Reviewerly/exHarmony huggingface dataset:
RIFLING: A reinforcement learning‐based GPU scheduler for deep learning research and development platforms

Abstract GPU platforms have been widely adopted in both academia and industry to support deep learning (DL) research and development (R&amp;D). Compared with giant companies who favor custom‐designed AI platforms, most small‐and‐medium‐sized enterprises, institutes and universities (EIUs) prefer to build or rent a cost‐effective GPU cluster, usually in a limited‐scale, to process diverse DL R&amp;D workloads. Therefore, more attention has been attracted by DL scheduling with the aim of improving the system efficiency and task performance. However, prior prediction‐based schedulers are limited in terms of their prediction accuracy and profiling overhead. Accordingly, in this article, we propose a reinforcement learning (RL)‐based online GPU scheduler, RIFLING , to model the scheduling problem as an online decision‐making process. Scheduling decisions are made according to Q‐learning, which is a typical RL method. RIFLING can achieve high scheduling efficiency based on the online exploring and exploiting of diverse scheduling strategies for various DL workloads, without the need for expensive offline profiling or sophisticated prediction model. We implement RIFLING as a plugin of Tensorflow, and deploy it on a distributed GPU cluster. Experiments demonstrate that RIFLING achieves up to 47.8% reductions and 19.6% improvements in makespan and average normalized processing rate respectively compared to the best available baseline without any manual intervention.
TransJury: Towards Explainable Transfer Learning through Selection of Layers from Deep Neural Networks

Training a neural network model from scratch is a computationally intensive operation. To alleviate this issue, researchers often employ "transfer learning" that transfers knowledge from a source data distribution to a target data distribution, instead of training the whole model from scratch. Typically, the last few layers of a pretrained convolutional neural network (CNN) are chosen for many transfer learning tasks where the outputs of those selected layers are combined to construct a feature space based on which a task-specific classification n etwork i s t rained o r fi ne-tuned. Th is arbitrary way of selecting layers, however, often fails to achieve the desired accuracy for the target task. What we need is an intelligent way of selecting layers from a pretrained model for a given task so that the additional overhead of successive training remains low. To this end, we propose a novel method, called TransJury, to find t he m ost s ignificant la yers from a pretrained mo del for transfer learning along with preserving the knowledge for the source domain. Through extensive experimentation on several target domain datasets, we show the supremacy of our approach in terms of lower training overhead and improved accuracy. By deploying MobileNet-v2, a lightweight CNN model pretrained on the ImageNet dataset on an edge device, we also discuss the future direction of this research.
Hybrid Marine Predator Algorithm with Simulated Annealing for Feature Selection

The Marine Predator Algorithm (MPA) is a recently proposed meta-heuristic algorithm based on the survival of the fittest theory. Despite being an outstanding optimization technique, the conventional MPA could not achieve the optimum value of the design parameter due to its premature convergence (PC). The predictive model failed to yield higher accuracy in the process of feature selection from the high-dimensional microarray datasets of life-threatening diseases due to the anomaly of PC in MPA. To address the shortcomings of the existing MPA in feature selection, this paper proposes an improved version of the MPA (iMPA) that uses simulated annealing (SA) to improve its ability to explore a larger design space. The performance of the iMPA is investigated on high-dimensional microarray cancer datasets. The experimental results of the proposed approach outperform conventional MPA optimization techniques and show significant developments in knowledge discovery from high-dimensional microarray datasets.
SAC-ABR: Soft Actor-Critic based deep reinforcement learning for Adaptive BitRate streaming

Adaptive Bit Rate (ABR) assignment plays a crucial role for ensuring satisfactory quality of experience (QoE) in video streaming applications. Recently the authors of [1] proposed to use reinforcement learning (RL) based asynchronous advantage actor-critic ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$A3C$</tex> ), an on-policy method, Pensieve, to improve ABR algorithms. It has shown to achieve a higher QoE as compared to other traditional ABR methods. However, Pensieve is sample inefficient and frail to different random seeds and hyperparameters. In this paper, we present soft actor-critic based deep reinforcement learning for adaptive bitrate streaming (SAC-ABR), an off-policy method, which improves the QoE as compared to other existing state-of-the-art ABR algorithms under a wide variety of network conditions. Based on the maximum entropy RL framework, SAC-ABR aims to maximize entropy while maximizing the expected rewards, hence achieving a better exploration-exploitation tradeoff as compared to on-policy ABR methods. We present the overall design together with the training and testing results of SAC-ABR, and evaluate its performance as compared to other state-of-the-art ABR algorithms. Our results show that SAC-ABR provides up to 27.42% higher average QoE as compared to Pensieve and much higher QoE when compared to other traditional fixed-rule based ABR algorithms.
Building High-throughput Neural Architecture Search Workflows via a Decoupled Fitness Prediction Engine

