Reviewerly/exHarmony huggingface dataset:
Predicting the Costs of Serverless Workflows

Function-as-a-Service (FaaS) platforms enable users to run arbitrary functions without being concerned about operational issues, while only paying for the consumed resources. Individual functions are often composed into workflows for complex tasks. However, the pay-per-use model and nontransparent reporting by cloud providers make it challenging to estimate the expected cost of a workflow, which prevents informed business decisions. Existing cost-estimation approaches assume a static response time for the serverless functions, without taking input parameters into account. In this paper, we propose a methodology for the cost prediction of serverless workflows consisting of input-parameter sensitive function models and a monte-carlo simulation of an abstract workflow model. Our approach enables workflow designers to predict, compare, and optimize the expected costs and performance of a planned workflow, which currently requires time-intensive experimentation. In our evaluation, we show that our approach can predict the response time and output parameters of a function based on its input parameters with an accuracy of 96.1%. In a case study with two audio-processing workflows, our approach predicts the costs of the two workflows with an accuracy of 96.2%.
Sigma Protocols for MQ, PKP and SIS, and Fishy Signature Schemes

This work presents sigma protocols to prove knowledge of: Our sigma protocols have soundness error 1/$$q'$$, where $$q'$$ is any number bounded by the size of the underlying finite field. This is much better than existing proofs, which have soundness error 2/3 or $$(q'+1)/2q'$$. The prover and verifier time our proofs are $$O(q')$$. We achieve this by first constructing so-called sigma protocols with helper, which are sigma protocols where the prover and the verifier are assisted by a trusted third party, and then eliminating the helper from the proof with a “cut-and-choose” protocol. We apply the Fiat-Shamir transform to obtain signature schemes with security proof in the QROM. We show that the resulting signature schemes, which we call the “MUltivariate quaDratic FIat-SHamir” scheme (MUDFISH) and the “ShUffled Solution to Homogeneous linear SYstem FIat-SHamir” scheme (SUSHSYFISH), are more efficient than existing signatures based on the MQ problem and the Permuted Kernel Problem. Our proof system can be used to improve the efficiency of applications relying on (generalizations of) Stern’s protocol. We show that the proof size of our SIS proof is smaller than that of Stern’s protocol by an order of magnitude and that our proof is more efficient than existing post-quantum secure SIS proofs.
Introducing clustering based population in Binary Gravitational Search Algorithm for Feature Selection

Feature Selection (FS) is an important aspect of knowledge extraction as it helps to reduce dimensionality of data. Among the numerous FS algorithms proposed over the years, Gravitational Search Algorithm (GSA) is a popular one which has been applied to various domains. However, GSA suffers from the problem of pre-mature convergence which affects exploration leading to performance degradation. To aid exploration, in the present work, we use a clustering technique in order to make the initial population distributed over the entire feature space and to increase the inclusion of features which are more promising. The proposed method is named Clustering based Population in Binary GSA (CPBGSA). To assess the performance of our proposed model, 20 standard UCI datasets are used, and the results are compared with some contemporary methods. It is observed that CPBGSA outperforms other methods in 12 out of 20 cases in terms of average classification accuracy. The relevant codes of the entire CPBGSA model can be found in the provided link: https://github.com/ManosijGhosh/Clustering-based-Population-in-Binary-GSA.
EXpectation Propagation LOgistic REgRession on permissioned blockCHAIN (ExplorerChain): decentralized online healthcare/genomics predictive model learning

Abstract Objective Predicting patient outcomes using healthcare/genomics data is an increasingly popular/important area. However, some diseases are rare and require data from multiple institutions to construct generalizable models. To address institutional data protection policies, many distributed methods keep the data locally but rely on a central server for coordination, which introduces risks such as a single point of failure. We focus on providing an alternative based on a decentralized approach. We introduce the idea using blockchain technology for this purpose, with a brief description of its own potential advantages/disadvantages. Materials and Methods We explain how our proposed EXpectation Propagation LOgistic REgRession on Permissioned blockCHAIN (ExplorerChain) can achieve the same results when compared to a distributed model that uses a central server on 3 healthcare/genomic datasets, and what trade-offs need to be considered when using centralized/decentralized methods. We explain how the use of blockchain technology can help decrease some of the problems encountered in decentralized methods. Results We showed that the discrimination power of ExplorerChain can be statistically similar to its counterpart central server-based algorithm. While ExplorerChain inherited some benefits of blockchain, it had a small increased running time. Discussion ExplorerChain has the same prerequisites as a distributed model with a centralized server for coordination. In a manner similar to secure multi-party computation strategies, it assumes that participating institutions are honest, but “curious.” Conclusion When evaluated on relatively small datasets, results suggest that ExplorerChain, which combines artificial intelligence and blockchain technologies, performs as well as a central server-based method, and may avoid some risks at the cost of efficiency.
Continuous Sign Language Recognition Through Cross-Modal Alignment of Video and Text Embeddings in a Joint-Latent Space

