Miracle-dz/UniFAR-FTUs huggingface dataset:
Area-Aware Routing and Spectrum Allocation for the Tidal Traffic Pattern in Metro Optical Networks

With the increasing bandwidth requirements for people and the development of urbanization, the movement of the population in the city (especially the supercity) has an increasing influence on the traffic distribution in both space and time dimensions. The imbalanced distribution results in the regional blocking of different areas in different time periods and reduces the spectrum resource utilization in elastic optical networks. To resolve this problem, this paper proposes a tidal traffic model to formulate a kind of tidal traffic phenomenon firstly. Based on the analysis for this model, the area-aware routing and spectrum allocation algorithm that focuses on the traffic adjustment in specific functional areas is proposed. And two benchmark algorithms named min-hop k-shortest path routing algorithm and occupied-slots-as-weight k-shortest path routing algorithm are introduced. The evaluation results show that, compared to the benchmark algorithms, the proposed area-aware algorithm could reduce the blocking probability efficiently from 2% to 47% with low time complexity.
A Practical Approach to Construct Profile Linkage Framework

When the number of social network users increases, the problem of profile linkage is recently enjoying popularity in deeper understanding online users' behaviors. Existing approaches address this issue at the expense of information loss, because they (1) exclude a significant portion of key information, (2) rank users with a lack of consideration on accuracy of profile. In this paper we propose Named-Entity Identity Linkage, a framework for exact social profile linkage. Different from traditional methods, our framework (1) finds out comprehensive information of virtual users by short-text named entity recognition, (2) builds users probabilistic profile library from heterogeneous platforms. Experiments on four popular social network systems show that the method is able to link users of different platforms correctly. It performs about 15% in accuracy and 10% in recall ratio better than state-of-the-art approaches.
“Coat & Pop”: On-Substrate Two-Step Composition Patterning Method of Liquid Metal Alloys for Flexible Devices

We have developed a novel on-substrate composition patterning process of room-temperature liquid metal alloys via a simple two-step method: “coat & pop”. This work present for the first time the integration of a single or several noble metals (Au, Ag or Cu) on Ga-based liquid metal droplets on a single substrate, by using a spontaneous chemical reaction then simple mechanical compression, to form biphasic and liquid composition patterns. The controllable deposition of noble metals encapsulation patterns and formation of several interfaced conductive biphasic intermetallic alloys on a single substrate were successfully demonstrated.
A Study of the IEEE802.11p (WAVE) and LTE-V2V Technologies for Vehicular Communication

V2V communication is being evolved as one of the most promising technologies in order to reduce road accidents, and traffic congestion. It also helps in providing road safety, traffic efficiency and infotainment applications. The vehicles are expected to share information about their speed, distance, velocity. This information could be leveraged to mitigate the problems leading to road accidents. In this paper, we present a detailed and in-depth study of two technologies- IEEE 802.11p and LTE-V2V being under active consideration for use in vehicular communication. In future work, we are willing to obtain the better results by considering IEEE 802.16e as well.
Towards Complete and Accurate Iris Segmentation Using Deep Multi-Task Attention Network for Non-Cooperative Iris Recognition

Iris images captured in non-cooperative environments often suffer from adverse noise, which challenges many existing iris segmentation methods. To address this problem, this paper proposes a high-efficiency deep learning based iris segmentation approach, named IrisParseNet. Different from many previous CNN-based iris segmentation methods, which only focus on predicting accurate iris masks by following popular semantic segmentation frameworks, the proposed approach is a complete iris segmentation solution, i.e., iris mask and parameterized inner and outer iris boundaries are jointly achieved by actively modeling them into a unified multi-task network. Moreover, an elaborately designed attention module is incorporated into it to improve the segmentation performance. To train and evaluate the proposed approach, we manually label three representative and challenging iris databases, i.e., CASIA.v4-distance, UBIRIS.v2, and MICHE-I, which involve multiple illumination (NIR, VIS) and imaging sensors (long-range and mobile iris cameras), along with various types of noises. Additionally, several unified evaluation protocols are built for fair comparisons. Extensive experiments are conducted on these newly annotated databases, and results show that the proposed approach achieves state-of-the-art performance on various benchmarks. Further, as a general drop-in replacement, the proposed iris segmentation method can be used for any iris recognition methodology, and would significantly improve the performance of non-cooperative iris recognition.
A Novel Ant Colony optimization Algorithm for Waste Collection Problem

