Reviewerly/exHarmony huggingface dataset:
Optimizing makespan and resource utilization for multi-DNN training in GPU cluster

Deep neural network (DNN) has been widely applied in many fields of artificial intelligence (AI), gaining great popularity both in industry and academia. Increasing the size of DNN models does dramatically improve the learning accuracy. However, training large-scale DNN models on a single GPU takes unacceptable waiting time. In order to speed up the training process, many distributed deep learning (DL) systems and frameworks have been published and designed for parallel DNN training with multiple GPUs. However, most of the existing studies concentrate only on improving the training speed of a single DNN model under centralized or decentralized systems with synchronous or asynchronous approaches. Few works consider the issue of multi-DNN training on the GPU cluster, which is the joint optimization problem of job scheduling and resource allocation. This paper proposes an optimizing makespan and resource utilization (OMRU) approach to minimize job completion time and improve resource utilization for multi-DNN training in a GPU cluster. Specifically, we first collect the training speed/time data of all DNN models by running a job for one epoch on a different number of GPUs. The OMRU algorithm, integrating job scheduling, resource allocation, and GPU reuse strategies, is then devised to minimize the total job completion time (also called makespan) and improve GPU cluster resource utilization. The linear scaling rule (LSR) is adopted for adjusting the learning rate when a DNN model is trained on multiple GPUs with large minibatch size, which can guarantee model accuracy without the other hyper-parameters tune-up. We implement the OMRU algorithm on the Pytorch with Ring-Allreduce communication architecture and a GPU cluster with 8 nodes, each of which has 4 NVIDIA V100 GPUs. Experimental results on image classification and action recognition show that OMRU achieves a makespan reduction of up to 30% compared to the baseline scheduling algorithms and reach an average of 98.4% and 99.2% resource utilization on image classification and action recognition, respectively, with the state-of-the-art model accuracy.
Keyword-based Vehicle Retrieval

Natural language-based vehicle retrieval system makes controlling a city-scale traffic system easy to maintain and adaptable to changing requirements. It provides a convenient means in managing traffic flows or detecting accidents related to a specific vehicle. Such a system is different from most query-based video retrieval systems because the language for traffic situations and visible objects in traffic video streams are limited. Existing techniques for language-based general video retrieval problems measure the similarity between language representations and video representations. Our system focuses on several features that can distinguish vehicles from others. Particularly, our proposed vehicle retrieval system defines a set of features that can differentiate a vehicle from others and calculates the similarity between queries and video frames based on the features. The proposed technique places our approach in the third place in the 2021 AI City Challenge.
A stabilization approach for a novel chaotic fractional-order discrete neural network

This paper aims to make a contribution to the topic of the stabilization of chaotic dynamics in fractional-order discrete systems by presenting a novel three-dimensional fractional-order discrete neural network formulated by the h-fractional difference operator. A novel theorem is illustrated with the aim of stabilizing the chaotic trajectories of the three-dimensional fractional-order discrete neural network at zero through proposing a quite simple linear control laws. Finally, certain simulation results are carried out to highlight the effectiveness of the stabilization approach proposed herein.
Artificial Intelligence in Digital Pathology: What Is the Future? Part 1: From the Digital Slide Onwards

This commentary aims to address the field of Artificial intelligence (AI) in Digital Pathology (DP) both in terms of the global situation and research perspectives. It has four polarities. First, it revisits the evolutions of digital pathology with particular care to the two fields of the digital cytology and the digital histology. Second, it illustrates the main fields in the employment of AI in DP. Third, it looks at the future directions of the research challenges from both a clinical and technological point of view. Fourth, it discusses the transversal problems among these challenges and implications and introduces the immediate work to implement.
Fast Local Spatial Verification for Feature-Agnostic Large-Scale Image Retrieval

Images from social media can reflect diverse viewpoints, heated arguments, and expressions of creativity, adding new complexity to retrieval tasks. Researchers working on Content-Based Image Retrieval (CBIR) have traditionally tuned their algorithms to match filtered results with user search intent. However, we are now bombarded with composite images of unknown origin, authenticity, and even meaning. With such uncertainty, users may not have an initial idea of what the search query results should look like. For instance, hidden people, spliced objects, and subtly altered scenes can be difficult for a user to detect initially in a meme image, but may contribute significantly to its composition. It is pertinent to design systems that retrieve images with these nuanced relationships in addition to providing more traditional results, such as duplicates and near-duplicates - and to do so with enough efficiency at large scale. We propose a new approach for spatial verification that aims at modeling object-level regions using image keypoints retrieved from an image index, which is then used to accurately weight small contributing objects within the results, without the need for costly object detection steps. We call this method the Objects in Scene to Objects in Scene (OS2OS) score, and it is optimized for fast matrix operations, which can run quickly on either CPUs or GPUs. It performs comparably to state-of-the-art methods on classic CBIR problems (Oxford 5K, Paris 6K, and Google-Landmarks), and outperforms them in emerging retrieval tasks such as image composite matching in the NIST MFC2018 dataset and meme-style imagery from Reddit.
Digital Literacy and its Relationship with Employee Performance in the 4IR