Continuous Sign Language Recognition (CSLR) refers to the challenging problem of recognizing sign language glosses and their temporal boundaries from weakly annotated video sequences.Previous methods focus mostly on visual feature extraction neglecting text information and failing to effectively model the intra-gloss dependencies.In this work, a cross-modal learning approach that leverages text information to improve vision-based CSLR is proposed.To this end, two powerful encoding networks are initially used to produce video and text embeddings prior to their mapping and alignment into a joint latent representation.The purpose of the proposed cross-modal alignment is the modelling of intra-gloss dependencies and the creation of more descriptive video-based latent representations for CSLR.The proposed method is trained jointly with video and text latent representations.Finally, the aligned video latent representations are classified using a jointly trained decoder.Extensive experiments on three well-known sign language recognition datasets and comparison with state-of-the-art approaches demonstrate the great potential of the proposed approach.
Decentralised federated learning with adaptive partial gradient aggregation

Federated learning aims to collaboratively train a machine learning model with possibly geo-distributed workers, which is inherently communication constrained. To achieve communication efficiency, the conventional federated learning algorithms allow the worker to decrease the communication frequency by training the model locally for multiple times. Conventional federated learning architecture, inherited from the parameter server design, relies on highly centralised topologies and large nodes-to-server bandwidths, and convergence property relies on the stochastic gradient descent training in local, which usually causes the large end-to-end training latency in real-world federated learning scenarios. Thus, in this study, the authors propose the adaptive partial gradient aggregation method, a gradient partial level decentralised federated learning, to tackle this problem. In FedPGA, they propose a partial gradient exchange mechanism that makes full use of node-to-node bandwidth for speeding up the communication time. Besides, an adaptive model updating method further reduces the convergence rate by adaptive increasing the step size of the stable direction of gradient descent. The experimental results on various datasets demonstrate that the training time is reduced up to compared to baselines without accuracy degrade.
Earthquake early warning message testing: Visual attention, behavioral responses, and message perceptions

Earthquake early warning (EEW) systems have the potential to facilitate the delivery of lifesaving information to individuals in the path of damaging shaking before it reaches them. While much attention has been devoted to measuring the technological feasibility in terms of reach, accuracy, and latency of EEW message delivery, one additional aspect that merits considerable attention is the effectiveness of alerts themselves; do they deliver actionable information that can lead to protective actions taken by message receivers? In this project, we investigated individual responses to a simulated EEW message in a laboratory environment. We employed eye-tracking technology to evaluate visual attention to specific message features; measured behavioral responses to the EEW message (i.e., the participants' ability to perform the recommended actions correctly); and collected self-reported perceptions of the messages (including participants’ intent to act in response to future EEW messages). We manipulated two variables selected to be likely to affect visual attention, behavioral responses, and message perceptions: 1) prior exposure to the relevant protective actions, and 2) the presence of informational icons about the protective actions within the message. We found that prior exposure to protective actions is the strongest predictor of behavioral response and message perceptions; however, the inclusion of informational icons had little effect on our results, suggesting a need for further research testing. The results of this study should inform recommendations for message design and public education and training, all of which are critical to the success of EEW.
A pointer meter recognition method based on virtual sample generation technology

At present, the pointer meter recognition methods utilize the traditional image processing techniques. Such techniques are complex, unstable, and unable to meet the requirements of real-time recognition. In order to address these problems, a recognition model based on convolutional neural networks is proposed in this study. However, it is difficult to obtain a large number of real instrument images to train the recognition model. In this paper, a novel virtual sample generation technology is proposed to generate a large number of images from a small number of real instrument images to train the recognition model. The proposed method does not require to pre-process the original images that are used in the trained pointer meter recognition model, just like end-to-end recognition model. The simulation data, the testbed data, and the engineering application show that the proposed method performs better than the compared methods under the interference of illumination and other complex application scenarios.
Real-time Out-of-distribution Detection in Learning-Enabled Cyber-Physical Systems

