Miracle-dz/UniFAR-FTUs huggingface dataset:
Audio IoT Analytics for Home Automation Safety

The aim of the paper is to perform audio analytics based on the audio sensor data that is continuously monitoring the home environment automatically through an audio Internet of Things (IoT) system. Domestic violence is one of the major problems in many cities nowadays. We have proposed a home automation system where IoT sensors records the audio in home environment continuously and the audio is sent to machine learning server where the audio is split into small clips and classified into different categories. The need of an automatic detection system is urgent for enforcing home safety and safe neighborhood. If IoT system detects any suspicious sound, it generates an emergency notification to nearest emergency services for possible action to be taken. The classification of audio such as gunshots, explosion, glass breaking, screaming and siren is based on shallow learning (Support vector machine, Decision tree, Random forest and Naïve Bayes) and deep learning (Convolutional neural network and Long short-term memory). Our experiments validated that Convolutional Neural Network shows the best performance (89% accuracy) compared to other machine learning algorithms.
Research on air Pollution Gases Recognition Method Based on LSTM Recurrent Neural Network and Gas Sensors Array

The integration of sensors array and pattern recognition to replace large scale analytical instruments is an important and feasible method for accurate and rapid measurement of atmospheric pollution gases. This paper proposed a quantitative detection method of mixed gases based on long short-term memory (LSTM) recurrent neural network, including data pre-processing, network structure design, model training and prediction process. This method can extract the deep characteristics of sensors array's responses automatically and match the complex nonlinear characteristics more accurately. It is proved that the proposed LSTM prediction model has a strong applicability and higher accuracy in the concentration identification of gas mixtures by comparing with the experiments of existing models.
A 1-D Tightly Coupled Dipole Array for Broadband mmWave Communication

A wideband phased array antenna for the use in 5G millimeter-wave (mmWave) mobile terminal is presented. The proposed array antenna was designed based on the tightly coupled dipole array (TCDA) technique to achieve a low profile and broadband impedance matching characteristic. Unlike the previously reported TCDAs implemented with 2D arrays on a vertical ground plane, the proposed 1D array antenna exhibited the maximum beam direction parallel to the ground, which is suitable for mobile terminal applications. Using a full-wave electromagnetic simulation tool, a <inline-formula> <tex-math notation="LaTeX">$1\times8$ </tex-math></inline-formula> TCDA was designed on a multilayer substrate including a reflector surface in the middle to replace a vertical ground plane. Prototypes were fabricated and measured with feeding networks designed for 0° and 30° scanning scenarios. The size of the proposed antenna (excluding the feeding network) was <inline-formula> <tex-math notation="LaTeX">$42.3\times 12.05\times0.535$ </tex-math></inline-formula> mm<sup>3</sup>. The measured bandwidth that subjects to S<sub>11</sub> < −10 dB criterion was 22.5–32.5 GHz, which covers most of the worldwide 5G mmWave frequency band candidates at the 20-GHz band range.
Feasibility of Serverless Cloud Services for Disaster Management Information Systems

Serverless is the new generation of cloud services that supports the pay-per-use policy in true spirit by charging only for the execution time of the hosted code. Amazon introduced serverless service of Lambda in 2014 and it is consider as the most popular serverless cloud service till date. This paper focuses on the serverless cloud services of Lambda and elaborates the importance of Lambda based serverless cloud services for hosting the disaster management information systems (DMIS). We have identified two repeatedly occurring phases of the life cycle of a DMIS. These phases are high activity phase and low activity phase. Our findings state that serverless cloud services are well-suited for both of these phases of a DMIS. Serverless reduce the operational cost during the low activity phase by detaching the code from running containers and it improves the scalability during the high activity phase by quickly assigning the already available containers from the container pool. However, this all comes with the price of reduced QoS for initial requests and our experimental results reflect the extent of this quality degradation.
Hierarchical Multi-layer Transfer Learning Model for Biomedical Question Answering

Biomedical semantic question answering refers to answering questions from a given contextual passage in the biomedical field. The traditional methods need complicated feature engineering and the end-to-end neural network models depend on the large scale dataset. We propose a hierarchical multi-layer transfer learning model to address the question answering task on the biomedical data which lacks of sufficient training data. The domain adaptation techniques are adopted to reinforce the performance, include fine-tuning and forgetting cost regularization which penalize the deviations from the source model’s parameters and avoid forgetting the knowledge of source domain. The distributed representation of the word is generated and domain knowledge of biomedical word embedding is integrated. Especially, the co-attention mechanism captures the question interaction clues for passage encoding. The open data set of 2017 BioASQ-Task 5B is used to evaluate the system performance. The results show that the domain adaptation techniques make the system get the state-of-the-art performance. Our model without any handcrafted feature achieves higher precision than the best solution for factoid question in 2017 BioASQ-Task 5B.
DeepMemIntrospect: Recognizing Data Structures in Memory with Neural Networks

