Miracle-dz/UniFAR-FTUs huggingface dataset:
Ranking Online Reviews Based on Consumer Preferences

To show a personalized review ranking list based on consumer preference is so necessary to the consumer that one can utilize online reviews effectively and efficiently to support his/her purchasing decisions. The paper formulates the ranking issue as an optimization problem and proposes a heuristic approximation algorithm to solve it stepwise and iteratively. In view of consumers' different reading behaviors, all possible review subsets that consumers would read from a ranking list are considered, and the objective of the algorithm is to maximize the expected matching degree between the consumer preference and the ranking list. Intensive experiments are conducted on real data, which reveal that ranking results from the proposed approach are better than those from the other related baseline methods.
Neural Network Music Genre Classification

Music genre classification utilizing neural networks has achieved some limited success in recent years. Differences in song libraries, machine learning techniques, input formats, and types of neural networks implemented have all had varying levels of success. This paper reviews some of the machine learning techniques utilized in this area. It also presents some initial research work on music genre classification. The research uses images of spectrograms generated from time-slices of songs as the input into a neural network to classify the songs into their respective musical genres.
On the Static CMOS Implementation of Magnitude Comparators

Digital magnitude comparators are used in computer systems to compare two binary numbers and determine if these are equal, or if one number is greater or less than the other. In this work, a new magnitude comparator’s architecture is presented. The proposed comparator architecture is designed in static CMOS logic and compared against the state of the art magnitude comparators in the literature, shows less area overhead, and for small input operands (which are commonly used in practice) presents lower delay and power-delay product.
Charge distribution on polymer insulator surface under AC voltage

Charge accumulation on an insulator surface under AC electric field is uncertain and is different from DC. In this paper, a method of extracting the phase of voltage truncation time is proposed. The characteristics of charge distribution on an insulator surface under AC voltage is obtained, and the relationship between the voltage phase and the surface charge distribution is investigated. The measurement results of charge distribution and the microscopic discharge process are compared and analyzed which can overcome the deficiency of the traditional off-line measuring method. In addition, the effect of voltage amplitude and insulator material characteristics on the surface charge are studied. It is shown that the charge distribution on an insulator surface shows a three-tier concentric circular structure, and the distribution characteristic of the charge is closely related to the phase of the AC voltage truncation time and the insulation material. The charge density is also affected by the voltage amplitude.
Fetal Cardiac Timing Events Estimation from Doppler Ultrasound Signal Cepstrum Analysis

Early diagnosis of the cardiac abnormalities during the pregnancy may reduce the risk of perinatal morbidity and mortality. Doppler ultrasound signals (DUS), which is commonly used for monitoring the fetal heart rate, can also be used for identifying the event timings of fetal cardiac valve motions. In this paper, we propose a non-invasive technique to identify the fetal cardiac timing events on the basis of analysis of fetal DUS (10 normal subjects and 6 abnormal subjects). We proposed using the cepstrum analysis which enabled the frequency contents of the Doppler signals to be linked to the opening and closing of the heart's valves (Aortic and mitral). The time intervals from R peak of fetal ECG to opening and closing of aortic valve were found to be 123.83±7.41 (msec) and 222.85±21.21 (msec), respectively. The time intervals from R peak to opening and closing of mitral valve were found to be 295.03±13.62 (msec) and 31.97±3.34 (msec), respectively. This feature is essential for diagnosing the disorder of cardiac rhythm, extremely used for diagnosis of heart abnormalities.
Adaptive Transient Computing for Power-Neutral Embedded Devices

Energy harvesting has emerged as a promising technology for small electronic devices to extend the battery run time and thereby enabling an increased autonomous operation. However, frequent charge and discharge cycles cause aging effects in the battery, which results in a loss of capacity and life time.Power-neutral transient computing systems avoid energy buffers by powering the load by the harvester directly. Usually, the power outputs of energy harvesters rely on arbitrary and transient environmental excitations. The resulting power losses are handled by checkpointing, where the volatile system state is backed up using non-volatile memories. The timely detection of upcoming power losses is essential for a reliable checkpointing process. Early detections allow a proactive power loss handling, which is important to ensure the finalization of atomic operations. However, common voltage threshold-based methods only allow short-term power loss detections since they do not adapt to the dynamics of the harvester.In this paper we propose a new methodology that allows an early power loss detection by exploiting physical characteristics of the harvester. The proposed approach points out new opportunities for transiently-powered devices, as it allows an adaptive and harvester-aware computing. We show how it facilitates a proactive scheduling that is used to ensure a successful finalization of atomic operations.
Low Loss Air-Filled Transmission Lines in LTCC Technology

