Miracle-dz/UniFAR-FTUs huggingface dataset:
A Stock Index Prediction Framework: Integrating Technical and Topological Mesoscale Indicators

With its growing importance in predicting future stock trends, nearly everyone watches the Chinese financial market. Traditional approaches typically employ a variety of statistical techniques or machine learning methods for stock index predicting, and often rely on analysis of technical indicators. In the existing literature, researchers rarely attempt to predict the stock index by using the topological features of temporal stock correlation networks. Keeping this in mind, we first calculate the correlation coefficient of any two stocks using the classic Visibility Graph Model (VGM). Then, by using the Planar Maximally Filtered Graph (PMFG) method, we generate temporal stock correlation networks from historical stock quantitative data. Next, we choose fourteen frequently adopted Technical Indicators (TIs) and five Topological Mesoscale Indicators (TMIs, extracted from the temporal stock correlation networks) as predictive variables of six machine learning classifiers. To improve forecast accuracy and to address potential overfitting problems, we modify the classic Sequential Backward Selection (SBS) algorithm to learn the most significant predictive variables for each classifier. We then conduct a series of comprehensive experiments on three Chinese stock indices to validate our prediction framework's performance. Experimental results show that using a combination of TIs and TMIs significantly improves forecast accuracy over conventional methods that use either TIs or TMIs exclusively.
SRAM On-Chip Monitoring Methodology for Energy Efficient Memory Operation at Near Threshold Voltage

Low power design by near-threshold voltage (NTV) operation is very attractive since it affords to considerably mitigate the sharp increase of power dissipation. However, one key barrier for the use of NTV operation is the significant increase of the SRAM failure. In this work, we propose an on-chip SRAM monitoring methodology that is able to accurately predict the minimum voltage, Vddmin, on each die that does not cause SRAM failure under a target confidence level. Precisely, we propose an SRAM monitor, from which we measure a maximum voltage, Vfail that causes functional failure on that SRAM monitor. Then, we propose a novel methodology of inferring SRAM Vddmin on each die from the measured Vfail of SRAM monitor on the same die where Vfail-Vddmin correlation table is built-up in design infra development phase, and Vddmin can be directly derived from the measured Vfail referencing the correlation table in silicon production phase. Through experiments, we confirm that our proposed methodology is able to save leakage power by 7.43%, read energy by 3.98%, and write energy by 4.06% in SRAM bitcell array over that by applying a uniform minimum voltage for all dies while meeting the same yield constraint.
IOTFLA : A Secured and Privacy-Preserving Smart Home Architecture Implementing Federated Learning

Slowly but steadily, the Internet of Things (IoT) is becoming more and more ubiquitous in our daily life. However, it also brings important security and privacy challenges along with it, especially in a sensitive context such as the smart home. In this position paper, we propose a novel architecture for smart home, called our, focusing on the security and privacy aspects, which combines federated learning with secure data aggregation. We hope that our proposition will provide a step forward towards achieving more security and privacy in smart homes.
Map-Matching-Based Cascade Landmark Detection and Vehicle Localization

For vehicle localization in highway situations, this paper proposes a map-matching based road facility detection and vehicle localization method with a camera, a low-cost global navigation satellite systems combined with an inertial navigation system (GNSS/INS), and a digital map. The proposed method adopts the cascade structure, which detects a road facility in each stage and gradually reduces the localization uncertainty based on the detection results. The proposed method consists of two stages. In the first stage, lane endpoints are detected and one camera position hypothesis for each lane is generated based on the detected lane endpoints. The localization uncertainty is reduced from a few meters to tens of cm. In the second stage, road sign regions of interest (ROI) are generated by projecting road signs of the map to an image based on each camera position hypothesis. Road signs are detected effectively within these ROIs and the best camera position hypothesis is selected based on the detection results. The proposed method significantly reduces the processing time and largely improves the road sign detection performance. Its processing time to detect a road sign in an image, whose resolution is $1280\times 1024$ , is 13ms on average without the help of any parallel processing H/W such as a graphic processing unit. Its detection performance has 100% recall and 100% precision, and this result is superior to that of the deep neural network detector whose name is You Only Look Once version 3 (YOLOv3). The localization precision of the proposed method is 20cm in average in highway situations.
PERSWADE-CORE: A Core Ontology for Communicating Socio-Environmental and Sustainability Science

