Miracle-dz/UniFAR-FTUs huggingface dataset:
Comprehensive Investigation on Electrical Properties of nLDMOS and pLDMOS Under Mechanical Strain

In this paper, we have comprehensively studied the performance boosts of both nLDMOS and pLDMOS under the mechanical strain. The electrical properties of LDMOS under the uniaxial tensile and compressive strains along the channel direction are examined thoroughly. We find that the uniaxial tensile strain benefits the nLDMOS, and the uniaxial compressive strain benefits the pLDMOS. Because the mechanical strain affects carriers’ transportation in bulk Si and inverted channel distinctly, the strain effects are strongly correlated with the gate voltage, drain voltage, and devices’ dimensions. It is found that the LDMOS with the longer gate length is more preferred for the strain. The piezoresistance coefficients of both nLDMOS and pLDMOS under the uniaxial strain are evaluated for the first time. It is also shown that the mechanical strain can enhance the drain current without the degradation of the breakdown voltage, which suggests a downshift of the ON-resistance versus breakdown voltage curve.
Reflection on Reflection in Applied Visualization Research

Reflection is a core method used by visualization researchers to generate knowledge from design practice. There is, however, a lack of standards to inform reflective practice and through which we can judge the quality of the reflection used in visualization research. Reflecting on this gap, we offer priorities for researchers looking to improve the use of reflection in applied visualization research.
Automatic Extraction of Equations in Medieval Arabic Algebra

In medieval Arabic, no symbols are used to express mathematical problems and solutions. Instead, specific Arabic terms are used to describe mathematical processes. Nevertheless, a close examination of the text styles and how information is encoded into natural language phrases reveals that the notations contain no ambiguity, which makes it possible to formulate the problem and solve it. In this paper, we explore the idea of automatically generating modern symbolic mathematical equations from natural language medieval Arabic Algebra texts. We will describe a simple machine translation system using Rule-based Machine Translation (MT) with Dictionary Approach in order to translate medieval verbal equation to a modern equation. We construct a new dataset from scratch to facilitate our study, as there is no previously published similar dataset. Our system was able to achieve correct translation results for cubic equations with 100% accuracy, and quartic, quintic equations with 96.66% and 94.01%, respectively.
That Seems Made Up: Deep Learning Classifiers for Fiction & Non Fiction Book Reviews

The thesis aims to take the first step towards automated extraction of the information found in book reviews, by using machine learning tools to assign a label of fiction or non fiction to the text. The thesis makes use of neural networks and performs experiments around architecture, hyper-parameters and text processing from which an optimized model is produced. The thesis enjoys certain successes; it was possible to match the state of the art achieved by (Kim, 2014) and computation was sped up considerably from the default to the optimized model by 13.8 seconds per 50 steps. Further it is confirmed by the thesis that labelling a sequence as fiction or non fiction can be performed most accurately with LSTM architectures and that contrary to (Reimers & Gurevych, 2017) every considered hyper parameter had a considerable impact on results.
Insertion-depth sensor embedded automatic grasping forceps for precise cornea grasping in keratoplasty

Keratoplasty is the surgery that replace the diseased cornea to the new donor cornea. Keratoplasty requires about 16 sutures with deep and regular suture depth. However controlling suture depth is difficult because of lack of depth information in microscope image, high transparency and thin thickness of the cornea. In this paper, we proposed sensor embedded surgical forceps which can automatically grasp the cornea with desired depth. The sensor can detect the moment that the forceps tip reaches to the desired suture depth. The sensor and the automatic grasping device was fabricated and preliminary experiment was conducted to investigate the feasibility of the device.
Enhanced Risk Minimization Framework for Cloud Computing Environment

