Miracle-dz/UniFAR-FTUs huggingface dataset:
Autonomous Ledge Detection and Landing with Multi-rotor UAV

As surveillance and reconnaissance utilizing UAVs become more prominent today thanks to the advancement in MEMS sensors and small yet powerful microprocessors, vertical-take-off-and-landing (VTOL) vehicles such as multirotor UAVs dominate this area due to its capability of hovering in the air. The endurance, however, is a downside of such operation. In this manuscript, we propose a surveillance solution with multi-rotor UAV by perching at the edge of roof near the target-of-interest, enabling long hour monitoring capability. Challenges on the mechanical design and autonomous ledge detection of the UAV will be addressed and possible solutions will be discussed. Flight experiments were conducted and positive results will be published in this manuscript.
Classification of Seizure Through SVM Based Classifier

Epilepsy is the most prevalent neural disorder characterized by abrupt and repetitive impairment of brain known as seizure, whose clinical symptoms are hyper synchronous activities of nerve cells in the brain. Since seizure, in general, occur very infrequently it is highly recommended for self-regulated disclosure of it during longstanding EEG measurement. The data handled in our work is publicly accessible online comprising of five classes. The segments in data set for each class were partitioned into two parts. Former comprised first 16 seconds (about 68%) of EEG which was used to train the network and rest of the signal were marked as test data. Statistical features for each class were evaluated. A supervised learning algorithm which is mostly used for classification and regression known as Support Vector Machine was used for classification of each set representing the different class. The classification results achieved taking all the five class was 91.42%. In order to confirm the accuracy of the classifier, the classifier is tested for different classification problem that were reported previously.
Contributions of prescribed and non-prescribed opioids to opioid related deaths: population based cohort study in Ontario, Canada

Abstract Objective To describe the contributions of prescribed and non-prescribed opioids to opioid related deaths. Design Population based cohort study. Setting Ontario, Canada, from 1 January 2013 to 31 December 2016. Participants All Ontarians who died of an opioid related cause. Exposure Active opioid prescriptions, defined as those with a duration overlapping the date of death, and recent opioid prescriptions, defined as those dispensed in the 30 and 180 days preceding death. Postmortem toxicology results from the Drug and Drug/Alcohol Related Death database were used to characterise deaths on the basis of presence of prescribed and non-prescribed (that is, diverted or illicit) opioids, overall and stratified by year and age. Results 2833 opioid related deaths occurred. An active opioid prescription on the date of death was relatively common but declined slightly throughout the study period (38.2% (241/631) in 2013 and 32.5% (278/855) in 2016; P for trend=0.03). Older people and women were relatively more likely to have an active opioid prescription at time of death. In 2016, 46% (169/364) of people aged 45-64 had an active opioid prescription compared with only 12% (8/69) among those aged 24 or younger (P for trend<0.001). Similarly, 46% (124/272) of women had an active opioid prescription at time of death compared with 26.4% (154/583) of men (P<0.001). Among people with active opioid prescriptions at time of death, 37.8% (375/993) also had evidence of a non-prescribed opioid on postmortem toxicology. By 2016, the non-prescribed opioid most commonly identified after death was fentanyl (41%; 47 of 115 cases). Among people without an active opioid prescription at time of death, fentanyl was detected in 20% (78/390) of deaths in 2013, increasing to 47.5% (274/577) by 2016 (P<0.001). Conclusions Prescribed, diverted, and illicit opioids all play an important role in opioid related deaths. Although more than half of all opioid related deaths still involved prescription drugs (either dispensed or diverted) in 2016, the increased rate of deaths involving fentanyl between 2015 and 2016 is concerning and suggests the need for a multifactorial approach to this problem that considers both the prescribed and illicit opioid environments.
AMI-Enabled Distribution Network Line Outage Identification via Multi-Label SVM

This letter proposes an effective data mining method for identifying distribution network line outages by leveraging data collected through Advanced Metering Infrastructure (AMI). The line outage identification method is developed based on a Multi-Label Support Vector Machine (ML-SVM) classification scheme that utilizes the status of customers’ smart meters as input data and accordingly identifies the outage/operational status of distribution lines. The <inline-formula> <tex-math notation="LaTeX">$\text{F}_{\beta }$ </tex-math></inline-formula>-score is proposed to validate the performance of the classifier through numerical simulations.
Analysis of Data Structures Involved in RPQ Evaluation

