Reviewerly/exHarmony huggingface dataset:
Lightweight and Scalable DAG based distributed ledger for verifying IoT data integrity

Verifying the integrity of IoT data in cloud-based IoT architectures is crucial for building reliable IoT applications. Traditional data integrity verification methods rely on a Trusted Third Party (TTP) that has issues of risk and operational cost by centralization. Distributed Ledger Technology (DLT) has a high potential to verify IoT data integrity and overcome the problems with TTPs. However, the existing DLTs have low transaction throughput, high computational and storage overhead, and are unsuitable for IoT environments, where a massive scale of data is generated. Recently, Directed Acyclic Graph (DAG) based DLTs have been proposed to address the low transaction throughput of linear DLTs. However, the integration of IoT Gateways (GWs) into the peer to peer (P2P) DLT network is challenging because of their low storage and computational capacity. This paper proposes Lightweight and Scalable DAG based distributed ledger for IoT (LSDI) that can work with resource-constrained IoT GWs to provide fast and scalable IoT data integrity verification. LSDI uses two key techniques: Pruning and Clustering, to reduce 1) storage overhead in IoT GWs by removing sufficiently old transactions, and 2) computational overhead of IoT GWs by partitioning a large P2P network into smaller P2P networks. The evaluation results of the proof of concept implementation showed that the proposed LSDI system achieves high transaction throughput and scalability while efficiently managing storage and computation overhead of the IoT GWs.
A Hybrid Control Scheme for 360-Degree Dynamic Adaptive Video Streaming Over Mobile Devices

A 360-degree streaming system can provide immersive, interactive, and autonomous experiences surrounding the user by means of viewpoint changes to see different angles of a 360-degree video. However, due to the limited capacity and highly dynamic conditions of cellular networks, high-resolution 360-degree video playback over mobile devices often suffers from playback freezing, and bandwidth waste is inevitably incurred in delivering out-of-view video data. In this paper, a hybrid control scheme is presented for segment-level continuous bitrate selection and tile-level bitrate allocation for 360-degree streaming over mobile devices to increase users’ quality of experience. First, a deep reinforcement learning (RL) method is proposed to predict the segment bitrate and avoid playback freezing. Second, a viewpoint-prediction-map-based cooperative bargaining game theory is proposed for bitrate allocation optimization to choose a suitable bitrate for each tile to reduce unreasonable bandwidth waste. The proposed scheme is compared with state-of-the-art approaches under a wide variety of mobile network conditions with multiple viewpoint traces and 360-degree video contents. The experimental results indicate that the proposed method outperforms the compared state-of-the-art approaches in terms of various experimental objectives on mobile devices.
The Role of Port Authority in New Blockchain Scenarios for Maritime Port Management: The Case of Denmark

The purpose of the paper is to determine and examine, to what extent blockchain scenarios for the shipping industry have practical explication from maritime ports’ perspective and how these are sync with ports’ long-term development strategies, particularly in Denmark. The present study involved qualitative interviews with representatives of the biggest maritime ports of Denmark, varied by location, volumes, operations and cargo type. Data saturation is achieved through several rounds of in-depth semi-structured interviews. Results showed uncertainties in the long-term investment strategy of the considered ports. While focused on land expansion and operation development, the port authorities lack inner-port coordination with related enterprises, which consequently affects overall efficiency. While the development strategy appears to be identical among the port authorities, it varies significantly within specific blockchain scenarios and port’s strategy regarding short-term port optimization. Besides, the role of port authority was debated. Authorities are willing to be more involved in supply chain operations as a consultancy rather than just a controlling party, yet are burden by the state restrictions. Unlike generally-discussed blockchain compatibility studies, the current research contributes by revealing core business uncertainties within port area development and communication. Moreover, the case could serve as a representation of small- to middle-size ports in the EU.
Synthesising 3D Facial Motion from “In-the-Wild” Speech

Synthesising 3D facial motion from speech is a crucial problem manifesting in a multitude of applications such as computer games and movies. Recently proposed methods tackle this problem in controlled conditions of speech. In this paper, we introduce the first methodology for 3D facial motion synthesis from speech captured in arbitrary recording conditions (“in-the-wild”) and independent of the speaker. For our purposes, we captured 4D sequences of people uttering 500 words, contained in the Lip Reading in the Wild (LRW) words, a publicly available large-scale in-the-wild dataset, and built a set of 3D blendshapes appropriate for speech. We correlate the 3D shape parameters of the speech blendshapes to the LRW audio samples by means of a novel time-warping technique, named Deep Canonical Attentional Warping (DCAW), that can simultaneously learn hierarchical non-linear representations and a warping path in an end-to-end manner. We thoroughly evaluate our proposed methods, and show the ability of a deep learning model to synthesise 3D facial motion in handling different speakers and continuous speech signals in uncontrolled conditions <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Video Question-Answering Techniques, Benchmark Datasets and Evaluation Metrics Leveraging Video Captioning: A Comprehensive Survey

