Reviewerly/exHarmony huggingface dataset:
GSSP: Eliminating Stragglers Through Grouping Synchronous for Distributed Deep Learning in Heterogeneous Cluster

Distributed deep learning has been widely used in training deep neural networks, especially for big models on massive datasets. Parameter Server (PS) architecture is the most popular distributed training framework, which can flexibly design the global parameter update manner. However, when scaling to complex heterogeneous clusters, stragglers make it difficult for existing distributed paradigms on PS framework to balance between synchronous waiting and staleness, which slows down the model training sharply. In this article, we propose <i>Grouping Stale Synchronous Parallel</i> (GSSP) scheme, which groups workers with similar performance together. Group servers coordinate intra-group workers using Stale Synchronous Parallel while they communicate with each other asynchronously to eliminate stragglers and refine the model weights. We further propose <i>Grouping Dynamic Tok-K Sparsification</i> (GDTopK), which dynamically adjusts the upload ratio for each group so as to make communication volume differentiated and mitigate inter-group iteration speed gap. We have conducted experiments on LeNet-5 on MNIST, ResNet-18, VGG-19 on Cifar-10, and Seq2Seq on Multi30k. Results show that GSSP accelerates the training by 46% <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 120%, with less than 1 percent accuracy drop. And GDTopK can make up for part of the lost accuracy.
Automatic Tuning of Stochastic Gradient Descent with Bayesian Optimisation

Many machine learning models require a training procedure based on running stochastic gradient descent. A key element for the efficiency of those algorithms is the choice of the learning rate schedule. While finding good learning rates schedules using Bayesian optimisation has been tackled by several authors, adapting it dynamically in a data-driven way is an open question. This is of high practical importance to users that need to train a single, expensive model. To tackle this problem, we introduce an original probabilistic model for traces of optimisers, based on latent Gaussian processes and an auto-/regressive formulation, that flexibly adjusts to abrupt changes of behaviours induced by new learning rate values. As illustrated, this model is well-suited to tackle a set of problems: first, for the on-line adaptation of the learning rate for a cold-started run; then, for tuning the schedule for a set of similar tasks (in a classical BO setup), as well as warm-starting it for a new task.
Syllable-Based Acoustic Modeling With Lattice-Free MMI for Mandarin Speech Recognition

Most automatic speech recognition (ASR) systems in past decades have used context-dependent (CD) phones as the fundamental acoustic units. However, these phone-based approaches lack an easy and efficient way for modeling long-term temporal dependencies. Compared with phone units, syllables span a longer time, typically several phones, thereby having more stable acoustic realizations. In this work, we aim to train a syllable-based acoustic model for Mandarin ASR with lattice-free maximum mutual information (LF-MMI) criterion. We expect that, the combination of longer linguistic units, the RNN-based model structure and the sequence-level objective function, can result in better modeling of long-term temporal acoustic variations. We make multiple modifications to improve the performance of syllable-based AM and benchmark our models on two large-scale databases. Experimental results show that the proposed syllable-based AM performs much better than the CD phone-based baseline, especially on noisy test sets, with faster decoding speed.
Machine learning pipeline for online shopper intention classification

Nowadays people prefer to buy online rather than buy on the spot. Online transactions make people's lives easier. This condition requires the seller to understand the characteristics of the intention of the prospective buyer. This research proposes a machine-learning pipeline to predict the customer behavior for e-commerce products. We compared several machine-learning algorithms to find the best algorithm to solve the problem then we deployed the model on a web application. Based on the experiment, the Random Forest algorithm can predict online shopper intention with 90% of accuracy.
Angelo Secchi and the Development of Geophysics in Italy

The various disciplines of Earth Sciences went through a crucial transition during the nineteenth century, as the naturalistic approach which had characterized the study of the planet since the Enlightenment was replaced by the application of modern experimental and quantitative methods, made possible by technological progress and by the invention of new instruments. Angelo Secchi played a leading role in this cultural revolution. Along with his work as a founder of modern astrophysics, meteorology, and applied sciences (vigorously promoting the importance of the useful applications of science), he can also be celebrated for having laid the foundations for the development of geophysics in Italy; as a result, between the end of the nineteenth century and the first decades of the twentieth century, Rome became the “moral capital” of that discipline. Secchi was also interested in geology and paleontology, following in the footsteps of Abbot Antonio Stoppani. Being also an amateur archaeologist, he spent time on field trips in the surroundings of Rome and in Central Apennines, making geological and paleontological observations (Chinnici, A biography of Angelo Secchi, Jesuit and scientist. Brill, 2019: 108–110; 199–200). Thus one can argue that Father Secchi was, in addition to everything else, a notable field geologist as well.
Identification of Interested Web Users Using Decision Tree Classifier

