Reviewerly/exHarmony huggingface dataset:
Quantum Solutions to Possible Challenges of Blockchain Technology

With the recent advent of quantum computing, the security of current blockchain cryptosystems can be compromised to a greater extent. Quantum algorithms such as Shor's large integer factorization algorithm and Grover's unstructured database search algorithm can offer exponential and quadratic speedup, respectively, in comparison to their classical counterparts. This can put threats on both public-key or asymmetric key cryptosystems and hash functions, which necessarily demands to migrate from classical cryptography to quantum-secure cryptography. This work first studies current and future classical state-of-the-art blockchain scalability and security primitives. The relevant quantum-safe blockchain cryptosystem initiatives which have been taken by Bitcoin, Ethereum, Corda, etc. are stated and compared with respect to key sizes, hash length, execution time, computational overhead, and energy efficiency. Moreover, the work aims to provide the different ways where quantum-aided solutions can overcome the challenges in blockchain technology and enhance scalability, security, and performance in the post-quantum era. Though quantum information itself is in the Noisy Intermediate Scale Quantum (NISQ) era and not yet developed to a high technology readiness level, this work also covers the current challenges and future research direction in the near-term, practical realization of post-quantum blockchain.
SHNE: Semantics and Homophily Preserving Network Embedding

Graph convolutional networks (GCNs) have achieved great success in many applications and have caught significant attention in both academic and industrial domains. However, repeatedly employing graph convolutional layers would render the node embeddings indistinguishable. For the sake of avoiding oversmoothing, most GCN-based models are restricted in a shallow architecture. Therefore, the expressive power of these models is insufficient since they ignore information beyond local neighborhoods. Furthermore, existing methods either do not consider the semantics from high-order local structures or neglect the node homophily (i.e., node similarity), which severely limits the performance of the model. In this article, we take above problems into consideration and propose a novel Semantics and Homophily preserving Network Embedding (SHNE) model. In particular, SHNE leverages higher order connectivity patterns to capture structural semantics. To exploit node homophily, SHNE utilizes both structural and feature similarity to discover potential correlated neighbors for each node from the whole graph; thus, distant but informative nodes can also contribute to the model. Moreover, with the proposed dual-attention mechanisms, SHNE learns comprehensive embeddings with additional information from various semantic spaces. Furthermore, we also design a semantic regularizer to improve the quality of the combined representation. Extensive experiments demonstrate that SHNE outperforms state-of-the-art methods on benchmark datasets.
Age Estimation from Facial Images using Transfer Learning and K-fold Cross-Validation

Automatic Age estimation has gained more and more interest in recent years due to its potential in many applications. Most techniques uses hand-crafted features to predict aging patterns, but not accurate enough to be employed effectively. Recent advances in deeply learned features extracted by Convolutional Neural Network (CNN) allows to design more accurate facial analysis. The aim of this paper is to explore the performance of different age estimation techniques that uses Deep Learning methods and to propose a variation of Transfer learning which uses K-fold cross validation on top of transfer learning. The experiment was carried out with UTKFace dataset using VGG16, ResNet50 and SENet50 models. The result demonstrates that our method is Superior to the existing methods in terms of performance.
Harnessing Unlabeled Data to Improve Generalization of Biometric Gender and Age Classifiers

With significant advances in deep learning, many computer vision applications have reached the inflection point. However, these deep learning models need large amount of labeled data for model training and optimum parameter estimation. Limited labeled data for model training results in overfitting and impacts their generalization performance. However, the collection and annotation of large amount of data is a very time consuming and expensive operation. Further, due to privacy and security concerns, the large amount of labeled data could not be collected for certain applications such as those involving medical field. Self-training, Co-training, and Self-ensemble methods are three types of semi-supervised learning methods that can be used to exploit unlabeled data. In this paper, we propose self-ensemble based deep learning model that along with limited labeled data, harness unlabeled data for improving the generalization performance. We evaluated the proposed self-ensemble based deep-learning model for soft-biometric gender and age classification. Experimental evaluation on CelebA and VISO B datasets suggest gender classification accuracy of 94.46 % and 81.00 %, respectively, using only 1000 labeled samples and remaining 199k samples as unlabeled samples for CelebA dataset and similarly,1000 labeled samples with remaining 107k samples as unlabeled samples for VISOB dataset. Comparative evaluation suggest that there is 5.74% and 8.47% improvement in the accuracy of the self-ensemble model when compared with supervised model trained on the entire CelebA and VISOB dataset, respectively. We also evaluated the proposed learning method for age-group prediction on Adience dataset and it outperformed the baseline supervised deep-learning learning model with a better exact accuracy of 55.55 ± 4.28 which is 3.92% more than the baseline.
SRNet

