Reviewerly/exHarmony huggingface dataset:
Learning Shared Semantic Space with Correlation Alignment for Cross-Modal Event Retrieval

In this article, we propose to learn shared semantic space with correlation alignment (S3CA) for multimodal data representations, which aligns nonlinear correlations of multimodal data distributions in deep neural networks designed for heterogeneous data. In the context of cross-modal (event) retrieval, we design a neural network with convolutional layers and fully connected layers to extract features for images, including images on Flickr-like social media. Simultaneously, we exploit a fully connected neural network to extract semantic features for text documents, including news articles from news media. In particular, nonlinear correlations of layer activations in the two neural networks are aligned with correlation alignment during the joint training of the networks. Furthermore, we project the multimodal data into a shared semantic space for cross-modal (event) retrieval, where the distances between heterogeneous data samples can be measured directly. In addition, we contribute a Wiki-Flickr Event dataset, where the multimodal data samples are not describing each other in pairs like the existing paired datasets, but all of them are describing semantic events. Extensive experiments conducted on both paired and unpaired datasets manifest the effectiveness of S3CA, outperforming the state-of-the-art methods.
Automatic Patrol and Inspection Method for Machinery Diagnosis Robot—Sound Signal-Based Fuzzy Search Approach

This paper developed the basic theories and techniques for a plant machinery diagnosis robot (MDR). The workplace of MDR is usually a large-scale plant or a place with a dangerous environment. It must autonomously carry out the condition monitoring of plant machinery with sensors in order to detect machinery faults for securing the safety of the plant. In this paper, the basic function, concept and structure of the MDR are discussed. The intelligent control method by rough set, fuzzy neural network (FNN) and self-location-azimuth correction for the MDR that patrols on the inspection route are proposed. Additionally, navigation and search method by fuzzy inference using extracted fault sound signals are also proposed, by which the MDR will be navigated to the near side of the faulty machine when the fault of a machine has been detected during inspection. Finally, examples of practical navigation and fault machine searching are also shown.
A Visual Analytic in Deep Learning Approach to Eye Movement for Human-Machine Interaction Based on Inertia Measurement

This paper proposes a hand free human-machine interaction (HMI) system to establish a novel way for communication between humans and computers. A regular interaction system based on the computer mouse puts the user's hand for too long in a pronation posture that increases inflammation in the wrist and hand. Additionally, the need for hand obstructs the use of computers for handicap people. In this paper, we develop a new pointing device for differently able people based on open and closed human eyes with inertia measurement that restrict to deal with carpal tunnel syndrome (CTS) for regular people and enables a novel way to interact with computers for the handicap people. The proposed system carries the human head gesture and eyes to perform the movement and clicking event of the mouse cursor. A combined three-axis accelerometer and gyroscope is used to detect the head gesture and translate it into the position of the mouse cursor on the computer monitor. To perform the left and right-clicking event, the user needs to shut down the left and right eye for a moment while opening another eye. This paper is also carried out the design of a deep learning approach to classify the individual openness and closeness of both human eyes with quite a high accuracy of 95.36% that ensures the comprehensive control over the clicking performance. The use of complementary filter removes the noise and drift from the obtained performance and confirms the smooth and accurate operation of the proposed device. An experimental validation is added to show the effectiveness of the proposed HMI system. The experimental details along with the performance evaluation prove that the proposed HMI system has extensive control over its performance for differently able people.
The Useful Quantum Computing Techniques for Artificial Intelligence Engineers

The hottest topics for many researchers in the past five years were Artificial Intelligence (AI) and machine learning. With many kinds of researches using machine learning, numerous AI engineers are still emerging. If the center of current research trends is on AI and machine learning, the center of near-future research trends will be on quantum computing techniques. The qubit implementation via superconductivity, diamond NitrogenVacancy (NV) center, ion-trap, and etc. has made quantum computers really exist. And cloud computing has made it possible for researchers around the world to use quantum computers remotely to their researches. The universalization of quantum computing techniques is no longer a story of the distant future, even more so for numerous AI engineers. This paper introduces some useful quantum computing techniques for AI engineers such as Quadratic Unconstrained Binary Optimization (QUBO), Variational Quantum Eigensolver (VQE), Quantum Approximate Optimization Algorithm (QAOA), and Harrow-Hassidim-Lloyd (HHL) algorithm.
Single-Objective/Multiobjective Cat Swarm Optimization Clustering Analysis for Data Partition

