Knowledge graph embedding for profiling the interaction between transcription factors and their target genes.

Interactions between transcription factor and target gene form the main part of gene regulation network in human, which are still complicating factors in biological research. Specifically, for nearly half of those interactions recorded in established database, their interaction types are yet to be confirmed. Although several computational methods exist to predict gene interactions and their type, there is still no method available to predict them solely based on topology information. To this end, we proposed here a graph-based prediction model called KGE-TGI and trained in a multi-task learning manner on a knowledge graph that we specially constructed for this problem. The KGE-TGI model relies on topology information rather than being driven by gene expression data. In this paper, we formulate the task of predicting interaction types of transcript factor and target genes as a multi-label classification problem for link types on a heterogeneous graph, coupled with solving another link prediction problem that is inherently related. We constructed a ground truth dataset as benchmark and evaluated the proposed method on it. As a result of the 5-fold cross experiments, the proposed method achieved average AUC values of 0.9654 and 0.9339 in the tasks of link prediction and link type classification, respectively. In addition, the results of a series of comparison experiments also prove that the introduction of knowledge information significantly benefits to the prediction and that our methodology achieve state-of-the-art performance in this problem.

Validation of Machine Learning-Aided and Power Line Communication-Based Cable Monitoring Using Measurement Data.

The implementation of power line communications (PLC) in smart electricity grids provides us with exciting opportunities for real-time cable monitoring. In particular, effective fault classification and estimation methods employing machine learning (ML) models have been proposed in the recent past. Often, the research works presenting PLC for ML-aided cable diagnostics are based on the study of synthetically generated channel data. In this work, we validate ML-aided diagnostics by integrating measured channels. Specifically, we consider the concatenation of clustering as a data pre-processing procedure and principal component analysis (PCA)-based dimension reduction for cable anomaly detection. Clustering and PCA are trained with measurement data when the PLC network is working under healthy conditions. A possible cable anomaly is then identified from the analysis of the PCA reconstruction error for a test sample. For the numerical evaluation of our scheme, we apply an experimental setup in which we introduce degradations to power cables. Our results show that the proposed anomaly detector is able to identify a cable degradation with high detection accuracy and low false alarm rate.

Cognitive information measurements: A new perspective.

From a traditional point of view, the value of information does not change during transmission. The Shannon information theory considers information transmission as a statistical phenomenon for measuring the communication channel capacity. However, in modern communication systems, information is spontaneously embedded with a cognitive link during the transmission process, which requires a new measurement that can incorporate continuously changing information values. In this paper, we introduce the concept of cognitive information value and a method of measuring such information. We first describe the characteristics of cognitive information followed by an introduction of the concept of cognitive information in measuring information popularity. The new measurement is based on the mailbox principle in the information value chain. This is achieved by encapsulating the information as a mailbox for transmission where the cognition is continuously implemented during the transmission process. Finally, we set up a cognitive communication system based on a combination of the traditional communication system and cognitive computing. Experimental results attest to the impact of incorporating cognitive value in the performance of 5G networks.

Secure Communications in CIoT Networks with a Wireless Energy Harvesting Untrusted Relay.

The Internet of Things (IoT) represents a bright prospect that a variety of common appliances can connect to one another, as well as with the rest of the Internet, to vastly improve our lives. Unique communication and security challenges have been brought out by the limited hardware, low-complexity, and severe energy constraints of IoT devices. In addition, a severe spectrum scarcity problem has also been stimulated by the use of a large number of IoT devices. In this paper, cognitive IoT (CIoT) is considered where an IoT network works as the secondary system using underlay spectrum sharing. A wireless energy harvesting (EH) node is used as a relay to improve the coverage of an IoT device. However, the relay could be a potential eavesdropper to intercept the IoT device's messages. This paper considers the problem of secure communication between the IoT device (e.g., sensor) and a destination (e.g., controller) via the wireless EH untrusted relay. Since the destination can be equipped with adequate energy supply, secure schemes based on destination-aided jamming are proposed based on power splitting (PS) and time splitting (TS) policies, called intuitive secure schemes based on PS (Int-PS), precoded secure scheme based on PS (Pre-PS), intuitive secure scheme based on TS (Int-TS) and precoded secure scheme based on TS (Pre-TS), respectively. The secure performances of the proposed schemes are evaluated through the metric of probability of successfully secure transmission ( P S S T ), which represents the probability that the interference constraint of the primary user is satisfied and the secrecy rate is positive. P S S T is analyzed for the proposed secure schemes, and the closed form expressions of P S S T for Pre-PS and Pre-TS are derived and validated through simulation results. Numerical results show that the precoded secure schemes have better P S S T than the intuitive secure schemes under similar power consumption. When the secure schemes based on PS and TS polices have similar P S S T , the average transmit power consumption of the secure scheme based on TS is lower. The influences of power splitting and time slitting ratios are also discussed through simulations.