Work in the oil and gas industry today in general has rapidly grow alongside with the advancement of digital technology. However, many employees are facing issues of coping and keeping up with the technological knowledge in completing their work. This study investigates the relationship between digital literacy and employee performance in the oil and gas industry in Malaysia. The technological, organizational and environmental (TOE) context was applied to measure the influencing factors. Stratified sampling method was used to attain information from two divisions of the organization. Data was analyzed using correlational analysis and multiple regression methods. Outcome of this study suggests that there is a positive relationship between digital literacy in technological context, organizational context, environmental context and employee performance. It is suggested that a good foundation in the technological and organizational infrastructure is needed to increase digital literacy among employees. For future research, readiness in embracing digital literacy should be made at the managerial level in this industry.
HILDIF: Interactive Debugging of NLI Models Using Influence Functions

Biases and artifacts in training data can cause unwelcome behavior in text classifiers (such as shallow pattern matching), leading to lack of generalizability.One solution to this problem is to include users in the loop and leverage their feedback to improve models.We propose a novel explanatory debugging pipeline called HILDIF, enabling humans to improve deep text classifiers using influence functions as an explanation method.We experiment on the Natural Language Inference (NLI) task, showing that HILDIF can effectively alleviate artifact problems in fine-tuned BERT models and result in increased model generalizability.
Applicability of Continuous, Stationary, and Discrete Wavelet Transforms in Engineering Signal Processing

Wavelet-based signal processing techniques are widely applied in multiple disciplines. However, few studies consider the applicability of different wavelet transforms in engineering signal processing fields. Based on the role of the wavelet transform, four engineering signal processing fields are classified, namely, singularity detection, denoising, time-frequency analysis, and sparse representation. Moreover, to clarify the confusion between wavelet transforms and the corresponding algorithms, this study compares the continuous, stationary, and discrete wavelet transforms and their corresponding algorithms, namely, the continuous wavelet convolution algorithm, á trous algorithm, and multiresolution algorithm, respectively. Both self-generated signals and engineering signals are applied to test the applicability of different wavelet-based algorithms in different engineering problems. The results show that all three wavelet-based algorithms could be applied in singularity detection; of these, the á trous algorithm was preferred for its translation invariance and filter property. Both the á trous and multiresolution algorithms could be applied in denoising due to their filter property together with decomposition and reconstruction algorithms. However, only the multiresolution algorithm could be applied in the time-frequency analysis and sparse representation due to its nonredundant property, filter property, and decomposition and reconstruction algorithms. These results provide references for engineers to select a proper wavelet-based algorithm in practice.
GANMIA: GAN-based Black-box Membership Inference Attack

Membership inference attacks (MIAs) against machine learning systems have drawn tremendous attention from information security researchers. By MIA, an adversary can speculate whether an individual data record is a member of the training set or not. Existing black-box MIA assumes that much information about the training data is available. Specifically, the attacker assumes that (s)he has the ability to query the target model without limitations or can access a sufficient dataset whose distribution is the same as the training data set. However, in a realistic scenario, MIAs usually come up with the limited number and the imbalanced proportion of target training datasets which cause significant challenges for MIAs. To launch an MIA in the realistic scenario, in this paper, we present a novel method called GANMIA, which generates synthetic data to augment the training samples of the shadow model for the black-box MIA by a Generative Adversarial Network (GAN). GANMIA firstly augments synthesized samples and then uses the generated samples to train the given shadow model to increase the training efficiency, and additionally improve the MIA’s performance. The experimental results show that the accuracy of the black-box MIA increases by 23% with the help of our synthetic data.
Collaborative Target Search Algorithm for UAV Based on Chaotic Disturbance Pigeon-Inspired Optimization

