Reviewerly/exHarmony huggingface dataset:
Mitigating Cold Start Problem in Serverless Computing with Function Fusion

As Artificial Intelligence (AI) is becoming ubiquitous in many applications, serverless computing is also emerging as a building block for developing cloud-based AI services. Serverless computing has received much interest because of its simplicity, scalability, and resource efficiency. However, due to the trade-off with resource efficiency, serverless computing suffers from the cold start problem, that is, a latency between a request arrival and function execution. The cold start problem significantly influences the overall response time of workflow that consists of functions because the cold start may occur in every function within the workflow. Function fusion can be one of the solutions to mitigate the cold start latency of a workflow. If two functions are fused into a single function, the cold start of the second function is removed; however, if parallel functions are fused, the workflow response time can be increased because the parallel functions run sequentially even if the cold start latency is reduced. This study presents an approach to mitigate the cold start latency of a workflow using function fusion while considering a parallel run. First, we identify three latencies that affect response time, present a workflow response time model considering the latency, and efficiently find a fusion solution that can optimize the response time on the cold start. Our method shows a response time of 28-86% of the response time of the original workflow in five workflows.
Learning in immersed collaborative virtual environments: design and implementation

Immersive virtual environments hold unexplored potential to scaffold and stimulate learning in multiple ways for the purpose of increasing potential learning gains. Yet, the number of implementations in educational settings remains very limited. One reason for limited implementation of immersive virtual environment applications may be a lack of recommendations for their effective design. Building on an extensive theoretical framework, this paper provides such recommendations, specific to immersive educational settings. The recommendations are divided into strategies to optimize cognitive load, foster collaborative learning, leverage platform-specific affordances, mitigate platform-specific limitations and to obtain additional benefits. We illustrate the implementation of these recommendations using a novel collaborative virtual reality environment shown to yield learning gains in two prior studies. Moreover, we detail how a non-invasive and cost-effective feature of automated performance analysis can monitor learning gains in collaborative virtual reality environments. The recommendations based on the example of a collaborative virtual reality environment pave the way for more such implementations to maximize benefits for learners and educators alike.
A combined calibration method of a mobile robotic measurement system for large-sized components

Product quality control of large-sized components with complex supports requires automatic and accurate 3D shape measurement. For accurate and efficient measurements of key local features (KLFs) on the mounting surface of multiple supports, we propose a flexible measurement method combining a laser tracker, a 3D scanner, and a mobile robot system. As for improving the overall measurement accuracy, we also introduce a calibration method based on a multidimensional combined collaboration target (MCCT) to establish correlation constraints between global and local calibration. Specifically, a global calibration method based on multidimensional geometric constraints provides a global measurement field. Additionally, a local calibration method derived from combination optimization model achieves accurate calibration of extrinsic parameters. We constructed the corresponding mobile robotic measurement system (MRMS) and conducted experiments. The experimental results demonstrate that the maximum and mean calibration errors are reduced from 0.045/0.0261 mm to 0.0231/0.0122 mm within 7 m, which features high accuracy.
The multi-objective inspection path-planning in radioactive environment based on an improved ant colony optimization algorithm

More and more occupational workers work in radioactive environment. Although measures are taken to keep the radiation dose in a safe range, the workers will suffer more radiation during the overhauling of nuclear power plants. The dose they suffer during the overhauling of nuclear power plants account for 80% of the total annual dose so it is necessarily to plan a reasonable inspection path for them according to the safety principle of as low as reasonably achievable (ALARA). An improved ant colony optimization (IACO) algorithm is proposed to solve the multi-objective inspection path-planning problem in radioactive environment. To improve the performance of the algorithm, we not only combine ant colony optimization (ACO) algorithm and chaos optimization algorithm, but also introduce pheromone differentiated update strategy and local search optimization strategy. Additionally, 5 experimental simulation cases are conducted and the results are compared to those from particle swarm optimization (PSO) algorithm, chaos particle swarm optimization (CPSO) algorithm and tradition ACO algorithm. In the first three cases, the probability of IACO algorithm finding the optimal path is obviously greater than that of PSO algorithm and CPSO algorithm. IACO algorithm is more efficient and stable. By analyzing an inspection path-planning case with 35 target positions and a case with 44 target positions in a more complex radioactive environment, IACO algorithm could find the path with less effective dose that ACO algorithm cannot find. Therefore, the effectiveness and validity of IACO to solve multi-objective inspection path-planning problem in radioactive environment are verified by experimental simulations and it can help workers reduce radiation exposure.
Q-learning embedded sine cosine algorithm (QLESCA)

