Reviewerly/exHarmony huggingface dataset:
Subphenotyping depression using machine learning and electronic health records

Abstract Objective To identify depression subphenotypes from Electronic Health Records (EHRs) using machine learning methods, and analyze their characteristics with respect to patient demographics, comorbidities, and medications. Materials and Methods Using EHRs from the INSIGHT Clinical Research Network (CRN) database, multiple machine learning (ML) algorithms were applied to analyze 11 275 patients with depression to discern depression subphenotypes with distinct characteristics. Results Using the computational approaches, we derived three depression subphenotypes: Phenotype_A (n = 2791; 31.35%) included patients who were the oldest (mean (SD) age, 72.55 (14.93) years), had the most comorbidities, and took the most medications. The most common comorbidities in this cluster of patients were hyperlipidemia, hypertension, and diabetes. Phenotype_B (mean (SD) age, 68.44 (19.09) years) was the largest cluster (n = 4687; 52.65%), and included patients suffering from moderate loss of body function. Asthma, fibromyalgia, and Chronic Pain and Fatigue (CPF) were common comorbidities in this subphenotype. Phenotype_C (n = 1452; 16.31%) included patients who were younger (mean (SD) age, 63.47 (18.81) years), had the fewest comorbidities, and took fewer medications. Anxiety and tobacco use were common comorbidities in this subphenotype. Conclusion Computationally deriving depression subtypes can provide meaningful insights and improve understanding of depression as a heterogeneous disorder. Further investigation is needed to assess the utility of these derived phenotypes to inform clinical trial design and interpretation in routine patient care.
An FPGA Accelerator for Real-Time Lossy Compression of Hyperspectral Images

Hyperspectral images offer great possibilities for remote studies, but can be difficult to manage due to their size. Compression helps with storage and transmission, and many efforts have been made towards standardizing compression algorithms, especially in the lossless and near-lossless domains. For long term storage, lossy compression is also of interest, but its complexity has kept it away from real-time performance. In this paper, JYPEC, a lossy hyperspectral compression algorithm that combines PCA and JPEG2000, is accelerated using an FPGA. A tier 1 coder (a key step and the most time-consuming in JPEG2000 compression) was implemented in a heavily pipelined fashion. Results showed a performance comparable to that of existing 0.18 μm CMOS implementations, all while keeping a small footprint on FPGA resources. This enabled the acceleration of the most complex step of JYPEC, bringing the total execution time below the real-time constraint.
Security and Privacy in Internet of Things-Enabled Smart Cities: Challenges and Future Directions

The digitalization of current urban spaces is realizing the vision of so-called smart cities, where security and privacy concerns could affect citizens' safety. This work discusses potential solutions derived from European Union research efforts to be considered in the coming years.
Quantum-inspired binary gravitational search algorithm to recognize the facial expressions

This paper addresses an autonomous facial expression recognition system using the feature selection approach of the Quantum-Inspired Binary Gravitational Search Algorithm (QIBGSA). The detection of facial features completely depends upon the selection of precise features. The concept of QIBGSA is a modified binary version of the gravitational search algorithm by mimicking the properties of quantum mechanics. The QIBGSA approach reduces the computation cost for the initial extracted feature set using the hybrid approach of Local binary patterns with Gabor filter method. The proposed automated system is a sequential system with experimentation on the image-based dataset of Karolinska Directed Emotional Faces (KDEF) containing human faces with seven different emotions and different yaw angles. The experiments are performed to find out the optimal emotions using the feature selection approach of QIBGSA and classification using a deep convolutional neural network for robust and efficient facial expression recognition. Also, the effect of variations in the yaw angle (front to half side view) on facial expression recognition is studied. The results of the proposed system for the KDEF dataset are determined in three different cases of frontal view, half side view, and combined frontal and half side view images. The system efficacy is analyzed in terms of recognition rate.
Regression-based methods for face alignment: A survey

Face alignment is the process of determining a face shape given its location and size in an image. It is used as a basis for other facial analysis tasks and for human-machine interaction and augmented reality applications. It is a challenging problem due to the extremely high variability in facial appearance affected by many external (illumination, occlusion, head pose) and internal factors (race, facial expression). However, advances in deep learning combined with domain-related knowledge from previous research recently demonstrated impressive results nearly saturating the unconstrained benchmark data sets. The focus is shifting towards reducing the computational burden of the face alignment models since real-time performance is required for such a highly dynamic task. Furthermore, many applications target devices on the edge with limited computational power which puts even greater emphasis on computational efficiency. We present the latest development in regression-based approaches that have led towards nearly solving the face alignment problem in an unconstrained scenario. Various regression architectures are systematically explored and recent training techniques discussed in the context of face alignment. Finally, a benchmark comparison of the most successful methods is presented, taking into account execution time as well, to provide a comprehensive overview of this dynamic research field.
CrowdQuake

