Reviewerly/exHarmony huggingface dataset:
Object Recognition Using Deep Learning

The deep learning approaches have drawn much focus of the researchers in the area of object recognition because of their implicit strength of conquering the shortcomings of classical approaches dependent on hand crafted features. In the last few years, the deep learning techniques have been made many developments in object recognition. This paper indicates some recent and efficient deep learning frameworks for object recognition. The up to date study on recently developed a deep neural network based object recognition methods is presented. The various benchmark datasets that are used for performance evaluation are also discussed. The applications of the object recognition approach for specific types of objects (like faces, buildings, plants etc.) are also highlighted. We conclude up with the merits and demerits of existing methods and future scope in this area.
Discovering Hypernymy in Text-Rich Heterogeneous Information Network by Exploiting Context Granularity

Text-rich heterogeneous information networks (text-rich HINs) are ubiquitous in real-world applications. Hypernymy, also known as is-a relation or subclass-of relation, lays in the core of many knowledge graphs and benefits many downstream applications. Existing methods of hypernymy discovery either leverage textual patterns to extract explicitly mentioned hypernym-hyponym pairs, or learn a distributional representation for each term of interest based its context. These approaches rely on statistical signals from the textual corpus, and their effectiveness would therefore be hindered when the signals from the corpus are not sufficient for all terms of interest. In this work, we propose to discover hypernymy in text-rich HINs, which can introduce additional high-quality signals. We develop a new framework, named HyperMine, that exploits multi-granular contexts and combines signals from both text and network without human labeled data. HyperMine extends the definition of "context" to the scenario of text-rich HIN. For example, we can define typed nodes and communities as contexts. These contexts encode signals of different granularities and we feed them into a hypernymy inference model. HyperMine learns this model using weak supervision acquired based on high-precision textual patterns. Extensive experiments on two large real-world datasets demonstrate the effectiveness of HyperMine and the utility of modeling context granularity. We further show a case study that a high-quality taxonomy can be generated solely based on the hypernymy discovered by HyperMine.
Multi-objective ensemble deep learning using electronic health records to predict outcomes after lung cancer radiotherapy

Accurately predicting treatment outcome is crucial for creating personalized treatment plans and follow-up schedules. Electronic health records (EHRs) contain valuable patient-specific information that can be leveraged to improve outcome prediction. We propose a reliable multi-objective ensemble deep learning (MoEDL) method that uses features extracted from EHRs to predict high risk of treatment failure after radiotherapy in patients with lung cancer. The dataset used in this study contains EHRs of 814 patients who had not achieved disease-free status and 193 patients who were disease-free with at least one year follow-up time after lung cancer radiation therapy. The proposed MoEDL consists of three phases: (1) training with dynamic ensemble deep learning; (2) model selection with adaptive multi-objective optimization; and (3) testing with evidential reasoning (ER) fusion. Specifically, in the training phase, we employ deep perceptron networks as base learners to handle various issues with EHR data. The architecture and key hyper-parameters of each base learner are dynamically adjusted to increase the diversity of learners while reducing the time spent tuning hyper-parameters. Furthermore, we integrate the snapshot ensembles (SE) restarting strategy, multi-objective optimization, and ER fusion to improve the prediction robustness and accuracy of individual networks. The SE restarting strategy can yield multiple candidate models at no additional training cost in the training stage. The multi-objective model simultaneously considers sensitivity, specificity, and AUC as objective functions, overcoming the limitations of single-objective-based model selection. For the testing stage, we utilized an analytic ER rule to fuse the output scores from each optimal model to obtain reliable and robust predictive results. Our experimental results demonstrate that MoEDL can perform better than other conventional methods.
Board-Level Functional Fault Identification using Streaming Data

High integration densities and design complexity of printed-circuit boards make board-level functional fault identification extremely difficult. Machine learning provides an opportunity to identify functional faults with high accuracy and thereby reduce repair cost. However, the large volume of manufacturing data comes in a streaming format and exhibits time-dependent concept drift in a production environment. These drawbacks limit the effectiveness of traditional machine-learning algorithms. We propose a diagnosis workflow that utilizes online learning to train classifiers incrementally with a small chunk of data at each step. These online learning algorithms adapt to concept drift quickly with carefully designed update rules. A hybrid algorithm is also proposed to handle the scenario that data for varying numbers of boards are collected at different times. Experimental results using two boards in high-volume production show that, with the help of online learning and the proposed hybrid algorithm, the F1-score for diagnosis can be improved from 57.3% to 78.9%.
A Proposed Framework for Emotion Recognition Using Canberra Distance Classifier