While describing visual data is a trivial task for humans, it is an intricate task for a computer.This is even more challenging if the visual data is a video.Comprehending a video and describing it is called Video Captioning.This involves understanding the semantics of a video and then generating human-like descriptions of the video.It requires the collaboration of both research communities of computer vision and natural language processing.The captions generated by video captioning can be further utilized for video retrieval, summarization, question-answering, etc. Video Question-Answering (video-QA) involves querying the system to obtain an answer in response.This paper presents a brief survey of the video captioning techniques and a comprehensive review of existing techniques, datasets, and evaluation metrics for the task of video-QA.Video-QA techniques rely on the attention mechanism to generate relevant results.The presented survey shows that recent works on Memory Networks, Generative Adversarial Networks, and Reinforced Decoders, have the capability to handle the complexities and challenges of video-QA.Additionally, the graph-based methods, although less explored, give very promising results.In this article, we have discussed the emerging research directions and various application areas of video-QA.
High Accuracy Conversational AI Chatbot Using Deep Recurrent Neural Networks Based on BiLSTM Model

In the modern world, chatbot programs are implementations that can be used to store data collected through a question and answer system and then can be applied in the Python program to optimize the results based on highly rated questions asked in a service center. The application of chatbots in the Python program can use various models. Specifically in this program, the BiLSTM model will be applied. The output produced from the chatbot program with the application of the BiLSTM model is in the form of accuracy and also data set that matches the information the program user enters in the chatbot's input dialog box. The selection of models that can be applied to the program is based on data which can affect program performance, with the objective of the program which can determine the high or low level of accuracy that will be generated from the results obtained through a program, which can be a major factor in deciding the selected model. Based on the various considerations that are the requirements for choosing a model of a program, in the end the BiLSTM model is selected will be applied to the program. In addition to model selection, the next step is to determine the method used in the program, in this program the greedy method is a form of implementation of the BiLSTM model with the aim that when running the program, data processing time can be faster, and increase the value of the model selected in program. In addition, supporting attributes such as the seq2seq model are a determining factor in a program that can function to verify whether data processing matches the criteria that can be used as new in data processing. In addition, a program evaluation method is needed that can be used to verify whether the program output matches the data expected by the user. Based on the application of the BiLSTM model into the chatbot, it can be concluded that with all program test results consisting of a variety of different parameter pairs, it is stated that Parameter Pair 1 (size-layer 512, num-layers 2, embedded-size 256, learning-rate 0.001, batch-size 32, epoch 20) from File 3 is the BiLSTM Chatbot with the avg accuracy value of 0.995217 which uses the BiLSTM model is the best parameter pair.
Verification of complementarity relations between quantum steering criteria using an optical system

The ability that one system immediately affects another one by using local measurements is regarded as quantum steering, which can be detected by various steering criteria. Recently, Mondal et al. [Phys. Rev. A 98, 052330 (2018)] derived the complementarity relations of coherence steering criteria, and revealed that the quantum steering of a system can be observed through the average coherence of a subsystem. Here, we experimentally verify the complementarity relations between quantum steering criteria by employing two-photon Bell-like states and three Pauli operators. The results demonstrate that if prepared quantum states can violate two setting coherence steering criteria and turn out to be steerable states, then they cannot violate the complementary settings criteria. Three measurement settings inequalities, which establish a set of complementarity relations between these two coherence steering criteria, are always obeyed by all prepared quantum states in experiment. In addition, we experimentally certify that the strengths of coherence steering criteria depend on the choice of coherence measure. In comparison with two setting coherence steering criteria based on ${l}_{1}$ norm of coherence and relative entropy of coherence, our experimental results show that the steering criterion based on skew information of coherence is the strongest in detecting the steerability of two-photon Bell-like states. Thus, our experimental demonstration can deepen the understanding of the relation between the quantum steering and quantum coherence.
Review of reinforcement learning research

Reinforcement learning is a type of machine learning. An important feature that distinguishes it from other types of learning is that reinforcement learning uses training information to evaluate actions taken. The correct action guides the choice of action. The agent is not told what action to do and what action should not be done. Instead, it tries to discover what action can produce the maximum reward. Therefore, reinforcement learning is a trial and error mechanism that learns through constant trial and error and feedback. Corresponding algorithms include dynamic programming, Monte Carlo methods, Q-Learning, TD-Learning, and Sarsa algorithms.
3D Object Detection and Instance Segmentation from 3D Range and 2D Color Images