Recently, low-cost acceleration sensors have been widely used to detect earthquakes due to the significant development of MEMS technologies. It, however, still requires a high-density network to fully harness the low-cost sensors, especially for real-time earthquake detection. The design of a high-performance and scalable networked system thus becomes essential to be able to process a large amount of sensor data from hundreds to thousands of the sensors. An efficient and accurate earthquake-detection algorithm is also necessary to distinguish earthquake waveforms from various kinds of non-earthquake ones within the huge data in real time. In this paper, we present CrowdQuake, a networked system based on low-cost acceleration sensors, which monitors ground motions and detects earthquakes, by developing a convolutional-recurrent neural network model. This model ensures high detection performance while maintaining false alarms at a negligible level. We also provide detailed case studies on two of a few small earthquakes that have been detected by CrowdQuake during its last one-year operation.
Research on Information Extraction of Technical Documents and Construction of Domain Knowledge Graph

With the rapid development of knowledge graph related technologies, domain knowledge graph has become a research hotspot in academia and industry. However, the domain knowledge graph for technical documents is not mature enough, and the semantic information implicit in unstructured technical documents has not been fully tapped. Combining the characteristics of technical documents, the paper proposes a TextCNN-based topic information extraction model and constructs a domain knowledge graph for technical documents. It uses the graph database Neo4j for knowledge storage and visualization. The information extraction model based on TextCNN can automatically extract the subject information of the document and the summary information such as title, ID, status, meeting, organization, etc. Experiments show that the model has high accuracy on the technical document dataset, which can effectively reduce the cost of manual annotation and data collation. At the same time, knowledge graph visualization can facilitate scientific researchers to search, track and update technical documents, which can show the evolution of technology more clearly.
Using the gameplay and user data to predict and identify causes of cybersickness manifestation in virtual reality games

Virtual reality (VR) is an imminent trend in games, education, entertainment, military, and health applications, as the use of head-mounted displays is accessible to everyone. While VR provides immersive experiences, it still does not offer an entirely perfect situation, mainly due to cybersickness (CS) issues. In this work, we propose a novel approach for predicting upcoming CS symptoms. Our solution is able to suggest whether the user of VR is entering into an illness situation. We adopted random forest classifiers and validated our solution using 16 different machine-learning techniques, which presented the best results. For training purposes, we built our own dataset through a CS profile questionnaire that we also propose in the present work. The questionnaire is focused on registering and identifying the user's susceptibility to CS, considering their historical conditions and also their response to the immersive environment developed by us. In this method, 86 individuals are selected and the developed questionnaire was put to them on different days, and the answers are compiled as dataset. Our proposal also identifying attributes responsible (causes and individual's parameters) for the observed stressful and uncomfortable situations.
4D Generic Video Object Proposals

Many high-level video understanding methods require input in the form of object proposals. Currently, such proposals are predominantly generated with the help of neural networks that were trained for detecting and segmenting a set of known object classes, which limits their applicability to cases where all objects of interest are represented in the training set. We propose an approach that can reliably extract spatio-temporal object proposals for both known and unknown object categories from stereo video. Our 4D Generic Video Tubes (4D-GVT) method combines motion cues, stereo data, and data-driven object instance segmentation in a probabilistic framework to compute a compact set of video-object proposals that precisely localizes object candidates and their contours in 3D space and time.
Efficient Neural Architecture for Text-to-Image Synthesis

Text-to-image synthesis is the task of generating images from text descriptions. Image generation, by itself, is a challenging task. When we combine image generation and text, we bring complexity to a new level: we need to combine data from two different modalities. Most of recent works in text-to-image synthesis follow a similar approach when it comes to neural architectures. Due to aforementioned difficulties, plus the inherent difficulty of training GANs at high resolutions, most methods have adopted a multi-stage training strategy. In this paper we shift the architectural paradigm currently used in text-to-image methods and show that an effective neural architecture can achieve state-of-the-art performance using a single stage training with a single generator and a single discriminator. We do so by applying deep residual networks along with a novel sentence interpolation strategy that enables learning a smooth conditional space. Finally, our work points a new direction for text-to-image research, which has not experimented with novel neural architectures recently.
Hierarchical Transfer Learning for Multilingual, Multi-Speaker, and Style Transfer DNN-Based TTS on Low-Resource Languages

