Reviewerly/exHarmony huggingface dataset:
Fundamental frequency feature warping for frequency normalization and data augmentation in child automatic speech recognition

Effective child automatic speech recognition (ASR) systems have become increasingly important due to the growing use of interactive technology. Due to the lack of publicly available child speech databases, young child ASR systems often rely on older child or adult speech for training data. However, there is a large acoustic mismatch between child and adult speech. This study proposes a novel fundamental frequency (fo)-based frequency warping technique for both frequency normalization and data augmentation to combat this acoustic mismatch and address the lack of available child speech training data. The technique is inspired by the tonotopic distances between formants and fo, developed to model human vowel perception. The tonotopic distances are reformulated as a linear relationship between fo and vowel formants on the Mel scale. This reformulation is verified using fo and formant measurements from child utterances. The relationship is further generalized such that the frequency warping technique only relies on two parameters. The LibriSpeech ASR corpus is used for training, and both the OGI Kids' Speech and CMU Kids Corpora are used for both training and testing. A single word ASR experiment and a continuous read speech ASR experiment are performed to evaluate the fo-based frequency normalization and data augmentation techniques. In the single word experiment, the system using fo-based frequency normalization significantly improved over the baseline system with no normalization, with a relative improvement of up to 22.3%, when the mismatch between training and testing data was large. In the continuous speech experiment, the combination of fo-based frequency normalization and data augmentation resulted in a relative improvement of 19.3% over the baseline. Additionally, in all experiments, the fo-based techniques outperformed other techniques such as vocal tract length normalization (VTLN) or vocal tract length perturbation (VTLP). Results were validated using Gaussian mixture model (GMM), deep neural network (DNN), and bidirectional long–short term memory (BLSTM) acoustic models.
Audits in Cybersecurity

The objective of this chapter is to provision a comprehensive literature review of the most relevant approaches for conducting cybersecurity audits. The study includes auditing perspectives for specific scopes and the best practices that many leading organizations are providing for security and auditing professionals to follow. The chapter reviews relevant features for auditing approaches in the following order: ISO/IEC 27001:2013, ISO/IEC 27002:2013, Control Objectives for Information and Related Technology (COBIT) 2019, Information Technology Infrastructure Library (ITIL) 4, AICPA, ISACA, NIST SP 800-53, NIST CSF v1.1, IIA, PCI DSS, ITAF, COSO, ENISA, NERC CIP, and CSAM.
MetaDetect: Uncertainty Quantification and Prediction Quality Estimates for Object Detection

In object detection with deep neural networks, the box-wise objectness score tends to be overconfident, sometimes even indicating high confidence in presence of incorrect predictions. Hence, the reliability of the prediction and therefore reliable uncertainties are of highest interest. In this work, we present a post processing method that for any given neural network provides predictive uncertainty and quality estimates. These estimates are learned by a post processing model that receives as input a hand-crafted set of transparent metrics in form of a structured dataset. From this set we learn two tasks for predicted bounding boxes. We discriminate between true positives ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$IoU&gt; 0.5$</tex> ) and false positives ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$IoU &lt;0.5$</tex> ) which we term meta classification, and we predict <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$IoU$</tex> values directly which we term meta regression. The probabilities of the meta classification model aim to learn the probabilities of success and failure and therefore provide a modelled predictive uncertainty estimate. Moreover, meta regression gives rise to a quality estimate. In numerical experiments, we use the publicly available YOLOv3 network and the Faster-RCNN network and evaluate meta classification and regression performance on the Kitti, Pascal VOC and COCO datasets. We demonstrate that our metrics are indeed well correlated with the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$IoU$</tex> . For meta classification we obtain classification accuracies of up to 99.61% and AUROCs of up to 99.84%. For meta regression we obtain an <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$R^{2}$</tex> value of up to 91.78%. These results yield significant improvements compared to the network's objectness score and other baseline approaches. Therefore, we obtain more reliable uncertainty and quality estimates which is particularly interesting in the absence of ground truth.
The effects of designing conversational commerce chatbots with expertise cues

