Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Design of a Blockchain-Based System to Facilitate Healthcare Data Sharing

Blockchain technology though originally designed for keeping financial ledgers, recently has found applications in many different fields including healthcare. Sharing healthcare data for research purposes will boost research innovation in this area. That being said, healthcare data sharing raises many privacy and security issues for the Patients who share their data. In this work, we present the potential of Blockchain technology to facilitate (i) private and auditable healthcare data sharing and (ii) healthcare data access permission handling by proposing a blockchain-based system architecture design.
Clothes Collocation Recommendations by Compatibility Learning

This paper introduces a simple, yet effective, framework for clothes collocation by considering compatibility between items. In particular, we treat title sentences as the features of clothing items, instead of using clothing images. For feature transformation, the long-short term memory (LSTM) network is utilized for mapping title sentences into a low-dimensional space. Features of query and candidate items learned by the Siamese LSTMs are synthesized into a style space by a compatibility matrix. We evaluate our framework on two large-scale datasets compiled from Amazon and Taobao, respectively. Extensive experimental results show the effectiveness of our method in comparison to several state-of-the-art methods.
Joint Cloudlet Selection and Latency Minimization in Fog Networks

Mobile edge or fog computing is a network architecture that brings the functionality of conventional centralized cloud to the edge nodes, which are in close proximity of the end devices in an Internet of Things (IoT) network. Fog networks have many advantages over traditional cloud networks, such as increased bandwidth utilization, enhanced security and privacy, better energy efficiency, improved performance, and support for mobility. The most critical requirement of a fog architecture is to minimize the end to end latency in IoT networks, particularly in scenarios where large number of end devices (IoT nodes) and distributed computing fog nodes (cloudlets) are present. In this paper, we formulated an optimization problem for joint cloudlet selection and latency minimization in a fog network, subjected to maximum work load and latency constraints. The problem can be epitomized as many-to-one matching game in which IoT nodes and cloudlets rank each other in order to minimize the latency. The proposed game belongs to a class of matching games with externalities. We propose an algorithm to solve this game which gives distributed and self-organizing solution. Extensive simulations have been carried out to validate the proposed algorithm.
For Small Merchants: A Secure Smartphone-Based Architecture to Process and Accept NFC Payments

EMV is the international protocol implemented to secure the communication between a client's payment device and a PoS during a contact or an NFC purchase transaction. It guarantees several important security properties such as authentication, authorization and integrity. However, researchers, in various studies, have analyzed this protocol and have shown that it is vulnerable to several kinds of attacks. In this paper, we propose an innovative security protocol that solves the EMV vulnerabilities in the context of a new NFC payment architecture. The latter is designed in this work and is especially destined for small merchants, allowing to replace the use of a mobile PoS by an NFC smartphone: we suggest, for a small merchant, to take advantage of his NFC smartphone for use as an NFC reader, and then, directly as a PoS to accept NFC purchases from client payment devices. We check the accuracy of our proposal using the Scyther security tool.
Tunable Metamaterial IR Emitter by Using MEMS Microheater

We proposed an effective metamaterial infrared (IR) emitter by using MEMS microheater with different pattern density. A highest heating efficiency of MEMS microheater is under the pattern density of 49%. The metamaterial is composed of $\pmb{\pi}$ ·shape nanostructures on the surface of MEMS microheater to form a metamaterial IR emitter. By changing gap width of $\pmb{\pi}$ {###} shape metamaterial, the emissivity of IR emitter is blue shift and enhances its emissivity. The emissivity of metamaterial IR emitter is 0.94 at wavelength of $\mathbf{5} \mu{\mathbf{m}}$. Such design is able to exhibit a narrowband wavelength in IR wavelength range.
Toward Monolithic Integration of OEOs: From Systems to Chips

The optoelectronic oscillator (OEO) has attracted a great deal of attention in recent years as a high-performance microwave source. Thanks to the high quality-factor (Q-factor) provided by the optical delay line, the OEO can generate spectrally pure microwave signals with ultralow phase noise. Various approaches for realization of OEO architectures for the generation of single-frequency microwave signals have been demonstrated in the past two decades. Seeded by the microwave signal generated by OEO, the generation of complex microwave waveforms has been demonstrated, which can be used in applications such as radar and communication systems. OEO has also been demonstrated to perform other specific functionalities in different applications, including optical pulses and frequency comb generation, optical sensing, low-power microwave signal detection, and optical signal processing. This paper reviews the developments of OEO over the past two decades. An integrated OEO with monolithically integrated photonic parts and radio frequency subsections is reported as a promising example toward the monolithic integration of OEO. Key features of OEO and prospects for future development of the integration of OEO are also described.
Neural DrugNet