The development of the World Wide Web has evolved in an enormous volume of data; consequently, drawing out of useful knowledge is a demanding research issue. Use of data mining methods to the Web mentioned as Web mining. It goals at finding and extracting hidden knowledge from Web pages, and services bring out useful information from Web resources and discover common patterns on the Web for examining user activities in network-based systems. The key aim of this paper is to find interested users for a particular website. Hence, the requirement is to build a classification model that can categorize users into interested and non-interested visitors for a particular website based on their access pattern using Web server logs by making use of decision tree classification technique. It will be useful for website owners, to get potential users who are more interested in their website, so that the administration can provide higher privileges for them. Users will be benefited by the special service, which is not available for other users.
Recognition and classification of water surface targets based on deep learning

Abstract Aiming at the shortcomings of low recognition rate and low calculation rate of surface unmanned ship in complex time-varying water surface environment, a surface target detection method based on Faster R-CNN is proposed in this paper. Firstly, the water surface image was enhanced by McCann Retinex method to improve the image quality under complex background. Secondly, a water surface target data set was established. Finally, based on Faster R-CNN algorithm, VGG, Resnet and Inception network structures were employed to test and analyze the data set. The results show that the detection method proposed in this paper can effectively complete the identification and classification of six categories of common targets on the water surface, which has significant guiding significance for the autonomous obstacle avoidance and maritime search and rescue of unmanned ships.
WITHDRAWN: Application of RFID technology in Library: A view

RFID ready to be applied within the library circulation processes in thieving detection controls. RFID based mostly systems move on the far side security to become tracing systems that association security with additional economical trailing of resources throughout the library, together with easier and quicker charging and discharging, stocktaking, and handling. RFID comprises sensible RFID labels, hardware and computer code provides libraries with simpler approach of handling their collections whereas providing larger client service to their patrons. RFID technology helps librarians to cut back valuable workers time spent scanning barcodes whereas provision and returning things. RFID may be a integration of radio frequency-based technology and semiconductor device technology. this text describes parts of RFID system in library like RFID tag, reader, security gate and antenna. any benefits of RFID technology in library and standards of RFID in library have additionally been explored.
WebTour 2021 Workshop on Web Tourism

Over the years, the Web has become a premier source of information in almost every area we can think about. When considering tourism, the Web became the primary source of information for travelers. When planning trips, people search for information about destinations, accommodations, attractions, means of transportation, in short, everything related to their future trip. Once done searching they reserve almost everything online. The blessing of the easily accessible information comes with the curse of information overload. This brings Web search techniques and recommendation systems come into play. This is especially true recently with the appearance of COVID-19 and the uncertainty and transformative power it brings to travelling. WebTour 2021 brings together researchers and practitioners working on developing and improving tools and techniques for improving users ability to better find relevant information that matches their needs.
Exploring the Time-efficient Evolutionary-based Feature Selection Algorithms for Speech Data under Stressful Work Condition

Initially, the goal of Machine Learning (ML) advancements is faster computation time and lower computation resources, while the curse of dimensionality burdens both computation time and resource. This paper describes the benefits of the Feature Selection Algorithms (FSA) for speech data under workload stress. FSA contributes to reducing both data dimension and computation time and simultaneously retains the speech information. We chose to use the robust Evolutionary Algorithm, Harmony Search, Principal Component Analysis, Genetic Algorithm, Particle Swarm Optimization, Ant Colony Optimization, and Bee Colony Optimization, which are then to be evaluated using the hierarchical machine learning models. These FSAs are explored with the conversational workload stress data of a Customer Service hotline, which has daily complaints that trigger stress in speaking. Furthermore, we employed precisely 223 acoustic-based features. Using Random Forest, our evaluation result showed computation time had improved 3.6 faster than the original 223 features employed. Evaluation using Support Vector Machine beat the record with 0.001 seconds of computation time.
An Improved Image Steganography Scheme Based on Partial Preservation Embedding Algorithm for Wireless Visual Sensor Networks