Drawing upon the computers-are-social-actors (CASA) paradigm, this study examined the effects of designing conversational commerce chatbots with expertise cues. Accordingly, these cues were operationalized through designation of chatbots as product-specific advisers, dialogues containing expertise-cued labels and social descriptors (e.g., "I am your personal expert adviser for sportswear!"), gender of chatbots, and appearance styles of chatbots. A within-subject laboratory experiment was conducted in which university undergraduates (n=71) viewed two videos displaying mock user-chatbot interactions — one featured the experimental condition (chatbots with expertise cues) while the other featured the control condition (chatbot without expertise cues). Compared against the control, the experimental condition elevated perceived source expertise of chatbots, platform trust ability and trust integrity, and purchase intention through the conversational commerce platform. The expertise cues effects on purchase intention was mediated by platform trust ability. Theoretical and practical implications are discussed through the lens of CASA and source credibility model in this paper.
Session-based Recommendation with Heterogeneous Graph Neural Networks

The aim of session-based recommendation is to predict the next-clicked item based on the anonymous behavior sequence. The existing works on session-based recommendation mainly capture the user preference within an individual session. This paper proposes a novel approach, called Session-based Recommendation with Heterogeneous Graph Neural Networks (SR-HGNN) to exploit cross-session information for better inferring the user preference of the current session. Specifically, we propose to use a heterogeneous graph to model the current session sequence and cross-session information simultaneously. After that, we come up with a novel model to pass messages along edges of different types hierarchically. Extensive experiments conducted on three real-world datasets demonstrate the superiority of SR-HGNN by comparing with different state-of-the-art baselines.
Comparing brain activity in virtual and non-virtual environments: A VR &amp; EEG study

The use of virtual reality (VR) technology in marketing, education and training is growing rapidly. However, the body of quantitative research comparing the cognitive impact of virtual with non-virtual environments is still small. To address this, the present research invited subjects to perform the same cognitive visuospatial task using both an immersive, head-mounted display (HMD) and a tablet computer. Cortical activity was then measured and compared in both environments using EEG. The results showed significant differences in cognitive load for the HMD condition, particularly in the frontal midline theta rhythm (4–8 Hz), a range commonly associated with a variety of executive functions. Explanations for this effect include potential misattribution of distance/object size in virtual space and the relative novelty of VR itself. Further research is needed to determine the underlying parameters that impact cognitive responses to complex tasks while immersed in a virtual environment.
Unsupervised low-light Image Enhancement with Quality-Task-Perception Loss

Unsupervised low-light image enhancement is a classic task in image processing area. Deep learning based methods are the main approaches to tackle this task due to their effectiveness in whole visual areas. However, blur image or fake color, insufficient enhancement and missing semantic information are three common issues when utilize these methods. In this paper, we propose a new hybrid loss named Quality-Task-Perception (QTP) to alleviate these three problems simultaneously. Specifically, this hybrid loss includes three components: quality, task and perception. The quality part overcomes the blur image or fake color problem by enforcing the similarity between image quality scores of enhancement results and those of samples in daytime domain. The task part tackles the insufficient enhancement issue by constraining enhancement results to have higher daytime probabilities, where daytime probabilities are achieved by our proposed new pipeline. The perception part copes with the missing semantic information by restricting the phase consistency between the low-light image and its enhanced version. As this new hybrid loss is special designed to deal with these three problems, a more accurate low-light image enhancement model can be learned. Comprehensive experiments on both supervised and unsupervised datasets demonstrate the effectiveness of our proposed approach.
String Theories Involving Regular Membership Predicates: From Practice to Theory and Back

Widespread use of string solvers in formal analysis of string-heavy programs has led to a growing demand for more efficient and reliable techniques which can be applied in this context, especially for real-world cases. Designing an algorithm for the (generally undecidable) satisfiability problem for systems of string constraints requires a thorough understanding of the structure of constraints present in the targeted cases. In this paper, we investigate benchmarks presented in the literature containing regular expression membership predicates, extract different first order logic theories, and prove their decidability, resp. undecidability. Notably, the most common theories in real-world benchmarks are \(\mathsf {PSPACE}\)-complete and directly lead to the implementation of a more efficient algorithm to solving string constraints.
Autonomous Cooperative Transportation System involving Multi-Aerial Robots with Variable Attachment Mechanism

Cooperative transportation by multi-aerial robots has the potential to support various payloads and improve failsafe against dropping. Furthermore, changing the attachment positions of robots according payload characteristics increases the stability of transportation. However, there are almost no transportation systems capable of scaling to the payload weight and size and changing the optimal attachment positions. To address this issue, we propose a cooperative transportation system comprising autonomously executable software and suitable hardware, covering the entire process, from pre-takeoff setting to controlled flight. The proposed system decides the formation of the attachment positions by prioritizing controllability based on the center of gravity obtained from Bayesian estimations with robot pairs. We investigated the cooperative transportation of an unknown payload larger than that of whole carrier robots through numerical simulations. Furthermore, we performed cooperative transportation of an unknown payload (with a weight of about 3.2 kg and maximum length of 1.76 m) using eight robots. The proposed system was found to be versatile with regard to handling unknown payloads with different shapes and center-of-gravity positions.
Learnability factors of AR usage performance: Validating through survey

