Reviewerly/exHarmony huggingface dataset:
Unleashing the Unused Potential of i-Vectors Enabled by GPU Acceleration

Speaker embeddings are continuous-value vector representations that allow easy comparison between voices of speakers with simple geometric operations. Among others, i-vector and x-vector have emerged as the mainstream methods for speaker embedding. In this paper, we illustrate the use of modern computation platform to harness the benefit of GPU acceleration for i-vector extraction. In particular, we achieve an acceleration of 3000 times in frame posterior computation compared to real time and 25 times in training the i-vector extractor compared to the CPU baseline from Kaldi toolkit. This significant speed-up allows the exploration of ideas that were hitherto impossible. In particular, we show that it is beneficial to update the universal background model (UBM) and re-compute frame alignments while training the i-vector extractor. Additionally, we are able to study different variations of i-vector extractors more rigorously than before. In this process, we reveal some undocumented details of Kaldi's i-vector extractor and show that it outperforms the standard formulation by a margin of 1 to 2% when tested with VoxCeleb speaker verification protocol. All of our findings are asserted by ensemble averaging the results from multiple runs with random start.
Slice-based Learning: A Programming Model for Residual Learning in Critical Data Slices

In real-world machine learning applications, data subsets correspond to especially critical outcomes: vulnerable cyclist detections are safety-critical in an autonomous driving task, and "question" sentences might be important to a dialogue agent's language understanding for product purposes. While machine learning models can achieve high quality performance on coarse-grained metrics like F1-score and overall accuracy, they may underperform on critical subsets---we define these as slices, the key abstraction in our approach. To address slice-level performance, practitioners often train separate "expert" models on slice subsets or use multi-task hard parameter sharing. We propose Slice-based Learning, a new programming model in which the slicing function (SF), a programming interface, specifies critical data subsets for which the model should commit additional capacity. Any model can leverage SFs to learn slice expert representations, which are combined with an attention mechanism to make slice-aware predictions. We show that our approach maintains a parameter-efficient representation while improving over baselines by up to 19.0 F1 on slices and 4.6 F1 overall on datasets spanning language understanding (e.g. SuperGLUE), computer vision, and production-scale industrial systems.
Attribute Rank based Weighted Decision Tree

Decision tree is a renowned classifier reformed by innumerable tree generation techniques to construct efficient trees. In this paper, a novel framework for constructing decision tree classifiers using ranked attributes and weights is presented. The tree is fabricated using proposed framework hierarchically from root to leaf using ranks and weights of attributes assigned to branches based on their contribution towards classification accuracy. The weights are perpetually updated until the tree furnishes maximum classification accuracy. This framework is experimented with cuckoo search, firefly search, wolf search and proposed limited lazy wolf search for ranking attributes and the experimental results illustrates the framework achieving excellent performance than the antecedent. For demonstrating the stability of proposed framework in all problems, numerous experiments are carried out using prominent benchmark datasets and a proposed crime propensity prediction dataset. The crime propensity prediction accuracy accomplished by fine tuning the proposed algorithm was 99.3617% directing crime free society.
Parameterized Convolutional Neural Networks for Aspect Level Sentiment Classification

We introduce a novel parameterized convolutional neural network for aspect level sentiment classification. Using parameterized filters and parameterized gates, we incorporate aspect information into convolutional neural networks (CNN). Experiments demonstrate that our parameterized filters and parameterized gates effectively capture the aspect-specific features, and our CNN-based models achieve excellent results on SemEval 2014 datasets.
Three-Dimensional Backbone Network for 3D Object Detection in Traffic Scenes

The task of detecting 3D objects in traffic scenes has a pivotal role in many real-world applications. However, the performance of 3D object detection is lower than that of 2D object detection due to the lack of powerful 3D feature extraction methods. To address this issue, this study proposes a 3D backbone network to acquire comprehensive 3D feature maps for 3D object detection. It primarily consists of sparse 3D convolutional neural network operations in the point cloud. The 3D backbone network can inherently learn 3D features from the raw data without compressing the point cloud into multiple 2D images. The sparse 3D convolutional neural network takes full advantage of the sparsity in the 3D point cloud to accelerate computation and save memory, which makes the 3D backbone network feasible in a real-world application. Empirical experiments were conducted on the KITTI benchmark and comparable results were obtained with respect to the state-of-the-art performance for 3D object detection.
Measurement of Luders band of cast iron material based on 3D-DIC de-correlation effect