The task of instance segmentation in videos aims to consistently identify objects at pixel level throughout the entire video sequence. Existing state-of-the-art methods either follow the tracking-by-detection paradigm to employ multi-stage pipelines or directly train a complex deep model to process the entire video clips as 3D volumes. However, these methods are typically slow and resource-consuming such that they are often limited to offline processing. In this paper, we propose SRNet, a simple and efficient framework for joint segmentation and tracking of object instances in videos. The key to achieving both high efficiency and accuracy in our framework is to formulate the instance segmentation and tracking problem into a unified spatial-relation learning task where each pixel in the current frame relates to its object center, and each object center relates to its location in the previous frame. This unified learning framework allows our framework to perform join instance segmentation and tracking through a single stage while maintaining low overheads among different learning tasks. Our proposed framework can handle two different task settings and demonstrates comparable performance with state-of-the-art methods on two different benchmarks while running significantly faster.
Learning to Select Relevant Knowledge for Neural Machine Translation

Most memory-based methods use encoded retrieved pairs as the translation memory (TM) to provide external guidance, but there still exist some noisy words in the retrieved pairs. In this paper, we propose a simple and effective end-to-end model to select useful sentence words from the encoded memory and incorporate them into the NMT model. Our model uses a novel memory selection mechanism to avoid the noise from similar sentences and provide external guidance simultaneously. To verify the positive influence of selected retrieved words, we evaluate our model on the single-domain dataset namely JRC-Acquis and multi-domain dataset comprised of existing benchmarks including WMT, IWSLT, JRC-Acquis, and OpenSubtitles. Experimental results demonstrate our method can improve the translation quality under different scenarios.
Audio-Visual Scene-Aware Dialog and Reasoning Using Audio-Visual Transformers with Joint Student-Teacher Learning

In previous work, we have proposed the Audio-Visual Scene-Aware Dialog (AVSD) task, collected an AVSD dataset, developed AVSD technologies, and hosted an AVSD challenge track at both the 7th and 8th Dialog System Technology Challenges (DSTC7, DSTC8). In these challenges, the best-performing systems relied heavily on human-generated descriptions of the video content, which were available in the datasets but would be unavailable in real-world applications. To promote further advancements for real-world applications, we proposed a third AVSD challenge, at DSTC10, with two modifications: 1) the human-created description is unavailable at inference time, and 2) systems must demonstrate temporal reasoning by finding evidence from the video to support each answer. This paper introduces the new task that includes temporal reasoning and our new extension of the AVSD dataset for DSTC10, for which we collected human-generated temporal reasoning data. We also introduce a baseline system built using an AV-transformer, which we released along with the new dataset. Finally, this paper introduces a new system that extends our baseline system with attentional multimodal fusion, joint student-teacher learning (JSTL), and model combination techniques, achieving state-of-the-art performances on the AVSD datasets for DSTC7, DSTC8, and DSTC10. We also propose two temporal reasoning methods for AVSD: one attention-based, and one based on a time-domain region proposal network.
LXM: better splittable pseudorandom number generators (and almost as fast)

In 2014, Steele, Lea, and Flood presented SplitMix, an object-oriented pseudorandom number generator (prng) that is quite fast (9 64-bit arithmetic/logical operations per 64 bits generated) and also splittable . A conventional prng object provides a generate method that returns one pseudorandom value and updates the state of the prng; a splittable prng object also has a second operation, split , that replaces the original prng object with two (seemingly) independent prng objects, by creating and returning a new such object and updating the state of the original object. Splittable prng objects make it easy to organize the use of pseudorandom numbers in multithreaded programs structured using fork-join parallelism. This overall strategy still appears to be sound, but the specific arithmetic calculation used for generate in the SplitMix algorithm has some detectable weaknesses, and the period of any one generator is limited to 2 64 . Here we present the LXM family of prng algorithms. The idea is an old one: combine the outputs of two independent prng algorithms, then (optionally) feed the result to a mixing function. An LXM algorithm uses a linear congruential subgenerator and an F 2 -linear subgenerator; the examples studied in this paper use a linear congruential generator (LCG) of period 2 16 , 2 32 , 2 64 , or 2 128 with one of the multipliers recommended by L’Ecuyer or by Steele and Vigna, and an F 2 -linear xor-based generator (XBG) of the xoshiro family or xoroshiro family as described by Blackman and Vigna. For mixing functions we study the MurmurHash3 finalizer function; variants by David Stafford, Doug Lea, and degski; and the null (identity) mixing function. Like SplitMix, LXM provides both a generate operation and a split operation. Also like SplitMix, LXM requires no locking or other synchronization (other than the usual memory fence after instance initialization), and is suitable for use with simd instruction sets because it has no branches or loops. We analyze the period and equidistribution properties of LXM generators, and present the results of thorough testing of specific members of this family, using the TestU01 and PractRand test suites, not only on single instances of the algorithm but also for collections of instances, used in parallel, ranging in size from 2 to 2 24 . Single instances of LXM that include a strong mixing function appear to have no major weaknesses, and LXM is significantly more robust than SplitMix against accidental correlation in a multithreaded setting. We believe that LXM, like SplitMix, is suitable for “everyday” scientific and machine-learning applications (but not cryptographic applications), especially when concurrent threads or distributed processes are involved.
Asymmetric optical cryptosystem for multiple images based on devil’s spiral Fresnel lens phase and random spiral transform in gyrator domain