This article proposes single-objective/multiobjective cat swarm optimization clustering algorithms for data partition. The proposed methods use the cat swarm to search the optimal. The position of the cat tightly associates with the clustering centers and is updated by two submodes: the seeking mode and the tracing mode. The seeking mode uses the simulated annealing strategy to update the cat position at a probability. Inspired by the quantum theories, the tracing mode adopts the quantum model to update the cat position in the whole solution space. First, the single-objective method is proposed and adopts the cohesion of clustering as the objective function, in which the kernel method is applied. For considering more objective functions to reveal diverse aspects of data, the multiobjective method is proposed and adopts both the cohesion and the connectivity as the objective functions. The Pareto optimization method is applied to balance the objectives. In the experiments, three kinds of data sets are used to examine the effectiveness of the proposed methods, which are three synthetic data sets, four data sets from the UCI Machine Learning Repository, and a field data set. Experimental results verified that the proposed methods perform better than the traditional clustering algorithms, and the proposed multiobjective method has the highest accuracy. Note to Practitioners-This article presents single-objective/multiobjective cat swarm optimization clustering analysis methods for data partition. Through automatically extracting meaningful or useful classes, clustering analysis could help the practitioners or the intelligent devices find the specific meanings of data, natural data structure, the data relationships, or other characteristics. The proposed methods use the cat swarm to search the optimal clustering result. One or more criterion functions could be selected as the optimization objectives. The time complexity of the multiobjective type is higher than that of the single-objective type. Therefore, in the industrial field, engineers should choose the number of the optimization objectives based on the actual requirements. The proposed methods could be widely used into industrial applications to deal with complex data sets. Future research could consider some more progressive optimization schemes to improve the effectiveness.
Quantum soft filtering for the improved security analysis of the coherent one-way quantum-key-distribution protocol

A precise security analysis of practical quantum key distribution (QKD) systems is an important step for improving their performance. Here we consider a class of quantum soft filtering operations, which generalizes the unambiguous state discrimination (USD) technique. These operations can be applied as a basis for a security analysis of the original coherent one-way (COW) QKD protocol since their application interpolates between beam-splitting (BS) and USD attacks. We demonstrate that a zero-error attack based on quantum soft filtering operations gives a larger amount of the information for Eve at a given level of losses. We calculate the Eve information as a function of the channel length. The efficiency of the proposed attack highly depends on the level of the monitoring under the maintenance of the statistics of control (decoy) states, and best-case results are achieved in the case of the absence of maintenance of control state statistics. Our results form additional requirements for the analysis of practical QKD systems based on the COW QKD protocol and its variants by providing an upper bound on the security.
Squeezing-enhanced quantum key distribution over atmospheric channels

Abstract We propose the Gaussian continuous-variable quantum key distribution using squeezed states in the composite channels including atmospheric propagation with transmittance fluctuations. We show that adjustments of signal modulation and use of optimal feasible squeezing can be sufficient to significantly overcome the coherent-state protocol and drastically improve the performance of quantum key distribution in atmospheric channels, also in the presence of additional attenuating and noisy channels. Furthermore, we consider examples of atmospheric links of different lengths, and show that optimization of both squeezing and modulation is crucial for reduction of protocol downtime and increase of secure atmospheric channel distance. Our results demonstrate unexpected advantage of fragile squeezed states of light in the free-space quantum key distribution applicable in daylight and stable against atmospheric turbulence.
Replay detection using CQT-based modified group delay feature and ResNeWt network in ASVspoof 2019

Automatic Speaker Verification (ASV) technology is vulnerable to various kinds of spoofing attacks, including speech synthesis, voice conversion, and replay. Among them, the replay attack is easy to implement, posing a more severe threat to ASV. The constant-Q cepstrum coefficient (CQCC) feature is effective for detecting the replay attacks, but it only utilizes the magnitude of constant-Q transform (CQT) and discards the phase information. Meanwhile, the commonly used Gaussian mixture model (GMM) cannot model the reverberation present in far-field recordings. In this paper, we incorporate the CQT and modified group delay function (MGD) in order to utilize the phase of CQT. Also, we present a simple 2D-convolution multi-branch network architecture for replay detection, which can model the distortion both in the time and frequency domains. The experiment shows that the proposed CQT-based MGD feature outperforms traditional MGD feature, and performance can be further improved by combining both magnitude-based and phase-based feature. Our best fusion system achieves 0.0096 min-tDCF and 0.39% EER on ASVspoof 2019 Physical Access evaluation set. Comparing with the CQCC-GMM baseline system provided by the organizer, the min-tDCF is relatively reduced by 96.09% and EER is relatively reduced by 96.46%. Our system is submitted to the ASVspoof 2019 Physical Access sub-challenge and won 1st place.
Hybrid adversarial‐discriminative network for leukocyte classification in leukemia

