Reviewerly/exHarmony huggingface dataset:
Bubble Margin: Motion Sickness Prevention While Reading on Smartphones in Vehicles

The shift towards autonomous driving will allow people to read and work while riding in such a vehicle. Some people suffer from motion sickness while being engaged in these activities in a moving vehicle since the signals of the visual and vestibular systems of the human body are mismatched. One approach to mitigate motion sickness is to provide visual cues for the affected person. We present Bubble Margin, a smartphone application to reduce motion sickness while reading in a moving vehicle. It is built as a stand-alone, semi-transparent overlay application that visualizes motion cues in form of bubbles at the margins of a smartphone while other applications may be used in the remaining area. Forces to move these bubbles are taken from smartphone sensors. An initial within-subject design study (N = 10) with participants performing a reading task in a real driving scenario showed mitigating effects on motion sickness symptoms.
DeepMoney: counterfeit money detection using generative adversarial networks

Conventional paper currency and modern electronic currency are two important modes of transactions. In several parts of the world, conventional methodology has clear precedence over its electronic counterpart. However, the identification of forged currency paper notes is now becoming an increasingly crucial problem because of the new and improved tactics employed by counterfeiters. In this paper, a machine assisted system—dubbed DeepMoney—is proposed which has been developed to discriminate fake notes from genuine ones. For this purpose, state-of-the-art models of machine learning called Generative Adversarial Networks (GANs) are employed. GANs use unsupervised learning to train a model that can then be used to perform supervised predictions. This flexibility provides the best of both worlds by allowing unlabelled data to be trained on whilst still making concrete predictions. This technique was applied to Pakistani banknotes. State-of-the-art image processing and feature recognition techniques were used to design the overall approach of a valid input. Augmented samples of images were used in the experiments which show that a high-precision machine can be developed to recognize genuine paper money. An accuracy of 80% has been achieved. The code is available as an open source to allow others to reproduce and build upon the efforts already made.
Representation Learning with Ordered Relation Paths for Knowledge Graph Completion

Incompleteness is a common problem for existing knowledge graphs (KGs), and the completion of KG which aims to predict links between entities is challenging.Most existing KG completion methods only consider the direct relation between nodes and ignore the relation paths which contain useful information for link prediction.Recently, a few methods take relation paths into consideration but pay less attention to the order of relations in paths which is important for reasoning.In addition, these path-based models always ignore nonlinear contributions of path features for link prediction.To solve these problems, we propose a novel KG completion method named OPTransE.Instead of embedding both entities of a relation into the same latent space as in previous methods, we project the head entity and the tail entity of each relation into different spaces to guarantee the order of relations in the path.Meanwhile, we adopt a pooling strategy to extract nonlinear and complex features of different paths to further improve the performance of link prediction.Experimental results on two benchmark datasets show that the proposed model OPTransE performs better than state-of-the-art methods.
Investigating the Effectiveness of BPE: The Power of Shorter Sequences

Byte-Pair Encoding (BPE) is an unsupervised sub-word tokenization technique, commonly used in neural machine translation and other NLP tasks.Its effectiveness makes it a de facto standard, but the reasons for this are not well understood.We link BPE to the broader family of dictionary-based compression algorithms and compare it with other members of this family.Our experiments across datasets, language pairs, translation models, and vocabulary size show that -given a fixed vocabulary size budget -the fewer tokens an algorithm needs to cover the test set, the better the translation (as measured by BLEU).
An Anti-Spam Detection Model for Emails of Multi-Natural Language

The spam is one of the illegal and negative practices that involves the use of email services to send unsolicited emails such as phishing for the purpose of scamming which influences the reliability of email. Investigations have been conducted from various perspectives in order to examine this spam problem and how it affects society. In this regard, many studies have been carried out with the aim of studying the effect of spam activity on finance, economy, marketing, business and management, while other studies have focused on studying the influence of spam on security and privacy. Consequently, the literature affords various anti-spam methods that blocks or filters spam emails. This paper investigates the existing anti-spam methods, highlights some current problems and carries out an improved anti-spam model. In this regard, a new agent-based of Multi-Natural Language Anti-Spam (MNLAS) model is proposed. The MNLAS model process in the spam filtering process of an email both visual information such as images and texts in English and Arabic languages. The Jade agent platform and Java environments are employed in the implementation of MNLAS model. The MNLAS model is tested on a 200 emails’ dataset and the results show that it is able to detect and filter various kinds of spam emails with high accuracy.
Training Multi-Speaker Neural Text-to-Speech Systems Using Speaker-Imbalanced Speech Corpora

