Reviewerly/exHarmony huggingface dataset:
Beyond Literal Meaning: Uncover and Explain Implicit Knowledge in Code Through Wikipedia-Based Concept Linking

When reusing or modifying code, developers need to understand the implicit knowledge behind a piece of code in addition to the literal meaning of code. Such implicit knowledge involves related concepts and their explanations. Uncovering and understanding the implicit knowledge in code are challenging due to the extensive use of abbreviations, scattered expressions of concepts, and ambiguity of concept mentions. In this paper, we propose an automatic approach (called CoLiCo) that can uncover implicit concepts in code and link the uncovered concepts to Wikipedia. Based on a trained identifier embedding model, CoLiCo identifies Wikipedia concepts mentioned in a given code snippet and excerpts a paragraph-level explanation from Wikipedia for each concept. During the process, CoLiCo resolves identifier abbreviation (i.e., concepts mentioned in the form of abbreviations) and identifier aggregation (i.e., concepts mentioned by an aggregation of multiple identifiers) based on identifier embedding and mining of identifier abbreviation/aggregation relations. Experimental study shows that CoLiCo outperforms a general entity linking approach by 38.7% in the correctness of concept linking and identifies 96.7% more correct concept linkings on a dataset with 629 code snippets. The concept linking is significant for program understanding in 54% code snippets. Our user study shows that CoLiCo can significantly shorten the time and improve the correctness in code comprehension tasks that intensively involve implicit knowledge.
Learning a Local-Global Alignment Network for Satellite Video Super-Resolution

Satellite video is a novel data source for earth observation, which can be applied in multiple fields for dynamic monitoring. It is always equipped with high temporal resolution at the cost of low spatial resolution of tiny moving objects. Video super-resolution (VSR) is utilized to improve the spatial resolution of satellite video and obtain high spatial-temporal resolution data. However, most existing VSR methods mainly focus on the local inter-frame information during feature alignment, which lack the ability to model long-distance correspondence. In this article, a novel two-branch alignment network with an efficient fusion module is proposed for satellite VSR. Both deformable convolution and transformer-like attention are employed to fully explore the local and global information between frames. Further, a fusion module is proposed to model the residuals between fusion features and compensate them for better fusion. Experiments on Jilin-1 satellite videos demonstrate that the proposed network can achieve comparable results to current state-of-the-art VSR methods with tiny parameters.
E-Learning Quality and Satisfaction of User

This study examines student satisfaction with using the e-learning concept. Other variables tested include user attitude, e-learning quality, and confirmation. This research will be carried out in 2022, with the object of study being students who use the e-learning concept. This study focuses on online behavior with a research method used by surveys of students. The type of data used for this research method is quantitative, so that the questionnaires distributed to students have answers in the form of numbers. Questionnaires were distributed to students online with a target of 500 respondents. Data from respondents were processed using the SmartPLS statistical tool, the results of the analysis were evaluated based on the research hypothesis. The results show that not all factors support the e-learning satisfaction of students. Only e-learning quality can increase student satisfaction. Meanwhile, the student’s attitude and confirmation were unable to keep them properly. This study provides an overview of the importance of e-learning quality when it is associated with user satisfaction with the concept of e-learning at universities.
Design and Prototype Development of Augmented Reality in Reading Learning for Autism

(1) Background: Augmented reality is no less popular than virtual reality. This technology has begun to be used in education fields, one of which is special education. Merging the real and virtual worlds is the advantage of augmented reality. However, it needs special attention in making software for children with special needs, such as children with autism. This paper presents an application prototype by paying attention to the characteristics of autistic individuals according to the Autism Guide, that has existed in previous studies. (2) Method: The method used in the development of this prototype is the Linear Sequential Model. Application development is made using Unity3D, Vuforia, and Adobe Illustrator by considering accessibility and other conveniences for developers. (3) Results: The prototype was developed with reference to the Autism Guide, then validated by media experts and autistic experts with the results of the assessment obtaining a score of 87.3/100 which is in the “Very Good” category and is suitable for use. (4) Conclusions: The development of a prototype that refers to the characteristics of children with autism needs to be considered so that what will be conveyed can be easily accepted.
Path planning for a tracked robot traversing uneven terrains based on tip‐over stability

