Reviewerly/exHarmony huggingface dataset:
A Two-phase Method to Balance the Result of Distributed Graph Repartitioning

With the increase in popularity of graph structured data arising in different areas such as Web, social network, communication network, knowledge graph, etc., there is a growing need for partitioning and repartitioning large graph data in a distributed system. However, the existing graph repartitioning methods are known for poor efficiency in the distributed environment and most of them lack a balance mechanism between edge cut and load balance. In this article, we introduce a new two-phase method to improve the result of distributed graph repartitioning. We first design a local method to identify all the potential candidate vertices that could improve the graph repartitioning result in load balance and edge cut at once in each partition locally. After that, we propose to migrate the selected vertices among the given initial partitions to improve the result of graph repartitioning. During this procedure, we propose to adopt a synchronous vertex migration method to balance both the edge cuts and load balance problems. Extensive experimental results demonstrate that the proposed method is more efficient than the existing methods in several aspects such as communication cost, running time, edge cut, and load balance. We also run SSSP and PageRank applications based on the graph repartitioning result on Giraph to indicate the efficiency of the proposed method.
Multi-Quartznet: Multi-Resolution Convolution for Speech Recognition with Multi-Layer Feature Fusion

In this paper, we propose an end-to-end speech recognition network based on Nvidia's previous QuartzNet [1] model. We try to promote the model performance, and design three components: (1) Multi-Resolution Convolution Module, re-places the original 1D time-channel separable convolution with multi-stream convolutions. Each stream has a unique dilated stride on convolutional operations. (2) Channel-Wise Attention Module, calculates the attention weight of each convolutional stream by spatial channel-wise pooling. (3) Multi-Layer Feature Fusion Module, reweights each convolutional block by global multi-layer feature maps. Our experiments demonstrate that Multi-QuartzNet model achieves CER 6.77% on AISHELL-1 data set, which outperforms original QuartzNet and is close to state-of-art result.
Arabic Speech Synthesis using Deep Neural Networks

Text-to-speech (TTS) synthesis is a rapidly growing field of research. Deep learning has shown impressive results in speech synthesis and outperformed the older concatenative and parametric methods. In this paper, speech synthesis using deep learning architectures is explored and two models are utilized in an end-to-end Arabic TTS system. The results of the two systems are compared to concatenative TTS system using the Mean Opinion Score (MOS) of the synthesized speech and indicates that deep learning based systems have outperformed the concatenative system when it comes to naturalness and intelligibility; moreover, it reduces system complexity.
PNL: Efficient long-range dependencies extraction with pyramid non-local module for action recognition

Long-range spatiotemporal dependencies capturing plays an essential role in improving video features for action recognition. The previously introduced non-local block, inspired by the non-local means, is designed to address this challenge and have shown excellent performance. However, the non-local block brings significant increase in computation cost to the original network. It also lacks the ability to model regional correlations in videos. To address the above limitations, we propose the Pyramid Non-Local (PNL) module, which extends the original non-local block by incorporating regional correlations at multiple scales through a pyramid structured module. This extension upscales the effectiveness of the original non-local block by attending to the interaction between different regions. Empirical results prove the effectiveness and efficiency of our PNL module, which achieves state-of-the-art performance of 83.09% on the Mini-Kinetics dataset, with decreased computational cost compared to the non-local block.
Adversarial Attacks are Reversible with Natural Supervision

We find that images contain intrinsic structure that enables the reversal of many adversarial attacks. Attack vectors cause not only image classifiers to fail, but also collaterally disrupt incidental structure in the image. We demonstrate that modifying the attacked image to restore the natural structure will reverse many types of attacks, providing a defense. Experiments demonstrate significantly improved robustness for several state-of-the-art models across the CIFAR-10, CIFAR-100, SVHN, and ImageNet datasets. Our results show that our defense is still effective even if the attacker is aware of the defense mechanism. Since our defense is deployed during inference instead of training, it is compatible with pre-trained networks as well as most other defenses. Our results suggest deep networks are vulnerable to adversarial examples partly because their representations do not enforce the natural structure of images.
A Systematic Review of Log-Based Cloud Forensics

