Reviewerly/exHarmony huggingface dataset:
Spam detection on social networks using cost-sensitive feature selection and ensemble-based regularized deep neural networks

Spam detection on social networks is increasingly important owing to the rapid growth of social network user base. Sophisticated spam filters must be developed to deal with this complex problem. Traditional machine learning approaches such as neural networks, support vector machines and Naïve Bayes classifiers are not effective enough to process and utilize complex features present in high-dimensional data on social network spam. Moreover, the traditional objective criteria of social network spam filters cannot cope with different costs assigned to type I and type II errors. To overcome these problems, here we propose a novel cost-sensitive approach to social network spam filtering. The proposed approach is composed of two stages. In the first stage, multi-objective evolutionary feature selection is used to minimize both the misclassification cost of the proposed model and the number of attributes necessary for spam filtering. Then, the approach uses cost-sensitive ensemble learning techniques with regularized deep neural networks as base learners. We demonstrate that this approach is effective for social network spam filtering on two benchmark datasets. We also show that the proposed approach outperforms other popular algorithms used in social network spam filtering, such as random forest, Naïve Bayes or support vector machines.
HUNER: improving biomedical NER with pretraining

Several recent studies showed that the application of deep neural networks advanced the state-of-the-art in named entity recognition (NER), including biomedical NER. However, the impact on performance and the robustness of improvements crucially depends on the availability of sufficiently large training corpora, which is a problem in the biomedical domain with its often rather small gold standard corpora.We evaluate different methods for alleviating the data sparsity problem by pretraining a deep neural network (LSTM-CRF), followed by a rather short fine-tuning phase focusing on a particular corpus. Experiments were performed using 34 different corpora covering five different biomedical entity types, yielding an average increase in F1-score of ∼2 pp compared to learning without pretraining. We experimented both with supervised and semi-supervised pretraining, leading to interesting insights into the precision/recall trade-off. Based on our results, we created the stand-alone NER tool HUNER incorporating fully trained models for five entity types. On the independent CRAFT corpus, which was not used for creating HUNER, it outperforms the state-of-the-art tools GNormPlus and tmChem by 5-13 pp on the entity types chemicals, species and genes.HUNER is freely available at https://hu-ner.github.io. HUNER comes in containers, making it easy to install and use, and it can be applied off-the-shelf to arbitrary texts. We also provide an integrated tool for obtaining and converting all 34 corpora used in our evaluation, including fixed training, development and test splits to enable fair comparisons in the future.Supplementary data are available at Bioinformatics online.
Perspectives on emerging directions in using IoT devices in blockchain applications

The space of Internet-of-things has exploded, with billions of interconnected devices ranging from mainframes to refrigerators and thermostats. These devices offer the promise of greater automation and control, and the ability for micro-level transactions that did not exist before. The advent of blockchain offers an intriguing path to managing distributed transactions in this new ecosystem. The use of blockchain in IoT applications is relatively new, especially at the lower end of the computing spectrum. Consequently, the roadmap for the future is unclear, and there are several challenges and questions that need to be addressed, such as trust, security, and efficiency. In this paper, we survey some of the promising applications that are being implemented including supply chains, smarter energy, and healthcare. We outline strategies for overcoming many of the challenges, which should lead to successful adoptions of blockchain for IoT. Finally, we sound a cautionary note on the potential cybersecurity implications in using IoT devices, including increased attack surfaces and device vulnerabilities.
Real-Time Privacy-Preserving Data Release Over Vehicle Trajectory

Intelligent connected vehicle trajectory data are of great value for data mining applications such as traffic management and commercial institutions. However, the leakage of sensitive trajectory makes the user hesitate to use the system if no privacy-preserving mechanism is adopted. In this paper, we propose a privacy-preserving mechanism with differential privacy called RPTR, which protects a vehicle's real-time trajectory data release. First, RPTR adopts a dynamic sampling method to process the trajectory data to meet the application load and practicability. Meanwhile, to ensure the data availability, ensemble Kalman filter based on users' position transfer probability matrix is used in the prediction calculation. Also, we construct the privacy budget allocation method based on regional privacy weight to provide better protection for regions with high user density. Through our analysis and experiments, RPTR not only protects the privacy of real-time trajectory data but also guarantees the data availability.
Control of Smart Home Operations Using Natural Language Processing, Voice Recognition and IoT Technologies in a Multi-Tier Architecture

