Miracle-dz/UniFAR-FTUs huggingface dataset:
Interpretable Emotion Recognition Using EEG Signals

Electroencephalogram (EEG) signal-based emotion recognition has attracted wide interests in recent years and has been broadly adopted in medical, affective computing, and other relevant fields. However, the majority of the research reported in this field tends to focus on the accuracy of classification whilst neglecting the interpretability of emotion progression. In this paper, we propose a new interpretable emotion recognition approach with the activation mechanism by using machine learning and EEG signals. This paper innovatively proposes the emotional activation curve to demonstrate the activation process of emotions. The algorithm first extracts features from EEG signals and classifies emotions using machine learning techniques, in which different parts of a trial are used to train the proposed model and assess its impact on emotion recognition results. Second, novel activation curves of emotions are constructed based on the classification results, and two emotion coefficients, i.e., the correlation coefficients and entropy coefficients. The activation curve can not only classify emotions but also reveals to a certain extent the emotional activation mechanism. Finally, a weight coefficient is obtained from the two coefficients to improve the accuracy of emotion recognition. To validate the proposed method, experiments have been carried out on the DEAP and SEED dataset. The results support the point that emotions are progressively activated throughout the experiment, and the weighting coefficients based on the correlation coefficient and the entropy coefficient can effectively improve the EEG-based emotion recognition accuracy.
Improving TCP Performance in Multi-Hop Coded Wireless Networks

Wireless Multi-Hop networks often rely on the use of the Transport Control Protocol (TCP) which provides high reliability and throughput performance at Transport Layer. Xor network coding does not benefit greatly from TCP's attributes. TCP takes into consideration the nature of networks; however, TCP does not acknowledge the network coding which can ease congestion by performing coding at intermediates nodes as in COPE. Meanwhile, the perspective of network coding has a more rigid approach that depends on relay nodes to perform coding rather than depending on the sender and the receiver exchange messages. In this paper, we investigate the different perspective to bridge the gap, as our understating is moved from throughput to Ratio of Loss Packet (RLP), delay and jitters. So we propose WeNC-TCP to appreciate the network coding nature functionality and incorporate better interaction between TCP at Transport layer, and network coding functionality which relies on between network and Data link layer, as a result, the average ratio of loss packets has improved up to 72%, and end-to-end Delays have reduced to up to 48%.
A 3DES Double–Layer Based Message Security Scheme

Nowadays, there are hundreds of applications that need to secure their transmission, where a lot of techniques were implemented and applied for this reason. Attackers are always breaking these secure techniques to penetrate the communication channel. Steganography and cryptography are two approaches that provide the channel with data security. Steganography hides the data by inserting it into digital media e.g. images, videos, and audios. Cryptography encrypts the data in a way that makes it unreadable for anyone other than the receiver. In this work, a well–secured algorithm is implemented through integrating the two layers, the cryptography layer and the steganography layer. In cryptography we applied the 3–DES technique. In steganography we embedded the secret message in an image using an enhanced least signicant bit (LSB) technique. We used the Peak Signal–to–Noise Ratio (PSNR), the mean squared error (MSE) and structure similarity index measure (SSIM) to evaluate the quality of the image after the embedding. In order to investigate the performance we simulated our proposed algorithm on different images and the results showed that the PSNR reaches up to 75 dB and MSE of 0.0018. Moreover, the obtained results were compared with some of the popular LSB steganographic methods. The comparison made shows that the proposed algorithm outperforms the common LSB steganographic methods in terms of the PSNR and the MSE, while maintaining an excellent SSIM.
Fiber Distribution Networks with Direct and External Modulation of Digital QAM-Signals

This We compare in detail the both available methods of intensity modulation in C-band: a direct one controlling injection current of distributed feedback laser or vertical cavity surface emitting laser, also an external one using electroabsorption modulator or lithium-niobate Mach-Zehnder modulator. In all cases, the same 64-position quadrature modulated signal at the radio-frequency carrier propagates over fiber fronthaul link of an analog Radio-over-Fiber system. Two limiting factors were taken into account: parasitic frequency chirp and fiber dispersion. In the result, the fiber distance of the link under study reaches within standard limit of 8% up to 33 km for a vertical cavity surface emitting laser, up to 55 km for a distributed feedback laser, up to 58 km for an electroabsorption modulator, and up to 62 km for a Mach-Zehnder modulator that are comparable with known data for individual devices under test. During the study of link at the carrier frequencies up to 10 GHz an intriguing effect was found for the case of a vertical cavity surface emitting laser, which consists in a drop in the slope of the error values with their increasing that is most likely explained by the presence of inverse frequency dependence of the adiabatic chirp factor’s magnitude and phase.
Functional Safety Oriented Integrated Design for Avionics System Resources: Multilayer Weighted Networks Model