Inexpensive devices that leverage cloud computing technology has proliferated the current market. With the increasing popularity and huge user base, the number of cybercrimes has also increased immensely. The forensics of the cloud has now become an important task. But due to the geographically distributed nature and multi-device capability of the cloud computing environment, the forensics of the cloud has become a challenging task. The logs generated by the cloud infrastructure provide the forensics investigator with major hints that may follow to reconstruct the crime scene chronology. This is highly critical for the forensics investigator to investigate the case. But the logs are not easily accessible, or they often fail to provide any critical clues due to poor logging practices. In this paper, initially, the importance of log-based cloud forensics has been discussed. Then, a taxonomy based on the survey of the literature has been furnished. Finally, the issues in the existing log-based cloud forensics schemes have been outlined and open research problems have been identified.
From Street Photos to Fashion Trends: Leveraging User-Provided Noisy Labels for Fashion Understanding

There is increased interest in using street photos to understand fashion trends. Though street photos usually contain rich clothing information, there are several technical challenges to their analysis. First, street photos collected from social media sites often contain user-provided noisy labels, and training models using these labels may deteriorate prediction performance. Second, most existing methods predict multiple clothing attributes individually and do not consider the potential to share knowledge between related tasks. In addition to these technical challenges, most fashion image datasets created by previous studies focus on American and European fashion styles. To address these technical challenges and understand fashion trends in Asia, we created RichWear, a new street fashion dataset containing 322,198 images with various text labels for fashion analysis. This dataset, collected from an Asian social network site, focuses on street styles in Japan and other Asian areas. RichWear provides a subset of expert-verified labels in addition to user-provided noisy labels for model training and evaluation. We propose the Fashion Attributes Recognition Network (FARNet) based on the multi-task learning framework to improve fashion recognition. Instead of predicting each clothing attribute individually, FARNet predicts three types of attributes simultaneously, and, once trained, this network leverages the noisy labels and generates corrected labels based on the input images. Experimental results show that this approach significantly outperforms existing methods. Applying the trained model to the RichWear dataset, we report Asian fashion trends and street styles based on predicted labels and image clusters from latent feature vectors.
Class-specific early exit design methodology for convolutional neural networks

Convolutional Neural Network-based (CNN) inference is a demanding computational task where a long sequence of operations is applied to an input as dictated by the network topology. Optimisations by data quantisation, data reuse, network pruning, and dedicated hardware architectures have a strong impact on reducing both energy consumption and hardware resource requirements, and on improving inference latency. Implementing new applications from established models available from both academic and industrial worlds is common nowadays. Further optimisations by preserving model architecture have been proposed via early exiting approaches, where additional exit points are included in order to evaluate classifications of samples that produce feature maps with sufficient evidence to be classified before reaching the final model exit. This paper proposes a methodology for designing early-exit networks from a given baseline model aiming to improve the average latency for a targeted subset class constrained by the original accuracy for all classes. Results demonstrate average time saving in the order of 2.09× to 8.79× for dataset CIFAR10 and 15.00× to 20.71× for CIFAR100 for baseline models ResNet-21, ResNet-110, Inceptionv3-159, and DenseNet-121.
Mobile and personalized learning system for computer science students

The era of Information and Communication Technology (ICT) allows people to learn without any limits “anywhere and anytime”. Mobile learning is considered as new and interesting form of learning due to the high spread and development of mobile technologies. In E-learning, the educational content is delivered through electronic technologies such as internet (mobility of learning content). Whereas, in M-learning, the mobility covers learning content, learners, and learning process. So, The M -learning is known as learning using mobile devices: smartphones, tablet computer ... etc. However, the mobile learning systems have some challenges or difficulties in term of the design, development and the technology usability. In this paper we develop a mobile system that enhances the learning process of the students of computer science. Moreover, our system provides several adaptation strategies to meet the needs of heterogeneous learners. The results of the tested system (on a group of second year license students) show the improvement in learning achievement of students and the big influence of our Mobile learning system on the degree of understanding of the courses
Empirical Insights from a Study on Outlier Preserving Value Generalization in Animated Choropleth Maps

Time series animation of choropleth maps easily exceeds our perceptual limits. In this empirical research, we investigate the effect of local outlier preserving value generalization of animated choropleth maps on the ability to detect general trends and local deviations thereof. Comparing generalization in space, in time, and in a combination of both dimensions, value smoothing based on a first order spatial neighborhood facilitated the detection of local outliers best, followed by the spatiotemporal and temporal generalization variants. We did not find any evidence that value generalization helps in detecting global trends.
Whole-Word Segmental Speech Recognition with Acoustic Word Embeddings