Cyber-physical systems (CPS) greatly benefit by using machine learning components that can handle the uncertainty and variability of the real-world. Typical components such as deep neural networks, however, introduce new types of hazards that may impact system safety. The system behavior depends on data that are available only during runtime and may be different than the data used for training. Out-of-distribution data may lead to a large error and compromise safety. The paper considers the problem of efficiently detecting out-of-distribution data in CPS control systems. Detection must be robust and limit the number of false alarms while being computational efficient for real-time monitoring. The proposed approach leverages inductive conformal prediction and anomaly detection for developing a method that has a well-calibrated false alarm rate. We use variational autoencoders and deep support vector data description to learn models that can be used efficiently compute the nonconformity of new inputs relative to the training set and enable realtime detection of out-of-distribution high-dimensional inputs. We demonstrate the method using an advanced emergency braking system and a self-driving end-to-end controller implemented in an open source simulator for self-driving cars. The simulation results show very small number of false positives and detection delay while the execution time is comparable to the execution time of the original machine learning components.
Deep learning multidimensional projections

Dimensionality reduction methods, also known as projections, are often used to explore multidimensional data in machine learning, data science, and information visualization. However, several such methods, such as the well-known t-distributed stochastic neighbor embedding and its variants, are computationally expensive for large datasets, suffer from stability problems, and cannot directly handle out-of-sample data. We propose a learning approach to construct any such projections. We train a deep neural network based on sample set drawn from a given data universe, and their corresponding two-dimensional projections, compute with any user-chosen technique. Next, we use the network to infer projections of any dataset from the same universe. Our approach generates projections with similar characteristics as the learned ones, is computationally two to four orders of magnitude faster than existing projection methods, has no complex-to-set user parameters, handles out-of-sample data in a stable manner, and can be used to learn any projection technique. We demonstrate our proposal on several real-world high-dimensional datasets from machine learning.
Complex-valued encoding metaheuristic optimization algorithm: A comprehensive survey

The number of publications related to complex-valued encoding metaheuristic optimization research is increasing the area of metaheuristic optimization is gaining in popularity. In this paper, we aim to provide researchers with a comprehensive and extensive overview of complex-valued encoding metaheuristic algorithms and applications for function optimization, engineering optimization design, and combination optimization. Compared with the basic metaheuristic algorithm, which are based on real-valued encoding or binary encoding, the complex-valued encoding metaheuristic algorithm expands the dimension of the search region and efficiently avoids the problem of falling into the local minimum. Finally, eight complex-valued encoding metaheuristic algorithms were used for 29 benchmark test functions and five engineering optimization design problems. Through the analysis and comparison of the results with statistical significance, the superiority of complex-value encoding was proved, and the complex-value encoding metaheuristic algorithm with the best performance was obtained. The purpose of this review is to present a relatively comprehensive list of all the complex-value encoding metaheuristic algorithms in the literature to inspire further research.
The effects of an augmented-reality ubiquitous writing application: a comparative pilot project for enhancing EFL writing instruction

The use of technology such as online software has been examined in English as a foreign language (EFL) writing contexts. However, few studies have incorporated context-aware ubiquitous technology into EFL writing instruction. To develop multimodal and digital literacy of target EFL undergraduates in this pilot project, the researchers designed and implemented an augmented-reality context-aware ubiquitous writing (ARCAUW) application, which aimed to increase long-term memory, motivation, and self-regulated cognition in participants' writing development. The pilot project compared the writing outcomes and learner perceptions of the proposed ARCAUW writing mode against the baseline mobile-assisted, classroom-based writing mode. The two writing modes differed in the way that metacognitive scaffolding tasks were carried out. Pre- and post-test results showed that (a) although both modes led to significant improvement in writing the process analysis essay, ARCAUW was conducive to the development of task schema in long-term memory, motivation, and self-regulation in writing, and (b) additional cognitive processing during AR-based learning led to mixed results in writing performance. It was concluded that ARCAUW should be incorporated into mobile-assisted writing courses to reach optimal instructional outcomes. Finally, a five-step procedure was offered to help EFL practitioners design and implement ubiquitous writing in their own teaching contexts.
SiamAtt: Siamese attention network for visual tracking