In the material tensile test, the fracture usually starts from generation of the Luders band. The Luders band can be measured by strain, but the conventional strain gauge cannot be used to measure due to undetermined position of the Luders band. The fracture phenomenon can be monitored by the full field strain measurement. However, the common full-field measurement method must cause delay because of the excessive calculation amount. Hence generation of the fracture cannot be measured in real-time. The 3D-DIC method is an efficient way of the full field non-contact measurement. By simplifying the 3D- DIC method and transforming the object of interest, this paper can focus on the generation of large strains, greatly reduce the amount of calculation and monitor the production and development of the Luders band in real time at last.
Semi-Supervised Voice Conversion with Amortized Variational Inference

In this work we introduce a semi-supervised approach to the voice conversion problem, in which speech from a source speaker is converted into speech of a target speaker. The proposed method makes use of both parallel and non-parallel utterances from the source and target simultaneously during training. This approach can be used to extend existing parallel data voice conversion systems such that they can be trained with semi-supervision. We show that incorporating semi-supervision improves the voice conversion performance compared to fully supervised training when the number of parallel utterances is limited as in many practical applications. Additionally, we find that increasing the number non-parallel utterances used in training continues to improve performance when the amount of parallel training data is held constant.
Domain Transfer in Dialogue Systems without Turn-Level Supervision

Task oriented dialogue systems rely heavily on specialized dialogue state tracking (DST) modules for dynamically predicting user intent throughout the conversation. State-of-the-art DST models are typically trained in a supervised manner from manual annotations at the turn level. However, these annotations are costly to obtain, which makes it difficult to create accurate dialogue systems for new domains. To address these limitations, we propose a method, based on reinforcement learning, for transferring DST models to new domains without turn-level supervision. Across several domains, our experiments show that this method quickly adapts off-the-shelf models to new domains and performs on par with models trained with turn-level supervision. We also show our method can improve models trained using turn-level supervision by subsequent fine-tuning optimization toward dialog-level rewards.
Layer Selection in Progressive Transmission of Motion-Compensated JPEG2000 Video

MCJ2K (Motion-Compensated JPEG2000) is a video codec based on MCTF (Motion- Compensated Temporal Filtering) and J2K (JPEG2000). MCTF analyzes a sequence of images, generating a collection of temporal sub-bands, which are compressed with J2K. The R/D (Rate-Distortion) performance in MCJ2K is better than the MJ2K (Motion JPEG2000) extension, especially if there is a high level of temporal redundancy. MCJ2K codestreams can be served by standard JPIP (J2K Interactive Protocol) servers, thanks to the use of only J2K standard file formats. In bandwidth-constrained scenarios, an important issue in MCJ2K is determining the amount of data of each temporal sub-band that must be transmitted to maximize the quality of the reconstructions at the client side. To solve this problem, we have proposed two rate-allocation algorithms which provide reconstructions that are progressive in quality. The first, OSLA (Optimized Sub-band Layers Allocation), determines the best progression of quality layers, but is computationally expensive. The second, ESLA (Estimated-Slope sub-band Layers Allocation), is sub-optimal in most cases, but much faster and more convenient for real-time streaming scenarios. An experimental comparison shows that even when a straightforward motion compensation scheme is used, the R/D performance of MCJ2K competitive is compared not only to MJ2K, but also with respect to other standard scalable video codecs.
A Survey of Fine-Grained Image Classification Based on Deep Learning

The deep learning technology has shown impressive performance in various vision tasks such as image classification and object detection. Recent advances of deep learning techniques bring encouraging performance to fine-grained image classification which aims to distinguish subordinate-level categories. This paper reviews the major deep learning concepts pertinent to fine-grained image analysis and summarizes to the field, briefly introduces the representative method of fine-grained image classification based on deep learning. At present, the field of fine classification has developed a variety of methods including multi-network learning, target part detection and visual attention. Each method is to obtain a distinguishing area in the image, which help the network learn more effective features to complete the classification and recognition of fine-grained images.
Review of Current Methods for Re-Identification in Computer Vision.

The problem of reidentification of a person in multiple cameras is a hot topic in computer vision research. The issue is with the consistent identification of a person in multiple cameras from different viewpoints and environmental conditions. Many computer vision researchers have been looking into methods that can improve the reidentification of people for many real-world purposes. There are new methods each year that expand and explore new concepts and improve the accuracy of reidentification. This paper will look at current developments and the past tends to find what has been done and what is being done to solve this problem. This paper will start off by introducing the topic as well as covering the basic concepts of the reidentification problem. Next, it will cover common datasets that are used in today's research. Then it will look at evaluation techniques. Then this paper will start to describe simple techniques that are used followed by the current deep learning techniques. This paper will cover how these techniques are used, what are some of their weaknesses and their strengths. It will conclude with an overview of some of the best models and show which models have the most promise and which models should be avoided.
Modeling of Ungrounded Tangibles on Mutual Capacitance Touch Screens