In this paper, we describe the system submitted for the shared task on Social Media Mining for Health Applications by the team Light. Previous works demonstrate that LSTMs have achieved remarkable performance in natural language processing tasks. We deploy an ensemble of two LSTM models. The first one is a pretrained language model appended with a classifier and takes words as input, while the second one is a LSTM model with an attention unit over it which takes character tri-gram as input. We call the ensemble of these two models: Neural-DrugNet. Our system ranks 2nd in the second shared task: Automatic classification of posts describing medication intake.
A 60-GHz, 15-dB Gain Range Digitally Controlled Phase-Inverting VGA With 0-dBm OP1 dB and 3° Phase Variation in 65-nm CMOS

A 60-GHz digitally controlled phase-inverting variable gain amplifier (PIVGA) is presented, with a gain control range from −9.4 to 6 dB, and a 180° phase inversion. Four digitally controlled nMOS switch arrays in the proposed PIVGA are used as a current-steering stage, which can reduce control complexity of the current-steering stage and reduce phase variation as well. A transformer to decouple input stage and current-steering stage instead of the nMOS transistor cascode topology is used to improve linearity. The PIVGA prototype is implemented in a 65-nm CMOS technology. Measured results show a gain range from −1 to 6 dB with a 0.5-dB linear-decibel fine gain step, an OP1 dB of 0 dBm, and a dc power consumption of 25 mW. As the gain is programmed over the entire 53–63 GHz, the phase shifts only varies by less than 3°.
One Intrusion Detection Method Based On Uniformed Conditional Dynamic Mutual Information

With the rapid development of our society, World Wide Web has turned to be an indispensible part of our daily life. Meanwhile, the network security is becoming more and more important. Intrusion Detection System (IDS), which serves to detect the abnormal activities in computers and internet, is often used to solve the network security problems. However, the IDS has to face and process the high dimensional data with high redundancy due to the increasing scale and dimension of the data, which causes the low efficiency of IDS. This paper proposes a new feature selection method for intrusion detection based on the Uniformed Conditional Dynamic Mutual Information (UCDMIFS), which can highly decrease the dimensionality and increase the detection accuracy. To examine our algorithm, the UCDMIFS algorithm is applied to the KDD Cup 99 data set and compared with other algorithms, such as support vector machine (SVM), to detect the intrusions. The experiments illustrate the efficiency of our algorithm.
Toward three-dimensional human action recognition using a convolutional neural network with correctness-vigilant regularizer

Abstract. Human action recognition is one of the raison d’être for doing human–computer interaction research, as it is highly vital in meeting the demands of modern society, such as automatic video surveillance for security, patient monitoring for recovery, content-based video retrieval, etc. In line with this, deep learning systems are fast becoming the defacto standard for object recognition, video understanding, and pattern recognition due to their inherent powerful feature learning ability from vast amount of data. It makes sense to capitalize on its great success and to further improve it for the complex task of action recognition. One of the contributions in this paper is an effective and yet simple method for encoding the spatiotemporal information from skeleton sequences into what we call temporal kinematic images. In the input encoding scheme, we embed various geometric relational features derived from the skeleton sequence in the form of our proposed skeletal optical flows (SOFs). SOFs collectively represent the variations of kinetic energy, angles between limbs, and pair-wise displacements between joints over consecutive frames of skeleton data, as color variations in the temporal kinematic images. Another contribution is our convolutional neural network with a correctness-vigilant regularizer. It is employed to exploit the discriminative features from the temporal kinematic image for human action recognition. Lastly, we additionally investigated an adaptive label smoothing technique employed toward the end of training iterations. Empirical results show that the efficiency of the proposed method is superior to existing works in terms of the generalizability of the generated model, training convergence speed, and the resulting classification accuracy on nine popular benchmarking datasets, such as MHAD, MSR Activity 3D, HDM05, MSR Daily Activity 3D, and the latest challenging databases, such as UTKinect-Action, NTU RGB+D, Northwestern-UCLA, UWA3DII, and SBU Kinect Interaction datasets.
Effects of 12 Weeks of Combined Exercise on Heart Rate Variability and Dynamic Pulmonary Function in Obese and Elderly Korean Women

