Miracle-dz/UniFAR-FTUs huggingface dataset:
iDrug: Pediatric Drug Interaction Modeling and Risk Evaluation Leveraging Prescription Big Data

Drug interaction caused by irrational co-prescribed drugs have become a serious problem in pediatric groups, due to the vulnerability of children's biological and psychological status and the lack dedicated pediatric drug instructions and standards. Traditional prescription review systems fails to provide accurate drug interaction risk evaluation when applied to the pediatric groups, since the pediatric drug information database used in these systems is usually incomplete and outdated. In this work, we propose iDrug, a data-driven approach to pediatric drug interaction modeling and risk evaluation. First, we propose a graph model to capture the drug co-prescription patterns from historical prescription data, and augment it with the existing drug instruction database to build a drug interaction graph. Then, we exploit a community detection algorithm to identify the intrinsic structures of the graph. Finally, we propose a drug interaction risk evaluation metric based on the community weights of the graph. We evaluate our framework with large-scale, real-world anonymized prescription big data collected from a tertiary hospital for two years. Results show that the risk evaluation approach achieves an accuracy above 84.2% in the test prescription set, and outperforms other baseline methods.
Software Defect Prediction on Unlabelled Dataset with Machine Learning Techniques

In this work, we are going to explore how to conduct defect software prediction on unlabelled datasets by exploiting unsupervised machine learning techniques. In previous literature, various approaches have been proposed over time with the aim of labelling an unlabelled dataset (i.e. classifying dataset instances/modules in terms of their defectiveness), they can usually rely on other software datasets, software experts or metrics’ thresholds. In this study, we intend to show the results obtained by exploiting CLAMI and CLAMI+, two approaches that overcome the various limitation of the previous ones adopted by researches, since they are independent on metrics’ threshold, do not need experts software knowledge and can be easily automated.Our prediction model takes as input a set of unlabelled datasets and machine learning techniques. Its output is composed of the values of several performance indicators on training datasets and predictions on test datasets. The latter can be employed to deduce information on the status of software code and, consequently, concentrate the software developers’ effort only where necessary.
Framework for Trustful Handover of M2M devices between Security Domains

The Internet of Things technology and services are becoming ubiquitous in multiple application domains like Smart Factory and Telecare. Some of the key requirements are security and flexibility. Identity Management and Access Control play a critical role allocating resources like IP connections and identifiers for secure and automatized onboarding of M2M devices as well as dynamic coupling of the M2M devices to the Application Servers, for assuring the communication reliability in case of maintenance or scaling of the infrastructure. This article analyses the requirements from multiple initiatives and standard organizations related to the M2M communication using 4G or 5G mobile networks and presents the concept and advantages of enabling dynamic cross-layer management for trustful handover between Security Domains that run their own core network. The concept validating demonstrators are presented and comparison with other solutions is also included.
Speaker Identification in Different Emotional States in Arabic and English

Speaker recognition is an important application of digital speech processing. However, a major challenge degrading the robustness of speaker-recognition systems is variation in the emotional states of speakers, such as happiness, anger, sadness, or surprise. In this paper, we propose a speaker recognition system corresponding to three states, namely emotional, neutral, and with no consideration for a speaker’s state (i.e., the speaker can be in an emotional state or neutral state), for two languages: Arabic and English. Additionally, cross-language speaker recognition was applied in emotional, neutral, and (emotional + neutral) states. Convolutional neural network and long short-term memory models were used to design a convolutional recurrent neural network (CRNN) main system. We also investigated the use of linearly spaced spectrograms as speech-feature inputs. The proposed system utilizes the KSUEmotions, emotional prosody speech and transcripts, WEST POINT, and TIMIT corpora. The CRNN system exhibited accuracies as high as 97.4% and 97.18% for Arabic and English emotional speech inputs, respectively, and 99.89% and 99.4% for Arabic and English neutral speech inputs, respectively. For the cross-language program, the overall CRNN system accuracy was as high as 91.83%, 99.88%, and 95.36% for emotional, neutral, and (emotional + neutral) states, respectively.
Design, Optimization and Energetic Evaluation of an Efficient Fully Powered Ankle-Foot Prosthesis With a Series Elastic Actuator

