Reviewerly/exHarmony huggingface dataset:
Automating reinforcement learning architecture design for code optimization

Reinforcement learning (RL) is emerging as a powerful technique for solving complex code optimization tasks with an ample search space. While promising, existing solutions require a painstaking manual process to tune the right task-specific RL architecture, for which compiler developers need to determine the composition of the RL exploration algorithm, its supporting components like state, reward, and transition functions, and the hyperparameters of these models. This paper introduces SuperSonic, a new open-source framework to allow compiler developers to integrate RL into compilers easily, regardless of their RL expertise. SuperSonic supports customizable RL architecture compositions to target a wide range of optimization tasks. A key feature of SuperSonic is the use of deep RL and multi-task learning techniques to develop a meta-optimizer to automatically find and tune the right RL architecture from training benchmarks. The tuned RL can then be deployed to optimize new programs. We demonstrate the efficacy and generality of SuperSonic by applying it to four code optimization problems and comparing it against eight auto-tuning frameworks. Experimental results show that SuperSonic consistently improves hand-tuned methods by delivering better overall performance, accelerating the deployment-stage search by 1.75x on average (up to 100x).
Quantum Computing in a Statistical Context

Quantum computing is widely considered a frontier of interdisciplinary research and involves fields ranging from computer science to physics and from chemistry to engineering. On the one hand, the stochastic essence of quantum physics results in the random nature of quantum computing; thus, there is an important role for statistics to play in the development of quantum computing. On the other hand, quantum computing has great potential to revolutionize computational statistics and data science. This article provides an overview of the statistical aspect of quantum computing. We review the basic concepts of quantum computing and introduce quantum research topics such as quantum annealing and quantum machine learning, which require statistics to be understood.
Dual-domain graph convolutional networks for skeleton-based action recognition

Skeleton-based action recognition is attracting more and more attention owing to the general representation ability of skeleton data. The Graph Convolutional Networks (GCNs) methods extended from Convolutional Neural Networks (CNNs) are proposed to directly extract spatial–temporal information from the graphs. Previous GCNs usually aggregate the skeleton information locally in the vertex domain. However, the focus on the local information brought about the limited representation ability in some actions containing overall dynamics in both spatial and temporal, which pulled down the overall accuracy of the model. Therefore, this paper proposes a more comprehensive two-stream GCN architecture containing the vertex-domain graph convolution and the spectral graph convolution based on Graph Fourier Transform (GFT). One stream utilizes an efficient vertex-domain graph convolution to obtain effective spatial–temporal information via Graph Shift Blocks (GSB), while the other brings the global spectral information from our improved Residual Spectral Blocks (RSB). According to the analysis of the experimental results, the action misalignment for certain actions is reduced. Moreover, along with other GCN methods that only focus on spatial–temporal information, our RSB strategies help improve their performance. DD-GCN is evaluated on three large skeleton-based datasets, NTU-RGBD 60, NTU-RGBD 120, and Kinetics-Skeleton. The experiment results demonstrate a comparable ability to the state-of-the-art.
Vibration Prediction of Flying IoT Based on LSTM and GRU

Drones, flying Internet of Things (IoT), have been widely used in several industrial fields, including rescue, delivery, military, and agriculture. Motors connected to a drone’s propellers play a crucial role in its movement. However, once the motor is damaged, the drone is at risk of falling. Thus, to prevent the drone from falling, an accurate and reliable prediction of motor vibration is necessary. In this study, four types of time series vibration data collected in the time domain from motors are predicted using long short-term memory (LSTM) and gated recurrent unit (GRU), and the accuracy and time efficiency of the predicted and actual vibration waveforms are compared and examined. According to the simulation results, the coefficient of determination, R2, and the root mean square error values obtained from the actual and predicted vibrations by the LSTM and GRU are similar. Furthermore, both the LSTM and GRU show excellent performance in forecasting future motor vibration, but GRU can predict future vibration about 22.79% faster than LSTM.
A classification framework for straggler mitigation and management in a heterogeneous Hadoop cluster: A state-of-art survey

Hadoop is the most economical and cheap software framework that allows distributed storage and parallel processing of more extensive data sets. Hadoop distributed file system (HDFS) allows distributed storage and parallel processing of vast data sets using MapReduce. However, Hadoop's current implementation believes that computing nodes connected in a cluster are homogeneous and distribute the tasks equally. This equal load distribution creates the load imbalance during storage, resource contention during task scheduling, hardware degradation by its excess use, and software misconfiguration during cluster management which are the leading causes of stragglers in heterogeneous Hadoop clusters. Due to hardware heterogeneity, Hadoop's performance degrades in the heterogeneous environment. In our study, the paper reviews and analyzes significant studies. It presents the new classification taxonomy to broadly classify the existing straggler management and mitigation techniques into two approaches: proactive and reactive. It analyses and compares the state of art studies and identifies their limitation based on their results. Finally, the systematic review discusses the open issues and the potential directions for future work to manage and mitigate stragglers from the heterogeneous Hadoop clusters.
Learning with virtual reality simulations: direct versus vicarious instructional experience