Facial Expression Recognition has become the preliminary research area due to its importance in human-computer interaction. Facial Expressions conveys the major part of information so it has vast applications in various fields. Many techniques have been developed in the literature but there is still a need to make the current expression recognition methods efficient. This paper represents proposed framework for face detection and recognizing six universal facial expressions such as happy, anger, disgust, fear, surprise, sad along with neutral face. Viola-Jones method and Face Landmark Detection method are used for face detection. Histogram of oriented gradients is used for feature extraction due to its superiority over other methods. To reduce the dimensionality of features Principal Component Analysis is used so that the maximum variation is preserved. Canberra distance classifier is used for classifying the expressions into different emotions. The proposed method is applied on Japanese Female Facial Expression Database and have evaluated that the proposed method outperforms many state-of-the-art techniques.
Active Learning via Membership Query Synthesis for Semi-Supervised Sentence Classification

Active learning (AL) is a technique for reducing manual annotation effort during the annotation of training data for machine learning classifiers.For NLP tasks, pool-based and stream-based sampling techniques have been used to select new instances for AL while generating new, artificial instances via Membership Query Synthesis was, up to know, considered to be infeasible for NLP problems.We present the first successful attempt to use Membership Query Synthesis for generating AL queries for natural language processing, using Variational Autoencoders for query generation.We evaluate our approach in a text classification task and demonstrate that query synthesis shows competitive performance to pool-based AL strategies while substantially reducing annotation time.
Does Financial Integration Matter for Financial Development? Evidence from the East Asian and Pacific Region

This paper assesses the impact of financial integration on financial development and establishes thresholds for materializing gains of financial advances from financial globalization using a sample of 34 countries from the East Asian and Pacific region. Following the approaches of Kose et al. (2011) and Asongu and De Moor (2016), we test non-linearity within the financial openness and financial development nexus through semi-parametric ordinary least-squares regression, and then, we develop threshold dynamics models. According to our findings, the effect of financial integration on financial development significantly changes across different financial inflows. When using external debt as a proxy for financial openness, there exists a robust significant inverted U-shaped relationship between financial integration and financial development. The empirical findings also suggest that the financial integration-development nexus is contingent on the level of trade openness, national income, and institutional quality. The results are robust to different measures of financial development and integration, the inclusion of other determinants of financial development, and considerations of endogeneity.
Depolarizing Channel Mismatch and Estimation Protocols for Quantum Turbo Codes

Quantum turbo codes (QTC) have shown excellent error correction capabilities in the setting of quantum communication, achieving a performance less than 1 dB away from their corresponding hashing bounds. Decoding for QTCs typically assumes that perfect knowledge about the channel is available at the decoder. However, in realistic systems, such information must be estimated, and thus, there exists a mismatch between the true channel information and the estimated one. In this article, we first heuristically study the sensitivity of QTCs to such mismatch. Then, existing estimation protocols for the depolarizing channel are presented and applied in an off-line manner to provide bounds on how the use of off-line estimation techniques affects the error correction capabilities of QTCs. Finally, we present an on-line estimation method for the depolarizing probability, which, different from off-line estimation techniques, neither requires extra qubits, nor increases the latency. The application of the proposed method results in a performance similar to that obtained with QTCs using perfect channel information, while requiring less stringent conditions on the variability of the channel than off-line estimation techniques.
Libra — A Differentiated View on Facebook’s Virtual Currency Project

Abstract Libra — a global virtual currency project initiated by Facebook — has been the subject of many controversial discussions since its announcement in June 2019. This paper provides a differentiated view on Libra, recognising that different development scenarios of Libra are conceivable. Libra could serve purely as an alternative payment system in combination with a dedicated payment token, the Libra coin. Alternatively, the Libra project could develop into a broader financial infrastructure for advanced financial services such as savings and loan products operating on the Libra Blockchain. Based on a comparison of the Libra architecture with other cryptocurrencies, the opportunities and challenges for the development of the respective Libra ecosystems are investigated from a commercial, regulatory and monetary policy perspective.
Unknown Vulnerability Risk Assessment Based on Directed Graph Models: A Survey