Sine Cosine Algorithm (SCA) was recognized as a lightweight, efficient, and has a clear math principal optimizer. However, SCA still suffers from a set of problems such as stagnation at local optima, a slow convergence curve, and a lack of efficient balancing between exploration and exploitation search modes. To mitigate these limitations and improve SCA performance, this study introduces a new version of SCA called QLESCA that smartly controls SCA parameters through an embedded Q-learning algorithm at run time. Each QLESCA agent evolves independently, and it has its own Q-table. The Q-table contains nine different states computed based on population density and distance from the micro population leader. As such, nine different actions are generated by Q-table to control QLESCA parameters, namely r1 and r3. These QLESCA parameters are responsible for adaptive switching from exploration/exploitation and vice versa. For each Q-table action, a reward value is given to a well-performing agent and a penalty to a non-performing agent. To verify the proposed algorithm's performance, QLESCA was evaluated with 23 continuous benchmarks, 20 large scale benchmark optimization functions, and three engineering design problems. In addition, the conducted analysis was compared with various SCA variant algorithms and other state-of-the-art swarm-based optimization methods. The numerical results demonstrate that the QLESCA was superior in terms of achieved fitness value. Statistical results confirm that QLESCA significantly outperforms other optimization algorithms. Additionally, the convergence curve outcomes show that the proposed QLESCA optimization obtains fast convergence against other conducted algorithms.
Role of Augmented Reality in Changing Consumer Behavior and Decision Making: Case of Pakistan

Marketers and advertisers ignore new technology and diverse marketing tactics when attempting to increase product exposure, customer engagement, customer behavior and buying intention in fashion accessory marketplaces in developing countries. This research sought to discover how the Augmented Reality (AR) experience influenced consumer behavior, buying intention and pleasure when purchasing a fashion item in developing countries. This study employs positivist ideas to investigate the connections between various factors, believing that reality is unwavering, stable, and static. Experiential marketing following stimulus exposure will gather cross-sectional data. The undertaken study has developed proper experimental design (within group) from business innovation models, for instance, uses and gratification and user experience models. User experience is disclosed by its four defining characteristics: hedonic quality (identification and simulation), aesthetic quality, and pragmatic quality. After encountering an enhanced user experience, users have a more favorable attitude about purchasing; in contrast, pleasure from using the application directly impacts buying intention. It was also shown that knowledge of AR apps impacts user experience and attitude. The novelty of this research is multifarious, for instance, the smart lab was used as a marketing technology to explore a virtual mirror of the Ray-Ban products. Secondly, the augmented reality experiential marketing activities have been developed by the developers as bearing in mind the four different aspects of the user experience—haptic, hedonic, aesthetic, and pragmatic. It should be functional, simple to learn and use, symmetrical, pleasant, and appealing, while fulfilling the unconscious emotional elements of a customer’s purchase. The research is the first known study in Pakistan to evaluate the influence of augmented reality on consumer proficiency and its consequent effects on attitude and satisfaction for fashion accessory brands. The research also advances the notion that application familiarity is the most important moderator between attitude and an augmented reality-enriched user experience, contradicting the prior studies, which focus on gender and age. This research has important theoretical implications for future researchers, who may wish to replicate the proposed final model in developed and developing countries’ fashion brands. This research also has imperative managerial implications for brand managers and marketing managers, who could include the recommendations of this study in their marketing strategies.
Comparative Research Directions of Population Initialization Techniques using PSO Algorithm