One yet-to-be-solved but very vital memory forensic problem is to recover data structure information from a specified memory range. Unlike previous studies relying on fixed signature of value or structure, DeepMemlntrospect is the first convolution neural network (CNN) based memory forensic system that can recover data structure information merely from raw memory without relying on signatures. Our experimental results demonstrate high accuracy with over 99% and also show significant performance improvement.
Real-Time On-Board Recognition of Locomotion Modes for an Active Pelvis Orthosis

To adapt to different locomotion modes or terrains, real-time human intents recognition is an essential skill to the control of lower-limb exoskeletons timely and precisely. In this paper, we propose a real-time on-board training and recognition method to identify locomotion-related activities for an active pelvis orthosis using two IMUs integrated into it. The designed on-board intent recognition system with a BPNN based algorithm realizes distinguish among six locomotion modes including standing, level ground walking, ramp ascending, ramp descending, stair ascending and stair descending, and deliver the recognition results for future control strategies. Experiments are conducted on one healthy subject including on-board training and online recognition parts. The overall recognition accuracy is 97.79% with the cost time of one recognition decision is about 0.9ms, which is sufficient short compared with the sample interval of 10ms. The experimental results validate the great performance of the proposed real-time on-board training and recognition method for future control of the lower-limb exoskeletons assisting in various locomotion modes or terrains.
Learning Dual Arm Coordinated Reachability Tasks in a Humanoid Robot with Articulated Torso

Performing dual arm coordinated (reachability)tasks in humanoid robots require complex planning strategies and this complexity increases further, in case of humanoids with articulated torso. These complex strategies may not be suitable for online motion planning. This paper proposes a faster way to accomplish dual arm coordinated tasks using methodology based on Reinforcement Learning. The contribution of this paper is twofold. Firstly, we propose DiGrad (Differential Gradients), a new RL framework for multi-task learning in manipulators. Secondly, we show how this framework can be adopted to learn dual arm coordination in a 27 degrees of freedom (DOF)humanoid robot with articulated spine. The proposed framework and methodology are evaluated in various environments and simulation results are presented. A comparative study of DiGrad with its parent algorithm in different settings is also presented.
ImgPricing: Everyone Can Earn Proper Rewards by Simply Taking Photos

A high-quality and large-scale image collection is a fundamental demand in the 3D reconstruction. Crowdsourcing can help us collect lots of diversified images. However, it is not easy to attract people to do this task due to their self-interest. Moreover, the collected images are quality-varying. Those low-quality images may disturb the performance of reconstruction. To avoid low-quality images and lead participants to collect high-quality data, we take images quality into account when allocating rewards. The rewards of participants should be proportionable with their contribution. In this paper, we propose a pricing mechanism, called ImgPricing, to determine the reward of participants in 3D reconstruction system. We model the process of image collection as a cooperative game, and regard each participant's contribution and corresponding image quality as critical factors when allocating rewards. ImgPricing differs from traditional pricing schemes, such as Shapley value, as it introduces the image sequence as an indispensable factor. Finally, we implement our design on the Android platform and evaluate its performance. We use some metrics, such as computational efficiency, fairness and anti-interference, to evaluate ImgPricing and compare with other traditional schemes. Our analyses show ImgPricing is superior to others in terms of computational efficiency and fairness.
Genome-Wide miRNA Expression Alterations in Nucleus Accumbens Provide Insights into Chronic Stress and Treatment in Depression