Nowadays, vulnerability attacks occur frequently. Due to the information asymmetry between attackers and defenders, vulnerabilities can be divided into known and unknown. Existing researches mainly focus on the risk assessment of known vulnerabilities. However, unknown vulnerabilities are more threatening and harder to detect. Therefore, unknown vulnerability risk assessment deserves the widespread attention. To model the exploit process, directed graph models are applied to vulnerability risk assessment. And security metrics are used to quantify the exploitability of vulnerabilities. In this paper, according to the data source of nodes, related works of unknown vulnerability risk assessment based on directed graph models are divided into two types. One is based on network-level data, the other is based on system-level data. The former is to visualize the network status, while the latter is to reflect the running process of the system. The concept and purpose of these directed graph models are given at first. Then, these models are analyzed from three aspects, including advantages, flaws and solutions. After that, challenges and solutions of unknown vulnerability risk assessment based on directed graph models are given. Meantime, security metrics for unknown vulnerability risk assessment based on directed graph models are summarized and classified. Finally, future work directions of unknown vulnerability risk assessment are discussed from the perspective of techniques and application trends. Consequently, this paper can fill in the lack of current survey on unknown vulnerability risk assessment based on directed graph models.
Domain-Agnostic Learning With Anatomy-Consistent Embedding for Cross-Modality Liver Segmentation

Domain Adaptation (DA) has the potential to greatly help the generalization of deep learning models. However, the current literature usually assumes to transfer the knowledge from the source domain to a specific known target domain. Domain Agnostic Learning (DAL) proposes a new task of transferring knowledge from the source domain to data from multiple heterogeneous target domains. In this work, we propose the Domain-Agnostic Learning framework with Anatomy-Consistent Embedding (DALACE) that works on both domain-transfer and task-transfer to learn a disentangled representation, aiming to not only be invariant to different modalities but also preserve anatomical structures for the DA and DAL tasks in cross-modality liver segmentation. We validated and compared our model with state-of-the-art methods, including CycleGAN, Task Driven Generative Adversarial Network (TD-GAN), and Domain Adaptation via Disentangled Representations (DADR). For the DA task, our DALACE model outperformed CycleGAN, TD-GAN, and DADR with DSC of 0.847 compared to 0.721, 0.793 and 0.806. For the DAL task, our model improved the performance with DSC of 0.794 from 0.522, 0.719 and 0.742 by CycleGAN, TD-GAN, and DADR. Further, we visualized the success of disentanglement, which added human interpretability of the learned meaningful representations. Through ablation analysis, we specifically showed the concrete benefits of disentanglement for downstream tasks and the role of supervision for better disentangled representation with segmentation consistency to be invariant to domains with the proposed Domain-Agnostic Module (DAM) and to preserve anatomical information with the proposed Anatomy-Preserving Module (APM).
A Novel Blockchain Oracle Implementation Scheme Based on Application Specific Knowledge Engines

In blockchain ecosystems, an Oracle is a service tool which provides real-world data for smart contracts and other blockchain applications. At present, there are several Oracle implementation schemes, e.g. centralized Oracles, decentralized Oracles, and hardware Oracles. However, these schemes typically suffer from single source of data and low scalability. Application Specific Knowledge Engine (ASKE) is an integrated topic/application-centered knowledge portal that supports effective information retrieval and analysis. Inspired by ASKE, in this paper, we propose a novel Oracle implementation scheme. The proposed scheme can realize multi-source data extraction and analysis, then working prototypes are demonstrated to show the validity of the scheme.
Preemptive and Low Latency Datacenter Scheduling via Lightweight Containers

Datacenters are evolving to host heterogeneous workloads on shared clusters to reduce the operational cost and achieve higher resource utilization. However, it is challenging to schedule heterogeneous workloads with diverse resource requirements and QoS constraints. On one hand, latency-critical jobs need to be scheduled as soon as they are submitted to avoid any queuing delays. On the other hand, best-effort long jobs should be allowed to occupy the cluster when there are idle resources to improve cluster utilization. The challenge lies in how to minimize the queuing delays of short jobs while maximizing cluster utilization. In this article, we propose and develop BIG-C, a container-based resource management framework for data-intensive cluster computing. The key design is to leverage lightweight virtualization, a.k.a, containers, to make tasks preemptable in cluster scheduling. We devise two types of preemption strategies: immediate and graceful preemptions and show their effectiveness and tradeoffs with loosely-coupled MapReduce workloads as well as iterative, in-memory Spark workloads. Based on the mechanisms for task preemption, we further develop job-level and task-level preemptive policies as well as a preemptive fair share cluster scheduler. Our implementation on Yarn and evaluation with synthetic and production workloads show that low job latency and high resource utilization can be both attained when scheduling heterogeneous workloads on a contended cluster.
Single- and multi-label classification of construction objects using deep transfer learning methods