Abstract In the real‐world environment, the path planning method of tracked robot is widely studied when driving on uneven terrain. How to solve the problem that the traditional path planning algorithm cannot adapt to uneven terrain because of the constraints of obstacle avoidance and path length is a challenge for tracked robots. In this paper, a stability‐based path planning framework for tracked robot is proposed to reduce the risk of rollover when the tracked robot passes through uneven terrain. First, a virtual plane method is proposed to estimate the attitude of tracked robot. Second, on this basis, a dynamic high‐stability path evaluation algorithm for tracked robot based on force angle stability margin (FASM) is proposed, which transforms the stability‐based path planning problem into a hypergraph problem. Moreover, considering that the optimization problem is strongly nonlinear and nonconvex, a hybrid algorithm of covariance matrix adaptive evolution strategy (CMAES) and Levenberg–Marquardt (LM) is designed under the framework of generalized graph optimization (G2O) to improve the solution efficiency. Finally, simulation and experiments show that the stability‐based path planning framework can effectively plan the high‐quality path, and the maximum stability of the tracked robot is 0.9156 when the robot crosses uneven terrain using optimal path 2.
An energy-efficient topology-aware virtual machine placement in Cloud Datacenters: A multi-objective discrete JAYA optimization

By the advent of cloud computing and the numerous related web applications, data center networks (DCNs) are becoming complex to provide all-to-all communications between underlying devices; it is done by spending a huge amount of electricity consumption. Energy consumption management is the first class concern for cloud providers in this energy hungry devices and also for the green computing goals. In the large DCs, the power management of hundreds or even many thousands idle switches can be a promising approach toward overall cost reduction. The virtual machine placement (VMP) scheme which is aware of both VMs affinity and underlying network topology for co-hosting dependent VMs as physically near as possible and lowering down power state of idle devices can enhance sustainability objectives. On the other hand, resource wastage lowers system utilization which leads more physical server usage causing more power consumption as a consequence. To address the issue, this paper presents an energy-efficient topology-aware VM placement scheme in the cloud DCs which is formulated to a multi-objective optimization problem with power consumption and resource wastage minimization perspective. To deal with this combinatorial problem, an advanced multi-objective discrete version of JAYA (MOD-JAYA) algorithm is presented since the search space of VMP is discrete in nature. The proposed algorithm is validated by intensively variable circumstances and simulations upon conducted scenarios. The simulation results prove the superiority of the proposed advanced MOD-JAYA algorithm in solving VMP problem in comparison with other existing schemes in terms of prominent assessment metrics.
Robustness of quantum reinforcement learning under hardware errors

Abstract Variational quantum machine learning algorithms have become the focus of recent research on how to utilize near-term quantum devices for machine learning tasks. They are considered suitable for this as the circuits that are run can be tailored to the device, and a big part of the computation is delegated to the classical optimizer. It has also been hypothesized that they may be more robust to hardware noise than conventional algorithms due to their hybrid nature. However, the effect of training quantum machine learning models under the influence of hardware-induced noise has not yet been extensively studied. In this work, we address this question for a specific type of learning, namely variational reinforcement learning, by studying its performance in the presence of various noise sources: shot noise, coherent and incoherent errors. We analytically and empirically investigate how the presence of noise during training and evaluation of variational quantum reinforcement learning algorithms affect the performance of the agents and robustness of the learned policies. Furthermore, we provide a method to reduce the number of measurements required to train Q-learning agents, using the inherent structure of the algorithm.
Secure-by-design serverless workflows on the Edge–Cloud Continuum through the Osmotic Computing paradigm

Nowadays, many Cloud companies adopt serverless computation based on the Function as a Service (FaaS) paradigm. Such an approach, built on the dynamic provisioning of serverless functions, has significantly altered the design of Cloud applications. Indeed, complex systems may now be viewed as a graph of serverless functions (i.e., workflow) distributed over the Cloud and Edge layers. However, building applications in such a complicated context and introducing security by design is still a non-trivial problem. Meanwhile, Osmotic Computing has advanced as a novel computational paradigm in recent years. This may serve as a suitable foundation for strengthening security in serverless applications throughout the Cloud–Edge Continuum. In this work, we rely on Osmotic Computing principles for modifying the architecture of OpenWolf, a novel serverless engine, and improving the execution time of ciphertext data by about 65%.
The Mediating Effect of the Internal Control System on the Relationship between the Accounting Information System and Employee Performance in Jordan Islamic Banks

The goal of the current study is to determine how the internal control system (ICS) in Jordanian Islamic banks affects the link between the accounting information system (AIS) and employee performance (EP). The study’s target audience is actual Jordanian Islamic Bank workers, and a sample of 92 respondents, representing a response rate of about 70.22%, was selected from a total of 131 participants, themselves divided among 105 branches of Islamic Banks in Jordan. According to the data, there is a clear correlation between the AIS, which comprises information quality (IQ), system quality (SQ1), and service quality (SQ2), and the EP in Jordanian Islamic Banks, at a significance level of 0.00. Furthermore, it was demonstrated that the impact of the ICS on the relationship between AIS and EP in Jordanian Islamic banks was statistically significant.
A Manifold-Level Hybrid Deep Learning Approach for Sentiment Classification Using an Autoregressive Model