Waste management, especially waste collection issue, has been an ultimate global issue. Particularly, problem of collecting waste in minimum time, especially in big cities, becomes a challenge that needs solution. In this paper, this problem is approached by a well-known problem called Vehicle Routing Problem (VRP). The objective is to generate collision-free trajectories along the process of collection while minimizing the traveling time of the slowest vehicle. A novel method to solve such the problem, namely, Augmented-Node-based Ant Colony optimization (ANACO), is introduced. The main feature of this method is the generation oftrajectories, i.e., paths and velocities, of all waste collection vehicles. The advantage of the proposed method is in the ability to provide velocity information besides path, which is totally different from the typical ACO algorithms that only provide path information. By the proposed method, the traveling time of the slowest vehicle can be minimized. Therefore, the requirement of fast-collecting capability can be satisfied. Simulation results are presented proving that a minimum-time and collision-free trajectories can be generated to solve the problem.
A Design of 6.8 mW All Digital Delay Locked Loop With Digitally Controlled Dither Cancellation for TDC in Ranging Sensor

This paper presents a design of 6.8 mW all digital delay locked loop (ADDLL) with digitally controlled dither cancellation (DCDC) for time to digital converter (TDC) in ranging sensors. ADDLL uses the accumulator (ACC) to control the delay of digitally controlled delay line (DCDL) during phase locking which utilizes less power and area as compared to analog delay locked loop (DLL). In the lock state, the ACC value dithers due to the closed loop operation. A digital controller is proposed to detect the lock state, performs dither cancelation and selects the optimum ACC value for controlling the delay of the replica DCDL for TDC operation. It helps the jitter reduction in the ADDLL. Additionally, it provides robustness against glitches, false locking and unlocking in a noisy environment. The ADDLL peak to peak jitter and RMS jitter at 625 MHz are 6.5 ps and 1.2 ps respectively. The ADDLL including DCDC is implemented on $0.18~\mu \text{m}$ CMOS technology with an operational range of 350~900 MHz. It consumes only 6.8 mW at 625 MHz power with 1.8 V power supply. The area utilization is 0.06 mm2.
Can a Smartband be Used for Continuous Implicit Authentication in Real Life

The use of cloud services that process privacy-sensitive information such as digital banking, pervasive healthcare, smart home applications requires an implicit continuous authentication solution, which will make these systems less vulnerable to the spoofing attacks. Physiological signals can be used for continuous authentication due to their uniqueness. Ubiquitous wrist-worn wearable devices are equipped with photoplethysmogram sensors, which enable us to extract heart rate variability (HRV) features. In this study, we show that these devices can be used for continuous physiological authentication for enhancing the security of the cloud, edge services, and IoT devices. A system that is suitable for the smartband framework comes with new challenges such as relatively low signal quality and artifacts due to placement, which were not encountered in full lead electrocardiogram systems. After the artifact removal, cleaned physiological signals are fed to the machine learning algorithms. In order to train our machine learning models, we collected physiological data using off-the-shelf smartbands and smartwatches in a real-life event. By applying a minimum quality threshold, we achieved a 3.96% Equal Error Rate. Performance evaluation shows that HRV is a strong candidate for continuous unobtrusive implicit physiological authentication.
Pre-Breakdown Streamer Propagation and Positive Lightning Breakdown Characteristics of Palm Oil Impregnated Aged Pressboard