The design of air-filled transmission lines in Low Temperature Co-Fired Ceramic (LTCC) technology is introduced. Striplines surrounded by air cavities inside two different multilayered LTCC substrates are described and their attenuation coefficients are calculated based on 3D field electromagnetic simulations. Transitions from these buried striplines to upper layers, for interconnection purpose, are also characterized. Compared to conventional dielectric-filled architectures, the proposed air-filled guiding structures, including transitions to upper layers, demonstrate significant gains on the attenuation coefficients from 1 GHz to 15 GHz. Simulation results show that their total attenuations are lower than 0.13 dB with better than 15 dB as return loss.
RRH selection and load balancing through Dynamic BBU-RRH Mapping in C-RAN

Cloud radio access network (C-RAN) is a novel architecture that supports the massive growth in traffic of the mobile network. Still, the connection blocking in the C-RAN network may occur due to two main reasons. The first one is not an optimal selection of the radio resource heads (RRHs) to serve the user demands. The second one is not optimal mapping between the baseband units (BBUs) and RRHs. In this paper, we propose a Markov decision process (MDP) based model for RRH selection to serve a connection demand in C-RAN that leads to the user blocking probability minimization as well as the operators’ revenue maximization. Moreover, the Ant Colony optimization (ACO) method is used to obtain the best BBU-RRH mapping that leads to further reduction of the user blocking probability and the quality of service (QoS) improvements. The presented simulation results show that the proposed techniques reduce the blocking probability when compared to Received signal strength (RSS) approach.
Performance of a Visual Fixation Model in an Autonomous Micro Robot Inspired by Drosophila Physiology

In nature, lightweight and low-powered insects are ideal model systems to study motion perception strategies. Understanding the underlying characteristics and functionality of insects' visual systems is not only attractive to neural system modellers but also critical in providing effective solutions to future robotics. This paper presents a novel modelling of dynamic vision system inspired by Drosophila physiology for mimicking fast motion tracking and a closed-loop behavioural response to fixation. The proposed model was realised on the embedded system in an autonomous micro robot which has limited computational resources. A monocular camera was applied as the only motion sensing modality. Systematic experiments including open-loop and closed-loop bio-robotic tests validated the proposed visual fixation model: the robot showed motion tracking and fixation behaviours similarly to insects; the image processing frequency can maintain 25 ~ 45Hz. Arena tests also demonstrated a successful following behaviour aroused by fixation in navigation.
On a Novel, Simplified Model Framework Describing Ascorbic Acid Concentration Dynamics

Ascorbic acid is an essential aminoacid which interacts in several parts of the human body metabolism. The ascorbic acid concentration is controlled by homeostasis, a biological, autonomous control function in the body. Humans are not able to produce ascorbic acid, and the ascorbic acid concentration is maintained by daily oral digestion. Ascorbic acid is buffered in various tissues, such as the adrenal glands, brain, muscles, and other. Excessive ascorbic acid is extracted from the body through urine and intestines. Measuring ascorbic acid concentration in the body is challenging, and special procedures must be followed when extracting a blood sample and performing a concentration analysis. This paper presents a novel, simplified model framework of the ascorbic acid homeostasis in the human body plasma and tissues, including tissues such as adrenal glands, brain, liver, muscles and bone marrow. These tissues also act as ascorbic acid concentration buffers, in case of low oral supply. The dynamic model framework is based on mass balances of ascorbate acid in various tissues, including tissue buffer terms involving the intestine, kidney, adrenal glands, and other critical and noncritical tissues. The interaction between buffer tissues and fluids is maintained by the body homeostasis using chemical transport protein molecules. Additional usage terms describing energy metabolism, body growth, and immune system response are also included. This dynamic model framework, including the ascorbic acid concentration control system, is simulated with assumed parameters based on available literature. The results indicates that further investigations using experiments, in addition to adaptive model parameter estimation schemes and additional model verification tests are needed in order to identify the various model framework parameters to fit the human body ascorbic acid homeostasis and pharmacokinetics.
AMDroid: Android Malware Detection Using Function Call Graphs

With the rapid development of the mobile Internet, Android has been the most popular mobile operating system. Due to the open nature of Android, c countless malicious applications are hidden in a large number of benign applications, which pose great threats to users. Most previous malware detection approaches mainly rely on features such as permissions, API calls, and opcode sequences. However, these approaches fail to capture structural semantics of applications. In this paper, we propose AMDroid that leverages function call graphs (FCGs) representing the behaviors of applications and applies graph kernels to automatically learn the structural semantics of applications from FCGs. We evaluate AMDroid on the Genome Project, and the experimental results show that AMDroid is effective to detect Android malware with 97.49% detection accuracy.
Random Number Generators Based on Discrete-time Chaotic Maps