Energy Management in Smart Cities Based on Internet of Things: Peak Demand Reduction and Energy Savings.

Around the globe, innovation with integrating information and communication technologies (ICT) with physical infrastructure is a top priority for governments in pursuing smart, green living to improve energy efficiency, protect the environment, improve the quality of life, and bolster economy competitiveness. Cities today faces multifarious challenges, among which energy efficiency of homes and residential dwellings is a key requirement. Achieving it successfully with the help of intelligent sensors and contextual systems would help build smart cities of the future. In a Smart home environment Home Energy Management plays a critical role in finding a suitable and reliable solution to curtail the peak demand and achieve energy conservation. In this paper, a new method named as Home Energy Management as a Service (HEMaaS) is proposed which is based on neural network based Q-learning algorithm. Although several attempts have been made in the past to address similar problems, the models developed do not cater to maximize the user convenience and robustness of the system. In this paper, authors have proposed an advanced Neural Fitted Q-learning method which is self-learning and adaptive. The proposed method provides an agile, flexible and energy efficient decision making system for home energy management. A typical Canadian residential dwelling model has been used in this paper to test the proposed method. Based on analysis, it was found that the proposed method offers a fast and viable solution to reduce the demand and conserve energy during peak period. It also helps reducing the carbon footprint of residential dwellings. Once adopted, city blocks with significant residential dwellings can significantly reduce the total energy consumption by reducing or shifting their energy demand during peak period. This would definitely help local power distribution companies to optimize their resources and keep the tariff low due to curtailment of peak demand.

Hybrid Bayesian Optimization-Based Graphical Discovery for Methylation Sites Prediction.

Protein methylation is one of the most important reversible post-translational modifications (PTMs), playing a vital role in the regulation of gene expression. Protein methylation sites serve as biomarkers in cardiovascular and pulmonary diseases, influencing various aspects of normal cell biology and pathogenesis. Nonetheless, the majority of existing computational methods for predicting protein methylation sites (PMSP) have been constructed based on protein sequences, with few methods leveraging the topological information of proteins. To address this issue, we propose an innovative framework for predicting Methylation Sites using Graphs (GraphMethySite) that employs graph convolution network in conjunction with Bayesian Optimization (BO) to automatically discover the graphical structure surrounding a candidate site and improve the predictive accuracy. In order to extract the most optimal subgraphs associated with methylation sites, we extend GraphMethySite by coupling it with a hybrid Bayesian optimization (together named GraphMethySite +) to determine and visualize the topological relevance among amino-acid residues. We evaluated our framework on two extended protein methylation datasets, and empirical results demonstrate that it outperforms existing state-of-the-art methylation prediction methods.

An Ensemble Surrogate-Based Coevolutionary Algorithm for Solving Large-Scale Expensive Optimization Problems.

Surrogate-assisted evolutionary algorithms (SAEAs) have shown promising performance for solving expensive optimization problems (EOPs) whose true evaluations are computationally or physically expensive. However, most existing SAEAs only focus on the problems with low dimensionality and they rarely consider solving large-scale EOPs (LSEOPs). To fill this research gap, this article proposes an ensemble surrogate-based coevolutionary optimizer for tackling LSEOPs. First, some local surrogate models are trained with low-dimensional data subsets by using feature selection on the large-scale decision variables, a part of which are used to build a selective ensemble surrogate for better approximating the target LSEOP. Then, a coevolutionary optimizer guided by the ensemble surrogate is designed by running two populations to cooperatively solve the target LSEOP and the simplified auxiliary problem. The information of offspring from the two populations is shared to facilitate the coevolution process, which can exploit the searching experience from the simplified auxiliary problem to help solving the target LSEOP. Finally, an effective infill selection criterion is used to update the ensemble surrogate and enhance its approximate performance. To evaluate the performance of the proposed algorithm, a number of well-known benchmark problems are used and the experimental results validate our superior performance over nine state-of-the-art SAEAs on most cases.

RNN-K: A Reinforced Newton Method for Consensus-Based Distributed Optimization and Control Over Multiagent Systems.