With the recent expansion of social media in the form of social networks, online portals, and microblogs, users have generated a vast number of opinions, reviews, ratings, and feedback. Businesses, governments, and individuals benefit greatly from this information. While this information is intended to be informative, a large portion of it necessitates the use of text mining and sentiment analysis models. It is a matter of concern that reviews on social media lack text context semantics. A model for sentiment classification for customer reviews based on manifold dimensions and manifold modeling is presented to fully exploit the sentiment data provided in reviews and handle the issue of the absence of text context semantics. This paper uses a deep learning framework to model review texts using two dimensions of language texts and ideogrammatic icons and three levels of documents, sentences, and words for a text context semantic analysis review that enhances the precision of the sentiment categorization process. Observations from the experiments show that the proposed model outperforms the current sentiment categorization techniques by more than 8.86%, with an average accuracy rate of 97.30%.
Overview of Voice Conversion Methods Based on Deep Learning

Voice conversion is a process where the essence of a speaker’s identity is seamlessly transferred to another speaker, all while preserving the content of their speech. This usage is accomplished using algorithms that blend speech processing techniques, such as speech analysis, speaker classification, and vocoding. The cutting-edge voice conversion technology is characterized by deep neural networks that effectively separate a speaker’s voice from their linguistic content. This article offers a comprehensive overview of the development status of this area of science based on the current state-of-the-art voice conversion methods.
Hybrid Classifiers for Spatio-Temporal Abnormal Behavior Detection, Tracking, and Recognition in Massive Hajj Crowds

Individual abnormal behaviors vary depending on crowd sizes, contexts, and scenes. Challenges such as partial occlusions, blurring, a large number of abnormal behaviors, and camera viewing occur in large-scale crowds when detecting, tracking, and recognizing individuals with abnormalities. In this paper, our contribution is two-fold. First, we introduce an annotated and labeled large-scale crowd abnormal behavior Hajj dataset, HAJJv2. Second, we propose two methods of hybrid convolutional neural networks (CNNs) and random forests (RFs) to detect and recognize spatio-temporal abnormal behaviors in small and large-scale crowd videos. In small-scale crowd videos, a ResNet-50 pre-trained CNN model is fine-tuned to verify whether every frame is normal or abnormal in the spatial domain. If anomalous behaviors are observed, a motion-based individual detection method based on the magnitudes and orientations of Horn–Schunck optical flow is proposed to locate and track individuals with abnormal behaviors. A Kalman filter is employed in large-scale crowd videos to predict and track the detected individuals in the subsequent frames. Then, means and variances as statistical features are computed and fed to the RF classifier to classify individuals with abnormal behaviors in the temporal domain. In large-scale crowds, we fine-tune the ResNet-50 model using a YOLOv2 object detection technique to detect individuals with abnormal behaviors in the spatial domain. The proposed method achieves 99.76% and 93.71% of average area under the curves (AUCs) on two public benchmark small-scale crowd datasets, UMN and UCSD, respectively, while the large-scale crowd method achieves 76.08% average AUC using the HAJJv2 dataset. Our method outperforms state-of-the-art methods using the small-scale crowd datasets with a margin of 1.66%, 6.06%, and 2.85% on UMN, UCSD Ped1, and UCSD Ped2, respectively. It also produces an acceptable result in large-scale crowds.
RSVG: Exploring Data and Models for Visual Grounding on Remote Sensing Data

In this paper, we introduce the task of visual grounding for remote sensing data (RSVG). RSVG aims to localize the referred objects in remote sensing (RS) images with the guidance of natural language. To retrieve rich information from RS imagery using natural language, many research tasks, like RS image visual question answering, RS image captioning, and RS image-text retrieval have been investigated a lot. However, the object-level visual grounding on RS images is still under-explored. Thus, in this work, we propose to construct the dataset and explore deep learning models for the RSVG task. Specifically, our contributions can be summarized as follows. 1) We build the new large-scale benchmark dataset of RSVG, termed RSVGD, to fully advance the research of RSVG. This new dataset includes image/expression/box triplets for training and evaluating visual grounding models. 2) We benchmark extensive state-of-the-art (SOTA) natural image visual grounding methods on the constructed RSVGD dataset, and some insightful analyses are provided based on the results. 3) A novel transformer-based Multi-Level Cross-Modal feature learning (MLCM) module is proposed. Remotely-sensed images are usually with large scale variations and cluttered backgrounds. To deal with the scale-variation problem, the MLCM module takes advantage of multi-scale visual features and multi-granularity textual embeddings to learn more discriminative representations. To cope with the cluttered background problem, MLCM adaptively filters irrelevant noise and enhances salient features. In this way, our proposed model can incorporate more effective multi-level and multi-modal features to boost performance. Furthermore, this work also provides useful insights for developing better RSVG models. The dataset and code will be publicly available at https://github.com/ZhanYang-nwpu/RSVG-pytorch.
Fourier-flow model generating Feynman paths