This paper presents the investigation on the breakdown characteristics and pre-breakdown streamer propagation of Palm Oil (PO) impregnated aged pressboard under positive lightning impulse voltages. The experimental work was carried out under a non-uniform field with needle-plane electrodes configuration. The streamer stopping length and breakdown voltage of 2 types of refined, bleached, and deodorized palm oil were examined in the presence of new and aged pressboards. The pressboard was placed in parallel to the needle-plane electrode at a gap distance of 50 mm. The lightning breakdown voltage was applied to the samples based on 1 shot per step rising voltage method under positive polarity as per IEC 60897. The presence of impregnated pressboard in both PO slightly increases the 50% positive lightning breakdown voltages. PO impregnated pressboards have lower 50% positive lightning breakdown voltages than MO. After subjected to ageing, the positive lightning breakdown voltages for PO and MO impregnated pressboards decrease. In the presence of aged pressboard, the streamers in PO generally propagate further than MO at the same voltage level.
DDPG-Based Decision-Making Strategy of Adaptive Cruising for Heavy Vehicles Considering Stability

The decision-making system of intelligent vehicles is the core component of an advanced driving system for both passenger vehicles and commercial vehicles. Finding ways to improve decision-making strategies to suit the complex and unfamiliar environments is a standing problem for traditional rule-based methods. This paper proposes a semi-rule-based decision-making strategy for heavy intelligent vehicles based on the Deep Deterministic Policy Gradient algorithm. Firstly, according to the car-following characteristics, the problems of high dimensions and a large amount of data in vehicle action space and state space are solved by dimension reduction and interval reduction to accelerate the training process. Subsequently, an accurate three-axle vehicle load model is established to calculate the load transfer rate value and carry out active control to increase the roll stability of heavy vehicles at high-speed corners. Furthermore, the Deep Deterministic Policy Gradient algorithm is developed based on the reward function and update function to achieve adaptive cruise control objectives for heavy vehicles on different curvature roads. Finally, the effectiveness and robustness of the algorithm are verified through simulation experiments.
Plasmonic Photonic Crystal Fiber Refractive Index Sensor Based on External Sensing

A double core photonic crystal fiber (PCF) refractive index (RI) sensor is proposed in this work established on surface plasmon resonance (SPR). The PCF-SPR based RI sensor is investigated numerically by using finite element method (FEM). The sensor performance is studied with the methods of wavelength and amplitude interrogation. Numerical analysis of the sensor shows that it can detect analyte RI in the range of 1.33 to 1.41. The proposed sensor demonstrates maximum wavelength sensitivity of 19,500 nm/RIU and 17,000 nm/RIU for x- and $y$- polarization, respectively. In addition, the sensor exhibits the maximum amplitude sensitivity of 947 RIU−1 and 727 RIU−1 for $x$- and $y$- polarization, respectively. The high sensitivity and wide sensing range indicate that the proposed sensor can be used to identify unknown biological and biochemical analytes.
A Novel Hybrid Deep Learning Model for Sentiment Classification

A massive use of social media platforms such as Twitter and Facebook by omnifarious organizations has increased the critical individual feedback on the situation, events, products, and services. However, sentiment classification plays an important role in the user’s feedback evaluation. At present, deep learning such as long short-term memory (LSTM), gated recurrent unit (GRU), bidirectionally long short-term memory (BiLSTM) or convolutional neural network (CNN) are prevalently preferred in sentiment classification. Moreover, word embedding such as Word2Vec and FastText is closely examined in text for mapping closely related to the vectors of real numbers. However, both deep learning and word embedding methods have strengths and weaknesses. Combining the strengths of the deep learning models with that of word embedding is the key to high-performance sentiment classification in the field of natural language processing (NLP). In the present study, we propose a novel hybrid deep learning model that strategically combines different word embedding (Word2Vec, FastText, character-level embedding) with different deep learning methods (LSTM, GRU, BiLSTM, CNN). The proposed model extracts features of different deep learning methods of word embedding, combines these features and classifies texts in terms of sentiment. To verify the performance of the proposed model, several deep learning models called basic models were created to perform series of experiments. By comparing, the performance of the proposed model with that of past studies, the proposed model offers better sentiment classification performance.
Task Space Position Control of Slider-Crank Mechanisms Using Simple Tuning Techniques Without Linearization Methods