Tangible user interface for touch screens, where interaction happens through tangible objects placed on the screen, is one possible option for addressing the lack of haptic feedback in touch screens. For the development of tangible user interface for mutual capacitance touch screens, it is of interest to be able to model how a tangible on the screen surface is seen by the touch screen controller. Finite Element Method simulations for this case is very demanding in terms of both computational resources and time. In this article, we present a computationally efficient model for simulating the capacitance image of arbitrarily shaped conductive sheets on full size mutual capacitance touch screen panels, with calculation time in the order of milliseconds. Output data from the model show good agreement with corresponding measurement data from experiments, with a root mean square deviation of 1.5% of the peak to peak of the measured values for the modeled screen area.
Construction and Application of Ship Data Mining Platform Based on Spark

With the explosive growth of ship information data brought by the intellectualization and digitization of waterborne traffic, it has become more and more important to process and apply massive ship data quickly and accurately.In view of the growing maturity and wide application of Spark large data technology in data mining field, this paper, against the characteristics of ship AIS data, chooses a series of large data technologies based on Spark to build the AIS data mining platform.According to the basic process of mining and processing, the overall framework of the platform is divided into three modules: database, Spark computing and mining, visualization.The AIS data of Sunda Strait with high ship density is used to process and mine the actual data to verify the availability of the platform.The results show that the platform works well and the three modules can work normally.And the AIS data of the Strait are processed and mined quickly and accurately.According to different requirements, it can display bar charts, polyline charts, histogram, trajectory charts and thermal charts and other commonly used data analysis graphics to achieve the effect of navigation assistance.
Sweetgrass AR: Exploring Augmented Reality as a Resource for Indigenous–Settler Relations

Augmented reality (AR) is increasingly used as a digital storytelling medium to reveal place-based content, including hidden histories and alternative narratives. In the context of Indigenous–settler relations, AR holds potential to expose and challenge representations of settler colonialism while invoking relational ethics and Indigenous ways of knowing. However, it also threatens to disseminate misinformation and commodify Indigenous Knowledge. Here, we focus on collaborative AR design practices that support critical, reflective, and reciprocal relationship building by teams composed of members from Indigenous and settler communities. After a short history of Indigenous media development in Canada, we describe how we operationalized a participatory AR design process to strengthen Indigenous–settler relations. We document a series of iterative design steps that teams can use to work through ethical, narrative, and technical choices made in the creation of culturally appropriate AR content, and draw attention to the potential and limitations of this emerging medium.
A Sentiment Score and a Rating Based Numeric Analysis Recommendations System: A Review

Online purchases have been significantly increasing in the web world. Some of the big giants who have dominated the E-Commerce market worldwide are Amazon, FlipKart, Walmart and many others. Data generation has increased exponentially and analysis of this kind of dynamic data poses a major challenge. Further, facilitating consumer satisfaction by recommending the right product is another main challenge. This involves a significant number of factors like review ratings, normalization, early rating, sentiment computations of a sentence consisting of conjunctions, categorizing the sentiment score as positive, negative and neutral score for a given product review. Finally, the product which has the highest positive and least negative score must be suggested for the end user. In this paper, we discuss the work done under rating based numerical analysis methods which considers the transactions done by the end user. In the second part of the paper, we present an overview of sentiment score computed and its significance in improving the efficiency of recommendation systems. The main objective of this review is to understand and analyze different methods used to improve the efficiency of the current recommendation systems, thereby enhancing the credibility of product recommendations.
Visual Servoing using Mixed Sensitivity $\mathbf{H}_{\infty}$ Control for Yaw-Pitch Camera Platform

This paper describes a robust control technique based on mixed sensitivity $\mathbf{H}_{\infty}$ control on visual servoing for Yaw-Pitch camera platform. $\mathbf{H}_{\infty}$ control technique can guarantee robustness and performance which can resist high disturbance and ensure stability in the closed-loop control system. The main function of the controller is to keep the object at the center of the field of an image displayed by the camera. The platform consists of two models, the first is motor servo dynamics and second is a single object movement model in image space. Weighting function is chosen such that the system could meet the performance specifications and robust stability requirements. The system used in this study is the SONY FCB-EV7520 camera which is placed on the Yaw-Pitch platform which consists of two servo motors. The control signal generated from the controller is used to adjust the servo motor so that it can direct the camera to the tracked object. The results obtained from the experiment show that the controller designed with mixed sensitivity $\mathbf{H}_{\infty}$ control has a good control behavior with fast transient and small overshoot.
Analyzing of data to dance performance judgement and usefulness investigation of raters random assignment design using Many-Facet Rasch measurement model