As an alternative but unified and more fundamental description for quantum physics, Feynman path integrals generalize the classical action principle to a probabilistic perspective, under which the physical observables' estimation translates into a weighted sum over all possible paths. The underlying difficulty is to tackle the whole path manifold from finite samples that can effectively represent the Feynman propagator dictated probability distribution. Modern generative models in machine learning can handle learning and representing probability distribution with high computational efficiency. In this study, we propose a Fourier-flow generative model to simulate the Feynman propagator and generate paths for quantum systems. As demonstration, we validate the path generator on the harmonic and anharmonic oscillators. The latter is a double-well system without analytic solutions. To preserve the periodic condition for the system, the Fourier transformation is introduced into the flow model to approach a Matsubara representation. With this novel development, the ground-state wave function and low-lying energy levels are estimated accurately. Our method offers a new avenue to investigate quantum systems with machine learning assisted Feynman Path integral solving.
Curriculum Reinforcement Learning From Avoiding Collisions to Navigating Among Movable Obstacles in Diverse Environments

Curriculum learning has proven highly effective to speed up training convergence with improved performance in a variety of tasks. Researchers have been studying how a curriculum can be constituted to train reinforcement learning (RL) agents in various application domains. However, discovering curriculum sequencing requires the ranking of sub-tasks or samples in order of difficulty, which is not yet sufficiently studied for robot navigation problems. It is still an open question what navigation strategies can be learned and transferred during multi-stage transfer learning from easy to hard. Furthermore, despite of some attempts of learning real robot manipulation tasks using curriculum, most of existing works are limited to toy or simulated settings rather than realistic scenarios. To address those issues, we first investigated how the model convergence in diverse environments relates to the navigation strategies and difficulty metrics. We found that only some of the environments can be trained from scratch, such as in a relatively open tunnel-like environment that only required wall following. We then carried out a two-stage transfer learning for more difficult environments. We found such approach effective for goal navigation, but failed for more complex tasks where movable obstacles may be on the navigation path. To facilitate more complex policies in the navigation among movable obstacles (NAMO) task, another curriculum with distance and pace functions appropriate to the difficulty of the environment was developed. The proposed scheme was proved effective and the strategies learned were discussed via comprehensive evaluations conducted in simulated and real environments.
Spatio-Temporal Adaptive Network With Bidirectional Temporal Difference for Action Recognition

Action Recognition is a fundamental task in computer vision field, with a wide range of applications in autonomous driving, security monitoring, etc. However, previous action recognition approaches usually suffer from the inappropriate spatio-temporal modeling or high computational consumption (e.g., 3D CNN). In this paper, we propose a novel Spatio-Temporal Adaptive Network (STANet) with bidirectional temporal difference, consisting of a Temporal Adaptive module (TA) and a Spatial Adaptive (SA) module, to sufficiently extract the crucial motion information and model the spatial pivotal appearance information from both forward and backward perspectives, respectively. Specifically, the Temporal Adaptive module uses bidirectional temporal differences to learn valuable motion trends and balance the static semantics and dynamic motion for a certain action during information fusion; while the Spatial Adaptive module uses the bidirectional temporal difference to obtain the spatio-channel attention to stress the discriminative position-relevant and semantic-relevant appearance features. Extensive experiments conducted on widely-used action recognition benchmarks UCF-101, HMDB-51, Something-Something V1, and Kinetics-400 prove the effectiveness of the proposed methods compared to other state-of-the-art approaches.
Detection of Crime Scene Objects using Deep Learning Techniques

Research on the detection of objects at crime scenes has flourished in the last two decades. Researchers have been concentrating on color pictures, where lighting is a crucial component, since this is one of the most pressing issues in computer vision, with applications spanning surveillance, security, medicine, and more. However, night time monitoring is crucial since most security problems cannot be seen by the naked eye. That's why it's crucial to record a dark scene and identify the things at a crime scene. Even when its dark out, infrared cameras are indispensable. Both military and civilian sectors will benefit from the use of such methods for night time navigation. On the other hand, IR photographs have issues with poor resolution, lighting effects, and other similar issues. Surveillance cameras with infrared (IR) imaging capabilities have been the focus of much study and development in recent years. This research work has attempted to offer a good model for object recognition by using IR images obtained from crime scenes using Deep Learning. The model is tested in many scenarios including a central processing unit (CPU), Google COLAB, and graphics processing unit (GPU), and its performance is also tabulated.