The Centre on Persuasive Systems for Wise Adaptive Living (PERSWADE) aims at developing and applying persuasive technologies and system science for social innovation that can help humanity to move toward sustainable, wise, adaptive living. The PERSWADE collaborative knowledge base needs to be designed with the intent to bring together, enrich and logically relate heterogeneous content, such as datasets, scientific literature and any kind of multimedia and social content, to support a participatory approach and help to translate science into action. PERSWADE-CORE, the foundation ontology described in this paper, plays a critical role in this by providing the backbone semantic infrastructure to enable collaboration through efficient data and knowledge integration, sharing and reuse. It also serves the purpose of clarifying and explaining the goals, functions and operations of the Centre. Because of its purpose, PERSWADE-CORE has been designed to be easy-to-use and easy-to-adapt by allowing generic, as well as more specific, relationships among concepts. The PERSWADE approach prioritizes interoperability and relies on the Semantic Web infrastructure. Furthermore, its design is intrinsically aimed at collaborative environments in which ontologies are expected to evolve as a response to users’ activity.
Tackling Multiple Byzantine Failures in Optical Networks Routed by means of Ant Colony Optimization

Byzantine failures in the routing protocol may disrupt the normal operation of the network, where authenticated nodes exhibit arbitrary or defective behavior. Ant Colony Optimization (ACO)-based routing algorithms are especially susceptible to those kinds of failures. By incorporating crankback re-routing extensions into the ACO algorithm, resilience against byzantine failures can be achieved. In this work, we evaluate the crankback mechanism and its overhead in the presence of many nodes of a wavelength-switched network engaging in three different types of byzantine behavior: misdirection of forward ants, dropping of forward ants, and dropping of backward ants. Simulations have demonstrated that the crankback mechanism can effectively mitigate the impact of multiple byzantine failures on the blocking probability without requiring an important increase of the path length or the setup time of the established lightpaths.
Harmonic Current Detection Method Using Nonlinear Load's DC Output Voltage and Current

This paper proposes a new harmonic current detection method using an average DC voltage and current of voltage source nonlinear load instead of a typical load current and a source voltage. By using a MATLAB Simulink tool, a model of single-phase AC system connected to a full bridge diode rectifier with capacitive load is designed. A step by step simulation results towards a generation of sinusoidal reference current and filtered reference current are presented and analysed. As a conclusion, the proposed harmonic detection method successfully achieved its objective to generate the reference current signals for the single-phase active power filter circuit.
Third-Order Nonlinear Optical Properties of Some Polycyclic Aromatic Hydrocarbons

In this work the nonlinear optical (NLO) properties of some polycyclic aromatic hydrocarbons (PAHs) are studied both in solutions and incorporated in thin films. The present study aims to investigate the effect of the structure of the PAH (i.e. geometry, number of rings, planarity, curvature, etc.) on their nonlinear optical (NLO) response. In that view, two O-doped (O-annulated), almost planar, PAHs (i.e., the 6Pp and the 6Fur) and a twisted bay-substituted quaterrylene are studied. The synthesis of these PAHs allowed the efficient tuning of their bandgap energy and their photophysical parameters, which on their turn allows for the efficient modulation of their third-order NLO response. So, both the nonlinear absorption and refraction of the PAHs of interest were determined and the corresponding third-order susceptibility χ(3) was deduced by means of the Z-scan technique, employing 35 ps laser pulses. The chemical stability together with the easy and efficient tunability of the photophysical properties of these hydrocarbons make them very interesting candidates for photonic and optoelectronic applications.
Adaptive Type-2 Fuzzy Control Scheme for Robust Teleoperation under Time-Varying Delay and Uncertainties

This study investigates the stability and performance issues in teleoperation systems due to latency and uncertainties. Interaction with the remote environment introduces unknown forces to the slave system that requires a robust control method for a stable operation. On the other side, network delays dramatically threatens the stability of the teleoperation process. Moreover, uncertainty sources such as human operator and system parameters further decreases the efficiency of the teleoperation task. Therefore, this paper proposes a type-2 fuzzy based adaptive control methodology to deal with the uncertainty concerns in teleoperation systems. First, the considered teleoperation system is expressed by type2 fuzzy rules. Then, applying Lyapunov-Krasovskii analysis method, an adaptive algorithm is developed for updating the control parameters to guarantee the system performance and stability. Finally, the effectiveness of the proposed adaptive type2 fuzzy control methodology is experimentally evaluated on a haptic teleoperation setup.
Smart Parking Model based on Internet of Things (IoT) and TensorFlow