Instance segmentation and object detection are significant problems in the fields of computer vision and robotics. We address those problems by proposing a novel object segmentation and detection system. First, we detect 2D objects based on RGB, depth only, or RGB-D images. A 3D convolutional-based system, named Frustum VoxNet, is proposed. This system generates frustums from 2D detection results, proposes 3D candidate voxelized images for each frustum, and uses a 3D convolutional neural network (CNN) based on these candidates voxelized images to perform the 3D instance segmentation and object detection. Results on the SUN RGB-D dataset show that our RGB-D-based system’s 3D inference is much faster than state-of-the-art methods, without a significant loss of accuracy. At the same time, we can provide segmentation and detection results using depth only images, with accuracy comparable to RGB-D-based systems. This is important since our methods can also work well in low lighting conditions, or with sensors that do not acquire RGB images. Finally, the use of segmentation as part of our pipeline increases detection accuracy, while providing at the same time 3D instance segmentation.
Limits of Transfer Learning

Transfer learning involves taking information and insight from one problem domain and applying it to a new problem domain. Although widely used in practice, theory for transfer learning remains less well-developed. To address this, we prove several novel results related to transfer learning, showing the need to carefully select which sets of information to transfer and the need for dependence between transferred information and target problems. Furthermore, we prove how the degree of probabilistic change in an algorithm using transfer learning places an upper bound on the amount of improvement possible. These results build on the algorithmic search framework for machine learning, allowing the results to apply to a wide range of learning problems using transfer.
Generalising the Scattered Property of Subspaces

Let V be an r-dimensional $${\mathbb{F}_{{q^n}}}$$ -vector space. We call an $${\mathbb{F}_{q}}$$ -subspace U of V h-scattered if U meets the h-dimensional $${\mathbb{F}_{{q^n}}}$$ -subspaces of V in $${\mathbb{F}_{q}}$$ -subspaces of dimension at most h. In 2000 Blokhuis and Lavrauw proved that $${\dim_{\mathbb{F}_{q}}}$$ U ≤ rn/2 when U is 1-scattered. Sub-spaces attaining this bound have been investigated intensively because of their relations with projective two-weight codes and strongly regular graphs. MRD-codes with a maximum idealiser have also been linked to rn/2-dimensional 1-scattered subspaces and to n-dimensional (r − 1)-scattered subspaces. In this paper we prove the upper bound rn/(h + 1) for the dimension of h-scattered subspaces, h > 1, and construct examples with this dimension. We study their intersection numbers with hyperplanes, introduce a duality relation among them, and study the equivalence problem of the corresponding linear sets.
Subspace Gaussian Mixture Model for Continuous Urdu Speech Recognition using Kaldi

Automatic Speech Recognition Systems (ASR) have significantly improved in recent years, where deep learning is playing an important role in the development of end to end ASR's. ASR is the task of converting spoken language into computer readable text. ASRs are becoming ever more prevalent way to interact with technology, thereby significantly closing the gap in terms of how humans interact with computers, making it more natural. Urdu is an under resourced language, for which training such a system requires a huge amount of data that is not readily available. In this paper we present improvements to the architecture of a statistical automatic speech recognition system for which the components involved in a statistical ASR have been explored in great detail. We also present the results on various statistical models that are trained for Urdu language. We choose the Kaldi toolkit for training the Urdu ASR using approximately 100 hours of transcribed data. The refined Subspace Gaussian Model gives a word error rate of 9% on the test set.
A High-speed Low-cost CNN Inference Accelerator for Depthwise Separable Convolution

This paper proposes a high-speed low-cost VLSI inference accelerator for depthwise separable convolution in deep convolutional neural networks (CNNs). The accelerator consists of parallel, pipelined depthwise convolution processing element array and pointwise convolution processing element array to improve system performance, with optimized hardware resource consumptions. Moreover, the PPE array can support fully-connected layers in CNNs. An FPGA prototype of the proposed accelerator was implemented. It executed a 12-layer simplified MobileNet model at a high speed above 15,000 frames per second (FPS) on $32\times 32$ images and 240 FPS on $224\times 224$ images.
How Does Visualization Help People Learn Deep Learning? Evaluating GAN Lab with Observational Study and Log Analysis