A fundamental ingredient of declarative graph query languages are regular path queries (RPQs). They provide an expressive yet compact way to match long and complex paths in a data graph by utilizing regular expressions. In this paper, we systematically explore and analyze the design space for the data structures involved in automaton-based RPQ evaluation. We consider three fundamental data structures used during RPQ processing: adjacency lists for quick neighborhood exploration, visited data structure for cycle detection, and the representation of intermediate results. We conduct an extensive experimental evaluation on realistic graph data sets and systematically investigate various alternative data structure representations and implementation variants. We show that carefully crafted data structures which exploit the access pattern of RPQs lead to reduced peak memory consumption and evaluation time.
Learning-Based Reference-Free Speech Quality Measures for Hearing Aid Applications

Objective measures of speech quality are highly desirable in benchmarking and monitoring the performance of hearing aids (HAs). Existing HA speech quality indices such as the hearing aid speech quality index (HASQI) are intrusive in that they require a properly time-aligned and frequency-shaped reference signal to predict the quality of HA output. Two new reference-free HA speech quality indices are proposed in this paper, based on a model that amalgamates perceptual linear prediction (PLP), hearing loss (HL) modeling, and machine learning concepts. For the first index, HL-modified PLP coefficients and their statistics were used as the feature set, which was subsequently mapped to the predicted quality scores using support vector regression (SVR). For the second index, HL-impacted gammatone auditory filterbank energies and their second-order statistics constituted the feature set, which was again mapped using SVR. Two databases involving HA recordings were collected and utilized for the evaluation of the robustness and generalizability of the two indices. Experimental results showed that the index based on the gammatone filterbank energies not only correlated well with HA quality ratings by hearing impaired listeners, but also exhibited robust performance across different test conditions and was comparable to the full-reference HASQI performance.
Z-scan measurements of water from 1150 to 1400  nm.

Understanding the nonlinear properties of water is essential for laser surgery applications, as well as understanding supercontinuum generation in water. Unfortunately, the nonlinear properties of water for wavelengths longer than 1064 nm are poorly understood. We extend the application of the Z-scan technique in water to determine its nonlinear refractive index (n2) and nonlinear absorption (β) for wavelengths in the 1150-1400 nm range, where linear absorption is also significant. We observe the wavelength-dependent variation of the nonlinear properties of water around the water absorption band.
Deep Sparse Coding for Non–Intrusive Load Monitoring

Energy disaggregation is the task of segregating the aggregate energy of the entire building (as logged by the smartmeter) into the energy consumed by individual appliances. This is a single channel (the only channel being the smart-meter) blind source (different electrical appliances) separation problem. The traditional way to address this is via stochastic finite state machines (e.g., factorial hidden Markov model). In recent times, dictionary learning-based approaches have shown promise in addressing the disaggregation problem. The usual technique is to learn a dictionary for every device and use the learned dictionaries as basis for blind source separation during disaggregation. Prior studies in this area are shallow learning techniques, i.e., they learn a single layer of dictionary for every device. In this paper, we propose a deep learning approach—instead of learning one level of dictionary, we learn multiple layers of dictionaries for each device. These multi-level dictionaries are used as a basis for source separation during disaggregation. Results on two benchmark datasets and one actual implementation show that our method outperforms state-of-the-art techniques.
Embracing invention similarity for the measurement of vertically overlapping claims

ABSTRACT Clear and well-defined patent rights can incentivize innovation by granting monopoly rights to the inventor for a limited period of time in exchange for public disclosure of the invention. However, with cumulative innovation, when a product draws from intellectual property held across multiple firms (including fragmented intellectual property or patent thickets), contracting failures may lead to suboptimal economic outcomes. However, an alternative theory, developed by a variety of scholars, contends that patent thickets have a more ambiguous effect. Researchers have developed several measures to gauge the extent and impact of cumulative innovation and the various channels of patent thickets. This paper contends that mis-measurement may contribute to the incoherence and overall lack of consensus within the patent thickets literature. Specifically, the literature is missing a precise measure of vertically overlapping claims. We propose a new measure of vertically overlapping claims that incorporates invention similarity to more precisely identify inventive overlap. The measure defined in this paper will enable more accurate measurement, and allow for novel economic research on cumulative innovation, fragmentation in intellectual property, and patent thickets within and across all patent jurisdictions.
Prescribed Performance Constrained Nonlinear Robust Control for Close Formation Flight

This paper presents a novel robust nonlinear control algorithm for close formation flight with guaranteed prescribed transient performance. The proposed control algorithm is composed of a constrained baseline controller and an uncertainty and disturbance estimator. Some performance specifications will be ensured by the baseline controller, such as the maximum overshoots, the least convergence rates, and the maximum steady tracking errors. Based on the baseline controller, an uncertainty and disturbance estimator is established to compensate for any model uncertainties and formation aerodynamic disturbances within the nonlinear aircraft model in close formation. The feasibility and efficiency of the proposed control algorithm are finally validated by numerical simulation.
Analysis and Comparative Study of Flux Weakening Capability in Fractional-Slot Concentrated Windings

