, we propose a lightweight ED-free algorithm to minimize an approximate MSE objective using per-node impulse responses of an ideal low-pass graph filter. Specifically, we first derive a proxy of the MMSE greedy sampling objective without matrix inverse. We then define node-dependent impulse responses of an ideal low-pass graph filter—analogous to the sinc function in traditional signal processing—which can be approximated very fast without ED. We use these response vectors to reformulate our derived sampling objective, then show that the optimal sampling set has supported low-pass impulse responses that are as orthogonal as possible—defaulting to uniform sampling for 1D regular kernels when the graph Fourier basis is DFT. For optimization, we propose a fast sampling algorithm to evaluate candidates via vector-vector multiplications by reusing previous greedy results. For faster sampling, we relax the MMSE-based greedy objective to a bounded approximation, so that candidate nodes can be easily appraised using simple scalar multiplications. Extensive experiments show that our sampling method achieved state-of-the-art sampling speed and had the best MSE performance among deterministic ED-free sampling methods in various scenarios.
Creation of column-oriented NoSQL databases automatically in Big Data environments and its impact on energy consumption

This study investigates the automatic creation of column-oriented NoSQL databases in Big Data environments and their impact on energy consumption. Traditional row-oriented databases face limitations in handling large volumes of data, resulting in slower query response times and energy inefficiencies. In contrast, column-oriented NoSQL databases store data in columns, enabling efficient compression, retrieval, and query processing. Innovative techniques are employed to automatically create these databases, optimizing performance and minimizing manual intervention. Storing data in a columnar format reduces storage requirements and power consumption while improving data locality and reducing I/O operations. This study emphasizes the benefits of adopting column-oriented NoSQL databases, including improved performance, scalability, and energy efficiency in Big Data environments.
GradQuant: Low-Loss Quantization for Remote-Sensing Object Detection

Convolutional neural network based methods have shown remarkable performance in remote sensing object detection. However, their deployment on resource-limited embedded devices is hindered by their high computational complexity. Neural network quantization methods have been proven effective in compressing and accelerating CNN models by clipping outlier activations and utilizing low-precision values to represent weights and clipped activations. Nonetheless, the clipping of outlier activations leads to distortion of object local features. Furthermore, the lack of enhanced overall feature mining exacerbates the degradation of detection accuracy. To address the limitations above, we propose an innovative clipping-free quantization method called GradQuant, which mitigates model’s quantization accuracy loss caused by clipping outlier activations and the lack of overall feature mining. Specifically, a bounded activation function (sigmoid-weighted tanh, SiTanh) is carefully designed to ensure that object features are represented within a limited range without clipping. On the basis of this, an activation substitute training (AST) method is co-designed to prompt models to focus more on non-outlier object features instead of outlier-like local ones. Extensive experiments on public remote-sensing datasets demonstrate the effectiveness of GradQuant method compared with other state-of-the-art quantization methods.
An Open Source Deep Learning Toolkit for Perception &amp; Cognition in Robotics and Other Areas

Deep Learning (DL) has provided powerful tools for a wide range of different application areas, including visual perception and cognition in robotics, media monitoring, image analysis, and others. However, deploying DL tools in real applications comes with several challenges, since DL requires different pipelines compared to traditional computer vision tools. For example, specialized software frameworks, along with the appropriate hardware should be used for training and inference, since DL models are often too resource-intensive to be directly deployed. This issue is even more important in embedded applications, such as robotics and drone vision where significant computational power and energy constraints exist. Furthermore, the fragmentation of the DL landscape can further slow down their integration, since models produced with different tools are often not interoperable. At the same time, DL tools are typically designed to follow a static inference paradigm. However, many systems can provide active perception capabilities that are usually not used by the current generation of DL tools. In this talk, we will discuss the aforementioned challenges and present the Open Deep Learning Toolkit for Robotics (OpenDR) which aims to overcome many of these limitations that occur in robotics and many other areas, focusing on visual perception applications. As part of the talk, we will also briefly showcase practical examples of using the OpenDR toolkit, discussing how tools can be optimized and adapted to different inference platforms that are often used.
Discussion and Lessons Learned