Augmented Reality has become an important learning medium in both educational and learning domain. AR has been in use within the game technology since long. Moreover, greater prospects of AR have been seen during learning and training, specifically in the developed countries. AR has contributed in enhancing the learnability of the users. In order to understand the impact of AR usage, this work starts by preparing the list of factors that influence the learnability with the AR usage. The list of seven (7) factors were identified through Literature Review (LR) using Kolb’s Experiential Learning Theory (ELT) 1984. Survey method was used to validate the factor’s questions, Next, the validated questions were given to student and academicians for answering through another survey for factor validation. Results showed the identified factors (Motivation, Better lab experience, Enhanced Focus, Satisfaction, Enable Visualization of Invisible Concepts, Better Learning and Performance and Confidence) within the Proposed Learnability Enhancement Model (LEM). Additionally, all the questions were validated with acceptance by experts and 32 academicians through the survey 1. Also, all the factors were accepted with 94% and above acceptance rate in survey 2. The significance of identifying these AR acceptance factors for enhancement of learnability will impact better learnability and performance with AR usage, the study will continue with testing the LEM efficiency through the simulation of AR usage on the real environment in the next phase of this work.
Generalized t-SNE Through the Lens of Information Geometry

t-SNE (t-distributed Stochastic Neighbor Embedding) is known to be one of the very powerful tools for dimensionality reduction and data visualization. By adopting the student’s t-distribution in the original SNE (Stochastic Neighbor Embedding), t-SNE achieves faster and more stable learning. However, t-SNE still poses computational complexity due to its dependence on KL-divergence. Our goal is to extend t-SNE in a natural way by the framework of information geometry. Our generalized t-SNE can outperform the original t-SNE with a well-chosen set of parameters. Furthermore, the experimental results for MNIST, Fashion MNIST and COIL-20, show that our generalized t-SNE outperforms the original t-SNE.
A Novel Handwritten Digit Classification System Based on Convolutional Neural Network Approach

An enormous number of CNN classification algorithms have been proposed in the literature. Nevertheless, in these algorithms, appropriate filter size selection, data preparation, limitations in datasets, and noise have not been taken into consideration. As a consequence, most of the algorithms have failed to make a noticeable improvement in classification accuracy. To address the shortcomings of these algorithms, our paper presents the following contributions: Firstly, after taking the domain knowledge into consideration, the size of the effective receptive field (ERF) is calculated. Calculating the size of the ERF helps us to select a typical filter size which leads to enhancing the classification accuracy of our CNN. Secondly, unnecessary data leads to misleading results and this, in turn, negatively affects classification accuracy. To guarantee the dataset is free from any redundant or irrelevant variables to the target variable, data preparation is applied before implementing the data classification mission. Thirdly, to decrease the errors of training and validation, and avoid the limitation of datasets, data augmentation has been proposed. Fourthly, to simulate the real-world natural influences that can affect image quality, we propose to add an additive white Gaussian noise with σ = 0.5 to the MNIST dataset. As a result, our CNN algorithm achieves state-of-the-art results in handwritten digit recognition, with a recognition accuracy of 99.98%, and 99.40% with 50% noise.
cDLRM: Look Ahead Caching for Scalable Training of Recommendation Models

Deep learning recommendation models (DLRMs) are typically composed of two sets of parameters: large embedding tables to handle sparse categorical inputs, and neural networks such as multi-layer perceptrons (MLPs) to handle dense non-categorical inputs. Current DLRM training practices keep both these parameters in GPU memory. But as the size of the embedding tables grow, this practice of storing model parameters in GPU memory requires dozens or even hundreds of GPUs. This is an unsustainable trend with severe environmental consequences. Furthermore, such a design forces only a few conglomerates to be the gate keepers of model training. In this work, we propose cDLRM which democratizes recommendation model training by allowing a user to train on a single GPU regardless of the size of embedding tables by storing all embedding tables in CPU memory. A CPU based pre-processor analyzes training batches to prefetch embedding table slices accessed by those batches and caches them in GPU memory just-in-time. An associated caching protocol on the GPU enables efficiently updating the cached embedding table parameters. cDLRM decouples the embedding table size demands from the number of GPUs needed for compute. We first demonstrate that with cDLRM it is possible to train a large recommendation model using a single GPU regardless of model size. We then demonstrate that with its unique caching strategy, cDLRM enables pure data parallel training. We use two publicly available datasets to show that a cDLRM achieves identical model accuracy compared to a baseline trained completely on GPUs, while benefiting from large reduction in GPU demand.
HackRL: Reinforcement learning with hierarchical attention for cross-graph knowledge fusion and collaborative reasoning