The use of powered ankle-foot prostheses for below-knee amputees leads to challenges like the peak power of the applied actuator and biomechanical features of the prosthesis foot. This paper proposes an efficient powered ankle-foot prosthesis with a series elastic actuator. By combining the geared five-bar spring (GFBS) mechanism and the traditional series elastic actuator (SEA), a series elastic with geared five-bar (SGFB) actuator is built. The new SGFB actuator has the benefits of both the GFBS and the SEA on mimicking biomechanics of the human ankle and reducing the peak power of the motor. The healthy walking gait in the experiment results indicates that the optimized SGFB prosthesis foot including a 150W Maxon DC motor can provide a 70kg subject enough net positive energy with an energy efficiency of 35.3% during normal speed walking in the treadmill trials. The experiment of the SGFB prosthesis foot in semi-active mode shows the advantage on closely mimicking the human biomechanics during the control dorsiflexion phase and the importance of injecting positive energy during the powered plantarflexion phase. The experiment results also show that the optimization of different parameters within the electromechanical model considering the efficiency of the whole drive train can effectively reduce the motor’s peak power to 132 W by making the motor more effective in high-power conditions.
SurfCNN: A Descriptor Accelerated Convolutional Neural Network for Image-Based Indoor Localization

Convolutional neural network (CNN) is a powerful tool for many data applications. However, its high dimension nature, large network size and computational complexity, and the need of large amount of training data make it challenging to be used in edge computing applications, which are becoming increasingly popular, relevant and important. In this paper, we propose a descriptor based approach to accelerate convolutional neural network training, reduce input dimension and network size, which greatly facilitates the use of CNN for edge computating and even cloud computing. By using image descriptors to extract features from original images, we report a simpler convolutional neural network with fast training speed, low memory usage and outstanding accuracy without the need for a pre-trained network as opposed to the state of art. In indoor localization, our SURF descriptors accelerated CNN (SurfCNN) can reach an average position error of 0.28 m and orientation error of 9.2°. Compared to the conventional CNN that uses original images as input, our algorithm reduces the dimension of the input features by a factor of 48 without impairing the accuracy. Further, at an extreme feature reduction of 14,440 times, our model still retains an average position error retained at 0.41 m and orientation error at 14°.
Identification Of Membrane-Linked Proteins Acting On Blood Red Cell Filtration. Study In Cerebrovascular Accident And Thrombosis

The blood red cell filtration (RCF) through nucleopore filters(5 μ ø) decrease in cerebrovascular accidents (CVA) and thrombosis. This disorder is related to circulating factors, especially fibrinogen and factor VIII. But after washing it also appears to be related to membrane-1 inked proteins. In this study, isolation and identification of these proteins were realized in patients with increased RCF. Have been here concerned 20 patients with CVA and/or other thrombosis, 8 days after the onset and compared to 30 controls. After the RC have been washed twice the proteins were eluted from the membrane, concentrated and submitted to usual electrophoresis and immunoelectrophoresis. The identification was made with specific antiserums against factor VIII, fibrinogen, fragments D and E, transferin, coeruloplasmin and using SDS polyacrylamid gel electrophoresis with, as controls, purified fibrinogen, fragments D and E, transferin, coeruloplasmin. The results were : in controls the eluted proteins are similar with an albumin/globulin ratio (A/G ) > 1. In patients with a high disorder of RCF the proteins were increased, with an inversion of the A/G. Two kinds of proteins were found : (1) a β population composed of inflammatory proteins, mainly transferin, (2), a β-α population consistuted by fibrin-related products. Concerning the whole population of patients with a high disorder in RCF, 80 % exhibited proteins induced by inflammation. In 20 % it was found proteins related to the coagulation, i.e. : fibrin related products. Finally, the disorder of RCF in thrombosis is much more due to these membrane disturbances than to circulating factors. During the clinical care of the patients it also has been observed that pentoxyphiline improved the RCF, certainly acting at the membrane level.
Identification and Implementation of Network Science Models for Centrality Measure using Social Network Analysis

A social structure constituting a set of nodes, representing social actors and edges or lines representing relation between these nodes or actors is a social network. Social network plays a vital role in circulation of information and innovation leading to analysis of the network and attracted attention in research field. The analysis of social network as a whole means, representation of all its actors and structure present in that social network forming part of a community. Community detection aims to divide the network into dense areas of graph. The dense regions usually correlates to entities which are familiar to each other, and form part of a community. Similar tastes and desires of the members in a community, enables exchange of information amongst various communities. Network science is the study of complex networks. There are various models defined in network science. Erdős–Rényi random graph model, Configuration model, Watts–Strogatz small world model, Barabási–Albert (BA) preferential attachment model, Mediation-driven attachment model, Fitness model are the various network science models. The current research work consists of application of these models on real world data and comparison between the final output with the use of R Studio.
H∞ Control for Time-Varying Cyber-Physical System Under Randomly Occurring Hybrid Attacks: The Output Feedback Case