In existing meta-heuristic algorithms, population initialization forms a huge part towards problem optimization. These calculations can impact variety and combination to locate a productive ideal arrangement. Especially, for perceiving the significance of variety and intermingling, different specialists have attempted to improve the presentation of meta-heuristic algorithms. Particle Swarm Optimization (PSO) algorithm is a populace-based, shrewd stochastic inquiry strategy that is motivated by the inherent honey bee swarm food search mechanism. Population initialization is an indispensable factor in the PSO algorithm. To improve the variety and combination factors, rather than applying the irregular circulation for the introduction of the populace, semi-arbitrary successions are more helpful. This examination presents a thorough overview of the different PSO initialization approaches which are dependent on semi-arbitrary successions systems. In this precise review, the best in class in the populace instatement is uncovered. The procedures are classified by utilizing a theoretical model that parts the cycle of populace introduction into two phases: that is, right now expressly or certainly utilized for reinstatement in every single present approach. The deliberate investigation unveils the potential examination zones of populace introduction and, furthermore, research holes, despite the fact that the fundamental center is to give the headings to future upgrade and advancement around there. This paper gives a deliberate study identified with this calculated model for the cutting edge of exploration, which is talked about in the predefined writing to date. The study is envisioned to be useful in examining the PSO algorithm in detail for the specialist. Likewise, the paper finds the proficiency of numerous quasi-random sequences (QRS) based on initialization approaches by looking at their exhibition analyzed for sixteen notable benchmark test problems.
Anomaly detection for hydropower turbine unit based on variational modal decomposition and deep autoencoder

Anomaly detection for hydropower turbine unit is a requirement for the safety of hydropower system. An unsupervised anomaly detection method employing variational modal decomposition (VMD) and deep autoencoder is proposed. VMD is employed to the data collected by multiple sensors to obtain the sub signal of each data. These sub signals in each time-period constitute two-dimensional data. The autoencoder based on convolutional neural network is used to complete unsupervised learning, and the reconstruction residual of autoencoder is used for anomaly detection. The experimental results show that the deep autoencoder can increase the interval between abnormal and normal data distribution, and VMD can effectively reduce the number of samples in the overlapping area. Compared with traditional autoencoder method, the proposed method improves the recall, precision and F1 scores by 0.140, 0.205 and 0.175, respectively. The proposed method achieves better anomaly detection performance than other methods.
Robust Image Hashing With Isomap and Saliency Map for Copy Detection

Compression technology for representing image is on demand for efficiently processing images in the Big Data era. Image hashing is an effective compression technology for computing a short representation based on visual content of input image. Currently, most reported image hashing algorithms have weakness in making a desirable classification between discrimination and robustness and thus can not reach good performance in copy detection. To address these issues, this paper proposes a new robust image hashing with Isometric Mapping (Isomap) and saliency map for copy detection. A key contribution is hash generation with saliency map determined by the Frequency Tuned (FT) method, which can guarantee robustness of the proposed image hashing. Another contribution is the use of Isomap in deriving hash from the FT-based saliency map. Since Isomap can discover the internal geometry features of image, the use of Isomap can learn discriminative image features and thus discrimination of the proposed image hashing is ensured. Experiments on open image databases are carried out. Comparison results illustrate that the proposed image hashing is better than some state-of-the-art algorithms in the performances of classification and copy detection.
An attention based dual learning approach for video captioning

Video captioning aims to generate sentences/captions to describe video contents. It is one of the key tasks in the field of multimedia processing. However, most of the current video captioning approaches utilize only the visual information of a video to generate captions. Recently, a new encoder–decoder–reconstructorarchitecture was developed for video captioning, which can capture the information in both raw videos and the generated captions through dual learning. Based on this architecture, this paper proposes a novel attention based dual learning approach (ADL) for video captioning. Specifically, ADL is composed of a caption generation module and a video reconstruction module. The caption generation module builds a translatable mapping between raw video frames and the generated video captions, i.e., using the visual features extracted from videos by an Inception-V4 network to produce video captions. Then the video reconstruction module reproduces raw video frames using the generated video captions, i.e., using the hidden states of the decoder in the caption generation module to reproduce/synthesize raw visual features. A multi-head attention mechanism is adopted to help the two modules focus on the most effective information in videos and captions, and a dual learning mechanism is adopted to fine-tune the performance of the two modules to generate final video captions. Therefore, ADL can minimize the semantic gap between raw videos and the generated captions by minimizing the differences between the reproduced and the raw videos, thereby improving the quality of the generated video captions. Experimental results demonstrate that ADL is superior to the state-of-the-art video captioning approaches on benchmark datasets.
Joint Client Selection and Bandwidth Allocation Algorithm for Federated Learning