Abstract An asymmetric cryptosystem is presented for encrypting multiple images in gyrator transform domains. In the encryption approach, the devil’s spiral Fresnel lens variable pure phase mask is first designed for each image band to be encrypted by using devil’ mask, random spiral phase and Fresnel mask, respectively. Subsequently, a novel random devil’ spiral Fresnel transform in optical gyrator transform is implemented to achieved the intermediate output. Then, the intermediate data is divided into two masks by employing random modulus decomposition in the asymmetric process. Finally, a random permutation matrix is utilized to obtain the ciphertext of the intact algorithm. For the decryption approach, two divided masks (private key and ciphertext) need to be imported into the optical gyrator input plane simultaneously. Some numerical experiments are given to verify the effectiveness and capability of this asymmetric cryptosystem.
Attention Neural Network Semblance Velocity Auto Picking with Reference Velocity Curve Data Augmentation

Semblance velocity analysis plays an indispensable role in seismic data processing. In order to avoid the huge time-cost when performed manually, some deep learning methods are proposed for automatic velocity picking from semblance. However, the application of existing deep learning methods is still restricted by the shortage of labels in practice. To solve this problem, we take semblance velocity analysis as a point-to-point regression problem at each time sample. A time window on semblance which can extract the block corresponding to a time-velocity (t-v) pair and the reference velocity curve (RVC) which can transform semblance randomly are employed together to augment the labeled data. We divide the development of data augmentation strategy into three progressive modes. The datasets from three modes are prepared for training designed attention neural network. The field experiments show that the attention neural network can produce reasonable results and the data augmentation strategy can effectively improve the velocity picking accuracy.
Building Maintainable Software Using Abstraction Layering

Increased software maintainability can help improve a company's profitability by directly reducing ongoing software development costs. Abstraction Layered Architecture (ALA) is a reference architecture for building maintainable applications, but its effectiveness in commercial projects has remained unexplored. This research, carried out as a 16-month joint industry-academic project, explores developing commercial code bases using ALA and the extent to which ALA improves maintainability. An existing application from Datamars, New Zealand, was re-developed by using ALA and compared with the original application. In order to carry out these comparisons, we developed suitable measures by adapting maintainability characteristics from the ISO 25010 family of standards. Specifically, we determined metrics to capture the five sub-characteristics of maintainability: modularity, reusability, analysability, modifiability, and testability; and used them to test our hypothesis that the use of ALA improved maintainability of the application. During the evaluation, we found that the modularity, reusability, analysability, and testability of the re-developed ALA application were higher than for the original application. The modifiability of the ALA-based application was lower in the short-term, but shown to trend upwards in the longer term. Our findings led to proposing a generalised ALA-based development method that promises a significant reduction in maintenance costs.
Fixed-Point Iteration Approach to Spark Scalable Performance Modeling and Evaluation

Companies depend on mining data to grow their business more than ever.To achieve optimal performance of Big Data analytics workloads, a careful configuration of the cluster and the employed software framework is required.The lack of flexible and accurate performance models, however, render this a challenging task.This paper fills this gap by presenting accurate performance prediction models based on Stochastic Activity Networks (SANs).In contrast to existing work, the presented models consider multiple work queues, a critical feature to achieve high accuracy in realistic usage scenarios.We first introduce a monolithic analytical model for a multi-queue YARN cluster running DAG-based Big Data applications that models each queue individually.To overcome the limited scalability of the monolithic model, we then present a fixed-point model that iteratively computes the throughput of a single queue with respect to the rest of the system until a fixed-point is reached.The models are evaluated on a real-world cluster running the widely-used Apache Spark framework and the YARN scheduler.Experiments with the common transaction-based TPC-DS benchmark show that the proposed models achieve an average error of only 5.6% in predicting the execution time of the Spark jobs.The presented models enable businesses to optimize their cluster configuration for a given workload and thus to reduce their expenses and minimize service level agreement (SLA) violations.Makespan minimization and per-stage analysis are examined as representative efforts to further assess the applicability of our proposition.
Multi-view 3D Smooth Human Pose Estimation based on Heatmap Filtering and Spatio-temporal Information