Most of the traditional researches on the IMA system safety focus on the dynamic scheduling and real-time management design of the resources. Although the integration design process is also important to guarantee the function safety, there are insufficient researches. The ultimate design object is to maximize the implementation of the IMA system resources integration under the premise of the system safety.
A Model of Human Orientation and Self Motion Perception during Body Acceleration: The Orientation Modeling System

Abstract : Spatial disorientation (SD) is a common cause of human-error-related aircraft mishaps, especially during flight within degraded visual environments. Aviation accident investigators often conduct qualitative perceptual analyses of mishaps when spatial disorientation is inferred as a cause. We have developed a quantitative perceptual model of human spatial orientation and have employed it to evaluate data from a variety of acceleration situations, in order to predict the self-orientation and motion perceptions a person will experience when subjected to various accelerations. The model was able to produce successful simulations ofmoment-by-moment orientation and self-motion perception data from a variety of acceleration situations. The model also allows for comparison with the outputs of other published models. The features and performance of our model are described in this report. The model has potential applications for aviation modeling, simulation, and human balance maintenance.
An Event-Triggered Robust Attitude Control of Flexible Spacecraft With Modified Rodrigues Parameters Under Limited Communication

The attitude regulation of spacecraft using continuous time execution of the control law is not always affordable for the low-cost satellites with limited wireless resources. Of late, within the ambit of control of systems over networks, event-triggered control has proved to be instrumental in ensuring acceptable closed-loop performance while respecting bandwidth constraints of the underlying network. Aligned with these design objectives, a robust event-triggered attitude control algorithm is proposed to regulate the orientation of a flexible spacecraft subjected to parametric uncertainties, external disturbances, and vibrations due to flexible appendages. The control law is developed using a state-dependent single feedback vector, which further assists in obeying the constrained network. The current information of this vector is updated to the onboard controller only when the predefined triggering condition is satisfied. Thus, the control input is updated through communication channel only when there is a need, which ultimately helps in saving the communication resources. The system trajectories, under the proposed approach, are guaranteed to be uniformly ultimately bounded (UUB) in a small neighborhood of origin by using a high gain. Moreover, the practical applicability of the proposed scheme is also proved by showing the Zeno free behavior in the proposed control, i.e., it avoids the accumulation of the triggering sequence. The numerical simulations results are indeed encouraging and illustrate the effectiveness of the designed controller. Moreover, the numerical comparative analysis shows that the proposed approach performs better than periodically sampled data technique and sliding mode-based event-triggered technique.
Quality Assessment of Smart Grid Data

Enormous amount of data gets generated in the Smart Grids (SGs) due to the large number of measuring devices, higher measurement rates and various types of sensors. Smart grid data contains important and critical information about the grid. Data driven applications are being developed for better planning, monitoring and operation of SGs. The outcome of data analytics heavily depends on the quality of SG data. However, not much work has been reported on the quality assessment of SG data. This paper addresses the objective assessment of SG data quality. Various dimensions of SG data quality are identified in this paper. Mathematical formulations are proposed to quantify the SG data quality. Proposed data quality metrics have been applied on the SCADA and PMU measurements collected from the Southern Regional Grid of India to demonstrate their effectiveness.
Computational memory-based inference and training of deep neural networks

In-memory computing is an emerging computing paradigm where certain computational tasks are performed in place in a computational memory unit by exploiting the physical attributes of the memory devices. Here, we present an overview of the application of in-memory computing in deep learning, a branch of machine learning that has significantly contributed to the recent explosive growth in artificial intelligence. The methodology for both inference and training of deep neural networks is presented along with experimental results using phase-change memory (PCM) devices.
Dynamic Adjustment of Storage Class Memory Capacity in Memory-Resource Disaggregated Hybrid Storage With SCM and NAND Flash Memory

