Reviewerly/exHarmony huggingface dataset:
Layout-aware Subfigure Decomposition for Complex Figures in the Biomedical Literature

Published scientific figure is a valuable information resource, but often occur as composite images. The ImageCLEF meeting presented a shared evaluation in 2016 to use machine learning to split these composite figures into components automatically. We adapted an existing high-performance object detection method to analyze the substructure of published biomedical figures by developing a novel multi-branch output convolution neural network to predict irregular panel layouts and provide augmented training data to drive learning. Our system has an accuracy of 86.8% on the 2016 ImageCLEF Medical dataset and 83.1% on a new dataset derived from open access papers from the INTACT database of molecular interactions.
Deep Quantization for MIMO Channel Estimation

Quantizers play a critical role in digital signal processing systems. In practice, quantizers are typically implemented using scalar analog-to-digital converters (ADCs), commonly utilizing a fixed uniform quantization rule which is ignorant of the task of the system. Recent works have shown that the performance of quantization systems utilizing scalar ADCs can be significantly improved by properly processing the analog signal prior to quantization. However, the implementation of such systems requires complete knowledge of the underlying model, which may not be available in practice. In this work we design task-oriented quantization systems with scalar ADCs using deep learning, focusing on the task of multiple-input multiple-output (MIMO) channel estimation. By utilizing deep learning, we construct a task-based quantization system, overcoming the need to explicitly recover the system model and to find the proper quantization rule for it. Our results indicate that the proposed method results in practical MIMO systems with scalar ADCs which are capable of approaching the optimal performance limits dictated by indirect rate-distortion theory, achievable using vector quantizers and requiring complete knowledge of the underlying statistical model.
Self-attention Based Prosodic Boundary Prediction for Chinese Speech Synthesis

Predicting prosodic boundaries from input text plays an important role in Chinese text-to-speech (TTS) system, which directly influences the naturalness and intelligibility of synthesized speech. In this paper, we propose to combine self-attention with multitask learning for prosodic boundary prediction. Self-attention is used to capture the dependency between two arbitrary characters in the input sentence, while multitask learning models the relationships between prosodic boundaries and lexicon words by setting word segmentation as an auxiliary task. The proposed method can generate prosodic boundary labels directly from Chinese characters and achieve the whole process end-to-end. Experimental results show the effectiveness of our proposed model and prove that the performance can be further improved by pretraining the model with extra word segmentation data.
Is infinity that far? A Bayesian nonparametric perspective of finite mixture models

Mixture models are one of the most widely used statistical tools when dealing with data from heterogeneous populations. Following a Bayesian nonparametric perspective, we introduce a new class of priors: the Normalized Independent Point Process. We investigate the probabilistic properties of this new class and present many special cases. In particular, we provide an explicit formula for the distribution of the implied partition, as well as the posterior characterization of the new process in terms of the superposition of two discrete measures. We also provide consistency results. Moreover, we design both a marginal and a conditional algorithm for finite mixture models with a random number of components. These schemes are based on an auxiliary variable MCMC, which allows handling the otherwise intractable posterior distribution and overcomes the challenges associated with the Reversible Jump algorithm. We illustrate the performance and the potential of our model in a simulation study and on real data applications.
Neural Network-Based Learning from Demonstration of an Autonomous Ground Robot

This paper presents and experimentally validates a concept of end-to-end imitation learning for autonomous systems by using a composite architecture of convolutional neural network (ConvNet) and Long Short Term Memory (LSTM) neural network. In particular, a spatio-temporal deep neural network is developed, which learns to imitate the policy used by a human supervisor to drive a car-like robot in a maze environment. The spatial and temporal components of the imitation model are learned by using deep convolutional network and recurrent neural network architectures, respectively. The imitation model learns the policy of a human supervisor as a function of laser light detection and ranging (LIDAR) data, which is then used in real time to drive a robot in an autonomous fashion in a laboratory setting. The performance of the proposed model for imitation learning is compared with that of several other state-of-the-art methods, reported in the machine learning literature, for spatial and temporal modeling. The learned policy is implemented on a robot using a Nvidia Jetson TX2 board which, in turn, is validated on test tracks. The proposed spatio-temporal model outperforms several other off-the-shelf machine learning techniques to learn the policy.
word2set: WordNet-Based Word Representation Rivaling Neural Word Embedding for Lexical Similarity and Sentiment Analysis