This study analyzed dance performance judgement evaluation data by applying Many-Facets Rasch measurement model(MFRM). Specifically, the purpose of this study is to examine the usefulness of the random assignment design(RAD) method by comparing the total inspection design(TID) method. In TID, five raters(rater A, B, C, D, E) assessed 40-subject(dancer) with two evaluation factors(art and skill factors). In RAD, three raters from the five raters were randomly assigned and assessed the 40-dancer with the two evaluation factors. SPSS 22.0 program and Facets 3.81.2 program were used for data analysis. The results of this study were as follows. First, in both designs(RAD and TID), the rater’s severity and the two factors difficulty are significant difference. Second, comparing the positions of the vertical ruler in the MFRM, the overall patterns in both designs are very similar to each other in terms of the three facets(rater’s severity, subject ability, evaluation factor difficulty). Third, in both designs, the raters were selected by bias assessment in the evaluation factors. In TID, it was shown that the rater A and rater D evaluated biasedly. However, in RAD, not only the rater A and rater D but also the rater B and rater C evaluated biasedly. Fourth, comparison of the dancer’s final ranking according to the two designs. The fair average was very high positive correlation between two designs(r=.998, p.001), and the final ranking of dancers was high positive correlation between two designs(ρ=.634, p.001).
Deep Learning for Cooperative Radio Signal Classification

Radio signal classification has a very wide range of applications in cognitive radio networks and electromagnetic spectrum monitoring. In this article, we consider scenarios where multiple nodes in the network participate in cooperative classification. We propose cooperative radio signal classification methods based on deep learning for decision fusion, signal fusion and feature fusion, respectively. We analyze the performance of these methods through simulation experiments. We conclude the article with a discussion of research challenges and open problems.
Cryptocurrency platform ecosystem: a systematic literature review from information systems perspective

A number of previous articles obtained from the IS research databases and conferences on cryptocurrency platform emphasize on protocol and network layers which are more of the technical perspective.However, there are very few articles that focused on the practitioners as they can be a source of knowledge on the ecosystem layer of the platform Review
An Auxiliary Classifier Generative Adversarial Framework for Relation Extraction

Relation extraction models suffer from limited qualified training data. Using human annotators to label sentences is too expensive and does not scale well especially when dealing with large datasets. In this paper, we use Auxiliary Classifier Generative Adversarial Networks (AC-GANs) to generate high-quality relational sentences and to improve the performance of relation classifier in end-to-end models. In AC-GAN, the discriminator gives not only a probability distribution over the real source, but also a probability distribution over the relation labels. This helps to generate meaningful relational sentences. Experimental results show that our proposed data augmentation method significantly improves the performance of relation extraction compared to state-of-the-art methods
Syntax-Aware Aspect-Level Sentiment Classification with Proximity-Weighted Convolution Network

It has been widely accepted that Long Short-Term Memory (LSTM) network, coupled with attention mechanism and memory module, is useful for aspect-level sentiment classification. However, existing approaches largely rely on the modelling of semantic relatedness of an aspect with its context words, while to some extent ignore their syntactic dependencies within sentences. Consequently, this may lead to an undesirable result that the aspect attends on contextual words that are descriptive of other aspects. In this paper, we propose a proximity-weighted convolution network to offer an aspect-specific syntax-aware representation of contexts. In particular, two ways of determining proximity weight are explored, namely position proximity and dependency proximity. The representation is primarily abstracted by a bidirectional LSTM architecture and further enhanced by a proximity-weighted convolution. Experiments conducted on the SemEval 2014 benchmark demonstrate the effectiveness of our proposed approach compared with a range of state-of-the-art models.
Real Time Motion Generation for Mobile Robot

The paper proposes a new real time motion generation technique for a mobile robot, which is able to find time-optimal motions along a curved path taking into account capabilities of the driving motors and ensuring the wheels rolling without skidding. The problem is converted to a time-optimal control of a second-order dynamic system under constraints on the control input, the first derivative of output, and mixed constraint on the control variable and the output derivative. After the state space discretization, the original problem is presented as a combinatorial one where the desired robot trajectory corresponds to a shortest path on the relevant graph. To find this path and ensure its real time implementation, a moving window strategy combined with dynamic programming is proposed. Advantages of this approach and its suitability to real time control are illustrated by a case study dealing the fastest motion of the mobile robot along a sinusoidal path.