Using storage class memories (SCMs) as nonvolatile cache of NAND flash memory is a promising solution for the high-performance storage. However, the problem is the high SCM cost per bit which is about ten times higher than NAND flash and the optimal SCM capacity is application dependent. The optimum SCM capacity is conventionally determined manually for every application operating on the data centers’ storages. To achieve high performance while reducing the overall storage cost, application-aware autonomous SCM capacity adjustment (3ASCA) method is utilized to observe the warm data in NAND flash by the ghost least recently used list. SCM capacity in the memory-resource disaggregated storage with hybrid use of SCM and NAND flash is autonomously adjusted and effectively utilized for different applications. By saving the SCM capacity with 3ASCA, multiple applications in the disaggregated storage can efficiently utilize the limited SCM capacity at the same time.
An ILI Mitigating Modulation Scheme for Molecular MIMO Communications

Multiple-input-multiple-output (MIMO) approach is recently proposed to the diffusion-based molecular communication realm in order to enhance communication performance. One of the most promising approaches of molecular MIMO, spatial multiplexing (SMUX), transmits parallel streams of data to achieve an increased throughput. Unfortunately, the performance of SMUX is limited by a high error floor due to severe interlink interference (ILI). Inspired by the relatively more sparse transmission strategy of the pulse-position modulation (PPM) scheme, this study proposes a PPM-based novel SMUX approach to the molecular MIMO literature. In the study, it is shown that the proposed approach combats ILI more effectively than the existing concentration-based SMUX approach and yields promising error performances.
Design and Evaluation of an Educational Simulation for the P-N Junction Diode

This paper discusses the design and the preliminary evaluation of an educational simulation of a P-N junction diode. The P-N diode is the most basic semiconductor device and exhibits a non-linear current vs. voltage dependence, which has been shown to pose many challenges to the students. A simulation was designed to include several important dynamic diagrams in both linear and logarithmic scales, so the students could see their dependence on the density of acceptor/donor atoms and on the applied voltage. The students could compare a silicon-based P-N diode with a germanium-based one for the same doping levels. An exploratory study was pursued with a class taking a 3rd year materials science course for engineering students which included a section on semiconductor physics. Various misconceptions before and after the activity were extracted through coding the answers. One of the biggest challenges was explaining reverse bias, for which half of the students provided incorrect explanations. These students’ evaluation will help improve the simulation in future iterations and inform the development of more sophisticated instruction tools to facilitate conceptual understanding.
Experimental Evaluation of Performance Anomaly in Mixed Data Rate IEEE802.11ac Wireless Networks

IEEE 802.11 wireless local area networks (WLANs) are shared networks, which use contention-based distributed coordination function (DCF) to share access to wireless medium. The performance of DCF mechanism depends on the network load, number of wireless nodes and their data rates. The throughput unfairness also known as performance anomaly is inherent in the very nature of mixed-data rate Wi-Fi networks. This unfairness exhibits itself through the fact that slow clients consume more airtime to transfer a given amount of data, leaving less airtime for fast clients. In this paper, we evaluate the performance anomaly of mixed rate wireless networks and present the results of practical experiments benchmarking throughput of a mixed rate 802.11ac wireless network. These results clearly show that even the most recent wireless standard still suffers from the airtime consumption unfairness. At the end of the paper we analyse related works offering possible solutions and discuss our approach to evade performance degradation in mixed data rate Wi-Fi environments.
Hybrid Self-Organization based Cluster Management Scheme for Group Mobility Aware MANET

The dynamic changes in cluster topology of Mobile Ad-hoc Network (MANET) can cause cluster management issues. Cluster management in the presence of mobility and cluster load balancing are among the most difficult issues to handle in mobility aware MANET. In this paper, we propose a self-organization based hybrid scheme for cluster management. This scheme is inspired from the behavioral study of firefly and dragonfly. Hybrid scheme consists of different phases which ensure cluster formation and its management. The cluster formation takes place by using energy aware approach of firefly algorithm while cluster management takes place using behavioral study of both dragonfly and firefly. A cluster merging technique is proposed for refining number of clusters in MANET to achieve cluster load balancing. The performance of proposed hybrid scheme is evaluated and compared with the existing scheme by considering the benchmarks of energy consumption, cluster head advertisement messages and topology distribution with respect to hop count.
Driver Gaze Detection Based on Deep Residual Networks Using the Combined Single Image of Dual Near-Infrared Cameras