Unmanned aerial vehicles (UAVs) have shown their superiority in military and civilian missions. In the face of complex tasks, many UAVs are usually needed to cooperate with each other. Therefore, multi-UAV cooperative target search has attracted more and more scholars’ attention. At present, there are many bionic algorithms for solving the cooperative search problem of multi-UAVs, including particle swarm optimization algorithm (PSO) and differential evolution (DE). Pigeon-inspired optimization (PIO) is a new swarm intelligence optimization algorithm proposed in recent years. It has great advantages over other algorithms in convergence, robustness, and accuracy, and has few parameters to be adjusted. Aiming at the shortcomings of the standard pigeon colony algorithm, such as poor population diversity, slow convergence speed, and the ease of falling into local optimum, we have proposed chaotic disturbance pigeon-inspired optimization (CDPIO) algorithm. The improved tent chaotic map was used to initialize the population and increase the diversity of the population. The disturbance factor is introduced in the iterative update stage of the algorithm to generate new individuals, replace the individuals with poor performance, and carry out disturbance to increase the optimization accuracy. Benchmark functions and UAV target search model were used to test the algorithm performance. The results show that the CDPIO had faster convergence speed, better optimization precision, better robustness, and better performance than PIO.
The Impact of Chatbots in Driving Digital Transformation

Digital transformation is the process of consuming digital technologies that drives business improvements and customer experience. Artificial intelligence plays a crucial role in businesses' digital transformation agendas. Technologies and algorithms are an important perspective in the implementation of chatbots. AI chatbots use natural language processing technology and offer solutions for modernizing traditional business processes. The key advantage of the implementation of chatbots in different domains is the impact by improving customers' experience and reducing costs. Chatbots are pieces of software that simulate human conversation through voice commands and/or text chats, intending to offer companies an approach on how to use this software to revolutionize their businesses. The aim of this paper is the analysis of the evaluation criteria; the study of insights into how chatbots can be implemented in the domains of banking, e-commerce, tourism, and call centres; and the discussion of some benefits and challenges of chatbots in driving the digital transformation of businesses.
Propositions-as-types and shared state

We develop a principled integration of shared mutable state into a proposition-as-types linear logic interpretation of a session-based concurrent programming language. While the foundation of type systems for the functional core of programming languages often builds on the proposition-as-types correspondence, automatically ensuring strong safety and liveness properties, imperative features have mostly been handled by extra-logical constructions. Our system crucially builds on the integration of nondeterminism and sharing, inspired by logical rules of differential linear logic, and ensures session fidelity, progress, confluence and normalisation, while being able to handle first-class shareable reference cells storing any persistent object. We also show how preservation and, perhaps surprisingly, progress, resiliently survive in a natural extension of our language with first-class locks. We illustrate the expressiveness of our language with examples highlighting detailed features, up to simple shareable concurrent ADTs.
Trilateral Attention Network for Real-Time Cardiac Region Segmentation

The accurate segmentation of cardiac images into anatomically meaningful regions is critical for the extraction of quantitative cardiac indices. The common pipeline for segmentation comprises regions of interest (ROIs) localization and segmentation stages that are independent of each other and typically performed using separate models. In this paper, we propose an end-to-end network, called Trilateral Attention Network (TaNet), for real-time region localization and segmentation. TaNet has a module for ROIs localization and three segmentation pathways: spatial pathway, handcrafted pathway, and context pathway. The localization module focuses segmentation attention on the desired region while learning the context relationship between different regions in the image. The localized regions are then sent to the three pathways for segmentation. The spatial pathway, which has regular convolutional kernels, is used to extract deep features at different levels of abstraction. The handcrafted pathway, which has hand-designed convolutional kernels, is used to extract a unique set of features complementary to the deep features. Finally, the context (or global) pathway is used to enlarge the receptive field. By jointly training TaNet for localization and segmentation, TaNet achieved superior performance, in terms of accuracy and speed, when evaluated on two echocardiography datasets for cardiac region segmentation.
Improving EFL Undergraduate Learners’ Speaking Skills Through Project-Based Learning Using Presentation

Project-based learning has been variously integrated into EFL instruction to promote learners’ language learning and skills. The objectives of this research study were to examine the effects of project-based learning using presentation on EFL undergraduate learners’ speaking skills. Furthermore, this study sought to explore learners’ opinions on the use of project-based learning using presentation. The participants of this study were 31 second-year undergraduate students who majored in Business English. The research instruments in this study included speaking tests, project-based learning instructions, questionnaires, and interviews. The obtained data were analyzed by quantitative and qualitative methods for the result interpretation. The findings of this study revealed that students’ speaking skills had significantly improved after the project-based learning implementation. Moreover, the students expressed positive opinions towards project-based learning using presentation as it was perceived to have helped improve their speaking skills. The findings from the interviews revealed that the project-based learning using presentation was positive for learners’ speaking skills according to their presentation tasks, scaffolding activities, and practice of integrative skills. A cooperative learning environment is perceived as useful for learners’ speaking skill improvement. However, time constraint posed a major challenge in the study. Further studies are recommended to encompass a community-based context for speaking skill improvement. Instructional implications derived from the findings include implementing project-based learning using presentation in order to provide learners with an authentic English speaking context and to promote the integrated skills.
Facial image noise classification and denoising using neural network