A novel tent map based random number generator(RNG) is introduced. Feasibility verification results from the experiment and simulation, show that the generated binary sequence fulfill statistical randomness tests without post processing, are presented. In the proposed RNG, bit sequences are produced by analyzing the distribution of the underlying signal. The proposed design can be applied for the other discrete-time chaotic maps and can be implemented in the integrated circuit.
Promoting Active Transportation via Information and Communication Technologies

According to several studies in recent years, active transportation such as walking and cycling are strongly correlated with job satisfaction and well-being. This paper investigates how information and communication technologies (ICT) influence factors that affect commuters willingness to engage in active transportation. Based on a survey study, we explore the necessary information that should be made available regarding the pedestrian environment so that commuters are willing to engage in active mobility. Further, we investigate the potential for a dedicated smartphone application that can be used by commuters as a means of encouraging active transportation. Through this ICT mobility enhancement, a plethora of benefits will be gained, not only by individuals, but by society, the economy and the natural environment.
Effect of connectivity measures on the identification of brain functional core network at rest

Magneto/Electro-encephalography (M/EEG) source connectivity is an emergent tool to identify brain networks with high time/space resolution. Here, we aim to identify the brain core network (s-core decomposition) using dense-EEG. We also evaluate the effect of the functional connectivity methods used and more precisely the effect of the correction for the so-called source leakage problem. Two connectivity measures were evaluated: the phase locking value (PLV) and phase lag index (PLI) that supposed to deal with the leakage problem by removing the zero-lag connections. Both methods were evaluated on resting state dense-EEG signals recorded from 19 healthy participants. Core networks obtained by each method was compared to those computed using fMRI from 487 healthy participants at rest (from the Human Connectome Project – HCP). The correlation between networks obtained by EEG and fMRI was used as performance criterion. Results show that PLV networks are closer to fMRI networks with significantly higher correlation values with fMRI networks, than PLI networks. Results suggest caution when selecting the functional connectivity methods and mainly methods that remove the zero-lag connections as it can severely affect the network characteristics. The choice of functional connectivity measure is indeed crucial not only in cognitive neuroscience but also in clinical neuroscience.
Optimizing Probability Threshold of Convolution Neural Network to Improve HRV-based Acute Stress Detection Performance

As stress is linked to numerous emotional and physical conditions, its timely detection and proper management is important for our health. Convolution neural network (CNN) has been shown to be promising in stress detection because it could automatically capture the discriminant information regarding physiological change from heart rate variability (HRV), usually derived from electrocardiogram (ECG) signals. This study proposed a two-step training method to improve the acute stress detection performance through optimizing the probability threshold of a CNN. The results showed that the average error rate was significantly reduced from 17.3 ± 9.2% to 9.2 ± 5.7% after probability threshold optimization, and the classification results were more balanced between stress and rest data. This study presented a simple method to improve stress detection performance using CNN without additional data, rendering benefits for the practical application of HRV-based stress measurement.
Frequency response data based disturbance observer design applicable to non-minimum phase systems

Disturbance observer (DOB) has been widely used in industrial field due to it's simplicity and effectiveness in rejecting disturbance. The performance of disturbance observer is greatly influenced by the bandwidth of low pass filter (Q filter). This paper proposes a novel way for optimizing bandwidth of Q filter by considering experimentally obtained frequency response data (FRD) of plant. By transforming all the nonliner constraints into convex constraints, the convex optimisation method can be employed to solve this problem easily. The simulation results verified the feasibility of proposed method and demonstrated that the optimized Q filter can gurantee the disturbance rejection performance.
Improved Pattern for ISO 26262 ASIL Decomposition with Dependent Requirements

Specification of requirements on the functional behaviour of system components is a central concern for the overall safety of software systems. Therefore, the methodology used for analysing failure modes resulting from requirement violations is of utmost importance to safety within the automotive industry. ISO 26262 is a standard for functional safety within the automotive industry, in which the concept of Automotive Safety Integrity Levels (ASILs) is defined. ASILs are assigned to requirements, and represents the risk associated with violating said requirements. As redundancy is introduced into systems, requirements are broken down and may have their ASILs lowered through ASIL decomposition. This paper examines ASIL decomposition as defined in ISO 26262, and identifies reasons for why the suggested pattern is insufficient for common use cases within the automotive industry. The paper also proposes an improved pattern, which is applied to an industrial case and analysed for its implications on system safety.
Feasibility of in-vivo estimation of the brachial artery area-pressure relation from CINE and real-time MRI during upper arm cuff inflations