This paper presents a detailed investigation of the winding inductance and the flux weakening capability in fractional-slot motors equipped with concentrated windings. An analytical model has been developed for evaluating the winding inductance by merging existent theories. Subsequently, a simple factor called weakening factor, which is proportional to the d-axis inductance and the pole pair, has been introduced to indicate the level of the flux weakening capability for different pole/slot combinations in the case where the air gap or slot leakage inductance is the dominant component. From the comparative study, it is shown that the multiple 4-pole/3-slot, i.e., ${\boldsymbol{S}_{\boldsymbol{pp}}} = 1/4$, can have the highest flux weakening capability resulting from the highest air gap inductance. The experimental results from several prototype motors, together with finite element analysis, are employed to confirm the validity of key analyses discussed in the paper.
Evaluation of Synthetic Speech Quality by Statistical Analysis of Voiced and Unvoiced Part Durations

The paper describes a system for automatic evaluation of differences in time duration, phrasing, and time structuring within an analysed sentence. The proposed system was successfully tested in evaluation of sentences originated from male and female voices and produced by a speech synthesizer using the unit selection method with two different prosody manipulation approaches. A detailed analysis shows great influence of the number of statistical parameters on correctness and precision of evaluated results. Larger size of the processed speech material has positive impact on stability of the evaluation process. The obtained results are in principal correlation with the evaluation based on the standard listening test method.
Adaptive Classification for Spam Detection on Twitter with Specific Data

At present, the popularity of Twitter is more and more increasing. Many users can find information that tweets to Twitter. Some information is beneficial, and at the meantime, some information is created from spammers who would like to promote their websites or services. They are harmful to normal users by using the Twitter channel to exploit common user interests, such as sharing malicious links and ads. In order to stop spammers, many researchers have proposed tools to filter those junk. However, the focus of recent works is how to create streaming spam detection methods. In this paper, we proposed the adaptive data classification for spam detection by using spam word lists and a commercial URL-based security tool. We analyzed data by Naïve Bayes algorithm with both data types including all data and specific data. It can help to improve the performance of the spam detector is better than usual. We can show our proposed methods fulfillment in the experiments result.
Power Trading in Secure Cooperative Relay Network with Cournot Duopoly Model

Cooperative relay network (CRN) has attracted an increasing interest as it plays an extremely significant role to improve security in wireless communication. The source, due to limited battery power is in-need of cooperation of a relay to communicate with the receiver. Thus a cooperation-request is broadcasted by the source specifying its quality of service (QoS) requirement in terms of desired secrecy rate. We present a secure power trading (SPT) model using Cournot duopoly based game theoretic approach in cooperative wireless communication network for power-efficiency and reliability with QoS performance requirement. A CRN model named SPT-CRN is thus formulated and analyzed as a Cournot duopoly game in this paper. Our analysis shows several interesting characteristics of SPT-CRN model and finally an equilibrium solution is obtained.
Privacy of Clients' Locations in Big Data and Cloud Computing

Amid the very hot issues, nowadays, the one related to Locations' privacy (GPS related) finds itself in top position. When it comes to talking about clients' locations in cloud or big data, the probable risk to privacy of clients' location is one of the major challenges should to be faced. In the recent years, a lot of developers and researchers have been paying attention to improve methods to provide privacy for data of clients' locations which it always processed by third-party. Big data could be like a puzzle for many researchers if they didn't understand it in the correct-side. Big data has to be understood as the process of gathering as much data as can be permitted in order to collect knowledge out of them (ideally in ground-breaking ways). so, this concept gives us attention that is privacy of clients' locations in cloud or big data could be under risks if it is used to collect knowledge or sell it to third-party. In our research, we try to show how we have implemented our algorithm (Diff-Anonym) in real data set (available at http://openaddresses.io) as to offer privacy for the clients' Locations in Big Data and cloud computing, as well as to improve our previous work which was simulation in normal data that appeared little differences in the results.
Latency Aware NOMA Based Device-to-Device Communication

Device-to-Device (D2D) links in cellular networks are expected to satisfy the increasing the local data traffic demands. This technique aims to admit more users into the network, providing spectral gains over traditional cellular communication. In this paper, we study a latency-aware resource allocation problem in which cellular devices in proximity communicate with each other directly. We consider the D2D link to underlay the cellular uplink where one cellular user and a group of D2D candidate users exist. Based on the channel state and the latency constraint, one of the D2D users are chosen to share the channel with the cellular user in non-orthogonal multiple access (NOMA) fashion. We formulate the problem as a mixed integer non-linear programming, where the sum data rate is maximized subject to the interference and latency restrictions, and evaluate the performance in terms of latency and throughput for different implementation strategies.
Gauging Ecliptic Sentiment

