Reviewerly/exHarmony huggingface dataset:
Experience of the SPSTL SB RAS in the Study of Open Tools to Support Scientific Research

The purpose of this study was to study online tools to support open science research processes, as a result of which a system of open tools to support scientific research (OIPSR) was built at the State Public Scientific and Technical Library of the Siberian Branch of the Russian Academy of Sciences on the online platform “Library for Open Science”. To create a system, an analysis of existing open tools to support research processes was carried out. Online tools have been identified from a number of different sources. The tools were evaluated in terms of openness, freeness and accessibility under restrictions. The final list of open research support tools contains 137 tools. As a result, a system was created based on the stages of the entire cycle of scientific research – planning, searching, analyzing, writing, publishing, disseminating, evaluating. The found and selected tools were grouped according to research activities for all stages of scientific research. The developed system will allow scientific researchers to increase the efficiency of their scientific work, reduce the time to search for the necessary publications and data, make research workflows more open and reliable, which will contribute to the promotion of open science practices in our country.
Using Blockchain to Ensure Supply Chain Traceability

Supply chains can span a huge number of countries, cross many borders, and require interoperation of a multitude of organizations. This vastness impacts business competitiveness since it adds complexity and can difficult securing traceability, chain of custody, and transparency. The authors propose that assuring chain of custody and traceability via blockchain allows organizations to demonstrate product provenance, integrity, and compliance. This work proposes that to effect true traceability the more complete approach is to connect both the supply chain actors (SCAs) and products identifications using digital certificates. A blockchain is used to manage the traceability of products and validation of the identities. Importing, verifying, and storing the certificates uses an off-chain data storage solution for products certificates. To create, validate the certificates, and setup the chain of trust, a public key infrastructure (PKI) was designed as part of the proposal. The results were architectural artifacts, including an Ethereum smart contract and a PKI-based certificate authentication system.
Identification of the Problem and Research Methodology

Internet and applications have become an essential part of life nowadays. We are now at the stage where we can think about future smart cities as we are having various driving forces in the form of web-based applications. The main phenomena behind data-computing can be considered as for keeping data persistent, secure, available, and relevant for the information in real time. In the chapter, the authors proposed blockchain representation-based application process that is a low cost and efficient application for true sense ubiquitous usage that is also the demand of current time. The implemented application is providing a huge data security scenario for a web application system, and it is dealing with the efficient information processing towards storage, processing, and access of the information in a real time. In this chapter, the authors provided various schemes for high availability of on-demand data with proper asymmetric algorithms techniques.
Disentangled Adversarial Domain Adaptation for Phonation Mode Detection in Singing and Speech

Phonation mode detection predicts phonation modes and their temporal boundaries in singing and speech, holding promise for characterizing voice quality and vocal health. However, it is very challenging due to the domain disparities between training data and unannotated real-world recordings. To tackle this problem, we develop a disentangled adversarial domain adaptation network, which adapts the phonation mode detection model with the structure of the convolutional recurrent neural network pre-trained on the source domain to the target domain without phonation mode labels. Based on our curated sung and spoken dataset for phonation mode detection, we demonstrate that the subject and the singing-speech mismatches cause performance decline. By disentangling domain-invariant phonation mode and domain-specific embeddings, our method greatly enhances the effectiveness and explainability of unsupervised adversarial domain adaptation. Experiments show that the performance drop caused by the subject mismatch is alleviated via adaptation, resulting in 44.7% and 6.8% improvement of the F-score for singing and speech, respectively. The singing and speech domain adaptation experiment indicates that a model trained on singing data can be adapted to speech, yielding an F-score of 0.56, commensurate with the F-score of 0.59 achieved using a model exclusively trained on speech data. By further investigating the disentangled embeddings, we find that the phonation mode feature shared by singing and speech is invariant to pitch. These results inspire reliable and versatile applications in voice quality evaluation and paralinguistic information retrieval.
SURT 2.0: Advances in Transducer-Based Multi-Talker Speech Recognition