This work applies a hierarchical transfer learning to implement deep neural network (DNN)-based multilingual text-to-speech (TTS) for low-resource languages. DNN-based system typically requires a large amount of training data. In recent years, while DNN-based TTS has made remarkable results for high-resource languages, it still suffers from a data scarcity problem for low-resource languages. In this article, we propose a multi-stage transfer learning strategy to train our TTS model for low-resource languages. We make use of a high-resource language and a joint multilingual dataset of low-resource languages. A pre-trained monolingual TTS on the high-resource language is fine-tuned on the low-resource language using the same model architecture. Then, we apply partial network-based transfer learning from the pre-trained monolingual TTS to a multilingual TTS and finally from the pre-trained multilingual TTS to a multilingual with style transfer TTS. Our experiment on Indonesian, Javanese, and Sundanese languages show adequate quality of synthesized speech. The evaluation of our multilingual TTS reaches a mean opinion score (MOS) of 4.35 for Indonesian (ground truth = 4.36). Whereas for Javanese and Sundanese it reaches a MOS of 4.20 (ground truth = 4.38) and 4.28 (ground truth = 4.20), respectively. For parallel style transfer evaluation, our TTS model reaches an F0 frame error (FFE) of 9.08%, 10.13%, and 8.43% for Indonesian, Javanese, and Sundanese, respectively. The results indicate that the proposed strategy can be effectively applied to the low-resource languages target domain. With a small amount of training data, our models are able to learn step by step from a smaller TTS network to larger networks, produce intelligible speech approaching the real human voice, and successfully transfer speaking style from a reference audio.
High-Level Modeling and Control of the Bebop 2 Micro Aerial Vehicle

This paper deals with a simplified dynamic model for the Parrot Bebop 2 micro aerial vehicle, whose parameters are identified through a least-mean square error procedure. Moreover, such a model is used to design high-level controllers to autonomously guide a single vehicle or a formation of two of them, in tasks of positioning and trajectory-tracking. The controllers thus designed are used in experiments of autonomous navigation, whose results validate the proposed model and the designed controllers.
Nanophotonic source of quadrature squeezing via self-phase modulation

Squeezed light is optical beams with variance below the shot noise level. They are a key resource for quantum technologies based on photons, and they can be used to achieve better precision measurements and improve security in quantum key distribution channels and as a fundamental resource for quantum computation. Here, we demonstrate an integrated source of squeezing based on four-wave mixing that requires a single laser pump, measuring 0.45 dB of broadband quadrature squeezing at high frequencies. We identify and verify that the current results are limited by excess noise produced in the chip and propose ways to reduce it. Calculations suggest that an improvement in the optical properties of the chip achievable with existing technology can develop scalable quantum technologies based on light.
A new approach to diagnosing prostate cancer through magnetic resonance imaging

This paper combines improved GrowCut and Zernik feature extraction and ensemble learning techniques such as KNN, SVM, and MLP algorithms to prostate cancer detection and lesion segmentation in MRI. We use improved GrowCut algorithm for segmentation of the suspected cancer area and the combination of machine learning algorithms such as KNN, SVM, and MLP in the ensemble learning system to detect prostate cancer. We found that the accuracy of this method, which is a combination of several methods, improved by about 20% compared to other methods. Other metrics such as precision, recall, and error of proposed method have been improved.
Approximating the total variation with finite differences or finite elements

We present and compare various types of discretizations which have been proposed to approximate the total variation (mostly, of a gray-level image in two dimensions). We discuss the properties of finite differences and finite elements based approach and compare their merits, in particular in terms of error estimates and quality of the reconstruction.
Using digital humanities and linguistics to help with terrorism investigations

This article seeks to offer a response to the digital transformation of forensic science by employing a tool-based linguistic analysis, integrated into the paradigm of digital humanities. It is a way to scientifically model the analysis of digital texts using digital methods. Computer science comes in support of linguistic skills in order to deal with investigative situations and help analyze criminal acts. It presents a case report thanks to the analysis of a corpus made up of 23 texts relating to criminal acts related to suspected terrorist groups with links to the far left. The goal is to help investigators by providing results which can help find stylistic similarities or exclusions between texts and thus potentially between the authors of those texts, offering authors profiling hypothsesis that may be included in the investigation process. While linguistics alone cannot solve such cases, a better understanding of language data, including topics, style and grammar, bring additional clues that can be very useful information in the investigation of crimes (linguists can “translate” information to investigators, so that it can be integrated to the investigation). Digital tools provide a form of objectification since they are based on statistical calculations which reveal regularities that are otherwise invisible to the naked eye. These tools, when used properly in investigations, can prove invaluable in extracting “clues” from the linguistic “traces” that make up texts.
Dasta – Alternative Linear Layer for Rasta