Purpose Leukemia is a lethal disease that is harmful to bone marrow and overall blood health. The classification of white blood cell images is crucial for leukemia diagnosis. The purpose of this study is to classify white blood cells by extracting discriminative information from cell segmentation and combining it with the fine‐grained features. We propose a hybrid adversarial residual network with support vector machine (SVM), which utilizes the extracted features to improve the classification accuracy for human peripheral white cells. Methods Firstly, we segment the cell and nucleus by utilizing an adversarial residual network, which contains a segmentation network and a discriminator network. To extract features that can handle the inter‐class consistency problem effectively, we introduce the adversarial residual network. Then, we utilize convolutional neural network (CNN) features and histogram of oriented gradient (HOG) features, which can extract discriminative features from images of segmented cell nuclei. To utilize the representative features fully, a discriminative network is introduced to deal with neighboring information at different scales. Finally, we combine the vectors of HOG features with those of CNN features and feed them into a linear SVM to classify white blood cells into six types. Results We used three methods to evaluate the effect of leukocyte classification based on 5000 leukocyte images acquired from a local hospital. The first approach is to use the CNN features as the input of SVM to classify leukocytes, which achieved 94.23% specificity, 95.10% sensitivity, and 94.41% accuracy. The use of the HOG features for SVM achieved 83.50% specificity, 87.50% sensitivity, and 85.00% accuracy. The use of combined CNN and HOG features achieved 94.57% specificity, 96.11% sensitivity, and 95.93% accuracy. Conclusions We propose a novel hybrid adversarial‐discriminative network for the classification of microscopic leukocyte images. It improves the accuracy of cell classification, reduces the difficulty and time pressure of doctors’ work, and economizes the valuable time of doctors in daily clinical diagnosis.
The token’s secret: the two-faced financial incentive of the token economy

Abstract Multi-sided platforms are omnipresent in today’s digital world. However, establishing a platform includes challenges: The platform utility usually increases with the number of participants. At an early stage, potential participants expect the platform utility to be low and lack an incentive to join (i.e., “chicken and egg” problem). Blockchain-enabled utility tokens hold the promise to overcome this problem. They supposedly provide a suitable financial incentive for their owners to join the platform as soon as possible. In the first half of 2018, investors seemed to believe in the presumption and spent more than US$ 17.6 billion in token sales. To date, we know little about this financial incentive in the context of the token economy. For this purpose, we model the token value development and the associated incentives in a multi-sided blockchain-enabled platform. The resulting findings suggest that blockchain-enabled utility tokens can help to overcome the “chicken and egg” problem. However, these tokens lead to contradictory incentives for platform participants, and can even inhibit platform usage. The contribution of our work is twofold: First, we develop one of the first models for token value development. Second, our research contributes to a deeper understanding of the utility token’s financial incentive.
Exploring Users' Learning Gains within Search Sessions

The area of search as learning is concerned with the optimization of search systems (that is, retrieval functions, user interface elements, etc.) for human learning ---this is in contrast to the currently dominant paradigm of optimizing the search experience by optimizing for relevance. While prior work typically considers learning as something that happens at some point during the search session, we are interested in when during the search session learning occurs. In order to answer this question, we here present the results of a user study ($N=64$) in which searchers were tasked with learning about a topic by searching the web for 20 minutes; they were prompted at regular intervals during the search session on their knowledge about the topic. We find that for study participants with little to no prior knowledge the learning gains are sublinear, while participants with some prior knowledge have the largest knowledge gains towards the end of the search session.
When artificial intelligence meets educational leaders’ data-informed decision-making: A cautionary tale

Artificial intelligence (AI) refers to a type of algorithms or computerized systems that resemble human mental processes of decision making. Drawing upon multidisciplinary literature that intersects AI, decision making, educational leadership, and policymaking, this position paper aims to examine promising applications and potential perils of AI in educational leaders’ data-informed decision making (DIDM). Endowed with ever-growing computational power and real-time data, highly scalable AI can increase efficiency and accuracy in leaders’ DIDM. However, misusing AI can have perilous effects on education stakeholders. Many lurking biases in current AI could be amplified. Of more concern, the moral values (e.g., fairness, equity, honesty, and doing no harm) we uphold might clash with using AI to make data-informed decisions. Further, missteps on the issues about data security and privacy could have a life-long impact on stakeholders. The article concludes with recommendations for educational leaders to leverage AI potential and minimize its negative consequences.
Combination of loss functions for robust breast cancer prediction