The Internet of Things (IoT) is an emerging Internet-based architecture, enabling the exchange of data and services in a global network. With the advent of the Internet of Things, more and more devices are connecting to the Internet in order to help people get and share data or program actions. In this paper, we introduce an IoT Agent, a Web application for monitoring and controlling a smart home remotely. The IoT Agent integrates a chat bot that can understand text or voice commands using natural language processing (NLP). With the use of NLP, home devices are more user-friendly and controlling them is easier, since even when a command or question/command is different from the presets, the system understands the user’s wishes and responds accordingly. Our solution exploits several available Application Programming Interfaces (APIs), namely: the Dialogflow API for the efficient integration of NLP to our IoT system, the Web Speech API for enriching user experience with voice recognition and synthesis features, MQTT (Message Queuing Telemetry Transport) for the lightweight control of actuators and Firebase for dynamic data storage. This is the most significant innovation it brings: the integration of several third-party APIs and open source technologies into one mash-up, highlighting how a new IoT application can be built today using a multi-tier architecture. We believe that such a tiered architecture can be very useful for the rapid development of smart home applications.
Guided Attention Inference Network

With only coarse labels, weakly supervised learning typically uses top-down attention maps generated by back-propagating gradients as priors for tasks such as object localization and semantic segmentation. While these attention maps are intuitive and informative explanations of deep neural network, there is no effective mechanism to manipulate the network attention during learning process. In this paper, we address three shortcomings of previous approaches in modeling such attention maps in one common framework. First, we make attention maps a natural and explicit component in the training pipeline such that they are end-to-end trainable. Moreover, we provide self-guidance directly on these maps by exploring supervision from the network itself to improve them towards specific target tasks. Lastly, we proposed a design to seamlessly bridge the gap between using weak and extra supervision if available. Despite its simplicity, experiments on the semantic segmentation task demonstrate the effectiveness of our methods. Besides, the proposed framework provides a way not only explaining the focus of the learner but also feeding back with direct guidance towards specific tasks. Under mild assumptions our method can also be understood as a plug-in to existing convolutional neural networks to improve their generalization performance.
Selective Audio Adversarial Example in Evasion Attack on Speech Recognition System

Deep neural networks (DNNs) are widely used for image recognition, speech recognition, and other pattern analysis tasks. Despite the success of DNNs, these systems can be exploited by what is termed adversarial examples. An adversarial example, in which a small distortion is added to the input data, can be designed to be misclassified by the DNN while remaining undetected by humans or other systems. Such adversarial examples have been studied mainly in the image domain. Recently, however, studies on adversarial examples have been expanding into the voice domain. For example, when an adversarial example is applied to enemy wiretapping devices (victim classifiers) in a military environment, the enemy device will misinterpret the intended message. In such scenarios, it is necessary that friendly wiretapping devices (protected classifiers) should not be deceived. Therefore, the selective adversarial example concept can be useful in mixed situations, defined as situations in which there is both a classifier to be protected and a classifier to be attacked. In this paper, we propose a selective audio adversarial example with minimum distortion that will be misclassified as the target phrase by a victim classifier but correctly classified as the original phrase by a protected classifier. To generate such examples, a transformation is carried out to minimize the probability of incorrect classification by the protected classifier and that of correct classification by the victim classifier. We conducted experiments targeting the state-of-the-art DeepSpeech voice recognition model using Mozilla Common Voice datasets and the Tensorflow library. They showed that the proposed method can generate a selective audio adversarial example with a 91.67% attack success rate and 85.67% protected classifier accuracy.
A Bi-LSTM mention hypergraph model with encoding schema for mention extraction

Natural language processing is a technique to process data such as text and speech. Some fundamental research includes named-entity recognition, which recognizes name entities (i.e., persons, companies) from texts; semantic parsing, which is used to convert a natural language utterance to the representation of logical form; and co-reference resolution, which extracts nouns (including pronouns, noun phrases) pointing to the same reference body. In this paper, we mainly focus on the task of mention extraction, which extract and classify overlapping or nested structure mentions. We proposed a neural-encoded mention-hypergraph (NEMH) model to use hypergraph to model overlapping or nested structure mentions and use neural networks to extract features for hypergraph automatically. Unlike the existing approaches, our hypergraph model can effectively capture nested mention entities with unlimited lengths. Also, the proposed model is highly scalable and the time complexity of the proposed model is linear in the number of mention classes and the number of input words. Extensive experiments are conducted on several standard datasets to demonstrate the effectiveness of the proposed model.
Inception Modules Enhance Brain Tumor Segmentation