In this paper, the $H_\infty $ control problem for a class of linear time-varying cyber-physical system (CPS) under randomly occurring hybrid attacks in a finite horizon is investigated. The hybrid attacks, including denial of service (DoS) attacks on both sensor-to-controller and controller-to-actuator communication channels and false data injection (FDI) attacks on sensors and actuators, aim to destroy the measurement data and control data in order to endanger the functionality of the closed-loop system. The purpose of this paper is to study the relationship between the attack injected signals and the controlled output, and to design the output feedback controller gains so that the $H_\infty $ performance of the closed-loop system is guaranteed over a given finite horizon, meanwhile, the impact of attack signals in the worst case on the linear quadratic performance can be reduced. In order to solve the above problems, both the methods of stochastic analysis and completing squares are utilized to establish the sufficient conditions for the existence of the desired controller, and a finite-horizon controller design algorithm is presented by solving two coupled backward recursive Riccati difference equations (RDEs) subject to some scheduled conditions. At last, the numerical simulation and experimental results are given out to demonstrate the effectiveness of the proposed approach.
Means for Measuring Control of Impulsive Overvoltage Caused by Thunderstorms

The analysis of national normative and technical requirements of the lightning protection systems shows the necessity of additional protecting measures from the direct strike of lightning or voltage induction by a remote discharge by determining protective devices choice and charts of their settings. The protection of information and communication systems and their electronic devices components is particularly dependent on the thunderstorms in the environment. Methods of calculation and protecting schemes from impulsive overvoltage recommended by international standards of IEC, are more hard and reliable. Therefore, it is proposed to apply in the circuits of protection against impulse overvoltage means of measurements. The proposed means for measuring control detects and fixes impulse deviations from sinusoidal form of network voltage. On this basis the application schemes of control devices for impulsive overvoltage are complemented, that substantially reduces the risk of fires and electronic device damage from thunderstorm origin. Implementation in the field of operation and maintenance of electronic and telecommunication equipment is proposed.
Therapeutic Impact of Follistatin-Like 1 on Myocardial Ischemic Injury in Preclinical Models

Background—Acute coronary syndrome is a leading cause of death in developed countries. Follistatin-like 1 (FSTL1) is a myocyte-derived secreted protein that is upregulated in the heart in response to ischemic insult. Here, we investigated the therapeutic impact of FSTL1 on acute cardiac injury in small and large preclinical animal models of ischemia/reperfusion and dissected its molecular mechanism. Methods and Results—Administration of human FSTL1 protein significantly attenuated myocardial infarct size in a mouse or pig model of ischemia/reperfusion, which was associated with a reduction of apoptosis and inflammatory responses in the ischemic heart. Administration of FSTL1 enhanced the phosphorylation of AMP-activated protein kinase in the ischemia/reperfusion–injured heart. In cultured cardiac myocytes, FSTL1 suppressed apoptosis in response to hypoxia/reoxygenation and lipopolysaccharide-stimulated expression of proinflammatory genes through its ability to activate AMP-activated protein kinase. Ischem.
Fine-Grained Sleep-Wake Behaviour Analysis

Sleep stages are traditionally assessed by experts from polysomnography measurements following specific guidelines. Sleep stage behaviour is subsequently used to detect anomalies and diagnose diseases in a laboratory setting. Recently, with the development of Internet of Things, there is a trend to use everyday technologies for sleep behaviour analysis at home, having the potential to eliminate expensive in-hospital monitoring. We propose a fine-grained sleep-wake behaviour analysis approach, which takes into consideration the influences of various factors, such as gender, health status and race. In addition, we investigate the combination of multiple data sources, in particular, actigraphy and heart rate variability, for enhancing model accuracy. Initial results show the proposed approach is recognising sleep and wake stages accurately and is providing a flexible recognition approach towards personalised sleep-based health monitoring.
Simulation of Microwave Circuit with PIN Diode by Field-Circuit Co-simulation

The discontinuous Galerkin time domain (DGTD) method and equivalent physical model are adopted to simulation microwave circuit with PIN diode. Compared with the existing field-circuit co-simulation method, the proposed method inherits the advantages of DGTD method. The final EM-circuit system equation is only related to the element connected with the circuit network. So only a small nonlinear equation stem from the equivalent physical model of PIN diode is solved at each time step. Numerical examples are given to demonstrate the capability of the proposed method.
RISE: Tomographic Image Reconstruction in Positron Emission Tomography

We present results from the application of Reconstructed Image from Simulations Ensemble (RISE) in Positron Emission Tomography (PET). The RISE implementation in PET is evaluated in this report using a 3-D Shepp-Logan software phantom featuring seven 3-D ellipsoids. The dataset was produced in the Geant4 Application for Tomographic Emission, on a cylindrical PET scanner and transformed and stored as a three-dimensional sinogram. Using the appropriate image quality metrics (CC, SSIM), we visually and quantitively compare our results to those obtained with the Maximum Likelihood Expectation Maximization (MLEM) method. The RISE results compare favorably to those of MLEM.
A Real-Time Hidden Anomaly Detection of Correlated Data in Wireless Networks