With the Great American Eclipse of 2017, the National Aeronautics and Space Administration's (NASA) production of social media content was crucial to the general public's awareness and understanding of the event. To date, it may have been NASA's largest production of an event spanning many social media platforms and hundreds of media outlets. Since NASA is government funded, the millions of dollars spent on their social media campaign of the eclipse requires evaluation. The primary goal with this paper is to understand how the public perceived the social media coverage that NASA provided, specifically in the world of Twitter, a free social networking microblogging service that allows registered members to broadcast short posts called tweets. The aforementioned goals are accomplished through sentiment analysis and the spotting of trends within Twitter data attached to the eclipse itself and to NASA. Furthermore, a framework is created that can be applied in the future to gauge the effectiveness of social media campaigns in a variety of categories in a simple and cost-effective manner.
Optimization of PID controller for Automatic voltage regulator system using Taguchi method

This paper presents a optimization of PID controller using Taguchi method for Automatic voltage regulator system. Taguchi method has orthogonal array that depends on number of factors and their levels. Taguchi method is useful when system parameters are subjected to change in a given range. This method reduces the variability of the response by reducing the number of experiments. The number of experiments can be reduced by varying the multiple factors together. This reduces computational burden on the AVR system. The main objective of this method is to find the combination of factors such that the system has lower values of overshoot, Rise time, settling time and steady state error. MINITAB 17 software is used for taguchi approach. MATLAB/Simulink software is used for the simulation. This response is then compared with fuzzy logic controller. PID optimized with Taguchi method has better dynamic response than fuzzy logic controller method.
Incorporating Statistical Machine Translation Word Knowledge Into Neural Machine Translation

Neural machine translation (NMT) has gained more and more attention in recent years, mainly due to its simplicity yet state-of-the-art performance. However, previous research has shown that NMT suffers from several limitations: source coverage guidance, translation of rare words, and the limited vocabulary, while statistical machine translation (SMT) has complementary properties that correspond well to these limitations. It is straightforward to improve the translation performance by combining the advantages of two kinds of models. This paper proposes a general framework for incorporating the SMT word knowledge into NMT to alleviate above word-level limitations. In our framework, the NMT decoder makes more accurate word prediction by referring to the SMT word recommendations in both training and testing phases. Specifically, the SMT model offers informative word recommendations based on the NMT decoding information. Then, we use the SMT word predictions as prior knowledge to adjust the NMT word generation probability, which unitizes a neural network based classifier to digest the discrete word knowledge. In this paper, we use two model variants to implement the framework, one with a gating mechanism and the other with a direct competition mechanism. Experimental results on Chinese-to-English and English-to-German translation tasks show that the proposed framework can take advantage of the SMT word knowledge and consistently achieve significant improvements over NMT and SMT baseline systems.
Binary Edwards Curves for Intrinsically Secure ECC Implementations for the IoT

: Even if recent advances in public key cryptography tend to focus on algorithms able to survive the post quantum era. At present, there is a urgent need to propose fast, low power and securely implemented cryptography to address the immediate security challenges of the IoT. In this document, we present a new set of Binary Edwards Curves which have been deﬁned to achieve the highest security levels (up to 284-bit security level) and whose parameters have been deﬁned to ﬁt IoT devices embedding 32-bit general purpose processors. We optimized the choice of the point generator with the w -coordinate to save a multiplication in the addition and doubling formulae. We manage to compute one step of the Montgomery Ladder in 4 multiplications and 4 squares. On top of the performance beneﬁts, cryptography over such curves have some intrinsic security properties against physical attacks.
Experimental Analysis of Quasi-Ballistic Transport in Advanced Si  ${n}$ FinFETs Using New Extraction Method

For the first time, the drain-bias dependence of mean free path, predicted by previous Monte Carlo simulations, is experimentally confirmed in Si <inline-formula> <tex-math notation="LaTeX">${n}$ </tex-math></inline-formula>FinFETs by using an improved formulation, considering the additional scatterings attributed to source/drain as well as the carrier degeneracy. This letter indicates that, for optimized Si <inline-formula> <tex-math notation="LaTeX">${n}$ </tex-math></inline-formula>FinFETs, the carrier velocity and drive current still increase monotonically with decreasing channel length. However, the degradation in mean free path leads to the saturation in ballistic ratio (~60%), resulting in quasi-ballistic transport for devices even with sub-20-nm channel length.