As virtual reality (VR) technologies have been increasingly adopted in the educational field, careful consideration and evaluation of instructional modes to support learning with VR are needed. This study focuses on evaluation of two instructional modes: direct interaction and manipulation of the VR learning environment; and the vicarious approach of observing how a novice peer and an expert learn with VR. An experimental design was utilized with two comparison conditions: direct VR interaction (n = 72) and the vicarious approach (n = 69). Participants' knowledge and emotional engagement were evaluated via self-report questionnaires. Results showed that though learning with the direct VR condition was associated with more intense emotional engagement, there was no significant difference in knowledge gains between the two study conditions. The findings suggest that each of the two instructional modes has specific instructional affordances and that the cognitive and emotional components of learning via VR should be further explored. Since learning vicariously through observing the dialogue between an expert and a peer can have immediate knowledge gains comparable to the direct VR interaction, there is an effective and more accessible opportunity for VR-based learning. Future research should evaluate this instructional effect on knowledge retention.
Automated design of CNN architecture based on efficient evolutionary search

Evolutionary Neural Architecture Search (ENAS) is a promising method for the automated design of deep network architecture, which has attracted extensive attention in the field of automated machine learning. However, the existing ENAS methods often need a lot of computing resources to design CNN architecture automatically. In order to achieve efficient and automated design of CNNs, this paper focuses on two aspects to improve efficiency. On the one hand, efficient CNN-based building blocks are introduced to ensure the effectiveness of the generated architectures and a triplet attention mechanism is incorporated into the architectures to further improve the classification performance. On the other hand, a random forest-based performance predictor is used in the fitness evaluation to reduce the amount of computation required to train each individual from scratch. Experimental results show that the proposed algorithm can significantly reduce the computational resources required and achieve competitive classification performance on the CIFAR dataset. Also, the architecture designed for the traffic sign recognition task exceeds the accuracy of manual expert design.
Adoption of Large-Scale Scrum Practices through the Use of Management 3.0

Software engineering companies have progressively incorporated agile project management methodologies. Initially, this migration occurred mostly in the context of startups, but in recent years it has also sparked interest from other companies with larger and more geographically dispersed teams. One of the frameworks used for large-scale agile implementation is the LeSS framework. This study seeks to explore how Management 3.0 principles can be applied in the context of the ten practices proposed in the LeSS framework. To this end, a qualitative research methodology based on four case studies is used to identify and explore the role of Management 3.0 in software management and development processes that adopt this agile paradigm. The findings show that the principles of Management 3.0 are relevant to the implementation of the LeSS framework practices, especially in fostering team values and personal values; however, distinct principles between the two paradigms are also identified, namely the greater rigidity of processes advocated in the LeSS framework and a greater focus on process automation.
Multi-Agent Deep Q Network to Enhance the Reinforcement Learning for Delayed Reward System

This study examines various factors and conditions that are related with the performance of reinforcement learning, and defines a multi-agent DQN system (N-DQN) model to improve them. N-DQN model is implemented in this paper with examples of maze finding and ping-pong as examples of delayed reward system, where delayed reward occurs, which makes general DQN learning difficult to apply. The implemented N-DQN shows about 3.5 times higher learning performance compared to the Q-Learning algorithm in the reward-sparse environment in the performance evaluation, and compared to DQN, it shows about 1.1 times faster goal achievement speed. In addition, through the implementation of the prioritized experience replay and the implementation of the reward acquisition section segmentation policy, such a problem as positive-bias of the existing reinforcement learning models seldom or never occurred. However, according to the characteristics of the architecture that uses many numbers of actors in parallel, the need for additional research on light-weighting the system for further performance improvement has raised. This paper describes in detail the structure of the proposed multi-agent N_DQN architecture, the contents of various algorithms used, and the specification for its implementation.
Exploring mineralization zones using remote sensing and aeromagnetic data, West Allaqi Area, Eastern-Desert, Egypt