Image denoising is an important aspect of image processing. Noisy images are produced as a result of technical and environmental flaws. As a result, it is reasonable to consider image denoising an important topic to research, as it also aids in the resolution of other image processing issues. The challenge, however, is that the traditional techniques used are time-consuming and inflexible. This article purposed a system of classifying and denoising noised images. A CNN and UNET based model architecture is designed, implement, and evaluated. The facial image dataset is processed and then it is used to train, valid and test the models. During preprocessing, the images are resized into 48*48, normalize, and various noises are added to the image. The preprocessing for each model is a bit different. The training and validation accuracy for the CNN model is 99.87% and 99.92% respectively. The UNET model is also able to get optimal PSNR and SSIM values for different noises.
Background Invariant Faster Motion Modeling for Drone Action Recognition

Visual data collected from drones has opened a new direction for surveillance applications and has recently attracted considerable attention among computer vision researchers. Due to the availability and increasing use of the drone for both public and private sectors, it is a critical futuristic technology to solve multiple surveillance problems in remote areas. One of the fundamental challenges in recognizing crowd monitoring videos’ human action is the precise modeling of an individual’s motion feature. Most state-of-the-art methods heavily rely on optical flow for motion modeling and representation, and motion modeling through optical flow is a time-consuming process. This article underlines this issue and provides a novel architecture that eliminates the dependency on optical flow. The proposed architecture uses two sub-modules, FMFM (faster motion feature modeling) and AAR (accurate action recognition), to accurately classify the aerial surveillance action. Another critical issue in aerial surveillance is a deficiency of the dataset. Out of few datasets proposed recently, most of them have multiple humans performing different actions in the same scene, such as a crowd monitoring video, and hence not suitable for directly applying to the training of action recognition models. Given this, we have proposed a novel dataset captured from top view aerial surveillance that has a good variety in terms of actors, daytime, and environment. The proposed architecture has shown the capability to be applied in different terrain as it removes the background before using the action recognition model. The proposed architecture is validated through the experiment with varying investigation levels and achieves a remarkable performance of 0.90 validation accuracy in aerial action recognition.
Status of Earthquake Early Warning in Switzerland

The Swiss Seismological Service (SED) at ETH has been developing methods and open-source software for Earthquake Early Warning (EEW) for more than a decade and has been using SeisComP for earthquake monitoring since 2012. The SED has built a comprehensive set of SeisComP modules that can provide EEW solutions in a quick and transparent manner by any seismic service operating SeisComP. To date, implementations of the Virtual Seismologist (VS) and Finite-Fault Rupture Detector (FinDer) EEW algorithms are available. VS provides rapid EEW magnitudes building on existing SeisComP detection and location modules for point-source origins. FinDer matches growing patterns of observed high-frequency seismic acceleration amplitudes with modeled templates to identify rupture extent, and hence can infer on-going finite-fault rupture in real-time. Together these methods can provide EEW for all event dimensions from moderate to great, if a high quality, EEW-ready, seismic network is available. In this paper, we benchmark the performance of this SeisComP-based EEW system using recent seismicity in Switzerland. Both algorithms are observed to be similarly fast and can often produce first EEW alerts within 4–6 s of origin time. In real time performance, the median delay for the first VS alert is 8.7 s after origin time (56 earthquakes since 2014, from M2.7 to M4.6), and 7 s for FinDer (10 earthquakes since 2017, from M2.7 to M4.3). The median value for the travel time of the P waves from event origin to the fourth station accounts for 3.5 s of delay; with an additional 1.4 s for real-time data sample delays. We demonstrate that operating two independent algorithms provides redundancy and tolerance to failures of a single algorithm. This is documented with the case of a moderate M3.9 event that occured seconds after a quarry blast, where picks from both events produced a 4 s delay in the pick-based VS, while FinDer performed as expected. Operating on the Swiss Seismic Network, that is being continuously optimised for EEW, the SED-ETHZ SeisComP EEW system is achieving performance that is comparable to operational EEW systems around the world.