This paper first proposes a smart parking model to reduce the wastage of time where it allocates the free parking spaces and helps the driver to navigate in the parking area. With the help of the Internet of Things (IoT) and Google Cloud, we can easily allocate the free spaces. Through an Android application, the user can see the free parking spaces and book the place. We have used Deep Learning algorithm for the user verification process and to navigate in the parking area, a navigation bot is developed using Deep Reinforcement Learning (RL). TensorFlow, a deep learning framework is used for developing the navigation bot and implementing the deep learning algorithm. In our experimental approach, we have got 98% accuracy rate in license plate detection and our proposed model can reduce the wastage of time by 50% which gives a better performance than the other traditional methods.
Designed Dynamic Reference With Model Predictive Control for Bidirectional EV Chargers

This paper presents a finite control set model predictive control (MPC) using a designed dynamic reference for bidirectional electric vehicle (EV) chargers. In the conventional MPC scheme, a PI controller is involved to generate an active power reference from the DC voltage reference. It is hard to find one fixed set of coefficients for all working conditions. In this paper, a designed dynamic reference based MPC strategy is proposed to replace the PI control loop. In the proposed method, a DC voltage dynamic reference is developed to formulate the inherent relationship between the DC voltage reference and the active power reference. Multi-objective control can be achieved in the proposed scheme, including controlling of the DC voltage, battery charging/discharging current, active power and reactive power, independently. Bidirectional power flow is operated effectively between the EV- and the grid-side. Experimental results are obtained from a laboratory three-phase two-stage bidirectional EV charger controlled by dSPACE DS1104. The results show that fast dynamic and good steady state performance of tracking the above objectives can be achieved with the proposed method. Compared with the system performance obtained by the conventional MPC method, the proposed method generates less active power ripples and produces a better grid current performance.
Drug Discovery Applications: A customized digital microfluidic biochip architecture/CAD flow

Digital Microfluidic Biochips (DMFBs) have significant potential to accelerate a wide range of drug discovery applications, including high-throughput screening, toxicity testing, and drug designing and development. On the other hand, the conventional approach for drug discovery is very time-consuming and error prone. For these applications, current DMFB architectures should be customized to speed up the drug discovery processes and reduce the cost and error of these reactions. Because many drug-related assays repetitively and concurrently use some operations, we developed a new DMFB architecture and the corresponding CAD flow for drug discovery applications. This architecture can accelerate the process by parallelizing the operations, decreasing the area and costs, and improving the execution time of bioassays.
Co-Optimizing Core Allocation, Mapping and DVFS in Streaming Programs with Moldable Tasks for Energy Efficient Execution on Manycore Architectures

Stream programming abstracts parallelism complexity by modeling a program as a set of streaming tasks. Tasks run repeatedly and can even be internally parallel, i.e., use one or multiple cores simultaneously (moldable). The throughput of the streaming application, as well as its energy consumption, depends strongly on scheduling, i.e., on how tasks are mapped to cores, and on the frequency at which they run. Crown scheduling is a scheduling method that reduces this problem's combinatorial complexity considerably by introducing a few additional restrictions especially on tasks' core allocation sizes and mapping. While it has previously been shown to outperform competing methods, the impact of these restrictions on the schedule quality has, up to now, never been analyzed quantitatively. In this paper, we first propose several crown scheduler improvements toward fewer restrictions. Also, we provide an Integer Linear Programming formulation that solves the same optimization problem without the inherent restrictions of crown scheduling. While in an extreme case an unrestricted schedule might use 3.7 times less energy than a crown schedule for a realistic execution platform model, we show that in practical benchmarks the difference is small while crown schedulers are significantly faster than unrestricted scheduling. We experimentally confirm this with benchmarks derived from random task collections, classic parallel algorithms as well as the Streamit benchmark suite.
Energy and Error Reduction using Variable Bit-width Optimization on Dynamic Fixed Point Format

In today's era of IoT, big data and AI, a lot of emerging applications are limited by the system's power and energy capability. Since many of these applications have relatively high resistance to computational errors, it becomes a hot topic to study the accuracy vs. energy trade-off. Several approaches have been proposed to optimize the bit-width in the fixed-point arithmetic operations. They mainly focus on the fraction part with little consideration on the integer part. In this paper, we take advantage of the dynamic fixed point format to expand the scope of bit-width optimization for both the fraction and integer parts. More specifically, we propose a real-time variable bit-width allocation technique to dynamically reduce the precision of the operands and hence balance the accuracy loss and the energy reduction. More importantly, existing approaches are application specific and the bit-width has to be optimized for each application, but our approach is designed for general purpose computations. We implement our approach and evaluate it on 9 commonly used test benchmarks. The results demonstrate that our technique can reduce the error rate on average by 57% over the traditional design given the same amount of energy budget. On the other hand, to reach the same level of computation accuracy, our technique can achieve more than 20% energy saving.
An Adaptive Chain-type Treadmill-Based Traction Testbed for the Wheeled Planetary Exploration Rover: Design, Modeling and Experimental Validation