Wadi-Allaqi district, in the southern part of the Eastern Desert of Egypt, is a historic well-known gold resource that has been exploited since the Pharaonic era. The study area represents the western segment of Wadi-Allaqi, about 220 km southeast Aswan city, Upper Egypt. It covers approximately 4702 km2. Exploring new potential mineralization zones was conducted by integrating the Landsat-8 and aeromagnetic data to map different surfaces of alteration zones and trace shallow and deep mineralized bodies. The corrected Landsat-Operational Land Imager (OLI) bands were processed using the Band Ratio (BR), Crosta, selective for the Minimum Noise Fraction (MNF) transformation, and False-Color Composition (FCC) techniques were used to detect the hydrothermal alteration zones and recognize the different geological units. The enhanced eigenimage (MNF1) was processed to delineate the fractures and weakness zones over the west Allaqi-Heini Suture (AHS). The shallow residual mineralized sources have been detected and mapped using the Tilt Derivative (TDR), First Derivative (FVD) and high pass filter techniques of aeromagnetic data. The depths to the mineralized magnetic sources, and hence the roots of gold and other minerals, were obtained using the power spectrum and Euler techniques. Accordingly, new gold and mineralization potential zones were successfully delineated over the study area's ophiolitic, metavolcanics, and granitic rocks. Lineaments deduced from aeromagnetic and remote sensing data represent the pathways of the ascending hydrothermal fluids. The main trends of the dominant structural systems were detected through the statistical analyses of these lineaments.
Augmented Reality in Education and Educational Games-Implementation and Evaluation: A Focused Literature Review

Studies on augmented reality (AR) in education and in particular AR games in education are gaining impetus worldwide. This area has been actively developing over the past decades relative to the dawn of the 4th industrial revolution and the rapid growth of digital technologies. The present review in the field of educational AR consists of a focused literature review on specific research questions regarding the effective utilization of AR in education and AR gaming applications in the everyday classroom. Moreover, the review studies knowledge and skills’ enhancement, teachers’ roles, relevant theories, and evaluation techniques. From the analysis of 78 selected articles, specific conclusions are drawn and a proposition of a series of recommendations and future research in certain areas of educational AR is portrayed. Respectively, considered, several acknowledged issues and limitations regarding the research and the subject area such as infrastructure, curriculum correspondence, AR games in classroom instruction and evaluation as well as the educators’ acceptance and contribution.
One-shot Video Graph Generation for Explainable Action Reasoning

Human action analysis is a critical yet challenging task for understanding diverse video content. Recently, to enable explainable reasoning of video actions, a spatio-temporal video graph structure was proposed to represent the video state changes at the semantic level. However, its requirement of tedious manual annotation of all the video frames is a serious limitation. Obviously, this approach would have a tremendously expanded applicability if the video graph generation process can be automated. In this paper, a One-Shot Video Graph (OSVG) generation approach is proposed for more effective explainable action reasoning, which only requires a one-time annotation of the objects in the starting frame of the video. We first estimate the predefined relevant objects across the temporal dimension by employing a proposed one-shot target-aware tracking strategy. This helps obtain the object locations and links objects simultaneously across all video frames. Then, the scene graph of each video frame can be constructed by an attribute detector and a relationship detector based on the estimated object locations. In addition, to further enhance the reasoning accuracy of performed actions, a video graph smoothing mechanism is designed with a fully-connected Conditional Random Field (CRF). By sequentially examining every state transition (including attributes and relationships) of the smoothed video graph, the actions occurring can be recognized using pre-defined rules. Experiments on the CAD-120++ dataset and a newly collected NTU RGBD++ dataset have verified that the proposed OSVG is able to outperform other state-of-the-art video action reasoning strategies on both state recognition and action recognition accuracy.
Underwater Single-Image Restoration with Transmission Estimation Using Color Constancy

The issue of underwater image restoration was investigated in this paper. Specifically, the color constancy of a single image was used to estimate the transmission map (TM), which can be used in the image formation model to restore the underwater image. First, the illumination component based on color constancy was used to estimate the refined TM without performing the guided filter or soft matting operation. Second, the statistical property of the pixel was used to fine-tune the color unbalance of underwater images. Finally, both qualitative and quantitative experimental results showed that the proposed method can not only obtain better restoration results, but also improve the real-time performance in different underwater scenes compared with other underwater image restoration methods.
Reservoir time series analysis: Using the response of complex dynamical systems as a universal indicator of change

We present the idea of reservoir time series analysis (RTSA), a method by which the state space representation generated by a reservoir computing (RC) model can be used for time series analysis. We discuss the motivation for this with reference to the characteristics of RC and present three ad hoc methods for generating representative features from the reservoir state space. We then develop and implement a hypothesis test to assess the capacity of these features to distinguish signals from systems with varying parameters. In comparison to a number of benchmark approaches (statistical, Fourier, phase space, and recurrence analysis), we are able to show significant, generalized accuracy across the proposed RTSA features that surpasses the benchmark methods. Finally, we briefly present an application for bearing fault distinction to motivate the use of RTSA in application.
Image Caption Generation using Deep Neural Networks