With the continuous improvement of encryption algorithms, some applications based on the architecture of wireless visual sensor networks have gradually shifted their attention to the imperceptibility and antijamming performance of secret images. To reduce the probability of secret images being detected, the current research focuses on hiding secret data in the least-significant bit of the cover image in the spatial domain or embedding data into the coefficients of the high-frequency band in the transformational domain, which usually leads to poor performance in a hostile environment. Therefore, some researchers proposed to substitute the coefficients of the medium-frequency band in the transformational domain with secret information to enhance the anti-interference performance. However, this idea would severely affect the imperceptibility of secret images. As a result, an improved version based on the partial preservation embedding algorithm was designed in this paper. Theory analysis and simulation results indicate that the proposed scheme performs better than the existing methods by directly substituting the coefficients of the medium-frequency band in the transformational domain, especially in the case of strong noise interference.
Active Probing for Improved Machine-Learned Recognition of Network Traffic

Information about the network protocols used by the background traffic can be important to the foreground traffic. Whether that knowledge is exploited via optimization through protocol selection (OPS) or through other forms of parameter tuning, a machine-learned classifier is one tool to identifying background traffic protocols. Unfortunately, global knowledge can be difficult to obtain in a dynamic distributed system like a shared, wide-area network (WAN). Previous techniques for protocol identification have focused on passive or end-point signals for classification. For example, end-to-end round trip time (RTT) can, especially when gathered as a time series, reveal a lot about what is happening on the network. Other related signals, such as bandwidth, and the number of retransmissions can also be used for protocol classification. However, as noted, these signals are typically gathered by passive means, which may limit their usefulness. We introduce and provide a proof-of-concept of active probing, which is the systematic and deliberate perturbation of traffic on a network for the purpose of gathering information. The time-series data generated by active probing improves our machine-learned classifiers because different network protocols react differently to the probing. Whereas passive probing might be limiting the time series observations to a period of steady state (e.g., saturated network), active probing forces the system out of that steady state. We show that active probing improves on prior work (with passive probing of RTT) by between 7% to 16% in additional accuracy (depending on the window size), and reaching 90% averages in precision, recall, and F1-scores.
A Holistic Neural Networks Classification for Wangiri Fraud Detection in Telecommunications Regulatory Authorities

Cybercrimes and fraud techniques are major threats to telecommunications sectors in the last decade, one of those fraud approaches called Wangiri fraud. Wangiri is a common type of fraud techniques in telecommunications sector, the definition is originated from a Japanese word that means (one & cut); as the fraudsters depend on a single ring method to gain illegal money from the subscribers. Consequently, the approaches that are used to detect fraud cases are used to classify subscribers based on their behaviors such as data extraction that identifies patterns in large datasets through a combination of statistical methods, artificial intelligence and databases. Neural networks are used to process & evaluate the given datasets; in order to uncover ambiguous communication and secret data patterns. This paper proposes the usage of AI neural networks to overcome the highly predictive wangiri fraud in a telecom dataset and to make an effective and convenient classification. ISTAT was used to test both accuracy and efficiency of the proposed method.
Linear Depth Deduction with Subformula Property for Intuitionistic Epistemic Logic

In their seminal paper Artemov and Protopopescu provide Hilbert formal systems, Brower–Heyting–Kolmogorov and Kripke semantics for the logics of intuitionistic belief and knowledge. Subsequently Krupski has proved that the logic of intuitionistic knowledge is PSPACE-complete and Su and Sano have provided calculi enjoying the subformula property. This paper continues the investigations around sequent calculi for Intuitionistic Epistemic Logics by providing sequent calculi that have the subformula property and that are terminating in linear depth. Our calculi allow us to design a procedure that for invalid formulas returns a Kripke model of minimal depth. Finally we also discuss refutational sequent calculi, that is sequent calculi to prove that formulas are invalid.
Synthetic Data for the Analysis of Archival Documents: Handwriting Determination

Archives contain a wealth of information and are invaluable for historical research. Thanks to digitization, many archives are preserved in a digital format, making it easier to share and access documents from an archive. Handwriting and handwritten notes are commonly found in archives and contain a lot of information that can not be extracted by analyzing documents with Optical Character Recognition (OCR) for printed text. In this paper, we present an approach for determining whether a scan of a document contains handwriting. As a preprocessing step, this approach can help to identify documents that need further analysis with a full recognition pipeline. Our method consists of a deep neural network that classifies whether a document contains handwriting. Our method is designed in such a way that we overcome the most significant challenge when working with archival data, which is the scarcity of annotated training data. To overcome this problem, we introduce a data generation method to successfully train our proposed deep neural network. Our experiments show that our model, trained on synthetic data, can achieve promising results on a real-world dataset from an art-historical archive.
Genetic Feature Fusion for Object Skeleton Detection