Neural networks (NN) are used in high-performance computing and high-throughput analysis to extract knowledge from datasets.Neural architecture search (NAS) automates NN design by generating, training, and analyzing thousands of NNs.However, NAS requires massive computational power for NN training.To address challenges of efficiency and scalability, we propose PENGUIN, a decoupled fitness prediction engine that informs the search without interfering in it.PENGUIN uses parametric modeling to predict fitness of NNs.Existing NAS methods and parametric modeling functions can be plugged into PENGUIN to build flexible NAS workflows.Through this decoupling and flexible parametric modeling, PENGUIN reduces training costs: it predicts the fitness of NNs, enabling NAS to terminate training NNs early.Early termination increases the number of NNs that fixed compute resources can evaluate, thus giving NAS additional opportunity to find better NNs.We assess the effectiveness of our engine on 6,000 NNs across three diverse benchmark datasets and three state of the art NAS implementations using the Summit supercomputer.Augmenting these NAS implementations with PENGUIN can increase throughput by a factor of 1.6 to 7.1.Furthermore, walltime tests indicate that PENGUIN can reduce training time by a factor of 2.5 to 5.3.
No-Reference Video Quality Assessment Using Voxel-Wise fMRI Models of the Visual Cortex

The performance of the human visual system is very efficient in many visual tasks such as identifying visual scenes, anticipating future actions based on the past observations, assessing the quality of visual stimuli, etc. A significant amount of effort has been directed towards finding quality aware representations of natural videos to solve the quality prediction task. In this work we present a novel no reference video quality assessment (NR-VQA) algorithm based on the functional Magnetic Resonance Imaging (fMRI) Blood Oxygen Level Dependent (BOLD) signal prediction with voxel-wise encoding models of the human brain. The voxel encoding models are learnt using deep features extracted from the AlexNet model to predict the fMRI response to natural video stimuli. We show that the curvature in the predicted voxel response time series provides good quality discriminability, and forms an important feature for quality prediction. Further, we show that the proposed curvature features in combination with the spatial index, temporal index and NIQE features deliver acceptable performance on the Video Quality Assessment (VQA) task on both synthetic and authentic distortion data-sets.
NVS-MonoDepth: Improving Monocular Depth Prediction with Novel View Synthesis

Building upon the recent progress in novel view synthesis, we propose its application to improve monocular depth estimation. In particular, we propose a novel training method split in three main steps. First, the prediction results of a monocular depth network are warped to an additional view point. Second, we apply an additional image synthesis network, which corrects and improves the quality of the warped RGB image. The output of this network is required to look as similar as possible to the ground-truth view by minimizing the pixel-wise RGB reconstruction error. Third, we reapply the same monocular depth estimation onto the synthesized second view point and ensure that the depth predictions are consistent with the associated ground truth depth. Experimental results prove that our method achieves state-of-the-art or comparable performance on the KITTI and NYU-Depth-v2 datasets with a lightweight and simple vanilla U-Net architecture.
Top interpretable neural network for handwriting identification

Machine learning (ML) has become one of the most promising tools in forensics, despite its dominant method of artificial neural networks (ANNs) suffering from the black-box problem. While forensic methodology demands explainability and evaluativity, neural networks are unexplainable, hence almost unfalsifiable. Our study was conducted to mitigate this problem in a case-like context, by creating a novel top interpretable neural network (TINN) for identification of the authors of handwritten documents. The idea of top interpretability assumes that it is irrelevant how the handwriting features are extracted from documents, as long as they are semantically sensible and the sole determinants of identification. The model was tasked with supervised extraction of handwriting characteristics and subsequent identification of the writers on that basis. The interpretable model not only outperformed all comparative models in terms of author identification, but also underperformed in terms of features extraction (achieving satisfactory results nonetheless). Visualizations of features-extracted by the model to perform its tasks-suggest that it considers rational and semantically sensible features of handwriting, but we are unable to determine whether it learned the exact features of handwriting we desired. The approach of top interpretability proved to be effective in terms of accuracy and interpretability. Furthermore, if we were to judge features extracted by such a network as unconvincing, then our approach is a highly efficient method of falsification. Lastly, the success of this study-performed on a small-scale identificational problem-suggests that a similar approach could yield better results on a large-scale identificational or verificational problem.
Research on coaxiality measurement system of large-span small-hole system based on laser collimation