Nowadays, traditional applications are becoming increasingly complex, so it is necessary to enhance computing competence and maintain stable environment. To meet these requirements, most of enterprises should purchase expensive hardware devices and software. As a result, cloud computing is developed to share hardware and software resources. Users are never plagued by insufficient storage space and never worry about loss of data because of disk crash. Meanwhile, the expenditure is reduced. However, cloud security is a significant problem which every enterprises and individual entrepreneur must face with. Recently, cloud computing security incidents happen frequently, and many enterprises suffer the security incidents even more than once. This paper aims to decrease risks related to cloud computing, improve security and prevent those risks from impacting business goals through assessing and managing the cloud computing environment risks. Further, this paper proposes a risk minimization framework (RMF) about risk minimization adapting to the situation and providing the algorithm of risk minimization process. The proposed frame is tested and implemented using C++ platform. Finally, the proposed RMF is compared with known risk minimization frameworks.
SEDAT: Sentiment and Emotion Detection in Arabic Text Using CNN-LSTM Deep Learning

Social media is growing as a communication medium where people can express online their feelings and opinions on a variety of topics in ways they rarely do in person. Detecting sentiments and emotions in text have gained considerable amount of attention in the last few years. The significant role of the Arab region in international politics and in the global economy have led to the investigation of sentiments and emotions in Arabic. This paper describes our system - SEDAT, to detect sentiments and emotions in Arabic tweets. We use word and document embeddings and a set of semantic features and apply CNN-LSTM and a fully connected neural network architectures to obtain performance results that show substantial improvements in Spearman correlation scores over the baseline models.
On Evaluation of Planar Drive Kinematics for Handling Tasks

Green production and energy efficiency in industrial environments are essential topics in recent political and scientific discussions. For realization, design processes of industrial applications such as robotic manipulators have to become energy aware. While most approaches regarding efficient design of robotic manipulators found in literature are based on complex optimization schemes, we present a novel, simply evaluable bound for the energy a kinematic structure will consume to fulfill a given motion task within a given time. The proposed estimate is based on optimal control methods for free rigid body mechanics, allowing an explicit solution in the considered case. After presenting and deriving the bound, we give numerical results for validation, yielding a deviation of only 0.1% to 7.2% compared to the numeric optimal control solution, depending on the considered kinematic structure. This confirms the proposed bound as non-conservative but suitable for estimating the energy consumption of a certain kinematic structure for a given motion task.
Tourist Attractions Classification using ResNet

Smart tourism is a keyword for describe the tourist on emerging forms of ICT. One application of smart tourism is to classify tourist attractions automatically, where the data in the form of pictures taken by tourists. However, there are some problems in application of tourist attractions classifications. First, in one place may have different objects and traits. Second, in some places may have a similar architecture, so it could be difficult for the system to classify the places. In this study, we focused on the tourist attractions in Jakarta and Depok using ResNet50. We divided this study into 2 scenarios. Scenario 1 is a model with 12 classes, and scenario 2 is a model with 16 classes. The results are ResNet50 has been able to handle both research problems, although not yet maximized, with average accuracy in scenario 1 is 92.17% and scenario 2 is 93.75%.
A Proposal for Reducing the Number of Trial-and-Error Searches for Deep Q-Networks Combined with Exploitation-Oriented Learning

Deep reinforcement learning has attracted attention for its application in deep Q-networks (DQNs). A DQN can attain superhuman performance, but it requires a large number of trial-and-error searches. To reduce the number of trial-and-error searches required for learning convergence in a DQN, multistep learning can be used and deep Q-networks with profit sharing (DQNwithPS) is one solution, but it has its own set of disadvantages. DQNwithPS optimizes a neural network by learning based on a DQN and profit sharing. However, multistep learning requires proper prefetching parameter tuning, and DQNwithPS has a learning performance degradation problem caused by profit sharing by not considering the expected rewards of a future episode. In this paper, we propose a learning-accelerated DQN combining multistep learning and DQNwithPS to cancel each disadvantage. The proposed method improves the prefetching parameter tuning in multistep learning by DQNwithPS and the learning performance degradation problem in DQNwithPS by multistep learning. By this method, we aim to reduce the number of trial-and-error searches compared to a DQN and DQNwithPS and to realize a manageable fast learning method.
Performance Analysis of MANET Networks Based on AODV Protocol in NS-3 Simulator