This chapter regroups the lessons learned and the key points in the previous sections for a general discussion leading to the basis of context-based intelligent assistant systems. The key points identified previously in the text are met in Table 6.1 where we kept the original numbering and ordering of the key points in the different chapters. They are grouped in the following section in five general themes which are visualized by colors of the cells of the key point numbers in Table 6.1. Hereafter, this chapter discusses the contextual elements, first individually and second by themes. Based on the finer approach of contextual elements proposed in the first section and, thus, about context in general, the third section proposes a new insight on decisional levels in terms of context. The chapter ends on the use of context modeling and use in a future type of intelligent systems, namely the context-based intelligent assistant systems.
Many-to-Many Multilingual Translation Model for Languages of Indonesia

Indonesia is home to over 700 languages and most people speak their respective regional languages aside from the lingua franca. In this paper, we focus on the task of multilingual machine translation for 45 regional Indonesian languages and introduced Indo-T5 which leveraged the mT5 sequence-to-sequence language model as a baseline. Performances of bilingual and multilingual fine-tuning methods were also compared, in which we found that our models have outperformed current state-of-the-art translation models. We also investigate the use of religious texts from the Bible as an intermediate mid-resource translation domain for low-resource translation domain specialization. Our findings suggest that this two-step fine-tuning approach is highly effective in improving the quality of translations for low-resource text domains. Our results show an increase in SacreBLEU scores when evaluated on the low-resource NusaX dataset. We release our translation models for other researchers to leverage.
A Cross-Trusted Authority Authentication Protocol for Internet of Vehicles Based on Blockchain

With the increasing number of vehicles connected to the Internet of Vehicles (IoV), to accommodate the evolving needs and patterns of new vehicles, passengers, and drivers, traditional single Trusted Authority (TA) authentication model may no longer be suitable for the IoV scenario, it is crucial to develop secure, lightweight, efficient, and cross-TA identity authentication and key agreement algorithms. In 2021, Xu et al. proposed a blockchain-based Roadside Unit (RSU)-assisted authentication and key agreement protocol for IoV. However, we describe that their protocol is vulnerable to identity guessing attacks, vehicle forgery attacks, and lacks of session key security and known session key secrecy, and propose a novel blockchain and elliptic curve cryptography-based cross-TA authentication and key agreement protocol for IoV. Our protocol uses Physical Unclonable Functions (PUF) and biometric keys to resist RSU capture attacks and Onboard Unit (OBU) intrusion attacks. Formal security proof and comparative analysis indicate that the proposed protocol can resist various known attacks and maintains lower computational complexity.
A Progressive Decoding Strategy for Action Recognition

In recent years, significant progress has been made in modeling temporal sequences and spatial structures in skeleton-based human action recognition. However, existing methods rely on explicit modeling of the inherent structure of the human body, which may result in reduced joint saliency and poor interpretability due to the sparsity of skeleton data and the relative smoothness of convolutions. This paper proposes a feature matching method based on the progressive decoding strategy. As human movement is a chain process, the strategy progressively decodes human pose features from the center to the periphery, using multi-level graph filters to obtain multi-frequency hierarchical graph features. Then the adaptive convolution kernels are constructed to match the local similarities between graph features. Self-similarity of the query set and the mutual similarity between the query set and the support set samples are calculated to analyze the entire posture of the human body, and similar skeletal features are distinguished according to the similarity of the node spectrum to differentiate between different action categories. Through experimental verification of two public data sets, the proposed method has better recognition accuracy and generalization of small sample behavior. The experiments show that the proposed method outperforms the existing methods on NTU RGB + D and Human36M Dataset.
Regulation of quantum communications in the “smart city” information system

Different countries are creating and improving information systems that increase the efficiency of city management. Their research takes place within the concept of a smart city. The article considers the formation of this concept in the Russian Federation and its evolution abroad. Information systems “smart city” involve the transfer of a significant amount of data, protected by the methods of classical cryptography, through communication lines. The level of development of quantum technologies allowed us to conclude that this method of information security will not be effective in the medium term. Smart city systems have already lost their cryptographic strength for retrospective attacks. The article analyzes data protection options for information system smart city under quantum threat conditions. Based on the classification of city services, the authors propose a differentiated approach to information security, combining post-quantum cryptography and quantum communication technologies. This will allow the optimal use of public resources and create a smart city information system resistant to quantum computer attacks. We completed this article within the Governmental Assignment: “Russian Legal System in the Realities of Digital Transformation of Society and State: Adaptation and Prospects for Responding to Modern Challenges and Threats (FSMW-2023-0006)”. Registration number: 1022040700002-6-5.5.1.
Extended <scp>melt‐conveying</scp> models for <scp>single‐screw</scp> extruders: Integrating domain knowledge into symbolic regression