Aiming at the problems of large-span small-hole system, such as long spatial span, small measured aperture, short hole depth, large distance between adjacent holes, limited measurement space, and lack of measurement benchmark, a non-contact coaxiality measurement system based on laser collimation with the laser displacement sensor as the core is proposed. According to the mathematical model, a position sensitive detector(PSD) and a motion platform with multiple degrees of freedom are utilized to establish a reference axis for parts at any position. Also, the relevant cross-section circle fitting and coaxiality evaluation algorithms are designed to solve the measurement problem. It is proved that this system has high coaxiality detection accuracy for the parts with large axial dimensions (total length is more than 1500 mm), small aperture (diameter is less than φ 10 mm) and large hole spacing (space between two holes is more than 750 mm), and the measurement error does not exceed 0.03 mm.
A 3D Object Recognition Method From LiDAR Point Cloud Based on USAE-BLS

Environmental perception provides the necessary information for unmanned ground vehicles to recognize and interact with surrounding objects. Velodyne light detection and ranging (LiDAR) is widely used for this purpose due to its significant advantages such as high precision and being uninfluenced by varying illuminations. However, the unstructured distribution of LiDAR point clouds always affects the performance of feature extraction and object recognition. Moreover, the numbers of parameters in most deep learning models of object recognition are very large and the training process costs lots of computation consumption. This paper proposes a broad learning system (BLS) variant with a unified space autoencoder (USAE) as a lightweight model to recognize 3D objects. When the proposed method was evaluated on the LiDAR point cloud dataset and ModelNet10 dataset, the experimental results indicated that the recognition accuracy of our USAE-BLS model was similar to that of state-of-the-art 3D object recognition models. Moreover, the USAE-BLS has a much smaller model size and shorter training time than that of the deep learning models.
Impact of COVID-19 and Renewed Awareness of Where We Could Improve

The author compared the data of the fall semester of 2019 with the fall semester of 2020 to discern the impact of COVID 19 in terms of resource sharing, document delivery activities in a flagship academic institution. The author aims to find out: Was there a huge decrease in requests submission caused by COVID-19, even after we resumed regular resource sharing and in-house document delivery activities in the fall of 2020? Which user groups by status and department/college affiliation were the heavy users of the GIFM service before and during COVID-19? Have patron’s format preferences changed since COVID-19? How successful was the fill rate when we requested ebook from other libraries? How has our participation in HathiTrust emergency temporary access played a role in fulfilling some of the requests? What were our fill rates? What were the main cancelation reasons for requests in borrowing and lending? What was the percentage of requests canceled by customers after we received the items from other libraries? Was there any correlation between customer canceled requests and not meeting their “needed by” date? Were there any human errors for canceled requests that need staff attention? What was the ratio of requests filled by RapidILL vs. OCLC?
Real-time static custom gestures recognition based on skeleton hand

Gesture recognition is one of the natural ways of human-computer interaction (HCI) that will positively affect their use. This paper presents an approach for real-time static gestures recognition based on the skeleton of a hand using a MediaPipe framework and Support Vector Machine(SVM) classification. The approach demonstrated high accuracy of recognition gestures 98.74% on a dataset of sign-digit-gestures as well as runtime 71 fps. Moreover, the approach is required only one camera sensor for recognition. The proposed approach can be extended for dynamic gesture recognition and used to control robots and computer devices.
Video Salient Object Detection Network with Bidirectional Memory and Spatiotemporal Constraints

Although deep learning-based video salient object detection networks have shown outstanding performance, there still exist problems such as the incompleteness of salient objects due to the lack of spatial saliency information, and the globally inconsistency of saliency results detected in long-term video sequences. To address these issues, we propose a novel video salient object detection network with bidirectional memory and spatiotemporal constraints. The global and local cues are first cascaded to extract spatial location information and internal structural details by residual skip connections. After obtaining reliable and fine-grained spatial saliency for a single frame, we design a DB-ConvLSTM module with bidirectional memory that retains the past and future information. A global temporal attention mechanism is added in the temporal dimension to correlate each frame with the whole video to obtain accurate salient objects of globally consistent. Extensive experiments have been conducted to demonstrate that the proposed model outperforms the other seven state-of-the-art models on four datasets, viz. DAVIS, FBMS, SegTrackV2, and UVSD.
A nonlocal method for image shadow removal