In this paper a comparative analysis of network performance regarding to Random Waypoint (RW) and Gauss-Markov (GM) mobility models within Ad hoc On Demand Distance Vector (AODV) protocol in Network Simulator 3 (NS-3) is given. Simulation results were compared according to throughput, percentage of packet loss, routing overhead, average end to end delay and jitter. In the simulations various node speeds and traffic loads are used. In order to obtain these network parameters a new software patch is developed for NS-3 simulator and used in this paper. The source code of this implementation is publicly available.
Model Predictive Control Approach on Packet Dropout Prevention of Networked Control Systems

A strategy towards stochastic packet dropout of networked control system is proposed in this paper. A reconfigured linear parameter-varying model of networked control system is established on the basis of some preliminaries and assumptions. A stochastic process of packet dropout in both the forward and feedback paths is modeled as a Bernoulli distribution sequence. An explicit model predictive controller with consideration of the worst case is employed in dealing with such a linear parameter-varying system. Finally, we present a Simulink numeric simulation and demonstrate both the stability and robustness of proposed control strategy in order to prove the feasibility and stability of proposed method.
Accurate Performance Bounds Calculation for Dynamic Voltage-Freq Islands in Best Effort NoCs

Dynamic voltage and frequency scaling (DVFS) is a technique used to meet the power budget limitations in multi-core embedded systems. DVFS is applied to Networks-on-Chip (NoC) as a major contributor of the dissipated power on-chip. We propose an analytic model to accurately calculate the performance bounds on the best effort NoC with multiple voltage-frequency islands. The model supports dual-port buffer or handshaking mechanisms. We also suggest a method to set the frequencies of the islands to decrease the dissipated power. We examine our method and models and show their effectiveness.
Evaluating Engagement of Virtual Reality Games Based on First and Third Person Perspective Using EEG and Subjective Metrics

Viewing perspective plays a significant role in gameplay experiences and especially in virtual reality (VR). However, it is still underexplored. In this paper we evaluate how different viewing perspectives relate to psychophysiological engagement, arousal, and valence metrics that are measured using brainwaves. We perform an experiment in which volunteers play a game using different perspectives and displays while their brainwaves are monitored by a commercial Electroencephalogram (EEG) headset. Using the EEG data, we calculate levels of engagement, arousal and valence using a metric from the literature, and then compare these results to answers from a subjective, self-reporting questionnaire. We then discuss how the psychophysiological metrics compare with the subjective ones in this study, verifying that their results do not always match.
ATT-Auth: A Hybrid Protocol for Industrial IoT Attestation With Authentication

This paper addresses the problem of developing attestation techniques for Industrial Internet of Things systems. To ensure hardware security, the proposed attestation protocols are based on physically unclonable functions. Moreover, to achieve scalability, the proposed protocols do not calculate the reference checksum for every prover at the verifier, instead they use timing information. Thus, to attest multiple devices in large-scale networks, such as device swarms, the proposed protocols use timing information to detect any unintentional or malicious modification to a device’s memory contents. The analysis on an Atmel micro controller shows that the proposed protocols have a high probability of detecting malware with significantly lower computation overhead.
A CMOS Squarer Based Nonlinear Filter for Spike Detection

In this paper it is presented a squarer based nonlinear operator for spike detection and its CMOS hardware implementation. The performance of the proposed operator is compared against the classical Nonlinear Energy Operator showing good agreement between both for signals with low Signal-to-Noise-Ratio. The proposed circuit implementation take advantage of its discrete time nature to reduce the power dissipation by turning off the most power hungry circuit when it is not necessary. It was designed aiming a standard CMOS 90nm process and simulations shown a power dissipation of 20nW for 1V supply voltage at 24KSps of sampling frequency; and occupies 0.014mm2.
Single-Sideband OFDM Transmission via a Silicon Microring IQ Modulator