Abstract The literature provides several analytical approximation methods for predicting the flow of non‐Newtonian fluids in single‐screw extruders. While these are based on various flow conditions, they were developed mostly for extruder screws with standard geometries. We present novel analytical melt‐conveying models for predicting the flow and dissipation rates of fully developed flows of power‐law fluids within three‐dimensional screw channels. To accommodate a broad range of industrial screw designs, including both standard and high‐performance screws, the main intention of this work was to significantly extend the scope of existing theories. The flow equations were first rewritten in a dimensionless form to reduce the mathematical problem to its dimensionless influencing parameters. These were varied within wide ranges to create a set of physically independent modeling setups, the flow and dissipation rates of which were evaluated by means of a finite‐volume solver. The numerical results were then approximated analytically using symbolic regression based on genetic programming. To support the regression analysis in finding accurate solutions, we integrated domain‐specific process knowledge in the preprocessing of the dataset. We obtained three regression models for predicting the flow and dissipation rates in melt‐conveying zones and tested their accuracy successfully against an independent set of numerical solutions. Highlights Flow of power‐law fluids in three‐dimensional screw channels Identification of independent influencing parameters by dimensional analysis Numerical parametric design study for a broad range of industrial applications Integration of domain knowledge in symbolic regression Surrogate models derived from numerical simulation results
TopKWS Algorithm in the Map-Reduce Paradigm for Cloud Computing QoS Recommendation System

With the evolution of Big Data, Cloud computing has become very popular because it enables the efficient use of the IT resources needed to manage these massive amounts of data. As a result, Cloud mar- kets have become very competitive, given their needs and the technologi- cal development that increasingly leverages Big Data infrastructures. As the growing number of various services in the cloud is rapidly evolving in the cloud market, selecting the best service from the vast amount of data is a great challenge. This paper presents a new TopK Selection Algorithm based on the Map-Reduce paradigm for recommending g the cloud QoS. This processing explores a combined System of recommendation called the n-CSRSS (New Cloud Service Recherche and Selection System) that helps the user choose the best services according to the their requirements. The experimental results show that our Recommender system can effectively recommend the right combination of Cloud services to consumers.
Development and Evolution of Software Product Lines Driven by Stakeholder Beliefs

The planning, realization, and release of a Software Product Line (SPL) are driven by features. Therefore, many high-level decisions about the evolution of an SPL are made at the feature level. However, a feature can involve many stakeholders with different expertise, and taking their opinions into account to make the right decisions is not trivial. In this paper, we propose using belief uncertainty in conjunction with feature models to assist in the evolution of SPLs by explicitly quantifying opinions. We outline three evolution scenarios in which subjective logic can be used to represent the opinions of stakeholders and explain in detail how to use subjective logic to make decisions in the context of the next release problem. We illustrate our ideas with a Smartwatch SPL. Finally, we discuss different ways of combining the opinions of stakeholders depending on the situation, the goals and the risks that can be assumed.
Bayesian Meta-Analysis of Software Defect Prediction With Machine Learning

Machine learning is widely used to predict software defect-prone components, facilitating testing and improving application quality. In a recent meta-analysis on binary classification for software defect prediction, the so-called researcher bias —i.e., the group who conducts the study— has been shown to play a critical role; the analysis, however, featured using proper null hypothesis testing statistical analysis alone. Since the null hypothesis testing is based on the so-called p-value, which is not the desired likelihood of the null hypothesis, it suffers from several important drawbacks. This article presents a Bayesian analysis of the same dataset, which overcomes the pitfalls of the null hypothesis testing approach and relaxes the assumptions of the methods used in the previous study. While the Bayesian analysis in this article identifies the software metrics as the most influential factor for a classifier's performance, researcher bias is still the second most important factor: the precautions against researcher bias are still critical to consider in the scope of software defect prediction endeavors. Further on, to confirm this finding, we analyze the data with more advanced Bayesian modeling, according to which we identify (1) classifiers with better performance, (2) the datasets whose instances are harder to predict, and (3) the metrics that impact the performance of a classifier.
Adapting the Database to Feature Changes in Software Product Lines