In federated learning (FL), if the participating mobile devices have low computing power and poor wireless channel conditions and/or they do not have sufficient data for various classes, a long convergence time is required to achieve the desired model accuracy. To address this problem, we first formulate a constrained Markov decision process (CMDP) problem that aims to minimize the average time of rounds while maintaining the numbers of trained data and trained data classes above certain numbers. To obtain the optimal scheduling policy, the formulated CMDP problem is converted into an equivalent linear programming (LP). Additionally, to overcome the problem of the curse of dimensionality in CMDP, we develop a joint client selection and bandwidth allocation algorithm (J-CSBA) that jointly selects appropriate mobile devices and allocates suitable amount of bandwidth to them at each round by considering their data information, computing power, and channel gain. Evaluation results validate that J-CSBA can reduce the convergence time by up to <inline-formula><tex-math notation="LaTeX">$49\%$</tex-math></inline-formula> compared to a conventional random scheme.
Learning Symbolic Operators for Task and Motion Planning

Robotic planning problems in hybrid state and action spaces can be solved by integrated task and motion planners (TAMP) that handle the complex interaction between motion-level decisions and task-level plan feasibility. TAMP approaches rely on domain-specific symbolic operators to guide the task-level search, making planning efficient. In this work, we formalize and study the problem of operator learning for TAMP. Central to this study is the view that operators define a lossy abstraction of the transition model of a domain. We then propose a bottom-up relational learning method for operator learning and show how the learned operators can be used for planning in a TAMP system. Experimentally, we provide results in three domains, including long-horizon robotic planning tasks. We find our approach to substantially outperform several baselines, including three graph neural network-based model-free approaches from the recent literature. Video: https://youtu.be/iVfpX9BpBRo. Code: https://git.io/JCT0g
Quantum Network: Security Assessment and Key Management

As an extension of quantum key distribution, secure communication among multiple users is an essential task in a quantum network. When the quantum network structure becomes complicated with a large number of users, it is important to investigate network issues, including security, key management, latency, reliability, scalability, and cost. In this work, we utilize the classical and graph theories to address two critical issues in a quantum network: security and key management. First, we design a communication scheme with the highest security level that trusts a minimum number of intermediate nodes. Second, when the quantum key is a limited resource, we design key management and data scheduling schemes to optimize the utility of data transmission. Our results can be directly applied to the current metropolitan and free-space quantum network implementations and potentially be a standard approach for future quantum network designs.
Generative Zero-Shot Learning for Semantic Segmentation of 3D Point Clouds

While there has been a number of studies on Zero-Shot Learning (ZSL) for 2D images, its application to 3D data is still recent and scarce, with just a few methods limited to classification. We present the first generative approach for both ZSL and Generalized ZSL (GZSL) on 3D data, that can handle both classification and, for the first time, semantic segmentation. We show that it reaches or outperforms the state of the art on ModelNet40 classification for both inductive ZSL and inductive GZSL. For semantic segmentation, we created three benchmarks for evaluating this new ZSL task, using S3DIS, ScanNet and SemanticKITTI. Our experiments show that our method outperforms strong baselines, which we additionally propose for this task.
Entropy regularization for unsupervised clustering with adaptive neighbors

Graph-based clustering has been considered as an effective kind of method in unsupervised manner to partition various items into several groups, such as Spectral Clustering (SC). However, there are three species of drawbacks in SC: (1) The effects of clustering is sensitive to the affinity matrix that is fixed by original data. (2) The input affinity matrix is simply based on distance measurement, which lacks of clear physical meaning under probabilistic prediction. (3) Additional discretization procedures still need to be operated. To cope with these issues, we propose a new clustering model, which refers to Entropy Regularization for unsupervised Clustering with Adaptive Neighbors (ERCAN), to dynamically and simultaneously update affinity matrix and clustering results. Firstly, the maximized entropy regularization term is introduced in probability model to avoid trivial similarity distributions. Additionally, we newly introduce the Laplacian rank constraint with ℓ0-norm to construct adaptive neighbors for sparsity and strength segmentation ability without extra discretization process. Finally, we present a novel monotonic function optimization method, which reveals the consistence between graph sparsity and neighbor assignment, to address the ℓ0-norm constraint in alternative optimization process. Comprehensive experiments show the superiority of our method with promising results.
Optimal representation in the form of logical functions of microinstructions of cryptographic algorithms (RSA, El-Gamal)

This article resolves the problem of logical representation of the classical asymmetric RSA public key encryption algorithms (Rivest, Shamir, and Adleman) and El Gamal in order to implement them on hard devices (microcontrollers). It is shown that the algorithms under study can be described by a set of micro-instructions consisting of a sequence of operators and, based on the analysis of graph-schemes, logical, algebraic and matrix representations of algorithms, construct a minimal logical representation for implementation on hard devices