In this work, a position control in task space for slider-crank mechanisms is presented. In order to apply linear controllers it is required to linearize the mechanism dynamics at an equilibrium point. However, complete dynamic knowledge is needed and the linearization technique gives an oversimplified model that affects the control performance. In this work, it is proposed a novel method to design task space controllers without using the complete knowledge of the mechanism dynamics and linearization methods. From the extended dynamic model of parallel robots, it can be seen that the end-effector (slider) dynamics is expressed as a linear system that can be used directly for the control design instead of the complete mechanism linear dynamics. The approach requires a minimal knowledge of the mechanism dynamics and avoids linearization methods. To verify our approach, it is used pole placement and sliding mode controllers whose gains are tuned according to the slider dynamics. A linear sensor is mounted at the slider to measure its position and avoids considering noise and disturbances at links before the slider. Simulations and experiments are presented to validate our approach using two kinds of slider-crank mechanisms.
Significance of Phase in DNN based speech enhancement algorithms

Most of the speech enhancement algorithms rely on estimating the magnitude spectrum of the clean speech signal from that of the noisy speech signal using either spectral regression or spectral masking. Because of difficulty in processing the phase of the short time Fourier transform (STFT), noisy phase is reused while synthesizing the waveform from the enhanced magnitude spectrum. In order to demonstrate the significance of phase in speech enhancement, we compare the phase obtained from different reconstruction methods, like Griffin and Lim, minimum phase, with that of the gold phase (clean phase). In this work, spectral magnitude mask (SMM) is estimated using deep neural networks to enhance the magnitude spectrum of the speech signal. The experimental results showed that gold phase outperforms the phase reconstruction methods in all the objective measures, illustrating the significance of enhancing the noisy phase in speech enhancement.
Sparse Bayesian Broad Learning System for Probabilistic Estimation of Prediction

Broad learning system (BLS) is proposed as a competitor to deep neural network, which is constructed using only single hidden layer instead of multilayered structure. It also manages incremental learning with intelligible mathematical derivation by pseudoinverse. Despite its empirical success, the limitations of BLS cannot be overlooked, especially for its sensibility to the number of feature nodes. For this reason, large model is required in training, which may easily cause overfitting. In this paper, a probabilistic model called sparse Bayesian broad learning system (SBBLS) is proposed to dispose of this defect by linearly-weighting a small group of basis functions from an enormous number of candidates within Bayesian framework. Outstanding sparsity and generalization can be observed in SBBLS. More distinctively, SBBLS is capable of outputting probabilistic estimation of prediction for further decision analysis while maintaining inherent properties of BLS simultaneously. Experimental results demonstrate that the proposed SBBLS model could achieve comparable performance to BLS both in regression and classification with probabilistic interpretability.
Distinguishing Similar Design Pattern Instances through Temporal Behavior Analysis

Design patterns (DPs) encapsulate valuable design knowledge of object-oriented systems. Detecting DP instances helps to reveal the underlying rationale, thus facilitates the maintenance of legacy code. Resulting from the internal similarity of DPs, implementation variants, and missing roles, approaches based on static analysis are unable to well identify structurally similar instances. Existing approaches further employ dynamic techniques to test the runtime behaviors of candidate instances. Automatically verifying the runtime behaviors of DP instances is a challenging task in multiple aspects. This paper presents an approach to improve the verification process of existing approaches. To exercise the runtime behaviors of DP instances in cases that test cases of legacy systems are often unavailable, we propose a markup language, TSML (Test Script Markup Language), to direct the generation of test cases by putting a DP instance into use. The execution of test cases is monitored based on a trace method that enables us to specify runtime events of interest using regular expressions. To characterize runtime behaviors, we introduce a modeling and specification method employing Allen's interval-based temporal relations, which supports variant behaviors in a flexible way without hard-coded algorithms. A prototype tool has been implemented and evaluated on six open source systems to verify 466 instances reported by five existing approaches with respect to five DPs. The results show that the dynamic analysis increases the F1-score by 53.6% in distinguishing similar DP instances.
Speech recognition for Indonesian language and its application to home automation