Visual attention has recently achieved great success and wide application in deep neural networks. Existing methods based on Siamese network have achieved a good accuracy–efficiency trade-off in visual tracking. However, the training time of Siamese trackers becomes longer for the deeper network and larger training data. Further, Siamese trackers cannot predict the target location well in fast motion, full occlusion, camera motion, and similar object scenarios. Due to these difficulties, we develop an end-to-end Siamese attention network for visual tracking. Our approach is to introduce an attention branch in the region proposal network that contains a classification branch and a regression branch. We perform foreground–background classification by combining the scores of the classification branch and the attention branch. The regression branch predicts the bounding boxes of the candidate regions based on the classification results. Furthermore, the proposed tracker achieves the experimental results comparable to the state-of-the-art tracker on six tracking benchmarks. In particular, the proposed method achieves an AUC score of 0.503 on LaSOT, while running at 40 frames per second (FPS).
Remote Sensing Image Registration Methodology: Review and Discussion

Image registration is the process of overlaying two or more multi sensor or multi temporal or multi resolution images of the same scene. Image registration is very crucial preprocessing step in any remote sensing image processing application. This paper presents a brief review of evolution of different image registration methodologies with challenges involved in it. Image registration basically aligns the two images (reference and sensed image) geometrically. In the first part of this paper, classical methods will be discussed which are mainly based on area based (Intensity based) approach and feature based approach. Ample amount of work has already been done in both methods in manual and automatic manner. In the second part all recent approaches will be discussed and compared with results, on the basis of registration accuracy, number of correct correspondence. In recent studies Convolutional Neural Network with deep learning algorithms are used in combination with traditional methods to boost the overall performance.
Query-controllable Video Summarization

When video collections become huge, how to explore both within and across videos efficiently is challenging. Video summarization is one of the ways to tackle this issue. Traditional summarization approaches limit the effectiveness of video exploration because they only generate one fixed video summary for a given input video independent of the information need of the user. In this work, we introduce a method which takes a text-based query as input and generates a video summary corresponding to it. We do so by modeling video summarization as a supervised learning problem and propose an end-to-end deep learning based method for query-controllable video summarization to generate a query-dependent video summary. Our proposed method consists of a video summary controller, video summary generator, and video summary output module. To foster the research of query-controllable video summarization and conduct our experiments, we introduce a dataset that contains frame-based relevance score labels. Based on our experimental result, it shows that the text-based query helps control the video summary. It also shows the text-based query improves our model performance. Our code and dataset: https://github.com/Jhhuangkay/Query-controllable-Video-Summarization.
A Flexible Privacy-Preserving Data Sharing Scheme in Cloud-Assisted IoT

Cloud-assisted Internet of Things (IoT) has become an increasingly popular technological trend as the performance of IoT applications can be greatly improved by delegating the cloud to manage massive IoT data. To protect the confidentiality of data outsourced from IoT devices to the cloud, cryptographic mechanisms are usually employed to encrypt the data in such a way that only the user designated by the data owner can decrypt the data. However, in the IoT multiuser environment, the encrypted data may also need to be shared with more users beyond the initially designated one. In this article, we propose a flexible privacy-preserving data sharing (FPDS) scheme in cloud-assisted IoT. With the FPDS scheme, an IoT user can encrypt data to a recipient by using identity-based encryption. More importantly, the IoT user can specify a fine-grained access policy to generate a delegation credential, and then send this credential to the cloud so that it can convert all the encrypted data satisfying the access policy into new ciphertexts that are readable to a new recipient. In this way, IoT users can share the data outsourced to the cloud in a flexible and privacy-preserving manner. Detailed security analysis shows that the FPDS scheme is secure against semitrusted cloud and malicious IoT users. Thorough theoretical and experimental analyses demonstrate the high efficiency of the scheme.
Convolutional neural networks for microseismic waveform classification and arrival picking

Microseismic data have a low signal-to-noise ratio (S/N). Existing waveform classification and arrival-picking methods are not effective enough for noisy microseismic data with low S/N. We have adopted a novel antinoise classifier for waveform classification and arrival picking by combining the continuous wavelet transform (CWT) and the convolutional neural network (CNN). The proposed CWT-CNN classifier is applied to synthetic and field microseismic data sets. Results show that CWT-CNN classifier has much better performance than the basic deep feedforward neural network (DNN), especially for microseismic data with low S/N. The CWT-CNN classifier has a shallow network architecture and small learning data set, and it can be trained quickly for different data sets. We have determined why CWT-CNN has better performance for noisy microseismic data. CWT can decompose the microseismic data into time-frequency spectra, where effective signals and interfering noise are easier to distinguish. With the help of CWT, CNN can focus on the specific frequency components to extract useful features and build a more effective classifier.