With the rise of the processing power of networked agents in the last decade, second-order methods for machine learning have received increasing attention. To solve the distributed optimization problems over multiagent systems, Newton's method has the benefits of fast convergence and high estimation accuracy. In this article, we propose a reinforced network Newton method with K -order control flexibility (RNN-K) in a distributed manner by integrating the consensus strategy and the latest knowledge across the network into local descent direction. The key component of our method is to make the best of intermediate results from the local neighborhood to learn global knowledge, not just for the consensus effect like most existing works, including the gradient descent and Newton methods as well as their refinements. Such a reinforcement enables revitalizing the traditional iterative consensus strategy to accelerate the descent of the Newton direction. The biggest difficulty to design the approximated Newton descent in distributed settings is addressed by using a special Taylor expansion that follows the matrix splitting technique. Based on the truncation on the Taylor series, our method also presents a tradeoff effect between estimation accuracy and computation/communication cost, which provides the control flexibility as a practical consideration. We derive theoretically the sufficient conditions for the convergence of the proposed RNN-K method of at least a linear rate. The simulation results illustrate the performance effectiveness by being applied to three types of distributed optimization problems that arise frequently in machine-learning scenarios.

Beacon-based opportunistic scheduling in wireless body area network.

Wireless Body Area Networks (WBANs) are one of the key technologies that support the development of digital health care, which has attracted increasing attention in recent years. Compared with general Wireless Sensor Networks (WSNs), WBANs have more stringent requirements on reliability and energy efficiency. Though WBANs are applied within limited transmission range, the on-body channel condition can be very challenging because of blocking or absorbing of signal. In this paper, we are looking into the design of Medium Access Control (MAC) protocols and propose an opportunistic scheduling scheme by applying heuristic scheduling and dynamic superframe length adjustment to improve the system performance. The simulations have been supplemented to show the advantages of the proposed solutions in outage rate performance, compared with existing solutions.

Cross-layer design for prompt and reliable transmissions over body area networks.

In this paper, we propose a cross-layer design to make ambulatory health monitoring via body area networks (BAN) more reliable and robust. The proposed design builds on our centralized body area network access scheme (CBAS), a receiver-initiated medium access control (MAC) scheme that improves the visibility of a BAN in a coexistent environment, where diverse networks with various physical and MAC protocols share the radio spectrum. The design enhances CBAS by incorporating a network layer scheme that improves the packet delivery ratio (PDR), while minimizing the need for multihop cooperative transmissions; thus, packet delay is less compromised to achieve higher PDRs. The MAC layer provides the network layer with local information about the quality of on-body links to enable the BAN to identify the most reliable links in a distributed manner. Extensive experimental results are presented, which give insights on how the proposed cross-layer design improves PDR and packet delay. Results show the effectiveness of the proposed design which takes advantage of dynamic scheduling and multihop relays as warranted by the link conditions.

Wireless body area network node localization using small-scale spatial information.

We present a new scheme to automatically identify the locations of wearable sensor nodes in a wireless body area network (WBAN). Instantaneous atmospheric air pressure readings are compared to map nodes in physical space. This enhancement enables unassisted sensor node placement, providing a practical solution to obtain and continuously monitor node locations without anchor nodes or beacons. To validate this localization scheme, a statistical analysis is conducted on a set of air pressure sensors and a prototype WBAN to examine the performance and limitations. Based on a 60 cm separation between nodes, indicative of the expected separation between limbs and placement positions along a patient's body, the measurements consistently exceeded p -value reliability within a 95% confidence interval. We also present and experimentally demonstrate an enhancement aiming to reduce false-positive (Type I) errors in conventional accelerometer-based on-body fall detection schemes. Our statistical analysis has shown that by continuously monitoring the patient's limb positions, the WBAN would be better able to discriminate "fall-like" motions from actual falls.

Mobility support for health monitoring at home using wearable sensors.

We present a simple but effective handoff protocol that enables continuous monitoring of ambulatory patients at home by means of resource-limited sensors. Our proposed system implements a 2-tier network: one created by wearable sensors used for vital signs collection, and another by a point-to-point link established between the body sensor network coordinator device and a fixed access point (AP). Upon experiencing poor signal reception in the latter network tier when the patient moves, the AP may instruct the sensor network coordinator to forward vital signs data through one of the wearable sensor nodes acting as a temporary relay if the sensor-AP link has a stronger signal. Our practical implementation of the proposed scheme reveals that this relayed data operation decreases packet loss rate down to 20% of the value otherwise obtained when solely using the point-to-point, coordinator-AP link. In particular, the wrist location yields the best results over alternative body sensor positions when patients walk at a 0.5 m/s.