Reasoning aiming at inferring implicit facts over knowledge graphs (KGs) is a critical and fundamental task for various intelligent knowledge-based services. With multiple distributed and complementary KGs, the effective and efficient capture and fusion of knowledge from different KGs is becoming an increasingly important topic, which has not been well studied. To fill this gap, we propose to explore cross-KG relation paths with the anchor links identified by entity alignment for the knowledge fusion and collaborative reasoning of multiple KGs. To address the heterogeneity of different KGs, this paper proposes a novel reasoning model named HackRL based on the reinforcement learning framework, which incorporates the long short-term memory and hierarchical graph attention in the policy network to infer indicative cross-KG relation paths from the history trajectory and the heterogeneous environment for predicting corresponding relations. Meanwhile, an entity alignment-oriented representation learning method is utilized to embed different KGs into a unified vector space based on the anchor links to reduce the impact of distinct vector spaces, and two training mechanisms, action mask and retrain with sampled paths, are proposed to optimize the training process to learn more successful indicative paths. The proposed HackRL is validated on three cross-lingual datasets built from DBpedia on the link prediction and fact prediction tasks. Experimental results demonstrate that HackRL achieves better performance on most tasks than existing methods. This work provides an industrially-applicable framework for fusing distributed KGs to make better decisions.
Experiencing Social Augmented Reality in Public Spaces

We present various views on the future use of augmented reality in public spaces. The views address enhanced walking, social activity, appropriation of public spaces, and futuristic social aspects of future outdoor augmented reality. Although we often refer to handheld augmented reality, the focus is on more natural ways of experiencing augmented reality. That is, not using a device at all (projection or mirror-based augmented reality) or using optical see-through smart eyewear such as smart glasses or lenses that allow the experience of ever-present vision and audio-based augmented reality.
Multiple Image Splicing Dataset (MISD): A Dataset for Multiple Splicing

Image forgery has grown in popularity due to easy access to abundant image editing software. These forged images are so devious that it is impossible to predict with the naked eye. Such images are used to spread misleading information in society with the help of various social media platforms such as Facebook, Twitter, etc. Hence, there is an urgent need for effective forgery detection techniques. In order to validate the credibility of these techniques, publically available and more credible standard datasets are required. A few datasets are available for image splicing, such as Columbia, Carvalho, and CASIA V1.0. However, these datasets are employed for the detection of image splicing. There are also a few custom datasets available such as Modified CASIA, AbhAS, which are also employed for the detection of image splicing forgeries. A study of existing datasets used for the detection of image splicing reveals that they are limited to only image splicing and do not contain multiple spliced images. This research work presents a Multiple Image Splicing Dataset, which consists of a total of 300 multiple spliced images. We are the pioneer in developing the first publicly available Multiple Image Splicing Dataset containing high-quality, annotated, realistic multiple spliced images. In addition, we are providing a ground truth mask for these images. This dataset will open up opportunities for researchers working in this significant area.
What-Where-When Attention Network for video-based person re-identification

Video-based person re-identification plays a critical role in intelligent video surveillance by learning temporal correlations from consecutive video frames. Most existing methods aim to solve the challenging variations of pose, occlusion, backgrounds and so on by using attention mechanism. They almost all draw attention to the occlusion and learn occlusion-invariant video representations by abandoning the occluded area or frames, while the other areas in these frames contain sufficient spatial information and temporal cues. To overcome these drawbacks, this paper proposes a comprehensive attention mechanism covering what, where, and when to pay attention in the discriminative spatial-temporal feature learning, namely What-Where-When Attention Network (W3AN). Concretely, W3AN designs a spatial attention module to focus on pedestrian identity and obvious attributes by the importance estimating layer (What and Where), and a temporal attention module to calculate the frame-level importance (when), which is embedded into a graph attention network to exploit temporal attention features rather than computing weighted average feature for video frames like existing methods. Moreover, the experiments on three widely-recognized datasets demonstrate the effectiveness of our proposed W3AN model and the discussion of major modules elaborates the contributions of this paper.