The estimation of 3D human poses from time-synchronized, calibrated multi-view video usually consists of two steps: (1) a 2D detector to locate the 2D coordinate point position of the joint via heatmaps for each frame and (2) a post-processing method such as the recursive pictorial structure model or robust triangulation to obtain 3D coordinate points. However, most existing methods are based on a single frame only. They do not take advantage of the temporal characteristics of the video sequence itself, and must rely on post-processing algorithms. They are also susceptible to human self-occlusion, and the generated sequences suffer from jitter. Therefore, we propose a network model incorporating spatial and temporal features. Using a coarse-to-fine approach, the proposed heatmap temporal network (HTN) generates temporal heatmap information, with an occlusion heatmap filter used to filter low-quality heatmaps before they are sent to the HTN. The heatmap fusion and the triangulation weights are dynamically adjusted, and intermediate supervision is employed to enable better integration of temporal and spatial information. Our network is also end-to-end differentiable. This overcomes the long-standing problem of skeleton jitter being generated and ensures that the sequence is smooth and stable.
Quality Assessment Methods for Textual Conversational Interfaces: A Multivocal Literature Review

The evaluation and assessment of conversational interfaces is a complex task since such software products are challenging to validate through traditional testing approaches. We conducted a systematic Multivocal Literature Review (MLR), on five different literature sources, to provide a view on quality attributes, evaluation frameworks, and evaluation datasets proposed to provide aid to the researchers and practitioners of the field. We came up with a final pool of 118 contributions, including grey (35) and white literature (83). We categorized 123 different quality attributes and metrics under ten different categories and four macro-categories: Relational, Conversational, User-Centered and Quantitative attributes. While Relational and Conversational attributes are most commonly explored by the scientific literature, we testified a predominance of User-Centered Attributes in industrial literature. We also identified five different academic frameworks/tools to automatically compute sets of metrics, and 28 datasets (subdivided into seven different categories based on the type of data contained) that can produce conversations for the evaluation of conversational interfaces. Our analysis of literature highlights that a high number of qualitative and quantitative attributes are available in the literature to evaluate the performance of conversational interfaces. Our categorization can serve as a valid entry point for researchers and practitioners to select the proper functional and non-functional aspects to be evaluated for their products.
Incorporating Multimodal Sentiments into Conversational Bots for Service Requirement Elicitation

In recent years, task-oriented conversational AI Bots have become very popular in many work and life scenarios with the goal of elicit and satisfy user requirements/intentions by natural language based interactions. To capture user intentions accurately, it is important to design efficient dialog strategy for guiding users to express their intentions by limited rounds of dialogue. However, intention acquisition methods based solely on semantics analysis do not work well especially for those complex user requirements/intentions. In this work, we design a conversational AI bot that could capture user intentions based on multimodal information including video, audio and texts. The bot could incorporate sentiments that are extracted from multimodal information into the bot's dialogue strategy. Sentiments are used for dynamically adjusting the questioning tactics during the dialogue. We design controlled experiments to explore the effectiveness of incorporating multimodal sentiments into the process of guiding users to expressing their intentions. Volunteers are asked to talk with the conversational AI Bot with rich emotions in real application scenarios. Experimental results show that our proposed approach could effectively improve the accuracy of user intention recognition and increase user satisfaction during dialogue. This work lays a solid foundation for the future's service solution design which is a key step in Service-Oriented Systems Engineering (SOSE).
A Novel Approach for Data Classification Using Neural Networks

In this paper, an approach of multi-view learning, with multilayer perceptron (MLP) and radial basis functions (RBF) with dynamic decay adjustment (DDA), has been proposed. Three different categories of semi-supervised learning are multi-view training, co-training and self-training. Here we have only used self-training and multi-view learning mechanisms to train the classifier. To test the accuracy of the algorithms, we have taken five real-time datasets from UCI Machine Learning Repository. The classifier is trained using the perceptron learning rule with its supervised and semi-supervised (self-training) versions and MLP with RBF (multi-view learning). The average classification accuracies have been compared and the proposed algorithm outperforms the former versions on the specified training sets. The significant improvement in performance obtained using multi-view learning can be used for various fields such as detecting changes of images, speech recognition and biometric identification.