Digital images are extensively used to increase the accuracy and timeliness of progress reports, safety training, requests for information (RFIs), productivity monitoring, and claims and litigation.While these images can be sorted using date and time tags, the task of searching an image dataset for specific visual content is not trivial.In pattern recognition, generating metadata tags describing image contents (objects, scenes) or appearance (colors, context) is referred to as multi-label image annotation.Given the large number and diversity of construction images, it is desirable to generate image tags automatically.Previous work has applied pattern matching to synthetic images or images obtained from constrained settings.In this paper, we present deep learning (particularly, transfer learning) algorithms to annotate construction imagery from unconstrained real-world settings with high fidelity.We propose convolutional neural network (CNN)-based algorithms which take RGB values as input and output the labels of detected objects.Particularly, we have investigated two categories of classification tasks: single-label classification, i.e., a single class (among multiple predefined classes) is assigned to an image, and multi-label classification, i.e., a set of (one or more) classes is assigned to an image.For both cases, the VGG-16 model, pre-trained on the ImageNet dataset, is trained on construction images retrieved with web mining techniques and labeled by human annotators.Testing the trained model on previously unseen photos yields an accuracy of ~90% for single-label classification and ~85% for multi-label classification, indicating the high sensitivity and specificity of the designed methodology in reliably identifying the contents of construction imagery.
Discrete-time high-order neural network identifier trained with high-order sliding mode observer and unscented Kalman filter

This paper presents a method to identify an unknown discrete-time nonlinear system, using high-order neural networks and high-order sliding mode algorithms, which may be subject to internal and external disturbances. Based on the information obtained from available system states, a high-order neural network model is proposed to approximate the system dynamics. Neural network weights are trained by means of the unscented Kalman filter and high-order sliding mode observer. A simulation example is included to illustrate effectiveness of the proposed scheme.
What goes into a word: generating image descriptions with top-down spatial knowledge

Generating grounded image descriptions requires associating linguistic units with their corresponding visual clues.A common method is to train a decoder language model with attention mechanism over convolutional visual features.Attention weights align the stratified visual features arranged by their location with tokens, most commonly words, in the target description.However, words such as spatial relations (e.g. next to and under) are not directly referring to geometric arrangements of pixels but to complex geometric and conceptual representations.The aim of this paper is to evaluate what representations facilitate generating image descriptions with spatial relations and lead to better grounded language generation.In particular, we investigate the contribution of four different representational modalities in generating relational referring expressions: (i) (pre-trained) convolutional visual features, (ii) spatial attention over visual features, (iii) top-down geometric relational knowledge between objects, and (iv) world knowledge captured by contextual embeddings in language models.
WPT-ANN and Belief Theory Based EEG/EMG Data Fusion for Movement Identification

The electromyography (EMG) and electroencephalography (EEG) are two frequently used modalities of bio-signals in the field of bio-robotics.However, it is insufficient to use a single isolated modality, due to the presence of artifacts, the lack of information, etc.To solve the problem, this paper proposes an EEG/EMG data fusion method that take advantages of both signals and overcome their drawbacks to achieve accurate identification of movements.The two types of bio-signals were preprocessed through wavelet packets transform (WPT) and classified by the artificial neural network (ANN).Then, the belief theory was introduced to allow for model uncertainty and imprecision, which adapts to the ambiguities and conflicts between sources.Experimental results show that the proposed EEG/EMG data fusion method outperformed the strategies based on only one modality.The research findings provide new impetus to bio-robotic applications.
ViGGO: A Video Game Corpus for Data-To-Text Generation in Open-Domain Conversation

The uptake of deep learning in natural language generation (NLG) led to the release of both small and relatively large parallel corpora for training neural models.The existing datato-text datasets are, however, aimed at taskoriented dialogue systems, and often thus limited in diversity and versatility.They are typically crowdsourced, with much of the noise left in them.Moreover, current neural NLG models do not take full advantage of large training data, and due to their strong generalizing properties produce sentences that look template-like regardless.We therefore present a new corpus of 7K samples, which (1) is clean despite being crowdsourced, (2) has utterances of 9 generalizable and conversational dialogue act types, making it more suitable for opendomain dialogue systems, and (3) explores the domain of video games, which is new to dialogue systems despite having excellent potential for supporting rich conversations.