In recent years, computer vision has made significant progress, primarily in the field of image classification and object detection and recognition. Describing the image content automatically using natural languages is challenging and has a tremendous potential impact. Here, the idea is to extract features from an image, generate captions, and convert the generated captions to speech. This work systematically analyses deep neural networks based image caption generation. With an image as an input, the model can output an English sentence that describes the content in the image by CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), and sentence generation. The generated caption is converted to audio using Google&#x0027;s Text to Speech (gTTS). These models are built on the Flickr 8k dataset consisting of 8000+ images. Usually, human beings tend to describe a scene using natural languages which are compact and concise. However, machine vision systems describe the scene/image by taking an image that is a two-dimensional array.
A back propagation NN to optimize the IoT network

In this paper, effective network optimization is carried out for the internet of things (IoT) with backpropagation neural we can improve the performance. An application area is the Internet of Things, where a massive amount of sensor data has to be classified. In this paper, we have to use backpropagation to reduce the network error, and the particle optimization network is further used to obtain the optimal weight and threshold of the network. This paper shows better performance in terms of reducing error rate after implementing neural networks for network optimization. Therefore, there is enormous demand for network optimization in the IoT for dynamic resource allocation in any infrastructure. The simulation process and the results demonstrate the effectiveness of the proposed approach in comparison to pieces of the proposed approach for the optimization problem.
Post-Quantum Succinct Arguments: Breaking the Quantum Rewinding Barrier

We prove that Kilian's four-message succinct argument system is post-quantum secure in the standard model when instantiated with any probabilistically checkable proof and any collapsing hash function (which in turn exist based on the post-quantum hardness of Learning with Errors). This yields the first post-quantum succinct argument system from any falsifiable assumption. At the heart of our proof is a new quantum rewinding procedure that enables a reduction to repeatedly query a quantum adversary for accepting transcripts as many times as desired. Prior techniques were limited to a constant number of accepting transcripts.
Privacy Preservation in Social Network Data using Evolutionary Model

Social Networks have seen an exponential increase in their user activity. The corresponding user data can be extremely valuable, for improving user experience, identifying trends to make business decisions, and analyzing local and global user behavior. There are concerns over how this data is handled, largely because there are no stringent measures to regulate its handling. Anonymizing user data is vital to ensure the re-identification risk is minimum while retaining as much possible useful information from it. This work proposes a novel method to anonymize user data and ensure it satisfies the three privacy constraints of K-Anonymity (KA), L-Diversity (LD), and T-Closeness (TC), while also keeping information loss from anonymization techniques to the minimum. This dual objective problem is solved through a minimization approach.
Unsupervised Scalable Multimodal Driving Anomaly Detection

Driving anomaly detection aims to identify objects, events or actions that can increase the risk of accidents, reducing road safety. While supervised approaches can effectively identify aspects related to driving anomalies, it is unfeasible to tabulate and address all potential driving anomalies. Instead, it is appealing to design unsupervised approaches that can automatically identify unexpected driving scenarios. This study formulates the detection of driving anomalies as a binary-discrimination task between expected and unexpected driving behaviors. We propose an unsupervised contrastive method using conditional generative adversarial networks (GANs) implemented with the attention model and the triplet loss function. A feature of our framework is its scalability, where it is easy to add new modalities. We consider five different modalities: the vehicle's CAN-Bus signals, driver's physiological signals, distance to nearby pedestrians, distance to nearby vehicles and distance to nearby bicycles. Our approach trains a conditional GAN to extract latent features from each of the five modalities. An attention model combines the latent representations from the modalities. The entire framework is trained with the triplet loss function to generate effective representations to discriminate normal and abnormal driving segments. We conduct experimental evaluations on the driving anomaly dataset (DAD), achieving improved performance over alternative approaches.
Quantum illumination with definite photon-number entangled states

Quantum illumination employs entangled states to detect a weakly reflecting target in a thermal bath. The performance of a given entangled state is evaluated from the minimum error probability in the asymptotic limit, which is compared against the optimal coherent state scheme. Using a relationship between the minimum error probability and quantum Fisher information, we compare the optimal performance of definite photon-number entangled states against that of coherent states under local repetitive strategies. When optimized under the constraint of a fixed total energy, we find that a coherent state outperforms the definite photon-number entangled states with the same signal energy.