Progress in the areas of multi-party computation (MPC) and fully homomorphic encryption (FHE) caused the demand of new design strategies, that minimize the number of multiplications in symmetric primitives. Rasta is an approach for a family of stream ciphers with an exceptional low AND depth, which equals the number of ANDs per encrypted bit. This is achieved in particular by randomizing parts of the computation with the help of a PRNG, implying that the security arguments rely on the provided randomness and the encryption/ decryption is potentially slowed down by this generation.In this paper we propose a variant of Rasta that achieves the same performance with respect to the AND depth and the number of ANDs per encrypted bit, but does not rely on a PRNG, i.e. is based on fixed linear layers.
Detecting Fine-Grained Cross-Lingual Semantic Divergences without Supervision by Learning to Rank

Detecting fine-grained differences in content conveyed in different languages matters for cross-lingual NLP and multilingual corpora analysis, but it is a challenging machine learning problem since annotation is expensive and hard to scale.This work improves the prediction and annotation of finegrained semantic divergences.We introduce a training strategy for multilingual BERT models by learning to rank synthetic divergent examples of varying granularity.We evaluate our models on the Rationalized English-French Semantic Divergences, a new dataset released with this work, consisting of English-French sentence-pairs annotated with semantic divergence classes and token-level rationales.Learning to rank helps detect finegrained sentence-level divergences more accurately than a strong sentence-level similarity model, while token-level predictions have the potential of further distinguishing between coarse and fine-grained divergences.ADV VERB ADJ NOUN how weak they are.
MetaPoison: Practical General-purpose Clean-label Data Poisoning

Data poisoning -- the process by which an attacker takes control of a model by making imperceptible changes to a subset of the training data -- is an emerging threat in the context of neural networks. Existing attacks for data poisoning neural networks have relied on hand-crafted heuristics, because solving the poisoning problem directly via bilevel optimization is generally thought of as intractable for deep models. We propose MetaPoison, a first-order method that approximates the bilevel problem via meta-learning and crafts poisons that fool neural networks. MetaPoison is effective: it outperforms previous clean-label poisoning methods by a large margin. MetaPoison is robust: poisoned data made for one model transfer to a variety of victim models with unknown training settings and architectures. MetaPoison is general-purpose, it works not only in fine-tuning scenarios, but also for end-to-end training from scratch, which till now hasn't been feasible for clean-label attacks with deep nets. MetaPoison can achieve arbitrary adversary goals -- like using poisons of one class to make a target image don the label of another arbitrarily chosen class. Finally, MetaPoison works in the real-world. We demonstrate for the first time successful data poisoning of models trained on the black-box Google Cloud AutoML API. Code and premade poisons are provided at this https URL
A Synset Relation-enhanced Framework with a Try-again Mechanism for Word Sense Disambiguation

Contextual embeddings are proved to be overwhelmingly effective to the task of Word Sense Disambiguation (WSD) compared with other sense representation techniques.However, these embeddings fail to embed sense knowledge in semantic networks.In this paper, we propose a Synset Relation-Enhanced Framework (SREF) that leverages sense relations for both sense embedding enhancement and a try-again mechanism that implements WSD again, after obtaining basic sense embeddings from augmented WordNet glosses.Experiments on all-words and lexical sample datasets show that the proposed system achieves new state-ofthe-art results, defeating previous knowledge-based systems by at least 5.5 F1 measure.When the system utilizes sense embeddings learned from SemCor, it outperforms all previous supervised systems with only 20% SemCor data.
Multi-label Few/Zero-shot Learning with Knowledge Aggregated from Multiple Label Graphs

Few/Zero-shot learning is a big challenge of many classifications tasks, where a classifier is required to recognise instances of classes that have very few or even no training samples.It becomes more difficult in multilabel classification, where each instance is labelled with more than one class.In this paper, we present a simple multi-graph aggregation model that fuses knowledge from multiple label graphs encoding different semantic label relationships in order to study how the aggregated knowledge can benefit multi-label zero/few-shot document classification.The model utilises three kinds of semantic information, i.e., the pre-trained word embeddings, label description, and pre-defined label relations.Experimental results derived on two large clinical datasets (i.e., MIMIC-II and MIMIC-III ) and the EU legislation dataset show that methods equipped with the multi-graph knowledge aggregation achieve significant performance improvement across almost all the measures on few/zero-shot labels.