Measuring lexical similarity using WordNet has a long tradition. In the last decade, it has been challenged by distributional methods, and more recently by neural word embedding. In recent years, several larger lexical similarity benchmarks have been introduced, on which word embedding has achieved state-of-the-art results. The success of such methods has eclipsed the use of WordNet for predicting human judgments of lexical similarity. We propose a new set cardinality-based method for measuring lexical similarity, which exploits the WordNet graph, obtaining a word representation, which we called word2set, based on related neighboring words. We show that the features extracted from set cardinalities computed using this word representation, when fed into a support vector regression classifier trained on a dataset of common synonyms and antonyms, produce results competitive with those of word-embedding approaches. On the task of predicting the lexical sentiment polarity, our WordNet set-based representation significantly outperforms the classical measures and achieves the performance of neural embeddings. Although word embedding is still the best approach for these tasks, our method significantly reduces the gap between the results shown by knowledge-based approaches and by distributional representations, without requiring a large training corpus. It is also more effective for less-frequent words.
HEALED: HEaling &amp; Attestation for Low-End Embedded Devices

We are increasingly surrounded by numerous embedded systems which collect, exchange, and process sensitive and safety-critical information. The Internet of Things (IoT) allows a large number of interconnected devices to be accessed and controlled remotely, across existing network infrastructure. Consequently, a remote attacker can exploit security vulnerabilities and compromise these systems. In this context, remote attestation is a very useful security service that allows to remotely and securely verify the integrity of devices’ software state, thus allowing the detection of potential malware on the device. However, current attestation schemes focus on detecting whether a device is infected by malware but not on disinfecting it and restoring its software to a benign state. In this paper we present HEALED – the first remote attestation scheme for embedded devices that allows both detection of software compromise and disinfection of compromised devices. HEALED uses Merkle Hash Trees (MHTs) for measurement of software state, which allows restoring a device to a benign state in a secure and efficient manner.
Corporate Management in the Age of AI

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MAD: A visual analytics solution for Multi-step cyber Attacks Detection

Software vulnerabilities represent one of the main weaknesses of an Information Technology (IT) system w.r.t. cyber attacks and nowadays consolidated official data, like the Common Vulnerability Exposure (CVE) dictionary, provide precise and reliable details about them. This information, together with the identification of priority systems to defend allows for inspecting the network structure and the most probable paths an attacker is likely to follow to reach sensible resources, with the main goal of identify suitable mitigation actions that reduce the risk of an attack. Some of these mitigation actions can be applied without further delay, some of them, instead, imply a high operational impact on the organization business that makes their usage convenient only when an attack is really on the way. Dealing with this issue is particularly challenging in the context of critical infrastructure where, even if patches are available, organization mission constraints create obstacles to their straightforward application. In this scenario, security operators are forced to deal with known vulnerabilities that cannot be patched and they spend a noticeable effort in proactive analysis, devising countermeasures that can mitigate the effect of a possible attack. This paper presents a Multi-step cyber Attack Detection (MAD) Visual Analytics solution aiming at assisting security operators in improving their network security by analyzing the possible attacks and identifying suitable mitigations. Moreover, during an attack, the system visually presents the security operator with the relevant pieces of information allowing a better comprehension of the attack status and its probable evolution, in order to make decisions on the possible countermeasures.
An academic review of current industrial and commercial cyber security social engineering solutions

The study aims to assess popular awareness training solutions and techniques used by organizations to defend and mitigate cyber security social engineering threats. Social engineering threats are the most unpredicted threats an organization faces, leading to loss of confidential data, finances, intellectual property, and consumer credibility. Therefore, it is very important that an organization is well prepared to defend its information systems against social engineering threats. Literature in this domain presents various types of contemporary training and awareness solutions used at the corporate level to address social engineering threats, with the most prominent being reviewed in this study. Latest training methods identified in this study include serious games, gamification, virtual labs, tournaments, simulations, and the use of other modern applications. Similarly, current awareness programs that educate against social engineering threats including video streaming, compliances, theme-based trainings, awareness campaigns, and conferences are also included.
A Lightweight Cryptographic Protocol with Certificateless Signature for the Internet of Things