Object skeleton detection requires the convolutional neural networks to recognize objects and their parts in the cluttered background, overcome the image definition degradation brought by the pooling layers, and predict the location of skeleton pixels in different scale granularity. Most existing object skeleton detection methods take great efforts into the designing of side-output networks for multiscale feature fusion. Despite the great progress achieved by them, there are still many problems that hinder the development of object skeleton detection, such as the manually designed network is labor-intensive and the network initialization depends on models pretrained on large-scale datasets. To alleviate these issues, we propose a genetic NAS method to automatically search on a newly designed architecture search space for adaptive multiscale feature fusion. Furthermore, we introduce a symmetric encoder-decoder search space based on reversing the VGG network, in which the decoder can reuse the ImageNet pretrained model of VGG. The searched networks improve the performance of the state-of-the-art methods on commonly used skeleton detection benchmarks, which proves the efficacy of our method.
Research and Application of Intelligent Learning System for Power Grid All-Element Simulation Based on Microservice

Abstract This paper introduces a development method of a micro-service-based power grid full-element simulation smart learning system. It explains in detail how to make full use of the microservice technology to develop services for the power system simulation training platform to solve the collaborative problems of different scopes, different characteristics, and different types of simulation calculations. The method can effectively use the computer resources of the training system, avoid the waste of resources of the training system on the simulation computing platform, and reduce the investment in hardware equipment. Finally, this article verifies the feasibility of the development method proposed in the article through a practical case.
Monitoring Indoor Living Spaces using Depth Information

Longer life expectancy is resulting in a steady increase in population that needs specific services to support their everyday routines. Public and private structures that provide services to these communities exist, however the increasing demands for service place these structures under stress and increased expenses. Assistive living systems can help reduce the demand and cost for these services by supporting the elderly at their homes, improving their quality of life in the process. In this paper we propose a solution that solely uses the depth information from RGB-D cameras to monitor the elderly within indoor living spaces. Deep learning on depth video data is used to detect the elderly and report the position to an application. This position information creates paths over time that can be monitored remotely by family members and caregivers to understand the behavior of the elderly and take appropriate action when needed. Experimental results show that the system manages to detect the person with an accuracy of 66.5% and a tracking accuracy of 59.1%.
UNET-Based Multi-Task Architecture for Brain Lesion Segmentation

Image segmentation is the task of extracting the region of interest in images and is one of the main applications of computer vision in the medical domain. Like other computer vision tasks, deep learning is the main solution to image segmentation problems. Deep learning methods are data-hungry and need a huge amount of data for training. On the other side, data shortage is always a problem, especially in the medical domain. Multi-task learning is a technique which helps the deep model to learn better representation from data distribution by introducing related auxiliary tasks. In this study, we investigate a research question to whether it is better to provide this auxiliary information as an input to the network, or is it better to use this task and design a multi-output network. Our findings suggest that however, the multi-output manner improves the overall performance, but the best result achieves when this extra information serves as auxiliary input information.
Gecko: Guaranteeing Latency SLO on a Multi-Tenant Distributed Storage System

Meeting tail latency Service Level Objective (SLO) as well as achieving high resource utilization is important to distributed storage systems. Recent works adopt strict priority scheduling or constant rate limiting to provide SLO guarantee but cause under-utilization resources. To address this issue, we first analyze the relationship between workload burst and latency SLO. Based on burst patterns and latency SLOs, we classify tenants into two categories: Postponement-Tolerable tenant and Postponement-Intolerable tenant. We then explore the opportunity to improve resource utilization by carefully allocating resources to each tenant type. We design Rate-Limiting-Priority scheduling algorithm to limit the impact of high priority tenants on low priority ones. Meanwhile, we propose Postponement-Aware scheduling algorithm which allows Postponement-Intolerable tenants to preempt system capacity from Postponement-Tolerable tenants. This helps to increase resource utilization. We propose a latency SLO guarantee framework Gecko. Gecko guarantees multi-tenant latency SLOs via combining the two proposed scheduling algorithms together with an admission control strategy. We evaluate Gecko with real production traces and the results show that Gecko admits 44% more tenants on average than state-of-the-art techniques meanwhile guaranteeing latency SLO.