Cancer detection can be formulated as a binary classification in a machine learning paradigm. Loss functions are a critical part of almost every machine learning algorithm. While each loss function comes up with its own advantages and disadvantages, in this paper, inspired by ensemble methods, we propose a novel objective function that is a linear combination of single losses. We then integrate the proposed objective function into an Artificial Neural Network (ANN) to diagnose breast cancer. By doing so, both the coefficients of loss functions and weight parameters of the ANN are learned jointly via backpropagation. As the patients’ data are sometimes very noisy, we evaluate our method by doing comprehensive experiments on Wisconsin Breast Cancer Diagnosis (WBCD) dataset at different noise levels. The experiments show its performance declines very slowly (from 0.97 to 0.96) compared to the peer methods with the increase of noise level.
Cryptocurrencies chaotic co‐movement forecasting with neural networks

In this study Non‐Linear forecasting models have been implemented to forecast the seven major cryptocurrencies. To the best of the authors knowledge, this is the first study to forecast the cryptocurrencies chaotic co‐movement forecasting using non‐linear models like Neural networks. The study finds that LSTM yields better result for lags 0 and 0‐3 and for large lags 0‐7, the ANN is the best. Further study confirms that predictions using variables like volume is not suitable for forecasting in any case. The findings of the study will impact Policy makers and investors.
Fast and Accurate Capitalization and Punctuation for Automatic Speech Recognition Using Transformer and Chunk Merging

In recent years, studies on automatic speech recognition (ASR) have shown outstanding results that reach human parity on short speech segments. However, there are still difficulties in standardizing the output of ASR such as capitalization and punctuation restoration for long-speech transcription. The problems obstruct readers to understand the ASR output semantically and also cause difficulties for natural language processing models such as NER, POS and semantic parsing. In this paper, we propose a method to restore the punctuation and capitalization for long-speech ASR transcription. The method is based on Transformer models and chunk merging that allows us to (1), build a single model that performs punctuation and capitalization in one go, and (2), perform decoding in parallel while improving the prediction accuracy. Experiments on British National Corpus showed that the proposed approach outperforms existing methods in both accuracy and decoding speed.
An Optimized Deep Neural Network Detecting Small and Narrow Rectangular Objects in Google Earth Images

Object detection is an important task for rapidly localizing target objects using high-resolution satellite imagery (HRSI). Although deep learning has been shown an efficient means of detection, object detection in HRSI remains problematic due to variations in object scale and size. In this article, we present a novel deep neural network (DNN) that combines double-shot neural network with misplaced localization strategy that adapts to object detection tasks in satellite images. This novel architecture optimizes the localization of small and narrow rectangular objects, which frequently appear in HRSI images, without accuracy loss on other size and width/height ratio objects. This method outperforms other state-of-art methods. We evaluated our proposed method on the NWPU VHR-10 public dataset and a new benchmark dataset (seven classes of small and narrow rectangular objects, SNRO-7). The NWPU VHR-10 dataset built a dataset for multiclass object detection; however, most labels are assigned in normal size and width/height ratios. SNRO-7 focuses on multiscale and multisize object detection and includes many small-size and narrow rectangular objects. We also evaluated the accuracy difference on DNN training and testing between gray scale and RGB datasets. The results of the experiment on object detection reveal that the mean average precision (MaP) of our method is 82.6% in NWPU VHR-10 and 79.3% in SNRO-7, which exceeds the MaPs of other state-of-the-art object detection neural networks. The model trained with the RGB dataset can achieve similar accuracy (around 79.0% MIoU) testing in both RGB and gray scale datasets. When training the model by mixing RGB and gray scale datasets in different ratios, the accuracy in the RGB channel significantly decreases with increasing gray scale images, but this does not influence the accuracy in the gray scale dataset.
NeuMMU

To satisfy the compute and memory demands of deep neural networks (DNNs), neural processing units (NPUs) are widely being utilized for accelerating DNNs. Similar to how GPUs have evolved from a slave device into a mainstream processor architecture, it is likely that NPUs will become first-class citizens in this fast-evolving heterogeneous architecture space. This paper makes a case for enabling address translation in NPUs to decouple the virtual and physical memory address space. Through a careful data-driven application characterization study, we root-cause several limitations of prior GPU-centric address translation schemes and propose a memory management unit (MMU) that is tailored for NPUs. Compared to an oracular MMU design point, our proposal incurs only an average 0.06% performance overhead.
A Survey of Blockchain Based on E-voting Systems

Blockchain technology as a decentralized and distributed public ledger in a P2P network has recently gained much attention. In this technology, a linked block structure is applied, and a trusted consensus mechanism is established to synchronize data modifications, making it possible to develop a tamper-proof digital platform for data storage and sharing. We think that blockchain could be used in various interactive online systems, such as the Internet of Things, supply chain systems, voting systems, etc. The scope of this survey is to shed light on some recent contributions of the security and privacy issues associated with e-voting based on blockchain. At the end of this paper, we provided a comparison for the security and privacy requirements of the existing e-voting systems based on blockchain.