The universality of smart-devices has brought rapid development and the significant advancement of ubiquitous applications for the Internet of Things (IoT). Designing new types of IoT-compatible cryptographic protocols has become a more popular way to secure IoT-based applications. Significant attention has been dedicated to the challenge of implementing a lightweight and secure cryptographic protocol for IoT devices. In this study, we propose a lightweight cryptographic protocol integrating certificateless signature and bilinear pairing crypto-primitives. In the proposed protocol, we elegantly refine the processes to account for computation-limited IoT devices during security operations. Rigorous security analyses are conducted to guarantee the robustness of the proposed cryptographic protocol. In addition, we demonstrate a thorough performance evaluation, where an IoT-based test-bed, i.e., the Raspberry PI, is simulated as the underlying platform of the implementation of our proposed cryptographic protocol. The results show the practicability of the proposed protocol.
A temporal epistemic logic with a non-rigid set of agents for analyzing the blockchain protocol

Abstract In this paper we provide a strongly complete axiomatization of a temporal epistemic logic in which non-rigid sets of agents are allowed. Using this framework, we prove a number of properties of the blockchain protocol with respect to the given set of axioms and premises.
Disclose More and Risk Less: Privacy Preserving Online Social Network Data Sharing

Many third-party services and applications have integrated the login services of popular Online Social Networks, such as Facebook and Google+, and acquired user information to enrich their services by requesting user's permission. Although users can control the information disclosed to the third parties in a certain granularity, there are still serious privacy risks due to the inference attack. Even if users conceal their sensitive information, attackers can infer their secrets by exploiting the correlations among private and public information with background knowledge. To defend against such attacks, we formulate the social network data sharing problem through an optimization-based approach, which maximizes the users' self-disclosure utility while preserving their privacy. We propose two privacy-preserving social network data sharing methods to counter the inference attack. One is the efficiency-based privacy-preserving disclosure algorithm (EPPD) targeting the high utility, and the other is to convert the original problem into a multi-dimensional knapsack problem (d-KP) using greedy heuristics with a low computational complexity. We use real-world social network datasets to evaluate the performance. From the results, the proposed methods achieve a better performance when compared with the existing ones.
Representation learning via serial autoencoders for domain adaptation

Domain adaption aims to promote the learning tasks in target domain by using the knowledge from source domain whose data distribution is different from target domain. The crucial problem in domain adaptation is learning more robust and higher-level feature representations to reduce the distribution divergences. Recently, deep learning methods based on autoencoders have been successfully applied in representation learning for domain adaptation. However, most existing methods of autoencoders rely on the single autoencoder model, which poses challenges for learning different characteristics of data. In this paper, we propose a new representation learning framework via serial autoencoders (SEAE), which extracts richer feature representations by serially connecting two different types of autoencoders. This framework consists of two encoding stages. In the first encoding stage, marginalized denoising autoencoder (MDAE) is applied to learn domain invariant features which occurred in both domains frequently. With the result of the first stage, stacked robust autoencoder via graph regularization (SRAEG) is used in the second encoding stage to improve the quality of feature representations. Additionally, SRAEG model can be computed in a closed form with less time cost. Experimental results demonstate the effectiveness of our proposed approach compared with several state-of-the-art baseline methods.
Transforming Heterogeneous Data into Knowledge for Personalized Treatments—A Use Case

Big data has exponentially grown in the last decade; it is expected to grow at a faster rate in the next years as a result of the advances in the technologies for data generation and ingestion. For instance, in the biomedical domain, a wide variety of methods are available for data ingestion, e.g., liquid biopsies and medical imaging, and the collected data can be represented using myriad formats, e.g., FASTQ and Nifti. In order to extract and manage valuable knowledge and insights from big data, the problem of data integration from structured and unstructured data needs to be effectively solved. In this paper, we devise a knowledge-driven approach able to transform disparate data into knowledge from which actions can be taken. The proposed framework resorts to computational extraction methods for mining knowledge from data sources, e.g., clinical notes, images, or scientific publications. Moreover, controlled vocabularies are utilized to annotate entities and a unified schema describes the meaning of these entities in a knowledge graph; entity linking methods discover links to existing knowledge graphs, e.g., DBpedia and Bio2RDF. A federated query engine enables the exploration of the linked knowledge graphs while knowledge discovery methods allow for uncovering patterns in the knowledge graphs. The proposed framework is used in the context of the EU H2020 funded project iASiS with the aim of paving the way for accurate diagnostics and personalized treatments.
User recommendations for intelligent personal assistants