When the available data of a target speaker is insufficient to train a high quality speaker-dependent neural text-to-speech (TTS) system, we can combine data from multiple speakers and train a multi-speaker TTS model instead.Many studies have shown that neural multi-speaker TTS model trained with a small amount data from multiple speakers combined can generate synthetic speech with better quality and stability than a speaker-dependent one.However when the amount of data from each speaker is highly unbalanced, the best approach to make use of the excessive data remains unknown.Our experiments showed that simply combining all available data from every speaker to train a multi-speaker model produces better than or at least similar performance to its speaker-dependent counterpart.Moreover by using an ensemble multi-speaker model, in which each subsystem is trained on a subset of available data, we can further improve the quality of the synthetic speech especially for underrepresented speakers whose training data is limited.
Leveraging Acoustic Cues and Paralinguistic Embeddings to Detect Expression from Voice

Millions of people reach out to digital assistants such as Siri every day, asking for information, making phone calls, seeking assistance, and much more. The expectation is that such assistants should understand the intent of the users query. Detecting the intent of a query from a short, isolated utterance is a difficult task. Intent cannot always be obtained from speech-recognized transcriptions. A transcription driven approach can interpret what has been said but fails to acknowledge how it has been said, and as a consequence, may ignore the expression present in the voice. Our work investigates whether a system can reliably detect vocal expression in queries using acoustic and paralinguistic embedding. Results show that the proposed method offers a relative equal error rate (EER) decrease of 60% compared to a bag-of-word based system, corroborating that expression is significantly represented by vocal attributes, rather than being purely lexical. Addition of emotion embedding helped to reduce the EER by 30% relative to the acoustic embedding, demonstrating the relevance of emotion in expressive voice.
Application of a Maximum Entropy Model for Mineral Prospectivity Maps

The effective integration of geochemical data with multisource geoscience data is a necessary condition for mapping mineral prospects. In the present study, based on the maximum entropy principle, a maximum entropy model (MaxEnt model) was established to predict the potential distribution of copper deposits by integrating 43 ore-controlling factors from geological, geochemical and geophysical data. The MaxEnt model was used to screen the ore-controlling factors, and eight ore-controlling factors (i.e., stratigraphic combination entropy, structural iso-density, Cu, Hg, Li, La, U, Na2O) were selected to establish the MaxEnt model to determine the highest potential zone of copper deposits. The spatial correlation between each ore-controlling factor and the occurrence of a copper mine was studied using a response curve, and the relative importance of each ore-controlling factor was determined by jackknife analysis in the MaxEnt model. The results show that the occurrence of copper ore is positively correlated with the content of Cu, Hg, La, structural iso-density and stratigraphic combination entropy, and negatively correlated with the content of Na2O, Li and U. The model’s performance was evaluated by the area under the receiver operating characteristic curve (AUC), Cohen’s maximized Kappa and true skill statistic (TSS) (training AUC = 0.84, test AUC = 0.8, maximum Kappa = 0.5 and maximum TSS = 0.6). The results indicate that the model can effectively integrate multi-source geospatial data to map mineral prospectivity.
Informing the Use of Visual Assets in Industrial Augmented Reality

Augmented Reality is considered one of the most promising technologies for Industry 4.0. Augmented Reality allows to display the needed information at the right time and to locate it in the desired space, superimposing it to the real world. In this way, it could simplify the work of industrial operators in a variety of tasks, from planning and commissioning to assembly and maintenance. Despite the growing research interest and efforts for the development of Augmented Reality applications and technologies for the industrial domain, Augmented Reality is still rarely used in real industrial procedures, often remaining at a conceptual level. In fact, developers fail to completely answer the actual needs of industries and workers because of a lack of knowledge of the context and the absence of guidelines to drive the design and development of Augmented Reality applications. One of the open issues is the choice of the proper visualization methods to display technical information in Augmented Reality, which is not trivial. Both the paper-based documentation, based on text and illustrations, and digital documentation, including CAD models and image or video-based tutorials, present different criticalities when adapted to the Augmented Reality technology. Other visual material has been developed specifically to be used in Augmented Reality, but it is difficult to use because of a lack of standards which are instead present for the former paper-based and digital documentation. In this work, we categorize and compare different Augmented Reality visual assets, illustrating their advantages and disadvantages and providing directions for future research.
Unsupervised Learning of Eye Gaze Representation from the Web