We experimentally demonstrate the generation of a single-sideband orthogonal frequency division multiplexed (OFDM) signal using an on-chip silicon photonics microring-based IQ modulator. Over 18-dB sideband suppression ratio is achieved for the wideband OFDM: 15.7-GHz data band and 2.7-GHz guard band. The 31.4-Gb/s signal was transmitted over 20 km of standard single-mode fiber with a bit error rate below the forward error correction threshold. While single-sideband continuous-wave signals have been produced with such hardware, this is the first demonstration of stable data transmission on the single sideband carrier.
36–40 GHz Tx/Rx Beamformers for 5G mm-Wave Phased-Array

A 36–40 GHz 4-element Tx and Rx beamformer chip set for the fifth generation communication millimeter wave (5G mm-Wave) phased-array applications is presented in this paper. The separated Tx/Rx beamformer chips have 4-bit (22.5°) phase control with 25°phase compensation, and 40 dB peak gain per channel with 20 dB gain control. All of the gate biases, gain and phase tuning functions are controlled by the mixed-signal integrated circuits embedded in the chips, programmable by the microcontroller. The chips are fabricated using TSMC 65-nm CMOS process. The measured $\mathbf{OP}_{\mathbf{l}\mathbf{d}\mathbf{B}}$ per channel of the Tx beamformer is 12 dBm, and NF (noise figure) of the Rx beamformer is 6–9 dB. The $\mathbf{Tx}/\mathbf{Rx}$ beamformers measurements performed less than 2 dB RMS gain error and 5.5° RMS phase error with 360° phase tuning, and $\pm 5^{\text{o}}$ phase variation with 20 dB gain tuning. To the author's knowledge, these chips are the first beamformers in 65-nm CMOS for the FCC 5G mm-Wave 37 GHz (37-38.6 GHz) and 39 GHz (38.6-40 GHz) phased-array applications. This chip set will be the unit cell to be integrated with antenna arrays to construct 32 or 64-element phased-array systems in the near future.
System Evolution Analytics: Deep Evolution and Change Learning of Inter-Connected Entities

There are many entities (or components) in a system that keeps on evolving over system states. The connection (or relationship) between entities also keep on evolving over system state, which makes series of evolving networks. Such networks can be studied over evolving state to provide system evolution information for analysis. This can be achieved with the help of hybrid mining approaches. The network rule information can be detected using network rule mining. The network subgraph information can be retrieved using network subgraph mining. The evolution information is detected using evolution mining. In this paper, we introduce a "System Evolution Analytics" model, which is explained using two pattern-mining techniques: network evolution rule mining and network evolution subgraph mining. The first technique retrieves network evolution rules (NERs), and the second technique retrieves network evolution subgraphs (NESs). The two techniques are prototyped as two System Evolution Analytics tools that are used to do experiments on six evolving systems. We demonstrated the application of the tools for the system evolution analysis.
3D Path Planning for Anterior Spinal Surgery Based on CT images and Reinforcement Learning*

Spinal internal fixation is one of the most complex operations, and the planning is the core process of preoperative preparation in robot-assisted surgery. Compared with posterior surgery, the anterior one is gradually accepted by the general public for more accuracy and less aesthetic impact. This paper proposes an anterior surgical path planning method based on the reinforcement learning. Firstly, the multi-task segmentation used for building the planning environment is performed on the chest-enhanced medical images, after which the 3D model of specified organs such as spine and vessels are reconstructed. Then, the surgical path is obtained by the Q-learning algorithm based on the model, while the $\varepsilon$-greedy policy is applied to guarantee the rapid convergence to global optimum. A curved limit scheme is proposed to solve the curse of dimensionality and improve the pathway searching efficiency. The experimental results indicate that the method performs well in the surgical 3D path planning. The path for the end of the robot from the entrance to the lesion site can be planned automatically to avoid the vital organs, even though without the prior knowledge of the environment.
Voronoi-Based Deployment of Multi-Agent Systems