This paper present a novel chain-type treadmill-based traction testbed for the wheeled planetary exploration rover. The main objective of this work is to develop a customized testing system executing traction loading to the rover body together with drag torque loading to the individual wheels, providing a synthetic evaluation of the mobility performance of the Mar rover platform. The overall system is composed of a traction loading subsystem to perform the task of traction loading and six modular chain-type treadmill subsystems to provide the drag torques. In order to improve the loading precision onto the target objects of the rover, an adaptive loading control strategy with friction compensation is proposed to satisfy the tailored loading requirements of individual rover wheels as well as the rover body. Finally, the comprehensive experiments of a planetary exploration rover prototype including the synchronous motion tracking, the traction capacity testing as well as the varied wheel drag torque loading are implemented to validate the effectiveness of the devised testbed.
A 1.8mW Perception Chip with Near-Sensor Processing Scheme for Low-Power AIoT Applications

In the past few years, the demand for intelligence of IoT front-end devices has dramatically increased. However, such devices face challenges of limited on-chip resources and strict power or energy constraints. Recent progress in binarized neural networks has provided promising solutions for front-end processing system to conduct simple detection and classification tasks by making trade-offs between the processing quality and the computation complexity. In this paper, we propose a mixed-signal perception chip, in which an ADC-free 32x32 image sensor and a BNN processing array are directly integrated with a 180nm standard CMOS process. Taking advantage of the ADC-free processing architecture, the whole processing system only consumes 1.8mW power, while providing up to 545.4 GOPS/W energy efficiency. The implementation performance and energy efficiency are comparable with the state-of-the-art designs in much more advanced CMOS technologies. This work provides a promising alternative for low-power IoT intelligent applications.
Lowpass MASH DQEFM for 4G Wireless Receivers

The design and simulation of a lowpass multistage noise shaping (MASH) Differential Quantizer based Error Feedback Modulator (DQEFM) architecture suitable for data conversion in 4G wireless receivers is presented. The DQEFM structure has been incorporated for obtaining benefits like relaxed op-amp requirements and reduced sensitivity to mismatch effects. The performance of the proposed modulator has been evaluated through circuit-level simulations using HSPICE. The circuit-level simulations performed with 45nm CMOS process indicate that the proposed modulator attains a peak Signal to Noise and Distortion Ratio (SNDR) of 58 dB over a bandwidth of 20 MHz while dissipating 5.9 mW of power. The low operating frequency, low power operation and a Figure of Merit (FoM) of 0.23 pJ/conv-step shows the better performance of the proposed lowpass MASH DQEFM architecture.
Temporal measures for analysis of emotional states from human electroencephalography signals

The objective of this study is to understand the temporal relationship between different brain waves and emotions using Electroencephalography (EEG) data. The temporal parameters such as mean, variance, standard-deviation, skewness and entropy has been extracted from the given database for emotion analysis using physiological signals (DEAP). Also, the Fast Fourier transform (FFT) has been used to analyze brain frequency bands from the time series EEG signals. The comparison between user emotion ratings and frequency bands has been done to confirm the EEG findings. To the collected data, the experimental results show how brain waves fluctuate with different types of emotions. These results can be helpful for Neurophysiological experts to collate the ground truth. Further, this will also help in assimilating the observations of different individuals with perspective of the brain activity due to the Mirror Neuron System (MNS). The promising performance of the statistical or temporal procedures with the EEG brain signals helps us to develop robust Human Interface Emotion Detection Systems which can be used for further research.
Multi-User and Multi-View Human Eyes' Detection and Tracking

This paper presents a framework on multi-user and multi-view human eyes' detection and tracking. First, it uses fives kinds of AdaBoost face detectors with four different sizes at each area of image to detect faces in turn. Then, to locate eyes' positions, four kinds of AdaBoost eye detectors are used and if the eye-detection above fails, the prior knowledge of human organs' positions in anatomy is applied as a spare method. Next, it uses the unscented filter to predict the targets' next possible positions. Finally, the detection method above is used to detect the third frame and amend the relative forecasting. And repeat above cycle until tracking over. This framework is robust to subject's eyes' blinking, closing, wearing glasses and partly sheltering in multi-face and multi-view to a certain extent for the optimized structure performance and reasonable selected features. And because of the nonlinear filtering, it can track targets in curves with changing speeds. It mainly fits most usual vertical head scenes in monitoring environment.