Nucleus Accumbens (NAc) is a key area for reward and response to stress, and microRNA plays a critical role in depression. However, the miRNA expression alteration in NAc during the stress-induced depression and antidepressant treatment, as well as its biological significance, remains mostly unexplored. We randomly selected 24 rats to control, chronic unpredictable mild stress (CUMS) and CUMS+Escitalopram treatment group (8 each). Nucleus Accumbens of rats from 3 groups were microRNA-sequenced. We applied differential expression analysis and co-expression analyses on expression data. After target gene prediction by Targetscan, network and pathway analysis were also performed. 18/8 differential expressing (DE) miRNA (FDR <0.05, log2FC >1 or <−1) are observed in control VS case/ case VS treatment comparison,2 of them are overlapping. We observed a negative correlation between log2FC(control vs. case) and log2FC (case vs. treatment). Weighted co-expression analysis discovered a module (M1) with a strong positive correlation with stress (R=0.96, P=0.003), and another module(M7) with a positive correlation with anhedonia (R=0.89, P=0.02). In miRNA-mRNA networks of M1, TFRC had most neighbors. Strikingly, pathway analysis of Kyoto encyclopedia of genes and genomes (KEGG) reveals enrichment in Ras signaling pathway of predicted target genes of miRNAs associated with both depression, treatment and anhedonia. indicating its potential involvement in depression. Conclusion: Escitalopram treatment can significantly reverse both behavioral and NAc miRNA abnormality caused by chronic stress. TFRC were strongly regulated in stress and/or antidepressant treatment. The Ras-signaling pathway may influence both onset, symptom and treatment response of stress-induced depression.
East Asian specific asthma associated variants were discovered via exome-sequencing

Rapid development in sequencing technology enabled us study a near complete catalogue of variants in human genome. We performed whole-exome sequencing to identify functional variants responsible for severe asthma in Korean population. We identified 10 variants of 6 candidate asthma genes $(\mathrm{P}\lt /p\gt\lt 10^{\mathbf{-5}}$ comprising GPR88,AGTRAP,GTP2IRD1,KANK1,DNHD1, and DCUN1D2. Our study provides possible new therapeutic targets for asthma.
Sensing-Enhanced Therapy System for Assessing Children With Autism Spectrum Disorders: A Feasibility Study

It is evident that recently reported robot-assisted therapy systems for assessment of children with autism spectrum disorder (ASD) lack autonomous interaction abilities and require significant human resources. This paper proposes a sensing system that automatically extracts and fuses sensory features, such as body motion features, facial expressions, and gaze features, further assessing the children behaviors by mapping them to therapist-specified behavioral classes. Experimental results show that the developed system has a capability of interpreting characteristic data of children with ASD, thus has the potential to increase the autonomy of robots under the supervision of a therapist and enhance the quality of the digital description of children with ASD. The research outcomes pave the way to a feasible machine-assisted system for their behavior assessment.
Fake Account Identification in Social Networks

Nowadays, the human influence often depends on the number of followers that an individual has in his/her own social media context. To this end, the presence of fake accounts is one of the most relevant problems and can potentially have a big impact on many real life and business activities. Fake followers are dangerous for social platforms, since they may alter concepts like popularity and influence, which might yield a strong impact on economy, politics, and society. Thus, it is necessary to devise new methodologies enabling the possibility to identify and characterize fake accounts. This work presents a novel technique to discriminate real accounts on social networks from fake ones. The technique exploits knowledge automatically extracted from big data to characterize typical patterns of fake accounts. We empirically evaluated the proposed technique on the Twitter social network, and achieved significant results in terms of discrimination capabilities.
Artificial Intelligence and Deep Learning Applications for Automotive Manufacturing

Artificial Intelligence (AI) and Deep Learning has been steadily gaining importance due it’s potential for a broad set of science and industry applications. The success of deep learning techniques has found many applications, e.g. in the domain of computer vision and natural language understanding. Developing AI applications is a complex task with many challenges related to data collection, model training, and deployment.In this paper, we evaluate architectures, models and deployment issues related to the usage of deep learning techniques in the automotive manufacturing domain. Particularly, we focus on different computer vision problems in automotive manufacturing processes, e.g., in logistics processes. We developed several deep learning models that help to improve the quality and efficiency of these processes. Finally, we provide an analysis of the architecture, datasets and models used, and provide performance metrics for each of the different models.
A new type of wavelet de-noising algorithm for lung sound signals

With the development of digital auscultation, the computer-based intelligent auscultation of respiratory sounds has drawn attention of researchers. However, the noises of acquired signals influence the further analysis of lung sound, so there is necessity to develop the noise reduction algorithm of lung sound signals. In this paper, a new type of noise reduction is proposed. The original signals are decomposed into 7 layers by wavelet transform. The locations of lung sound part are obtained in the sub-signals by the mean values of autocorrelation coefficients. The noises between lung sound parts are reduced by setting zero directly. The noises in the lung sound parts are filtered by a Chebyshev type I band-pass filter. The de-noising results are judged by two means. One is the subjective judgement of internal physicians and the de-noising effect is accepted by doctors without distortion. The other is the classification result of sound types in the further research by BP neural network and the classification accuracy can reach 85%.
DNVMCFS: The Direct Hybrid NVM File System for the Application