Automatic eye gaze estimation has interested researchers for a while now. In this paper, we propose an unsupervised learning based method for estimating the eye gaze region. To train the proposed network "Ize-Net" in self-supervised manner, we collect a large `in the wild' dataset containing 1,54,251 images from the web. For the images in the database, we divide the gaze into three regions based on an automatic technique based on pupil-centers localization and then use a feature-based technique to determine the gaze region. The performance is evaluated on the Tablet Gaze and CAVE datasets by fine-tuning results of Ize-Net for the task of eye gaze estimation. The feature representation learned is also used to train traditional machine learning algorithms for eye gaze estimation. The results demonstrate that the proposed method learns a rich data representation, which can be efficiently finetuned for any eye gaze estimation dataset.
Pulsed entanglement measured by parametric amplifier assisted homodyne detection

Balanced homodyne detection relies on a beam splitter to superpose the weak signal input and strong local oscillator.However, recent investigation shows that a high gain phase sensitive amplifier (PSA) can be viewed as homodyne detector, in which the strong pump of PSA serves as the local oscillator [1].Here, we analyze a new method of measuring the continuous variable entanglement by assisting a balanced homodyne detector with the PSA and implement it experimentally.Before measuring quadrature amplitude with the balanced homodyne detectors, two entangled fields generated from a pulse pumped fiber optical parametric amplifier are simultaneously coupled into the PSA.We find that the normalized noise for both the difference and sum of the quadrature amplitudes of the two entangled fields fall below the shot noise limit by about 4.6 dB, which is the record degree of entanglement measured in optical fiber systems.The experimental results illustrate that the advantages of the new measurement method include but not limit to tolerance to detection loss and characterizing entanglement with only one homodyne detector.The influence of mode-mismatching due to multi-mode property of entanglement on the measured noise reduction can also be greatly mitigated, indicating the new method is advantageous over the traditional measurement in multi-mode case.
A privacy protection approach for XML-based archives management in a cloud environment

Purpose The security of archival privacy data in the cloud has become the main obstacle to the application of cloud computing in archives management. To this end, aiming at XML archives, this paper aims to present a privacy protection approach that can ensure the security of privacy data in the untrusted cloud, without compromising the system availability. Design/methodology/approach The basic idea of the approach is as follows. First, the privacy data before being submitted to the cloud should be strictly encrypted on a trusted client to ensure the security. Then, to query the encrypted data efficiently, the approach constructs some key feature data for the encrypted data, so that each XML query defined on the privacy data can be executed correctly in the cloud. Findings Finally, both theoretical analysis and experimental evaluation demonstrate the overall performance of the approach in terms of security, efficiency and accuracy. Originality/value This paper presents a valuable study attempting to protect privacy for the management of XML archives in a cloud environment, so it has a positive significance to promote the application of cloud computing in a digital archive system.
Nonlocal Sar Image Despeckling by Convolutional Neural Networks

Nonlocal methods are state-of-the-art in SAR despeckling, thanks to their ability to exploit image self-similarity. Given sufficient training data, however, methods based on deep learning have proven highly competitive. Therefore, to take the best of both approaches, we investigate the use of deep learning to improve nonlocal despeckling. We use plain non-iterative nonlocal means despeckling, with weights provided, for each estimation window, by a suitably trained deep CNN. Experiments on synthetic and real SAR data prove this approach to outperform conventional nonlocal methods.
Mixed Multi-Head Self-Attention for Neural Machine Translation

Recently, the Transformer becomes a stateof-the-art architecture in the filed of neural machine translation (NMT).A key point of its high-performance is the multi-head selfattention which is supposed to allow the model to independently attend to information from different representation subspaces.However, there is no explicit mechanism to ensure that different attention heads indeed capture different features, and in practice, redundancy has occurred in multiple heads.In this paper, we argue that using the same global attention in multiple heads limits multi-head self-attention's capacity for learning distinct features.In order to improve the expressiveness of multi-head self-attention, we propose a novel Mixed Multi-Head Self-Attention (MMA) which models not only global and local attention but also forward and backward attention in different attention heads.This enables the model to learn distinct representations explicitly among multiple heads.In our experiments on both WAT17 English-Japanese as well as IWSLT14 German-English translation task, we show that, without increasing the number of parameters, our models yield consistent and significant improvements (0.9 BLEU scores on average) over the strong Transformer baseline. 1
Blockchain consortium networks: Adding security and trust in financial services