Magnetic resonance images of brain tumors are routinely used in neuro-oncology clinics for diagnosis, treatment planning, and post-treatment tumor surveillance. Currently, physicians spend considerable time manually delineating different structures of the brain. Spatial and structural variations, as well as intensity inhomogeneity across images, make the problem of computer-assisted segmentation very challenging. We propose a new image segmentation framework for tumor delineation that benefits from two state-of-the-art machine learning architectures in computer vision, i.e., Inception modules and U-Net image segmentation architecture. Furthermore, our framework includes two learning regimes, i.e., learning to segment intra-tumoral structures (necrotic and non-enhancing tumor core, peritumoral edema, and enhancing tumor) or learning to segment glioma sub-regions (whole tumor, tumor core, and enhancing tumor). These learning regimes are incorporated into a newly proposed loss function which is based on the Dice similarity coefficient (DSC). In our experiments, we quantified the impact of introducing the Inception modules in the U-Net architecture, as well as, changing the objective function for the learning algorithm from segmenting the intra-tumoral structures to glioma sub-regions. We found that incorporating Inception modules significantly improved the segmentation performance (p < 0.001) for all glioma sub-regions. Moreover, in architectures with Inception modules, the models trained with the learning objective of segmenting the intra-tumoral structures outperformed the models trained with the objective of segmenting the glioma sub-regions for the whole tumor (p < 0.001). The improved performance is linked to multiscale features extracted by newly introduced Inception module and the modified loss function based on the DSC.
Generative Adversarial Speaker Embedding Networks for Domain Robust End-to-end Speaker Verification

This article presents a novel approach for learning domain-invariant speaker embeddings using Generative Adversarial Networks. The main idea is to confuse a domain discriminator so that it cannot tell if embeddings are from the source or target domains. We train several GAN variants using our proposed framework and apply them to the speaker verification task. On the challenging NIST-SRE 2016 dataset, we are able to match the performance of a strong baseline x-vector system. In contrast to the the baseline systems which are dependent on dimensionality reduction (LDA) and an external classifier (PLDA), our proposed speaker embeddings can be scored using simple cosine distance. This is achieved by optimizing our models end-to-end, using an angular margin loss function. Furthermore, we are able to significantly boost verification performance by averaging our different GAN models at the score level, achieving a relative improvement of 7.2% over the baseline.
Parallelizing Word2Vec in Shared and Distributed Memory

Word2vec is a widely used algorithm for extracting low-dimensional vector representations of words. State-of-the-art algorithms including those by Mikolov et al. [1] , [2] have been parallelized for multi-core CPU architectures, but are based on vector-vector operations with “Hogwild” updates that are memory-bandwidth intensive and do not efficiently use computational resources. In this paper, we propose “HogBatch” by improving reuse of various data structures in the algorithm through the use of minibatching and negative sample sharing, hence allowing us to express the problem using matrix multiply operations. We also explore different techniques to distribute word2vec computation across nodes in a computer cluster, and demonstrate good strong scalability up to 32 nodes. The new algorithm is particularly suitable for modern multi-core/many-core architectures, especially Intel's latest Knights Landing processors, and allows us to scale up the computation near linearly across cores and nodes, and process hundreds of millions of words per second, which is the fastest word2vec implementation to the best of our knowledge. We released the source code for reproducible research and general usage.
Temporal–Spatial Mapping for Action Recognition

Deep learning models have enjoyed great success for image related computer vision tasks such as image classification and object detection. For video related tasks such as human action recognition, however, the advancements are not as significant yet. The main challenge is the lack of effective and efficient models in modeling the rich temporal-spatial information in a video. We introduce a simple yet effective operation, termed temporal-spatial mapping, for capturing the temporal evolution of the frames by jointly analyzing all the frames of a video. We propose a video level 2D feature representation by transforming the convolutional features of all frames to a 2D feature map, referred to as VideoMap. With each row being the vectorized feature representation of a frame, the temporal-spatial features are compactly represented, while the temporal dynamic evolution is also well embedded. Based on the VideoMap representation, we further propose a temporal attention model within a shallow convolutional neural network to efficiently exploit the temporal-spatial dynamics. The experiment results show that the proposed scheme achieves state-of-the-art performance, with 4.2% accuracy gain over the temporal segment network, a competing baseline method, on the challenging human action benchmark dataset HMDB51.
New Modality: Emoji Challenges in Prediction, Anticipation, and Retrieval

Over the past decade, emoji have emerged as a new and widespread form of digital communication, spanning diverse social networks and spoken languages. We propose treating these ideograms as a new modality in their own right, distinct in their semantic structure from both the text in which they are often embedded as well as the images which they resemble. As a new modality, emoji present rich novel possibilities for representation and interaction. In this paper, we explore the challenges that arise naturally from considering the emoji modality through the lens of multimedia research, specifically the ways in which emoji can be related to other common modalities such as text and images. To do so, we first present a large-scale data set of real-world emoji usage collected from Twitter. This data set contains examples of both text-emoji and image-emoji relationships within tweets. We present baseline results on the challenge of predicting emoji from both text and images, using state-of-the-art neural networks. Further, we offer a first consideration into the problem of how to account for new, unseen emoji-a relevant issue as the emoji vocabulary continues to expand on a yearly basis. Finally, we present results for multimedia retrieval using emoji as queries.
Generalization and Expressivity for Deep Nets