Research into the prevention of driver inattention by detecting the driver gaze has become more vital, as traffic accidents due to driver inattention have increased. In a vehicle environment, the conventional gaze-detection methods include detecting the driver gaze using single or multiple cameras. When a single camera is used to detect the driver gaze, excessive rotation of the driver’s head may prevent the eye region from being accurately detected, thereby reducing the gaze-detection accuracy. To address this issue, researchers previously attempted gaze detection using dual cameras. However, these methods selectively use the information obtained from each camera; thus, accuracy improvement is limited because the information is not simultaneously used. In addition, the processing complexity increases when images obtained from dual cameras are simultaneously processed. Accordingly, this paper proposes a method to detect the driver’s gaze position in the vehicle. This is the first study to calculate the driver gaze via a deep convolutional neural network (CNN) that simultaneously uses image information acquired from the dual near-infrared light cameras. Previous research selectively used one of the images acquired from the dual cameras, and the existing CNN-based gaze-detection methods use multiple deep CNNs for the driver eyes and facial images. However, the proposed method uses one CNN model that integrates all information acquired from the dual cameras into one three-channel image and uses it as an input for the network, thereby increasing the recognition reliability and reducing the computational cost. We conducted experiments based on a self-built driver database that comprised the images from 26 participants (Dongguk dual-camera-based gaze database) and the Columbia gaze dataset, which is an open database. The results demonstrate that the proposed method shows better performance than the existing methods.
Classifying the Unstructured IT Service Desk Tickets Using Ensemble of Classifiers

Manual classification of IT service desk tickets may result in routing of the tickets to the wrong resolution group. Incorrect assignment of IT service desk tickets leads to reassignment of tickets, unnecessary resource utilization and delays the resolution time. Traditional machine learning algorithms can be used to automatically classify the IT service desk tickets. Service desk ticket classifier models can be trained by mining the historical unstructured ticket description and the corresponding label. The model can then be used to classify the new service desk ticket based on the ticket description. The performance of the traditional classifier systems can be further improved by using various ensemble of classification techniques. This paper brings out the three most popular ensemble methods ie, Bagging, Boosting and Voting ensemble for combining the predictions from different models to further improve the accuracy of the ticket classifier system. The performance of the ensemble classifier system is checked against the individual base classifiers using various performance metrics. Ensemble of classifiers performed well in comparison with the corresponding base classifiers. The advantages of building such an automated ticket classifier systems are simplified user interface, faster resolution time, improved productivity, customer satisfaction and growth in business. The real world service desk ticket data from a large enterprise IT infrastructure is used for our research purpose.
A Novel Reconfigurable CMOS Compatible Ka Band Bandstop Structure Using Split-Ring Resonators and Vanadium Dioxide (VO2) Phase Change Switches

The article presents the design, fabrication and characterization of a novel complementary metal-oxide-semiconductor (CMOS) compatible reconfigurable Ka band bandstop structure using split ring resonators (SRR) while employing the Vanadium Dioxide (VO2) phase change (PC) thermally triggered transition. The work focuses on the VO2 thin film conductivity levels challenges on silicon dioxide (SiO2)/ silicon (Si) substrates caused by the limited conductivity in the metallic state of the VO2 films versus their non-zero conductivity in the insulating state. We characterize first various samples of VO2 thin films deposited on SiO2/Si substrates and present different fabricated filters responses with several VO2 switches dimensions. The filters show higher bandstop rejection levels than previously reported VO2 based CMOS compatible bandstop filters for the Ka band and displays a higher reconfigurable range from: 29.7 GHz-38.7 GHz. The filters while introducing a new compact tuning mechanism present the first VO2 reconfigurable SRR bandstop structures for the Ka band.
Discovering Topic Representative Terms for Short Text Clustering

Clustering short texts are one of the most important text analysis methods to help extract knowledge from online social media platforms, such as Twitter, Facebook, and Weibo. However, the instant features (such as abbreviation and informal expression) and the limited length of short texts challenge the clustering task. Fortunately, short texts about the same topic often share some common terms (or term stems), which can effectively represent a topic (i.e., supported by a cluster of short texts), and we also call them topic representative terms. Taking advantage of topic representative terms, it is much easier to cluster short texts by grouping short texts into the most similar topic representative term groups. This paper provides a novel topic representative term discovery (TRTD) method for short text clustering. In our TRTD method, we discover groups of closely bound up topic representative terms by exploiting the closeness and significance of terms. The closeness of the topic representative terms is measured by their interdependent co-occurrence, and the significance is measured by their global term occurrences throughout the whole short text corpus. The experimental results on real-world datasets demonstrate that TRTD achieves better accuracy and efficiency in short text clustering than the state-of-the-art methods.