Software Product Lines (SPL) support the development of families of software products that share a set of core assets but differ in certain features. To generate a new product, the engineer selects the desired features and the SPL assembles and adapts the implementation of the core assets. In real scenarios, we may need to update a product by adding a feature not initially selected. Similarly, we may need to remove a feature that is no longer necessary. Modifying the selection of features of a product in use poses a challenge from the point of view of the product's database. If the added/removed features affect the database schema, we may need to adapt the schema and the data stored in the database. This paper addresses this scenario and proposes an evolution model to define actions to be executed in the database when features are added or removed. Our proposal allows us to model those adaptations and to automate them when modifying the selection of features of a product. The evolution model describes changes to be made in the database, each composed of different actions that adapt certain elements of the database. Changes are associated with the features that may trigger their execution, and the change's actions are associated with the data model elements they affect. In this way, the evolution model supports automatic adaptation of the database, and we keep traceability between features and the elements of the data model they affect.
Automata Learning for Dynamic Software Product Lines

Automata learning is a fundamental techique for building behavioral models by actively interacting with black-box systems. Through four decades of research the community has come up with many algorithms and tools for automata learning, of which this tutorial will provide an overview. Moreover, researchers have proposed several extensions of automata learning algorithms to evolving systems, i.e., systems that change in time, as well as variability-intensive systems, i.e., systems that change in configuration space. In this tutorial, we provide an overview of such extensions and show how they can be applied to the field of dynamic software product lines.
Second Tutorial on the Universal Variability Language

The Universal Variability Language (UVL) is a textual feature modeling approach that is developed in regular exchange with the community. Over the last years, the adoption of UVL in variability-modeling tools is steadily increasing. Our tutorial covers different aspects of working with UVL, such as editing, format conversion, and automated analysis, and shows recent advances in tool support.
Power-Efficient Stochastic Number Generator Design and Implementation for Encryption

Stochastic computing utilizes random bit streams to encode continuous data efficiently. One important aspect is the stochastic number generator (SNG) that encodes numbers into random binary bit streams. This work presents power-efficient 4-bit and 8-bit SNGs by using a modified Non-linear Feedback Shift Register (NLFSR) as the random number source and a Weighted Binary Generator (WBG) as the probability conversion circuit. A bipartite approach is employed to reduce power and delay consumption in the SNGs. Additionally, the concept of sharing a single random number source with multiple probability conversion circuits is explored to achieve further power savings. This project offers enhanced power efficiency in SNGs, contributing to advancements in stochastic computing and power circuit designs. The main application of this project is encryption, as random number sequences are required for various stages of encryption protocols.
Balancing Variability and Costs in Software Product Lines

This paper provides a detailed testing strategy for Software Product Lines (SPLs), aiming to balance the variability offered with its associated cost. Utilizing feature modeling, combinatorial testing, and deployment-based testing, the strategy addresses the unique challenges of testing SPLs, including the large number of possible feature interactions.
Optimization of OLAP In-Memory Database Management Systems with Processing-In-Memory Architecture

With the growing popularity of Processing-In-Memory (PIM) technology, many sectors of the industry are willing to take advantage of this new technology. However, the state-of-the-art applications are not optimized to fully utilize the PIM capabilities. In this paper, an in-memory database is analyzed and its functions whose executions cause the majority of CPU clock cycles are identified. Factors such as running time and cache locality are studied and processes causing long running times are accelerated with the PIM technology. The results show that by utilizing the proposed optimization methods, there is an overall speedup of 110.94% in the selected functionalities in the database management system. Furthermore, a deep analysis of the results is provided, summarizing key observations and programming recommendations for the in-memory database developers, and providing guidelines on where to take advantage of this new memory technology, and where to avoid it.
A Programmatic and Scalable Approach to Making Data Management Machine-Actionable

The data management plan (DMP), while seen by many as an ancillary document during a grant application, is a rich source of contextual information that is key to ensuring researchers, funders, and institutions follow the best possible and most appropriate research data management (RDM) practices. Unfortunately, the current practice is to transmit this information to the funder as a PDF or Word file through their web portals. As optimizing internal workflows and information sharing is a priority across the research space, retooling DMPs as machine-readable and machine-actionable will enable leveraging of key information to build RDM strategies collectively. Similarly, there is a growing need to streamline workflows, reuse information and reduce the burden on researchers.