While a rapidly growing number of people want to learn artificial intelligence (AI) and deep learning, the increasing complexity of such models poses significant learning barriers. Recently, interactive visualizations, such as TensorFlow Playground and GAN Lab, have demonstrated success in lowering these barriers. However, there has been little work in evaluating these tools with human subjects. This paper presents two studies on evaluating GAN Lab, an interactive tool designed to help people learn how Generated Adversarial Networks (GANs) work. First, through an observational study, we investigate how the tool is used and what users learn from their usage. Second, we conduct a log analysis of the deployed tool to investigate how its visitors engage with GAN Lab. Based on the studies and our experience in developing and successfully deploying the tool, we provide design considerations and discuss further evaluation challenges for interactive educational tools for deep learning.
Holistic Web Application Security Visualization for Multi-Project and Multi-Phase Dynamic Application Security Test Results

As the number of web applications and the corresponding number and sophistication of the threats increases, creating new tools that are efficient and accessible becomes essential.Although there is much research concentrating on network security visualizations, there are only a few studies considering the web application vulnerabilities' possible visualization options.Consequently, to fill this gap, this research centers around a novel perception configuration to improve web application vulnerability monitoring.This study forms a generic data structure based on data sources that might be readily associated and commonly available for the majority of the web applications.The primary contribution of this study is a new dashboard tool for visualizing dynamic application security test results.Another contribution is the metrics/measures that the tool presents.The paper also describes a validation study in which participants answered quiz questions upon using the tool prototype.For the case study, sample data has been generated using the OWASP ZAP scanner tool and a prototype has been implemented to be used for validation purposes.This study allows the investigation of fifty metrics/measures for the multi-project/phase environment that enhances its benefits if the user aims to monitor a series of analyses' results and the changes between them for more than one web project.
High Speed, Approximate Arithmetic Based Convolutional Neural Network Accelerator

Convolutional Neural Networks (CNNs) for Artificial Intelligence (AI) algorithms have been widely used in many applications especially for image recognition. However, the growth in CNN-based image recognition applications raised challenge in executing millions of Multiply and Accumulate (MAC) operations in the state-of-the-art CNNs. Therefore, GPUs, FPGAs, and ASICs are the feasible solutions for balancing processing speed and power consumption. In this paper, we propose an efficient hardware architecture for CNN that provides high speed, low power, and small area targeting ASIC implementation of CNN accelerator. To realize low cost inference accelerator. we introduce approximate arithmetic operators for MAC operators, which comprise the key datapath components of CNNs. The proposed accelerator architecture exploits parallel memory access, and N-way high speed and approximate MAC units in the convolutional layer as well as the fully connected layers. Since CNNs are tolerant to small error due to the nature of convolutional filters, the approximate arithmetic operations incur little or no noticeable loss in the accuracy of the CNN, which we demonstrate in our test results. For the approximate MAC unit, we use Dynamic Range Unbiased Multiplier (DRUM) approximate multiplier and Approximate Adder with OR operations on LSBs (AOL) which can substantially reduce the chip area and power consumption. The configuration of the approximate MAC units within each layer affects the overall accuracy of the CNN. We implemented various configurations of approximate MAC on an FPGA, and evaluated the accuracy using an extended MNIST dataset. Our implementation and evaluation with selected approximate MACs demonstrate that the proposed CNN Accelerator reduces the area of CNN by 15% at the cost of a small accuracy loss of only 0.982% compared to the reference CNN.
Machine Learning in Stem Cells Research: Application for Biosafety and Bioefficacy Assessment

The applicability of machine learning-based analysis in the field of biomedical field has been very beneficial in determining the biological mechanism and validation for a wide range of biological scenarios.This approach is also gaining momentum in various stem cells research activities, specifically for stem cells characterization and differentiation pattern.The adoption of similar computational approaches to study and assess biosafety and bioefficacy risks of stem cells for clinical application is the next progression.In particular where tumorigenicity has been one of the major concerns in stem cells therapy.There are many factors influencing tumorigenicity in stem cells which may be difficult to capture under conventional laboratory settings.In addition, given the possible multifactorial etiology of tumorigenicity, defining a onesize-fits-all strategy to test such risk in stem cells might not be feasible and may compromise stem cells safety and effectiveness in therapy.Given the increase in biological datasets (which is no longer limited to genomic data) and the advancement of health informatics powered by state-of-the-art machine learning algorithms, there exists a potential for practical application in biosafety and bioefficacy of stem cells therapy.Here, we identified relevant machine learning approaches and suggested protocols intended for stem cells research focusing on the possibility of its usage for stem cells biosafety and bioefficacy assessment.Ultimately, generating models that may assist healthcare professionals to make a better-informed decision in stem cell therapy.