Along with the rapid development of deep learning in practice, theoretical explanations for its success become urgent. Generalization and expressivity are two widely used measurements to quantify theoretical behaviors of deep nets. The expressivity focuses on finding functions expressible by deep nets but cannot be approximated by shallow nets with similar number of neurons. It usually implies the large capacity. The generalization aims at deriving fast learning rate for deep nets. It usually requires small capacity to reduce the variance. Different from previous studies on deep nets, pursuing either expressivity or generalization, we consider both the factors to explore theoretical advantages of deep nets. For this purpose, we construct a deep net with two hidden layers possessing excellent expressivity in terms of localized and sparse approximation. Then, utilizing the well known covering number to measure the capacity, we find that deep nets possess excellent expressive power (measured by localized and sparse approximation) without essentially enlarging the capacity of shallow nets. As a consequence, we derive near-optimal learning rates for implementing empirical risk minimization on deep nets. These results theoretically exhibit advantages of deep nets from the learning theory viewpoint.
Runtime Monitoring Neuron Activation Patterns

For using neural networks in safety critical domains, it is important to know if a decision made by a neural network is supported by prior similarities in training. We propose runtime neuron activation pattern monitoring - after the standard training process, one creates a monitor by feeding the training data to the network again in order to store the neuron activation patterns in abstract form. In operation, a classification decision over an input is further supplemented by examining if a pattern similar (measured by Hamming distance) to the generated pattern is contained in the monitor. If the monitor does not contain any pattern similar to the generated pattern, it raises a warning that the decision is not based on the training data. Our experiments show that, by adjusting the similarity-threshold for activation patterns, the monitors can report a significant portion of misclassfications to be not supported by training with a small false-positive rate, when evaluated on a test set.
BAMHealthCloud: A biometric authentication and data management system for healthcare data in cloud

Advancements in the healthcare industry have given rise to the security threat to the ever growing e-medical data. The healthcare data management system records patient’s data in different formats such as text, numeric, pictures and videos leading to data which is big and unstructured. Also, hospitals may have several branches in different geographical locations. Sometimes, for research purposes, there is a need to integrate patients’ health data stored at different locations. In view of this, a cloud-based healthcare management system can be an effective solution for efficient health care data management. But the major concern of cloud-based healthcare system is the security aspect. It includes theft of identity, tax fraudulence, bank fraud, insurance frauds, medical frauds and defamation of high profile patients. Hence, a secure data access and retrieval is needed in order to provide security of critical medical records in healthcare management system. Biometric based authentication mechanism is suitable in this scenario since it overcomes the limitations of token theft and forgetting passwords in the conventional token id-password mechanism used for providing security. It also has high accuracy rate for secure data access and retrieval. In the present paper, a cloud-based system for management of healthcare data BAMHealthCloud is proposed, which ensures the security of e-medical data access through a behavioral biometric signature-based authentication. Training of the signature samples for authentication purpose has been performed in parallel on Hadoop MapReduce framework using Resilient Backpropagation neural network. From rigorous experiments, it can be concluded that it achieves a speedup of 9 times, Equal error rate (EER) of 0.12, the sensitivity of 0.98 and specificity of 0.95. Performance comparison of the system with other state-of-art-algorithms shows that the proposed system preforms better than the existing systems in literature.
Dynamic Prediction in Clinical Survival Analysis Using Temporal Convolutional Networks

Accurate prediction of disease trajectories is critical for early identification and timely treatment of patients at risk. Conventional methods in survival analysis are often constrained by strong parametric assumptions and limited in their ability to learn from high-dimensional data. This paper develops a novel convolutional approach that addresses the drawbacks of both traditional statistical approaches as well as recent neural network models for survival. We present Match-Net: a missingness-aware temporal convolutional hitting-time network, designed to capture temporal dependencies and heterogeneous interactions in covariate trajectories and patterns of missingness. To the best of our knowledge, this is the first investigation of temporal convolutions in the context of dynamic prediction for personalized risk prognosis. Using real-world data from the Alzheimer's disease neuroimaging initiative, we demonstrate state-of-the-art performance without making any assumptions regarding underlying longitudinal or time-to-event processes-attesting to the model's potential utility in clinical decision support.