This paper proposes a new model for image shadow removal. The model reformulates a recent osmosis model with nonlocal differential operators. This allows to benefit from distant pixels similarities and thus improves restoration results. Some properties of this model are established and discussed, making it suitable for our application. Experimental results show that the nonlocal model obtained very good qualitative and quantitative results compared with state-of-the-art techniques.
FaceHack: Attacking Facial Recognition Systems Using Malicious Facial Characteristics

Recent advances in machine learning have opened up new avenues for its extensive use in real-world applications. Facial recognition, specifically, is used from simple friend suggestions in social-media platforms to critical security applications for biometric validation in automated border control at airports. Considering these scenarios, security vulnerabilities of such facial recognition systems pose serious threats with severe outcomes. Recent work demonstrated that Deep Neural Networks (DNNs), typically used in facial recognition systems, are susceptible to backdoor attacks; in other words, the DNNs turn malicious in the presence of a unique trigger. Detection mechanisms have focused on identifying these distinct trigger-based outliers statistically or through reconstructing them. In this work, we propose the use of facial characteristics as triggers to backdoored facial recognition systems. Additionally, we demonstrate that these attacks can be realised on real-time facial recognition systems. Depending on the attack scenario, the changes in the facial attributes may be embedded artificially using social-media filters or introduced naturally through facial muscle movements. We evaluate the success of the attack and validate that it does not interfere with the performance criteria of the model. We also substantiate that our triggers are undetectable by thoroughly testing them on state-of-the-art defense and detection mechanisms.
FedPrune: Personalized and Communication-Efficient Federated Learning on Non-IID Data

Federated learning (FL) has been widely deployed in edge computing scenarios. However, FL-related technologies are still facing severe challenges while evolving rapidly. Among them, statistical heterogeneity (i.e., non-IID) seriously hinders the wide deployment of FL. In our work, we propose a new framework for communication-efficient and personalized federated learning, namely FedPrune. More specifically, under the newly proposed FL framework, each client trains a converged model locally to obtain critical parameters and substructure that guide the pruning of the network participating FL. FedPrune is able to achieve high accuracy while greatly reducing communication overhead. Moreover, each client learns a personalized model in FedPrune. Experimental results has demonstrated that FedPrune achieves the best accuracy in image recognition task with varying degrees of reduced communication costs compared to the three baseline methods.
Accessibility Analysis Model of College Students’ Employment Education Resources Based on Mobile Search Algorithm

This paper introduces the mobile search algorithm into the research of employment accessibility measurement. It not only proves the effectiveness of the method, but also analyzes the characteristics and formation mechanism of College Students’ employment accessibility in the research case, and puts forward the corresponding control strategies. The results show that the employment accessibility of college students is not evenly distributed, but presents an obvious center periphery pattern. This paper puts forward the planning means of the accessibility of College Students’ employment education resources to promote the balanced distribution of employment and population, the reform of the employment structure and the reasonable improvement of policy.
Analyzing metadata of Russian open access repositories in science and technology for using in RNPLS&T’s system of Single Open Information Archive

RNPLS&T has been designing and developing the Single Open Infor- mation Archive (SOIA) to integrate all digital full-text resources generated or otherwise acquired by RNPLS&T. SOIA is intended to use not only its own content sources but also metadata collected from other open access repositories, in particular Russian repositories. The goal of this analysis is to draw the list of available sources that meet the following criteria: OAI-PMH provider technical accessibility, continuity (regularity) of updating. The authors present the findings of their analysis and describe the challenges revealed. The list of Russian open accessrepositories is drawn for metadata intended to be used in RNPL&T's SOIA as extra source of information. The article is prepared within the framework of the State Order No 075-01300-20-00 "Design and development of the system of Open Archive of Integrated Information Library Resources of Russian National Public Library for Science and Technology as a modern system of knowledge management in digital environment: On the way to Open Science" for the years 2020–2022.
Novel quaternion discrete shifted Gegenbauer moments of fractional-orders for color image analysis

Orthogonal moments (OMs) are used to extract features from color images. OMs with fractional orders are better than the OMs with integer orders due to their ability to extract fine features. This paper defined novel quaternion orthogonal shifted Gegenbauer moments (FrQSGMs) of fractional orders for color image analysis and recognition. Since both shifted Gegenbauer polynomials and the input digital images are defined in the domain [0, 1] × [0, 1], the proposed FrQSGMs did not need any image mapping or image interpolation. The invariance to geometric transformations of the proposed FrQSGMs is derived by expressing these moments in geometric moment invariants of fractional order. We conduct various experiments to test the accuracy, invariance to RST, sensitivity to noise, recognition of similar color images, and computational times. The proposed descriptors outperformed the existing orthogonal moments with fractional orders.