The Streaming Unmixing and Recognition Transducer (SURT) model was proposed recently as an end-to-end approach for continuous, streaming, multi-talker speech recognition (ASR). Despite impressive results on multi-turn meetings, SURT has notable limitations: (i) it suffers from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">leakage</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">omission</i> related errors; (ii) it is computationally expensive, due to which it has not seen adoption in academia; and (iii) it has only been evaluated on synthetic mixtures. In this work, we propose several modifications to the original SURT which are carefully designed to fix the above limitations. In particular, we (i) change the unmixing module to a mask estimator that uses dual-path modeling, (ii) use a streaming zipformer encoder and a stateless decoder for the transducer, (iii) perform mixture simulation using force-aligned subsegments, (iv) pre-train the transducer on single-speaker data, (v) use auxiliary objectives in the form of masking loss and encoder CTC loss, and (vi) perform domain adaptation for far-field recognition. We show that our modifications allow SURT 2.0 to outperform its predecessor in terms of multi-talker ASR results, while being efficient enough to train with academic resources. We conduct our evaluations on 3 publicly available meeting benchmarks — LibriCSS, AMI, and ICSI, where our best model achieves WERs of 16.9%, 44.6% and 32.2%, respectively, on far-field unsegmented recordings. We release training recipes and pre-trained models: <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><uri>https://sites.google.com/view/surt2</uri></monospace> .
Sequential Learning for Modeling Video Quality of Delivery Metrics

Video streaming traffic growth poses a challenge for many video content providers to maintain high video quality on their networks. Modern day networks are highly dynamic due to the adaptation of routing protocols, time-varying loads, and adaptive codecs. We consider how these dynamics should be incorporated in learning algorithms. We ask how can sequential learning be used in order to account for time-varying trends in learning algorithms that perform forecasts in time-varying video delivery systems? We propose two approaches. The first is called ASAPjitter. It uses a Recurrent Network (RN) to forecast gradients in a time series of jitter measurements for a video stream and a Convolutional Neural Network to classify the forecasts. The second approach is called FEATjitter. It maps the jitter time series to a higher dimensional feature domain. This novel feature domain transform has the aim of enhancing the quality of prediction and classification of future video jitter measurements. Jitter captures varying congestion levels present in the network. Three parameters are extracted from the jitter time series: its period, base and rate of decay. These parameters are used to train an artificial RN to perform forecasts. A batch learning approach such as the Neural Network (NN) or otherwise referred to a Multi-Layer Perceptron (MLP) is used for classifying the feature domain forecasts. Experimental results demonstrate how sequential learning can be used effectively to predict and classify jitter using Deep Learning (DL) frameworks. The accuracy achieved by ASAPjitter is 84.5% compared to 91.6% for FEATjitter. Performance gains in terms of increased classification accuracy are due to the learning algorithm operating in this feature-space. Performance gains in terms of forecasts are more effective in the time-domain. Our approach contributes to the development of more effective algorithms for managing video streaming traffic in dynamic network environments.
Trainable Weights for Multitask Learning

The research on multi-task learning has been steadily increasing due to its advantages, such as preventing overfitting, averting catastrophic forgetting, solving multiple inseparable tasks, and coping with data shortage. Here, we question whether to incorporate different orderings of feature levels based on distinct characteristics of tasks and their interrelationships in multitask learning. While in many classification tasks, leveraging the features extracted from the last layer is common, we hypothesized that given different characteristics of tasks there might be a need to encompass different representation levels, i.e., different orderings of feature levels. Hence, we utilized the knowledge of different representation levels by features extracted from the various blocks of the main module and applied trainable parameters as weights on the features. This indicates that we optimized the solution to the question by learning to weigh the features in a task-specific manner and solving tasks with a combination of newly weighted features. Our method SimPara presents a modular topology of multitask learning that is efficient in terms of memory and computation, effective, and easily applicable to diverse tasks or models. To show that our approach is task-agnostic and highly applicable, we demonstrate its effectiveness in auxiliary task learning, active learning, and multilabel learning settings. This work underscores that by simply learning weights to better order the features learned by a single backbone, we can incur better task-specific performance of the model.
Denoising-Based Decoupling-Contrastive Learning for Ubiquitous Synthetic Face Images

With the improvement of generative models such as GPT-4, GANs, and diffusion models, synthetic face images are increasingly pervading the current digital environment. Various face editing software based on generative models is already commercially available, which can edit face image attributes, including changing age, makeup, hair, scars, gender, etc. Existing face recognition methods trend to employ synthetic face images to augment datasets during large-scale training. However, the involvement of low-quality synthetic data can impair the feature extraction ability, consequently affecting recognition performance. Furthermore, face editing can potentially be applied for illegal or criminal purposes, such as criminals uploading edited face images for disguising themselves, thereby reducing the accuracy of face recognition models. To mitigate the negative impact of synthetic face images, we propose a denoising-based decoupling-contrastive learning (DDCL) method for extracting more benign features from synthetic data. By designing a siamese network structure with two branches, the framework extracts robust features from natural and synthetic images with contrastive learning mechanism. Subsequently, the bi-directional coding-based feature decoupling module filters out features of synthetic images before proceeding to identity recognition. Experimental results demonstrate that our method can alleviate the negative impact of synthetic face images and achieves the highest recognition accuracy for both synthetic and natural data.
A Lightweight Deep Neural Network Using a Mixer-Type Nonlinear Vector Autoregression

