Miracle-dz/UniFAR-FTUs huggingface dataset:
Study of Behaviors of Motion Models in High-Order Systems

This paper presents several proposed motion models of high-order physical systems in three main concepts called macro-scale, regular-scale, and nano-scale. In fact, it is very difficult to find an exact numerical solution for the high-order differential equations because all numerical methods only yield the approximate solutions. In addition, loop gain is not widely used in many negative feedback systems because it is an approximation value. To overcome the limitations of the high-order differential equations and the loop gain, the waveforms of the physical periodic motions are expressed by helix functions at time variation, and the characteristics of complex functions are used to examine the behaviors of the transmission spaces and the transmission networks in the different motion models including the Earth's motion, the simple pendulum systems, and the electronic systems. Furthermore, the force of attraction and the friction or the resistance in the different scales obey the conservation law and the superposition principle; therefore, three superposition formulas are introduced to derive the transfer functions in high-order mechatronic systems. The operating regions, the effects of the overshoot phenomena, the breaking chemical bonds, and the difference between negative and positive feedbacks in these systems are also introduced. As a result, the use of complex functions, helix waves, and superposition principle leads to a complete control theory with which many behaviors of the physical systems can be explained and predicted easily.
Energy-Efficient Virtual Machine Migration Approach For Optimization Of Cloud Data Centres

Cloud computing inherits sharing of data from pool of resources existing in data centres when ever demanded. The imminent requirement for this purpose is proficiency of the data centre for fulfilment of this coveted objective. The pursuit of energy-efficient peak performance level is challenged by a simultaneous hike of energy consumption. To negotiate this problem of excessive energy consumption the enhancement of system’s proficiency is of paramount importance. It is addressed by achieving better resource utilization by dint of the mechanism of virtual machine migration where placement of virtual machines on physical machines is suitably addressed for improved resource utilization and reduced energy consumption as a consequence. The process of virtual machine migration incurs enhanced network-related traffic hence it is challenged by constraint network capacity. Inter - data centre virtual machine migration having network capacity constraints reveals itself as an NP-Hard problem. This can be addressed by applying heuristic algorithms to secure optimal virtual machine migration solution. For attainment of green cloud computing solutions the virtual machine migration is augmented by certain other techniques which are used in data centres. These techniques minimize the operational costs, CO2 emission and power consumption by suitable resource allocation, virtualization and virtual machine migration.
An Efficient Algorithm for the Anchored k-Core Budget Minimization Problem

Online social networking companies devote much effort to maximize the engagement of users. Most existing methods assume that the budget is already known and focus on heuristic solutions to maximize the number of users that keep engaged. However, many real-life applications aim to keep the engagement of the majority of users in the social network. In such scenarios, the budget is flexible, and the goal is to minimize the budget used to keep these users active. In this paper, we define and study the anchored k-core budget minimization problem. Given an undirected graph G, a degree constraint k and a quota q for engaged users, this problem aims to minimize the budget that assures at least q users not in k-core keeping engaged.We propose CLOCK, an efficient greedy algorithm to solve the anchored k-core budget minimization problem. The main idea is to delete the anchored vertex with the lowest score in every iteration while maintaining the required number of engaged users. By considering the influence among the anchored vertices, a scoring function is well designed to identify the anchored vertices with high quality. Then we propose two merging strategies to get better anchored vertices. In addition, we dynamically maintain all anchored vertices by a local update strategy which avoids many redundant calculations. Finally, we conduct extensive experiments on real world datasets, demonstrating the effectiveness and efficiency of CLOCK.
Exploratory Data Analysis in SAP IQ Using Query-Time Sampling

As businesses continue to consume and produce ever-growing volumes of data, exploratory data analysis (EDA) is becoming an integral part of everyday operations. While online analytical processing (OLAP) systems in general – and column-oriented relational database management systems (RDBMS) in particular – are equipped with powerful tools to plough through petabytes of data, analytical queries may take seconds to execute, which is not always desirable in exploratory data analysis. Data scientists often need tools for fast visualization of data, and they are interested in identifying subsets of data that need further drilling-down before running computationally expensive analytical functions. In this paper, we describe our early work on extending SAP IQ (a disk-based columnar RDBMS) to support approximate query processing for exploratory data analysis using a technique known as query-time sampling. Specifically, we introduce two classes of novel samplers: (i) a stratified sampler with randomized row access to address the early-row bias problem in sampling, and (ii) hash-based equi-join samplers that are outlier-aware. We demonstrate how SAP IQ’s polymorphic table function (PTF) technology can be utilized to implement these samplers as new query plan operators.
SING: Sequence Indexing Using GPUs