Background: We investigated whether a combination of aerobic and resistance exercise administered over a period of 12 weeks enhanced heart rate variability (HRV) and dynamic pulmonary function (DPF) in obese and elderly Korean women. Methods: The study was conducted in 2016 in the Konkuk University (Seoul, Korea). The study participants included 20 older obese women [aged 66.4±0.8years; >30 BMI and >30% in percent body fat]. The subjects were divided into a non-exercise group (n=10, control group; CON) and a combined exercise group (n=10, experimental group; EXP). Total power (TP), low frequency (LF), high frequency (HF), and LH/HF ratio were measured as frequency-domain methods. Salivary cortisol levels were analyzed by ELISA. The participants underwent dynamic pulmonary function (DPF) test. Results: The EXP group showed a significantly decrease in body weight (P=0.002) and % body fat (P<0.001) following 12 weeks of combined exercise training. The CON group revealed a significant increase in LF (P=0.011), LF/HF ratio (P<0.001), salivary cortisol (P=0.015) and decrease in HF (P=0.003). However, the EXP presented a significant decrease in LF (P=0.006) and salivary cortisol (P=0.046), and a significantly increase in MVV (P<0.001). Conclusion: Twelve wk of combined aerobic and resistance exercise improves heart rate variability, reducing mental stress in obese older women. In addition, the exercise program was found to be effective in reducing body fat and improving lung function in obese elderly women in East Asian countries with similar body composition and cultural patterns. Therefore, further collaborative research is needed to investigate obese older women in East Asian countries.
Doppler Vital Signs Detection in the Presence of Large-Scale Random Body Movements

Noncontact detection of human vital signs based on miniaturized Doppler radar systems (DRSs) can be widely used in healthcare and biomedical applications. Although significant progresses have been achieved, a reliable wireless vital signs detection in the presence of large-scale random human body movements remains a technical challenge. In this paper, based on a comprehensive use of the high-dynamic-range radar architecture and linearized Doppler phase demodulation algorithms, we further introduce matched filters to retrieve the respiration and heartbeat spectra completely concealed under the wideband noise floor caused by large-scale body movements. Along with an existing 5.8-GHz system, a single-board integrated DRS operating at 24 GHz was designed to verify the effectiveness of the used architecture and algorithms. Experimental results comply with the theoretical expectation. The obtained results imply the potential to implement practical bioradar systems for human noncontact vital signs detections.
Beta Event-Related Desynchronization Can Be Enhanced by Different Training Programs and Is Correlated With Improved Postural Control in Individuals With Parkinson’s Disease

The purpose of this paper was to investigate the effects of a specific exercise (SE) training program focusing on balance and muscle strengthening, and a turning-based treadmill (TT) training program on cortical desynchronization and postural control in Parkinson’s disease (PD). The 18 patients with PD were recruited and randomly assigned to the SE group, TT training group or control exercise group and participated in 12 30 min training sessions focusing on balance and streng-thening, TT training, or general exercise training, respectively, followed by 10 min of over-ground walking in each session for 4 to 6 weeks. The outcomes included alpha event-related desynchronization (ERD), beta ERD, postural control ability indicated by postural instability and gait disorder (PIGD), the step/quick turn test (SQT), and the sensory organization test (SOT). All measurements were assessed at baseline and after training. The results ( ${n} =6$ for each group) showed that both the SE and TT groups had improved beta ERD, but not alpha ERD, in the Cz area, PIGD score, and turn sway/time in the SQT compared with the CE group. Furthermore, postural control ability was positively correlated with beta ERD in the Cz area. However, there was no significant correlation between SOT total score and alpha ERD in the Cz area. This paper showed that beta ERD in the central area and postural control can be improved with balance training, along with lower extremity muscle strengthening exercise and TT training, in patients with PD. Furthermore, improvement in beta ERD in the central area correlated with improvements in postural control ability. This trial was registered at http://www.anzctr. org.au/ (ACTRN12616000198426).
2,3‐Dihydrofurans as Potential Cytotoxic and Antibacterial Agents: Tandem Knoevenagel−Michael Cyclization for the Synthesis of 2,3‐Dihydrofurans by Using α‐Tosyloxy Ketone Precursors

Novel 2,3‐dihydrofuran derivatives were synthesized through a tandem Knoevenagel−Michael cyclization in good yield by reacting α‐tosyloxy ketone, 5,5‐dimethyl‐1,3‐cyclohexanedione, and various aldehydes in the presence of phthalazine in acetonitrile. These compounds were subjected to in vitro antibacterial screening against eight micro‐organisms by using diffusion method and also in vitro cytotoxicity screening against four human cancerous cell lines by applying MTT assay. Some of the compounds showed impressive activities.
Unsupervised Bayesian Classification of a Hyperspectral Image Based on the Spectral Mixture Model and Markov Random Field