Objective: We investigate the basic feasibility of estimating the brachial artery area-pressure relationship from MRI data obtained during pressure cuff inflations in-vivo. Methods: We acquired cross-sectional real-time MR images and cardiac-gated CINE MR images from the upper arm of a single male subject at rest during supra-systolic pressure cuff inflations and deflations. We estimate from the MR images the lumen area changes of the brachial artery, and, simultaneously, from the cuff pressure the systemic blood pressure of the subject. We reconstruct the area-pressure curve from two real-time and three CINE independent measurements. Results: The area-pressure curve can be reconstructed, and it is plausible and appears largely consistent with the literature using other methods. Conclusion: MR imaging during pressure cuff inflations is an easy to use, non-invasive candidate method to estimate the brachial artery pressure-area curve.
Sperm-templated magnetic microrobots

Spermatozoa are efficient microswimmers which perform a bending wave motion for their forward propulsion. By binding positively charged iron oxide microparticles to the surface of negatively charged bull spermatozoa, we fabricate sperm-templated biohybrid magnetic microrobots. This study presents an easy and cost-efficient method to obtain magnetic microswimmers that are actuated by oscillating magnetic fields and take advantage of the intrinsic flexibility of the sperm tails for their propulsion in fluidic environment. In this article, we present the actuation of such sperm-templated flexible magnetic microsrobots under influence of an oscillating magnetic field and investigate their performance on the solid-liquid and liquid-gas interface.
Exploring the Impact of Evolutionary Computing based Feature Selection in Suicidal Ideation Detection

The ubiquitous availability of smartphones and the increasing popularity of social media provide a platform for users to express their feelings, including suicidal ideation. Suicide prevention by suicidal ideation detection on social media lights the path to controlling the rapidly increasing suicide rates amongst youth. This paper proposes a diverse set of features and investigates into feature selection using the Firefly algorithm to build an efficient and robust supervised approach to classifying tweets with suicidal ideation. The development of a suicidal language to create three diverse, manually annotated datasets leads to the validation of the proposed model. An in-depth result and error analysis lead to an accurate system for monitoring suicidal ideation on social media along with the discovery of optimal feature subsets and selection methods using a penalty based Firefly algorithm.
A Cross-Platform Comparison of Affymetrix, Agilent, and Illumina Microarray Reveals Functional Genomics in Colorectal Cancer Progression

Colorectal cancer is one of the most common cancers with the second highest mortality rate in the world. The microarray can be used to collect gene expression alteration information from many tissue samples that will be useful to understand colorectal cancer from the molecular level. However, the mechanism behind the progression from normal to cancer is not fully understood. Here, a cross-platform comparison among three common microarray platforms (Affymetrix, Agilent, and Illumina) was applied. As results, we found a significant correlation of purine metabolism and p53 signaling pathway role in colorectal cancer progression. Purine metabolism can control the regulation of cell proliferation which involve hydro-lyase activity on organelle lumen. Meanwhile, genetic alterations in p53 signaling pathways could control some hallmarks of cancer. These two terms might play important roles in inducing normal colorectal cells into cancer.
Collision Recognition in Multihop IEEE 802.15.4-Compliant Wireless Sensor Networks

Collisions caused by the hidden terminal effects may result in severe packet corruption and performance degradation in multihop IEEE 802.15. 4-compliant wireless sensor networks (WSNs). In order to avoid such collisions through scheduling protocols, it is important to first recognize these collisions by distinguishing them from some other noncollision cases (e.g., path loss, multipath fading, shadow fading, and IEEE 802.11 interference), which may also lead to similar consequences. In this paper, we focus on the collision recognition problem in multihop IEEE 802.15. 4-compliant WSNs. First, through a series of measurements of the error properties in various collision and noncollision scenarios, we investigate the statistical behaviors of error patterns including the bit error rate and error position distribution, which reveal obvious differences between collision and noncollision cases in terms of bit- and symbol-level error position distribution. Based on these observations, we further propose a machine learning-based collision recognition mechanism by inserting some redundant blocks in a data frame. The inserted blocks are known to both the sender and receiver, thereby it enables the receiver to recognize the error patterns only according to the redundant sequences. Moreover, a mutual information-guided byte selection technique is also provided to effectively improve the recognition accuracy. Finally, the proposed mechanism is verified under three different transmission environments. The experimental results show that the proposed mechanism achieves good recognition accuracy over 90% with 94% coding efficiency.