The Non-Volatile Memory (NVM) device has higher read and write speed compared with disks and SSDs, and it is the hotspot to improve the efficiency of computer system. But it also makes the I/O software stack become the bottleneck of application's efficiency instead of storage devices. We analyse I/O software stack when the application accesses the NVM device and design a direct hybrid NVM file system which can bypass the VFS to reduce the extra time overhead of accessing the NVM device for the application. The application oriented direct file system interface is design to allow the application directly access the NVM device which reducing the response time and improving the I/O performance of applications. On this basis, we implement the prototype of DNVMCFS and use Fio to test its performance comparing with PMFS, NVMCFS and Ext4 on PMBD respectively. The results show that DNVMCFS can improve the I/O performance by 42.3% compared with PMFS, 50% compared with NVMCFS and 53.7% compared with Ext4 on PMBD. Meanwhile, DNVMCFS can also increase the IOPS where Filebench is used to simulate the real application test. The results show that DNMVCFS raises the I/O performance and IOPS by 25%. These results verify that DNVMCFS provides higher I/O performance and better adaptability.
Weighted matrix factorization based data fusion for predicting lncRNA-disease associations

Increasing biomedical studies have demonstrated important associations between lncRNAs and various human complex diseases. Developing data integrative models can boost the performance of lncRNA-disease association identification. However, existing models generally have to transform heterogenous data into homologous networks, and then sum up these networks into a composite network for integrative prediction. The transformation may conceal the intrinsic structure of the heterogeneous data, and the summation process may suffer from noisy networks. Both these issues compromise the performance. In this paper, we introduce a Weighted Matrix Factorization based data fusion solution to predict LncRNA-Disease Associations (WMFLDA). WMFLDA first directly encodes the inter-associations between different types of biological entities (such as genes, lncRNAs, and Disease Ontology terms) via a heterogeneous network, which also encodes multiple intra-association networks of entities of the same type. Next, it assigns weights to these inter-association and intra-association matrices, and performs collaborative low-rank matrix factorization to explore the latent relationships between entities. After that, it simultaneously optimizes these weights and low-rank matrices. In the end, it uses the optimized low-rank matrices and weights to reconstruct the lncRNA-disease association matrix and accomplish the prediction. WMFLDA achieves a larger area under the receiver operating curve (by at least 7.61%), and a larger area under the precision-recall curve (by at least 5.49%) than competitive data fusion approaches in different experimental scenarios. WMFLDA can not only maintain the intrinsic structure of the association matrices, but can also selectively and differentially combine them. The codes and datasets are available at http: //mlda.swu. edu.cn/codes.php? name=WMFLDA
A General Overview of Privacy-Preserving Big Data Management and Analytics Models, Methods and Techniques in Specific Domains: Static and Dynamic Distributed Environments

Privacy-preserving big data management and analytics is gaining the momentum within the research community, and several current research efforts aim to provide solutions to the challenges that emerge when models, techniques and algorithms must be delivered on top of massive, distributed big data repositories, especially with regards to emerging distributed settings such as Clouds and social networks. In this paper, at the convergence of the contexts of static and dynamic distributed environments, we provide a general overview of models, issues and approaches, along with some reference frameworks. Indeed, both static and dynamic distributed environments are relevant cases of settings where the privacy of big data turns to be critical. Finally, we discuss emerging research directions.
Fast Bag-Of-Words Candidate Selection in Content-Based Instance Retrieval Systems

Many content-based image search and instance retrieval systems implement bag-of-visual-words strategies for candidate selection. Visual processing of an image results in hundreds of visual words that make up a document, and these words are used to build an inverted index. Query processing then consists of an initial candidate selection phase that queries the inverted index, followed by more complex reranking of the candidates using various image features. The initial phase typically uses disjunctive top-k query processing algorithms originally proposed for searching text collections.Our objective in this paper is to optimize the performance of disjunctive top-k computation for candidate selection in content-based instance retrieval systems. While there has been extensive previous work on optimizing this phase for textual search engines, we are unaware of any published work that studies this problem for instance retrieval, where both index and query data are quite different from the distributions commonly found and exploited in the textual case. Using data from a commercial large-scale instance retrieval system, we address this challenge in three steps. First, we analyze the quantitative properties of index structures and queries in the system, and discuss how they differ from the case of text retrieval. Second, we describe an optimized term-at-a-time retrieval strategy that significantly outperforms baseline term-at-a-time and document-at-a-time strategies, achieving up to 66% speed-up over the most efficient baseline. Finally, we show that due to the different properties of the data, several common safe and unsafe early termination techniques from the literature fail to provide any significant performance benefits.