Abstract This article explores blockchain consortium networks as an emerging technology for financial institutions. Blockchain is quickly becoming recognized for its value as an immutable ledger within the financial services industry. While Blockchain provides direct benefits in fraud prevention and transaction efficiency, consortium networks specifically address a need for increased trust and security in shared business processes. Also discussed are strategic decisions needed for implementing blockchain consortium networks at the enterprise level with topics ranging from membership choices to use of cloud services.
Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks

When training clinical prediction models from electronic health records (EHRs), a key concern should be a model's ability to sustain performance over time when deployed, even as care practices, database systems, and population demographics evolve. Due to de-identification requirements, however, current experimental practices for public EHR benchmarks (such as the MIMIC-III critical care dataset) are time agnostic, assigning care records to train or test sets without regard for the actual dates of care. As a result, current benchmarks cannot assess how well models trained on one year generalise to another. In this work, we obtain a Limited Data Use Agreement to access year of care for each record in MIMIC and show that all tested state-of-the-art models decay in prediction quality when trained on historical data and tested on future data, particularly in response to a system-wide record-keeping change in 2008 (0.29 drop in AUROC for mortality prediction, 0.10 drop in AUROC for length-of-stay prediction with a random forest classifier). We further develop a simple yet effective mitigation strategy: by aggregating raw features into expert-defined clinical concepts, we see only a 0.06 drop in AUROC for mortality prediction and a 0.03 drop in AUROC for length-of-stay prediction. We demonstrate that this aggregation strategy outperforms other automatic feature preprocessing techniques aimed at increasing robustness to data drift. We release our aggregated representations and code to encourage more deployable clinical prediction models.
Inductive representation learning on temporal graphs

Inductive representation learning on temporal graphs is an important step toward salable machine learning on real-world dynamic networks. The evolving nature of temporal dynamic graphs requires handling new nodes while learning temporal patterns. The node embeddings, which become functions of time under the temporal setting, should capture both static node features and evolving topological structures. Moreover, node and topological features may exhibit temporal patterns that are informative for prediction, of which the temporal node embeddings should also be aware. We propose the temporal graph attention (TGAT) layer to effectively aggregate temporal-topological neighborhood features as well as learning time-feature interactions. For TGAT, we use the self-attention mechanism as the building block and develop the novel functional time encoding technique based on the classical Bochner's theorem from harmonic alaysis. By stacking TGAT layers, the network learns node embeddings as functions of time and can inductively infer embeddings for both new and observed nodes whenever the graph evolves. The proposed approach handles both node classification and link prediction task, and can be naturally extended to aggregate edge features. We evaluate our method with transductive and inductive tasks under temporal setting with two benchmark and one industrial dataset. Our TGAT model compares favorably to state-of-the-art baselines and prior temporal graph embedding approaches.
Research on Target Detection Based on Distributed Track Fusion for Intelligent Vehicles

Accurate target detection is the basis of normal driving for intelligent vehicles. However, the sensors currently used for target detection have types of defects at the perception level, which can be compensated by sensor fusion technology. In this paper, the application of sensor fusion technology in intelligent vehicle target detection is studied with a millimeter-wave (MMW) radar and a camera. The target level fusion hierarchy is adopted, and the fusion algorithm is divided into two tracking processing modules and one fusion center module based on the distributed structure. The measurement information output by two sensors enters the tracking processing module, and after processing by a multi-target tracking algorithm, the local tracks are generated and transmitted to the fusion center module. In the fusion center module, a two-level association structure is designed based on regional collision association and weighted track association. The association between two sensors' local tracks is completed, and a non-reset federated filter is used to estimate the state of the fusion tracks. The experimental results indicate that the proposed algorithm can complete a tracks association between the MMW radar and camera, and the fusion track state estimation method has an excellent performance.
SANVis: Visual Analytics for Understanding Self-Attention Networks

Attention networks, a deep neural network architecture inspired by humans' attention mechanism, have seen significant success in image captioning, machine translation, and many other applications. Recently, they have been further evolved into an advanced approach called multi-head self-attention networks, which can encode a set of input vectors, e.g., word vectors in a sentence, into another set of vectors. Such encoding aims at simultaneously capturing diverse syntactic and semantic features within a set, each of which corresponds to a particular attention head, forming altogether multi-head attention. Meanwhile, the increased model complexity prevents users from easily understanding and manipulating the inner workings of models. To tackle the challenges, we present a visual analytics system called SANVis, which helps users understand the behaviors and the characteristics of multi-head self-attention networks. Using a state-of-the-art self-attention model called Transformer, we demonstrate usage scenarios of SANVis in machine translation tasks. Our system is available at http://short.sanvis.org.