This paper considers a decentralized control scheme for Voronoi-based deployment of discrete-time multiagent dynamical systems within multi-dimensional static convex polytopic environments. The deployment objective is to drive the multi-agent system to a static configuration in which coverage of the environment is optimized. To this end, local control laws steer each agent towards a Chebyshev center of its associated time-varying polytopic Voronoi-neighborhood. By introducing a novel time-varying interaction graph, mechanisms enforcing consensus on intra-neighbor distances among subsets of agents are uncovered. Subsequently the interaction graph is exploited to provide both proofs of convergence as well as structural characterizations of static configurations.
Information-Theoretic Analysis of the Speed-Accuracy Tradeoff with Feedback

Human movements are inherently variable and involve some feedback mechanism. A study of the positional variance in a tapping task reveals that the variance profiles are unimodal in time. In the variance-decreasing phase, the aiming task can be modeled by a Shannon-like communication scheme where information is transmitted from a "source"—determined by the distance to the target at maximum variance—to a "destination"—the movement endpoint—over a "channel" with feedback perturbed by Gaussian noise. Thanks to the feedback link, we show that the variance decreases exponentially at a rate given by the channel capacity. This is confirmed on real data. The proposed information-theoretic model has promise to improve our understanding of human aimed movements.
An Experimental Study on Generative Adversarial Network and Visual Experience Mining for Domain Adaptive Change Detection

This paper addresses the problem of cross-domain change detection from a novel perspective of image-to-image translation. In general, change detection aims to identify interesting changes between a given query image and a reference image of the same scene taken at a different time. This problem becomes a challenging one when query and reference images involve different domains (e.g., time of the day, weather, and season) due to variations in object appearance and a limited amount of training examples. In this study, we address the above issue by leveraging a generative adversarial network (GAN). Our key concept is to use a limited amount of training data to train a GAN-based image translator that maps a reference image to a virtual image that cannot be discriminated from query domain images. This enables us to treat the cross-domain change detection task as an in-domain image comparison. This allows us to leverage the large body of literature on in-domain generic change detectors. As a part of our contribution, we investigate to what extent the usage of the translation GAN can alleviate the issue of domain shift by developing and evaluating several change detection algorithms. In addition, we also consider the use of visual place recognition as a method for mining more appropriate reference images over the space of virtual images. Experimental results on cross-season change detection using the publicly available North Campus Long-Term autonomy dataset validate the efficacy of the proposed approach.
An Attention-Based Air Quality Forecasting Method

Air pollution is threatening human's health since the industrial revolution, but there are not efficient ways to solve air pollution, so forecasting air quality has become an efficient measure to prevent citizens from hurting of heavy air pollution. In this paper, we proposed an advanced Seq2Seq (Sequence to Sequence) model called attention-based air quality forecasting model (ABAFM) whose RNN encoder is replaced by pure attention mechanism with position embedding. This improvement not only reduces the training time of Seq2Seq model with attention but also enhances the robustness of Seq2Seq models. We implemented ABAFM in Olympic center and Dongsi monitoring stations in Beijing to forecast PM2.5 in future 24 hours. The experimental results showed that the proposed model outperformed the related arts, especially in sudden changes.
Development of a Visual Cueing System Using Immersive Virtual Reality for Object-Centered Neglect in Stroke Patients

Unilateral spatial neglect (USN) is defined as failure to report, respond, or adjust to novel or meaningful stimuli presented to the side opposite a brain lesion. This symptom deteriorates the rehabilitation efficiency and hinders activities of daily living. USN is classified into 2 sub-categories: body-centered neglect and object-centered neglect. No optimal intervention has been proposed for treating object-centered neglect. Thus, we developed a visual cueing system using immersive virtual reality to improve object-centered neglect. A feasibility study showed 2 typical measurements for object-centered neglect tended to improve the symptom, these results provide preliminary evidence for designing successful rehabilitation programs using a visual cueing system in patients with object-centered neglect.