Segmental models are sequence prediction models in which scores of hypotheses are based on entire variable-length segments of frames. We consider segmental models for whole-word ("acoustic-to-word") speech recognition, with the feature vectors defined using vector embeddings of segments. Such models are computationally challenging as the number of paths is proportional to the vocabulary size, which can be orders of magnitude larger than when using subword units like phones. We describe an efficient approach for end-to-end whole-word segmental models, with forward-backward and Viterbi decoding performed on a GPU and a simple segment scoring function that reduces space complexity. In addition, we investigate the use of pre-training via jointly trained acoustic word embeddings (AWEs) and acoustically grounded word embeddings (AGWEs) of written word labels. We find that word error rate can be reduced by a large margin by pre-training the acoustic segment representation with AWEs, and additional (smaller) gains can be obtained by pre-training the word prediction layer with AGWEs. Our final models improve over prior A2W models.
Contradistinguisher: A Vapnik's Imperative to Unsupervised Domain Adaptation

Recent domain adaptation works rely on an indirect way of first aligning the source and target domain distributions and then train a classifier on the labeled source domain to classify the target domain. However, the main drawback of this approach is that obtaining a near-perfect domain alignment in itself might be difficult/impossible (e.g., language domains). To address this, inspired by how humans use supervised-unsupervised learning to perform tasks seamlessly across multiple domains or tasks, we follow Vapnik's imperative of statistical learning that states any desired problem should be solved in the most direct way rather than solving a more general intermediate task and propose a direct approach to domain adaptation that does not require domain alignment. We propose a model referred to as Contradistinguisher that learns contrastive features and whose objective is to jointly learn to contradistinguish the unlabeled target domain in an unsupervised way and classify in a supervised way on the source domain. We achieve the state-of-the-art on Office-31, Digits and VisDA-2017 datasets in both single-source and multi-source settings. We demonstrate that performing data augmentation results in an improvement in the performance over vanilla approach. We also notice that the contradistinguish-loss enhances performance by increasing the shape bias.
Word Similarity Based Label Smoothing in Rnnlm Training for ASR

Label smoothing has been shown as an effective regularization approach for deep neural networks. Recently, a context-sensitive label smoothing approach was proposed for training RNNLMs that improved word error rates on speech recognition tasks. Despite the performance gains, its plausible candidate words for label smoothing were confined to n-grams observed in training data. To investigate the potential of label smoothing in model training with insufficient data, in this current work, we propose to utilize the similarity between word embeddings to build a candidate word set for each target word, where by doing so, plausible words outside the n-grams in training data may be found and introduced into candidate word sets for label smoothing. Moreover, we propose to combine the smoothing labels from the n-gram based and the word similarity based methods to improve the generalization capability of RNNLMs. Our proposed approach to RNNLM training has been evaluated for n-best list rescoring on speech recognition tasks of WSJ and AMI, with improved experimental results on word error rates confirming its effectiveness.
Animation of virtual medical system under the background of virtual reality technology

Abstract Virtual reality technology is a technology that uses a variety of scientific technologies such as computer systems, image displays, and sensors to perform human–computer interaction. Since the advent of virtual reality technology, the virtual reality + medical model has been highly expected. At present, three‐dimensional (3D) animation technology can be used in combination with virtual reality technology in medical systems. 3D animation technology can transform the transformed human medical image into a 3D character model, and animate the corresponding bones, and observe the model on the screen and operate the model like real surgery, completing virtual medical content learning, virtual medical skill training, virtual surgery preview. Applying 3D animation technology to virtual reality surgery training can effectively bypass the limitations of traditional surgery training modes, form multiple interactive real‐time effects, and bring a rich perceptual experience to the audience. Based on the above background, the research content of this article is the study of virtual medical system animation under the background of virtual reality technology. This paper analyzes the feasibility of virtual reality technology and 3D animation modeling, and designs a virtual medical system based on this. Finally, through experimental simulations, the results show that the gap h is getting larger and larger after the first approximation, and the gap between the Euler explicit method and the true value is larger, while the implicit method is more stable, and the gap does not increases too quickly with the increase of h . Hence choosing the implicit method to calculate Δ t to obtain a larger value will not affect the stability of the solution.
UAS Batch Path Planning With a Space-Time Graph

Unmanned Aircraft Systems (UAS) provide the platform of many applications such as efficient package delivery, monitoring, surveillance, search and rescue, and more. As the density of UAS deployed in urban areas for delivery and monitoring operations is expected to increase in the near future, it becomes critical to design an efficient collision-free UAS path planning system to be used by a centralized traffic manager. In this article we propose an on-demand, scalable, collision-free trajectory planning mechanism that processes routing requests in batches, with the objective of improving the ratio of successfully admitted requests. The planner transforms the input map to a unit graph discretized on the safety distance and computes trajectories using search in the derived space-time graph. The planner generates trajectories that are by design conflict-free and deadlock-free. The performance of our algorithm is analyzed with simulations. We measure admission ratio, the delay overhead vs. optimal path delay, the algorithm running time, and we compare results with prior work. The proposed batch planner yields better admission ratio and delay overhead in low and medium density scenarios. In all scenarios it has a running time that is 4-7 times shorter, showing its scalability in practical situations.
Target detection of banana string and fruit stalk based on YOLOv3 deep learning network