Adoption of intelligent personal assistants (IPA) is on the rise. Published studies on IPAs often focus on the analysis and critique of existing IPA features without understanding specific user needs that the technology aims to address. We present an exploratory study that gathered user recommendations for the design of their ideal IPA. The study relied on focus group and content analysis methods for data collection and analysis. Major themes in participants’ recommendations for IPA design were identified and included feature improvements (e.g. speech recognition, input/output modalities, device feedback); customizability and increased control over IPA features and functions; transparency and understanding of IPA hardware, software, and data management processes; personification; compatibility with third-party platforms; accessibility; and aesthetics. Many of these recommendations are rooted in basic user experience design principles and have been previously discussed in the context of PDAs (personal digital assistants) and other technology. Addressing these recommendations would advance IPA technology and improve user experiences with it.
Semantic Query-by-example Speech Search Using Visual Grounding

A number of recent studies have started to investigate how speech systems can be trained on untranscribed speech by leveraging accompanying images at training time. Examples of tasks include keyword prediction and within- and across-mode retrieval. Here we consider how such models can be used for query-by-example (QbE) search, the task of retrieving utterances relevant to a given spoken query. We are particularly interested in semantic QbE, where the task is not only to retrieve utterances containing exact instances of the query, but also utterances whose meaning is relevant to the query. We follow a segmental QbE approach where variable-duration speech segments (queries, search utterances) are mapped to fixed-dimensional embedding vectors. We show that a QbE system using an embedding function trained on visually grounded speech data outperforms a purely acoustic QbE system in terms of both exact and semantic retrieval performance.
Fast block distributed CUDA implementation of the Hungarian algorithm

The Hungarian algorithm solves the linear assignment problem in polynomial time. A GPU/CUDA implementation of this algorithm is proposed. GPUs are massive parallel machines. In this implementation, the alternating path search phase of the algorithm is distributed by several blocks in a way to minimize global device synchronization. This phase is very important and has a big contribution to the execution time. Other advanced features also implemented are: parallel graph traversal; the parallel detection of multiple alternating paths in a single iteration; a simplified and fast matrix compression that stores the zeros of the slack matrix, resulting in very fast graph traversal; highly optimized reductions for the initial slack matrix calculation and update. This results in a fast implementation for moderate size problems.
Visualization of Expert Evaluations of the Smartness of Sociopolises with the Help of Radar Charts

The article covers the issue of visualization of the evaluation results by experts of various types of complex systems. The peculiarities of expert evaluation system “smartness sociopolis” are analyzed, namely, in such settlements are the main place of residence of the majority of humanity on the planet and the basic methods of visualization of expert assessments. The advantages of presenting the results of expert evaluation with the help of data visualization tools in comparison with text or tabular are noted. The radar charts have been selected as one of the tools for visualizing the evaluation results, which provide an opportunity to present peculiarities of the use of various criteria, to distinguish more weighty criteria and to take into account the opinion of each expert. The article proposes a mathematical model for evaluating of “smartness of sociopolis”.
Neural Approaches to Automated Speech Scoring of Monologue and Dialogue Responses

We present Neural Network (NN) approaches to the automated assessment of non-native spontaneous speech in a monologic task and a simulated dialogic task. Three attention-based Bidirectional Long Short-Term Memory (BLSTM) Recurrent Neural Networks (RNN) are employed to learn three dimensions (i.e., delivery, language use, and content) of scoring rubrics for the spoken responses. The prompts or turn history information are encoded to low-dimensional vectors by either a BLSTM-RNN or an end-to-end memory network (MemN2N) and used as the conditions of the inputs of the NN for rating the subscore of content. The three subscores are fused together to generate a holistic score. The experimental results show that our approaches significantly outperform the conventional approaches to speech scoring and the correlations of automatically predicted scores with the reference human scores are higher than human-human agreement levels for both tasks.
Exact gaussian process regression with distributed computations

Gaussian Processes (GPs) are powerful non-parametric Bayesian models for function estimation, but suffer from high complexity in terms of both computation and storage. To address such issues, approximation methods have flourished in the literature, including model approximations and approximate inference. However, these methods often sacrifice accuracy for scalability.
FTVR in VR

Spherical fish tank virtual reality (FTVR) displays attempt to create a virtual "crystal ball" experience using head-tracked rendering. Almost all of these systems have omitted stereo cues, making them easy to build, but it is not clear how much this omission degrades the 3D experience. In this study, we evaluate performance and subjective effects of stereo on 3D perception and interaction tasks with a spherical FTVR display. To control for calibration error and tracking latency, we perform the evaluation on a simulated spherical display in VR. The results of our study provide a clear recommendation for the design and use of spherical FTVR displays: while omitting stereo may not be readily apparent for users, their performance will be significantly degraded (20% - 91% increase in median task time). Therefore, including stereo viewing in spherical displays is critical for use in FTVR.