The practical aspects of developing an Automatic Speech Recognition System with Hidden Markov Toolkit are reviewed. Steps are explained concerning hardware, software, libraries, applications and computer programs used. The common procedure to rapidly apply the speech recognition system is summarized. The procedure is illustrated, to implement a speech-based electrical switch in home automation for the Indonesian language. The main key of the procedure is to match the environment for training and testing using the training data recorded from the testing program, HVite. Often the silence detector of HVite is wrongly triggered by noises because the microphone is too sensitive. This problem is mitigated by simply scaling down the volume. In this sub-word phone-based speech recognition, noise is included in the training database and labeled particularly. Illustration of the procedure is applied to a home automation application. Electrical switches are controlled by Indonesian speech recognizer. The results show a 100% command completion rate.
Online Travel Service Aggregators and Customer Satisfaction

Internet economy in India is growing rapidly and the rising ‘netizen’ population makes it both attractive and lucrative for marketers. Data affordability has added to the growth story and inching the country towards becoming ‘a trillion-dollar digital economy by 2025’ as per [1] PwC report (2018). However the digital marketplace is flooded with number of business models vying with each other. Manufactures are selling directly thorough their website but cannot ignore strong players like Make My Trip, GoIbibo etc. and are present on these sites as well to reach the customers. The current paper looks at the digital marketplace in India and delineates where the customer is shopping for their travel needs and why they are going to these sites. It delves into the customer satisfaction level with the websites and identifies the gap therein. The paper is relevant for various stakeholders as it provides clarity about the reasons why customers are going to the third party sites for making their purchases when the companies own website is present for the same and the gap between their expectations and perceptions.
Dependency Solving Is Still Hard, but We Are Getting Better at It

Dependency solving is a hard (NP-complete) problem in all non-trivial component models due to either mutually incompatible versions of the same packages or explicitly declared package conflicts. As such, software upgrade planning needs to rely on highly specialized dependency solvers, lest falling into pitfalls such as incompleteness—a combination of package versions that satisfy dependency constraints does exist, but the package manager is unable to find it. In this paper we look back at proposals from dependency solving research dating back a few years. Specifically, we review the idea of treating dependency solving as a separate concern in package manager implementations, relying on generic dependency solvers based on tried and tested techniques such as SAT solving, PBO, MILP, etc. By conducting a census of dependency solving capabilities in state-of-the-art package managers we conclude that some proposals are starting to take off (e.g., SAT-based dependency solving) while—with few exceptions—others have not (e.g., outsourcing dependency solving to reusable components). We reflect on why that has been the case and look at novel challenges for dependency solving that have emerged since.
T/R-Switch Composed of 3 High-Voltage MOSFETs with 12.1 µW Consumption that can Perform Per-channel TX to RX Self-Loopback AC Tests for 3D Ultrasound Imaging with 3072-channel Transceiver

This paper presents an area- and power-efficient TX/RX-isolation switch implemented in a 3072-ch ultrasound transceiver IC. The proposed dynamic gate-source shunt topology, which utilizes a negative-HV-transmit-driven shunt switch, eliminates area- and power-hungry HV level shifters and ensures OFF during TX periods. In addition, source-driven HV-PMOS for ON/OFF control enables both gate charging and discharging using single HV-PMOS, thus contributing to area reduction. Moreover, by preparing an attenuation mode, a per-channel TX to RX self-loopback test can be performed. This function provides an on-wafer AC test without probing 3072 electrodes and can be applied to field diagnosis of assembled ultrasound probes.