Manual picking of banana bunch is a dangerous, inefficient and labor-intensive aerial work. To solve this problem, in order to realize the intelligent operation of picking, it is a prerequisite to solve the identification and positioning of banana and handle. Combined with the real-time and high efficiency of yolov3 deep learning network in target detection, this paper uses the framework of keras + tensorflow to build Yolov3 model. The average accuracy of banana stalk and banana string target detection is 88.45% and 97.96% respectively.
Fuzzy Logic Testing Approach for Measuring Software Completeness

Due to advancements in science and technology, software is constantly evolving. To adapt to newly demanded requirements in a piece of software, software components are modified or developed. Measuring software completeness has been a challenging task for software companies. The uncertain and imprecise intrinsic relationships within software components have been unaddressed by researchers during the validation process. In this study, we introduced a new fuzzy logic testing approach for measuring the completeness of software. We measured the fuzzy membership value for each software component by a fuzzy logic testing approach called the fuzzy test. For each software component, the system response was tested by identifying which software components in the system required changes. Based on the measured fuzzy membership values for each software component, software completeness was calculated. The introduced approach scales the software completeness between zero and one. A software component with a complete membership value indicates that the software component does not require any modification. A non-membership value specifies that the existing software component is no longer required in the system or that a new software component is required to replace it. The partial membership value specifies that the software component requires few new functionalities according to the new software requirements. Software with a partial membership value requires partial restructuring and design recovery of its components. Symmetric design of software components reduces the complexity in the restructuring of software during modification. In the study, we showed that by using the introduced approach, high-quality software that is faultless, reliable, easily maintained, efficient, and cost-effective can be developed.
Privacy protection of online social network users, against attribute inference attacks, through the use of a set of exhaustive rules

A malicious data miner can infer users’ private information in online social networks (OSNs) by data mining the users’ disclosed information. By exploring the public information about a target user (i.e. an individual or a group of OSN users whose privacy is under attack), attackers can prepare a training data set holding similar information about other users who openly disclosed their data. Using a machine learning classifier, the attacker can input released information about users under attack as non-class attributes and extract the private information as a class attribute. Some techniques offer some privacy protection against specific classifiers;, however, the provided privacy can be under threat if an attacker uses a different classifier (rather than the one used by the privacy protection techniques) to infer sensitive information. In reality, it is difficult to predict the classifiers involved in a privacy attack. In this study, we propose a privacy-preserving technique which first prepares a training data set in a similar way that an attacker can prepare and then takes an approach independent of the classifiers to extract patterns (or logic rules) from the training data set. Based on the extracted rule set, it then suggests the target users to hide some non-class attribute values and/or modify some friendship links for protecting their privacy. We apply our proposed technique on two OSN data sets containing users’ attribute values and their friendship links. For evaluating the performance of the proposed technique, we use conventional classifiers such as Na $$\ddot{\text {i}}$$ ve Bayes, Support Vector Machine and Random Forest on the privacy-protected data sets. The experimental results show that our proposed technique outperforms the existing privacy-preserving algorithms in terms of securing privacy while maintaining the data utility.
On the Effects of Absumption for XCS with Continuous-Valued Inputs

The rule-based XCS Classifier System (XCS) aims at forming classifiers which are as general as possible to achieve an optimal performance level. A too high generalization pressure may lead to over-general classifiers degrading the performance of XCS. To date, no method exists for XCS for real-valued input spaces (XCSR) to handle over-general classifiers ensuring an accurate population. The Absumption mechanism and the Specify operator, both developed for XCS with binary inputs, provide a promising basis for over-generality handling in XCSR. This paper introduces adapted versions of Absumption and Specify by proposing different identification and specialization strategies for the application in XCSR. To determine their potential, the adapted techniques will be evaluated in different classification problems, i.e., common benchmarks and real-world data from the agricultural domain, and in a multi-step problem.
Oral English Speech Recognition Based on Enhanced Temporal Convolutional Network

In oral English teaching in China, teachers usually improve students' pronunciation by their subjective judgment. Even to the same student, the teacher gives different suggestions at different times. Students' oral pronun... | Find, read and cite all the research you need on Tech Science Press