The design of a lightweight deep learning model would be an ideal solution for overcoming resource limitations when implementing artificial intelligence in edge sites. In this study, we propose a lightweight deep neural network that uses a Mixer-type architecture based on nonlinear vector autoregression (NVAR), which we refer to as Mixer-type NVAR. We applied overlapping patch embedding to enrich the image input and Sequencer architecture for vertical and horizontal operation inside the Mixer-type NVAR. We utilized a window partition technique and general quadratic positional encoding to increase the performance of the proposed model. Our model achieved a top-1 accuracy of 82.48 for the CIFAR-10 dataset with 0.159Mparameters and 98.36 for MNIST with 0.106Mparameters. Moreover, we evaluated its throughput on a central processing unit, which was 190.1 images per second for CIFAR-10 and 106.7 images per second for the MNIST dataset. These results are competitive with the state-of-the-art convolutional neural network-based model, MLP-Mixer, and the traditional reservoir-computing-based Mixer model with the same tuning of hyperparameters.
Qualitative Survey on Artificial Intelligence Integrated Blockchain Approach for 6G and Beyond

Utilizing the 0.1 to 10 THz spectrum in the next-generation wireless communication networks holds potential for futuristic applications. However, managing resources to accommodate numerous devices raises privacy and security concerns. Further, technology proliferation entwines devices, infrastructure complexity, and resources. Indeed, the transition from 5G (fifth-generation) to 6G (sixth-generation) signifies a progression towards high-speed data rates, minimal latency, and seamless integration of artificial intelligence, enabling ground-breaking applications and services. However, it complicates network management, privacy, resource allocation, and data processing. Notably, integrating Blockchain Technology (BCT) and Machine Learning (ML) is a promising solution, enhancing security, decentralization, trust in ML decisions, and efficient data sharing. This survey thoroughly reviews the integrated ML and BCT, showcasing their collaborative enhancement of network security, decentralization, trust in ML decisions, immutability, and efficient model sharing. Furthermore, we also delve into various distinctive topics, such as BCT-enabled spectrum refarming, rate splitting multiple access, 6G radar-based communication, reconfigurable intelligent surfaces, visible light communication, and integrated sensing and communication. Moreover, it also explores the integration of ML and BCT in novel 6G communication technologies, including molecular, holographic, and semantic communication. Finally, critical open issues, challenges, solutions, and futuristic scope are identified for forthcoming researchers.
Optimized Local Path Planner Implementation for GPU-Accelerated Embedded Systems

Autonomous vehicles are latency-sensitive systems. The planning phase is a critical component of such systems, during which the in-vehicle compute platform is responsible for determining the future maneuvers that the vehicle will follow. In this letter, we present a GPU-accelerated optimized implementation of the Frenet Path Planner, a widely known path planning algorithm. Unlike the current state of the art, our implementation accelerates the entire algorithm, including the path generation and collision avoidance phases. We measure the execution time of our implementation and demonstrate dramatic speedups compared to the CPU baseline implementation. Additionally, we evaluate the impact of different precision types (double, float, and half) on trajectory errors to investigate the tradeoff between completion latencies and computation precision.
Categorical Weighting Domination for Imbalanced Classification With Skin Cancer in Intelligent Healthcare Systems

In the field of dermatological diseases, especially for skin cancer, machine learning (ML) methods are used to classify melanoma and nevus using skin images. ML techniques result in high accuracy of diagnostic tasks since they are trained on balanced datasets. However, MLs working with imbalanced datasets produce erroneous results on precision, sensitivity, and specificity measured criteria. To deal with this problem, an augmentation approach combined with a category seesaw is used for the compensation factor, increasing the penalty of misclassified instances to avoid the false positives of the tail categories. This paper presents an approach to improve the efficiency of DCNN for classifying multi-class medical images on imbalanced datasets. The solution consists of three major contributions: (1) feature extraction based on some backbone models with customizing fully connected layers for classifier layers, (2) optimizing loss function (LF) and training parameters, (3) solving the problem of imbalanced samples using optimizing domination of weights between asymmetric classes with majority and minority categories. The method was evaluated and analyzed using the ISIC2018 benchmark dataset. Some well-known backbones were used for this study, e.g., EfficientNets, MobileNets, and DenseNets. The use of these backbones is to demonstrate that our methods are more efficient and stable in both light and heavy DCNN architectures. We also provide comparisons with existing methods that deal with the imbalance problem, e.g., data augmentation (AU), downsamples, customizing LF, and focal loss method (FL) for focusing on hard samples. Experimental results showed that these methods achieve good performance. However, there are several problems caused by generating new samples, and weighting samples, such as data overloading to train classifier models, a corrupt problem when applied to imbalanced data. Moreover, the FL method produced insufficient results on various DCNN backbones. Differently, our approach solves the imbalanced dataset based on boosting the sample weights of the minority and reducing the impact ratio of samples in majority categories. This strategy results in high precision and stable performance with various DCNN models without augmenting the dataset. As a result, our approach achieves more efficiency than other methods in some specific evaluation criteria as follows: higher than the FL method with 2.73% recall, 2.63% precision, 2.81% specificity, and 3.09% F1 using EfficientNet backbones; higher than AU method with 5.16% recall, 5.97% precision, 8.93% specificity, 6.16% F1 using DenseNet backbones.
CNN Workloads Characterization and Integrated CPU–GPU DVFS Governors on Embedded Systems