Wireless networks have been generating a plethora of unstructured and highly-correlated big data with hidden anomalies. The anomalies may bring inaccurate predictions of network behaviors, which further lead to inefficient system designs such as proactive caching placement. Current Random Matrix Theory (RMT) approaches are unable to detect hidden anomalies with a satisfying tolerance of data correlation. We present a novel data Decomposition aided Random Matrix Theory (DC-RMT) framework, which enables a real-time anomaly detection of large scale multi-dimensional and highly-correlated data. The detection results show that the proposed DC-RMT methodology can detect anomalies with an accuracy of 28 times better than RMT applied without data decomposition. The prediction results present a 6 times higher accuracy than data with anomaly, which will facilitate the identification of regions of interests, and contribute to the improvement of resource allocation efficiency and user QoE.
A high-resolution Vernier delay generator using delay-adjustable carry chains on FPGAs

Delay generator (DG) generating a digitally programmed time interval acts as the core component in various modern physical experiments. Most traditional designs are based on application specific integrated circuit (ASIC) platform causing long development period and high cost. This work presents a high-resolution Vernier DG architecture based on field programmable gate array (FPGA) platform, which utilizes a novel delay line structure built on multiplexed carry chain. The carry chain structure guarantees the resolution rather small. The multiplexer laid next to the carry chain makes the actual delay of the delay line dynamically adjustable. This structure can be automatically compiled by PC without manual intervene while maintaining very high resolution. A DG prototype circuit was implemented on a Stratix III FPGA and experimental tests were conducted to evaluate the performance. The obtained resolution is 23.9 ps, the differential nonlinearity (DNL) lies in the range of -0.62 least significant bit (LSB) ~ 0.59 LSB and the integral nonlinearity (INL) lies in the range of -0.14 LSB ~ 0.01 LSB. All the performances show state-of-the-art quality.
A Multi-Feature DDoS Detection Schema on P4 Network Hardware

Data plane programmability is a promising technology that enables rapid control loops for the detection and mitigation of cyber-attacks. In this context, we propose an in-network architecture for DDoS attack detection combining important traffic metrics of malicious traffic. These pertain to number of flows and packet symmetry, maintained for protected subnets and utilized to identify anomalies. Appropriate alarms are triggered within time-based epochs and conveyed to external mitigation systems. We assess our DDoS detection schema in P4-enabled SmartNICs in terms of detection accuracy and packet processing performance. As input to our accuracy experiments we use real publicly available traffic traces. Furthermore, performance stress tests were conducted using high speed packet generators. Results exhibit that our approach is applicable in typical enterprise and/or carrier environments, featuring packet rates of 1–2 Mpps for l0G links.
Grid Fusion of Rough Sea Surface and Ship for Composite EM Scattering and Image Simulation

Mesh merging and fusion is great important for electromagnetic (EM) scattering simulation of target embedded in extended rough surface, such as ships on ocean, stones on planetary surface etc. This paper uses computer graphics methods to extract the intersection contour of sea level cutting ship model. Then the odd-even rule and Delaunay triangle rule are used to perform mesh fusion of two groups of meshes (target mesh and rough surface mesh). Lastly, the fused meshes are used for high frequency (HF) EM scattering calculation and radar one-dimensional range image simulation of different ship models. The computation results present different radar echo signals for different ship models, which can be further used for target identification with 1D range image signal.
Learning to Track by Bi-Directional Long Short-Term Memory Networks

Invisualtracking, deeplearningbasedtrackershave demonstrated strong competitiveness under many challenging conditions such as background clutters and occlusions. Several recent studies have devoted to defeating such conditions by using Recurrent Neural Networks (RNNs) to explore temporal smoothness and stable deep representation. In this paper, we propose a tracker based on Bi-directional Long Short-Term Memory network (Bi-LSTM) under the tracking-by-detection paradigm. In particular, the proposed tracker adapts the YOLO algorithm to detect the target in single frame, and combines both detected target and tracked target in Bi-LSTM. Since BiLSTM learns the representation of current frame from both historical frames and subsequent frames, the proposed tracker can simultaneously incorporate forward plays and backward plays. This brings two advancements: 1) Bi-LSTM extends visual features into temporal domain and controls motions of the target, and2)Bi-LSTMpredictsreliabletargetlocationbyincorporating both past and future motions. We evaluated the proposed tracker on the visual benchmark OTB challenge videos and confirmed its effectiveness by comparing with the representative trackers to illustrate the feasibility of managing trace memory.