Typical unsupervised classification of hyperspectral imagery (HSI) uses a Gaussian mixture model to determine intensity similarity of pixels. However, the existence of mixed pixels in HSI tends to reduce the effectiveness of the similarity measure and leads to large classification errors. Since a semantic class is always dominated by a particular endmember, a mixed pixel can be better classified by identifying the dominant endmember. By exploiting the spectral mixture model (SMM) that describes the endmember-abundance pattern of mixed pixels, the discriminative ability of HSI can be enhanced. A Bayesian classification approach is presented for spatial–spectral HSI classification, where the data likelihood is built upon the SMM, and the label prior is based on a Markov random field (MRF). The new approach has three key characteristics. First, instead of using intensity similarity, the new approach uses the abundance-endmember pattern of each pixel and classifies a pixel by its dominant endmember. Second, to integrate the SMM into a Bayesian framework, a data likelihood is designed based on the SMM to reflect the influence of the dominant endmember on the conditional distribution of the mixed pixel given the class label. Third, the resulting maximum a posteriori problem is solved by the expectation–maximization (EM) algorithm, in which the E-step adopts a graph-cut approach to estimate the class labels, and the M-step adopts a purified-means approach to estimate the endmembers. Experiments on both simulated and real HSIs demonstrate that the proposed method can exploit the spatial–spectral information of HSI to achieve high accuracy in unsupervised classification of HSI.
Synaptic dysfunction in neurodegenerative and neurodevelopmental diseases: an overview of induced pluripotent stem-cell-based disease models

Synaptic dysfunction in CNS disorders is the outcome of perturbations in physiological synapse structure and function, and can be either the cause or the consequence in specific pathologies. Accumulating data in the field of neuropsychiatric disorders, including autism spectrum disorders, schizophrenia and bipolar disorder, point to a neurodevelopmental origin of these pathologies. Due to a relatively early onset of behavioural and cognitive symptoms, it is generally acknowledged that mental illness initiates at the synapse level. On the other hand, synaptic dysfunction has been considered as an endpoint incident in neurodegenerative diseases, such as Alzheimer's, Parkinson's and Huntington's, mainly due to the considerably later onset of clinical symptoms and progressive appearance of cognitive deficits. This dichotomy has recently been challenged, particularly since the discovery of cell reprogramming technologies and the generation of induced pluripotent stem cells from patient somatic cells. The creation of ‘disease-in-a-dish’ models for multiple CNS pathologies has revealed unexpected commonalities in the molecular and cellular mechanisms operating in both developmental and degenerative conditions, most of which meet at the synapse level. In this review we discuss synaptic dysfunction in prototype neurodevelopmental and neurodegenerative diseases, emphasizing overlapping features of synaptopathy that have been suggested by studies using induced pluripotent stem-cell-based systems. These valuable disease models have highlighted a potential neurodevelopmental component in classical neurodegenerative diseases that is worth pursuing and investigating further. Moving from demonstration of correlation to understanding mechanistic causality forms the basis for developing novel therapeutics.
Managing Currency Risk

This book demonstrates how exporters’ decisions regarding choice of invoice currency can be influenced by many factors including firm size, product competitiveness, intra/inter-firm trades, and the geography of export destination. The aim is to enhance our understanding of exporters’ behavior in terms of managing currency risk. It contains detailed research and insightful data focusing on Japanese exporters and shows how they face an important trade-off in choosing the invoice currency. If exports are invoiced in yen, then exchange rate fluctuations will pass through to retail prices ultimately affecting sales volumes. However, if they choose to invoice in the importer’s currency, then sales volumes are largely unchanged.
The role of conserved proteins DrpA and DrpB in nitrate respiration of Thermus thermophilus

Summary In many Thermus thermophilus strains, nitrate respiration is encoded in mobile genetic regions, along with regulatory circuits that modulate its expression based on anoxia and nitrate presence. The oxygen‐responsive system has been identified as the product of the dnrST (dnr) operon located immediately upstream of the nar operon (narCGHJIKT), which encodes the nitrate reductase (NR) and nitrate/nitrite transporters. In contrast, the nature of the nitrate sensory system is not known. Here, we analyse the putative nitrate‐sensing role of the bicistronic drp operon (drpAB) present downstream of the nar operon in most denitrifying Thermus spp. Expression of drp was found to depend on the master regulator DnrT, whereas the absence of DrpA or DrpB increased the expression of both DnrS and DnrT and, concomitantly, of the NR. Absence of both proteins made expression from the dnr and nar operons independent of nitrate. Polyclonal antisera allowed us to identify DrpA as a periplasmic protein and DrpB as a membrane protein, with capacity to bind to the cytoplasmic membrane. Here, we propose a role for DrpA/DrpB as nitrate sensors during denitrification.