Dynamic power management (DPM) techniques on mobile systems are indispensable for deep learning (DL) inference optimization, which is mainly performed on battery-based mobile and/or embedded platforms with constrained resources. To this end, we characterize CNN workloads using object detection applications of YOLOv4/-tiny and YOLOv3/-tiny, and then propose integrated CPU–GPU DVFS governor policies that scale integrated pairs of CPU and GPU frequencies to improve energy–delay product (EDP) with negligible inference execution time degradation. Our results show up to 16.7% EDP improvements with negligible (mostly less than 2%) performance degradation using object detection applications on NVIDIA Jetson TX2.
Differentiable Slimming for Memory-Efficient Transformers

Transformer models are continuously achieving state-of-the-art performance on a wide range of benchmarks. To meet demanding performance targets, the number of model parameters is continuously increased. As a result, state-of-the-art Transformers require substantial computational resources prohibiting their deployment on consumer-grade hardware. In the literature, overparameterized Transformers are successfully reduced in size with the help of pruning strategies. Existing works lack the ability to optimize the full architecture, without incurring significant overheads, in a fully differentiable manner. Our work proposes a single-stage approach for training a Transformer for memory-efficient inference and various resource-constrained scenarios. Transformer blocks are extended with trainable gate parameters, which attribute importance and control information flow. Their integration into a differentiable pruning-aware training scheme allows the extraction of extremely sparse subnetworks at runtime, with minimal performance degradation. Evaluative pruning results, at the attention head and layer levels, illustrate the memory efficiency of our trained subnetworks under various memory budgets.
Revolutionizing Perimeter Intrusion Detection: A Machine Learning-Driven Approach With Curated Dataset Generation for Enhanced Security

Perimeter intrusion detection systems (PIDS) play a crucial role in safeguarding critical infrastructures from unauthorized access and potential security breaches. Security is the main concern everywhere in the world. There are already many PIDS available, but the PID systems are still lacking in terms of probability of detection, false intrusion, and the activity recognition of intrusion. To solve the above problem, we designed a prototype for PIDS using a DHT22 temperature and humidity sensor, vibration sensor SW- 420 Module Pinout, Mini PIR motion sensor, and Arduino UNO. After collecting the data from above mentioned sensors we applied machine learning algorithms DBSCAN to cluster the data points and K-NN classification to classify those clusters in one-dimensional data, but the results were not much satisfying. From there we got the motivation to improve the algorithm and applied it to two-dimensional data. The existing DBSCAN is not efficient due to its high complexity and the varying densities. To overcome these issues in this algorithm, we have improved the existing DBSCAN to ST-DBSCAN where we have used the estimation for the epsilon value and used the Manatton distance formula to find out the distance between points which produces 94.9853% accuracy on our dataset. Another contribution of the proposed work is that we have developed our own dataset named STPID-dataset, captured from security cameras installed in various locations which can be used by future researchers.
An Approximate Parallel Annealing Ising Machine for Solving Traveling Salesman Problems

Annealing-based Ising machines have emerged as high-performance solvers for combinatorial optimization problems (COPs). As a typical COP with constraints imposed on the solution, traveling salesman problems (TSPs) are difficult to solve using conventional methods. To address this challenge, we design an approximate parallel annealing Ising machine (APAIM) based on an improved parallel annealing algorithm. In this design, adders are reused in the local field accumulator units (LAUs) with half-precision floating-point representation of the coefficients in the Ising model. The momentum scaling factor is approximated by a linear, incremental function to save hardware. To improve the solution quality, a buffer-based energy calculation unit selects the best solution among the found candidate results in multiple iterations. Finally, approximate adders are applied in the design for improving the speed of accumulation in the LAUs. The design and synthesis of a 64-spin APAIM show the potential of this methodology in efficiently solving complicated constrained COPs.