Data series similarity search is a core operation for several data series analysis applications across many domains. This has attracted lots of interest that led to the development of several indexing techniques. Nevertheless, these techniques fail to deliver the similarity search time performance that is needed for interactive exploration, or analysis of large data series collections. We propose SING, the first data series index designed to take advantage of Graphics Processing Units (GPUs). SING is an in-memory index that uses CPU+GPU co-processing (as well as SIMD, multi-core and multi-socket architectures), in order to accelerate similarity search. Our experimental evaluation with synthetic and real datasets shows that SING is up to 5.1x faster than the state-of-the-art parallel in-memory approach, and up to 62x faster than the state-of-the-art parallel serial scan algorithm. SING achieves exact similarity search query times as low as 32msec on 100GB datasets, which enables interactive data exploration on very large data series collections.
A Hetero-Dielectric Double-Gate Junctionless FET with Spacer for Improved Device Performances

In this paper, we lay out a simulation study of DC parameters for a hetero-dielectric junctionless field transistor along with the best possible combination of high-k/low-k spacer dielectric on both ends of the gate oxides of the device. Various parameters, such as ON-current, subthreshold leakage current and current switching ratio are analyzed and compared with single-material gate (i.e. conventional) DG-JLFET. A significant improvement in the current switching ratio is observed for the presented device when the length ratio of two gate oxides is kept at an optimum value with high-k and low-k spacer on source and drain end, respectively. It is mainly attributed due to the fact that different spacer and gate dielectric introduced at both the ends alters the fringing electric field along the channel, mainly at channel-drain interface, and eventually reduces the OFF-state current by at least 100 fold as compared to that of SMG-JLFET.
Design and Simulation of Power efficient SRAM

Portable or handheld devices are growing at a fast pace nowadays and that is motivating designers to go for advanced low power circuit designs. The challenge is to reduce the existing topologies keeping the performance unhindered. Technology advances have always been accompanied by rise in the density of memory design. Power efficient memory designs are actively being used in biomedical sensors and space applications. The various designs in Static Random Access Memory (SRAM) circuit such as power dissipation, read and write cycle delays and signal-to-noise margin are modified by change in transistor sizing. In the present work, variation of width in a fixed technology length, also called as the aspect ratio in case of non-uniform lengths is chosen such that it best suits the requirements. The effect of change in transistor size on power dissipation of the SRAM cell circuit is discussed. The 6T SRAM cell configuration is one of the most widely used because of its speed and lesser sensitivity to noise and soft errors. The results show that the implemented circuit has lower power dissipation and comparable delays to the existing circuits. The proposed circuit has a power dissipation of 53pW and a delay of 77ps @Vdd=1.8V on 250nm technology length.
Accurate and Efficient Matrix Completion Using Cascaded Deep Neural Network

The matrix completion problem, which recover the missing data from the observed ones, had been widely studied in recent years. Although deep learning techniques had been applied in varies fields, limited works had done on matrix recovery. In this paper, we proposed a new deep neural network (DNN) model by integrating optimization theory and deep learning technique to solve the matrix completion problem. A cascaded neural network that contains the idea of alternating optimization is trained, and the application of SAR data reconstruction and imaging is used for evaluation. Experimental results shown that the proposed model can achieve better performance with less computational complexity when the sampling rate is sufficiently low.
Multiclass Classification of Cardiac Rhythms on Short Single Lead ECG Recordings using Bidirectional Long Short-Term Memory Networks

The recognition of cardiac rhythms is a topic of great relevance, particularly using short single-lead ECG recordings, due to its potential to early detect cardiovascular diseases and take actions quickly to preserve peoples wellbeing. Among cardiac arrhythmias, atrial fibrillation is the most common sustained cardiac arrhythmia, with significant mortality and morbidity rates. Several approaches have been conceived to identify cardiac rhythms, from the comparison of heart rate with adaptive and fixed thresholds to the application of deep and machine learning techniques. In this work, the classification of three cardiac rhythms (normal, atrial fibrillation, and other arrhythmias), as well as the identification of noise recordings, were performed using bidirectional LSTM networks that exploit the ECG signal (a representation of the cardiac electric activity) and time series containing information about the auricular and ventricular activities. A Monte Carlo 10-fold cross-validation of 10 iterations was performed to assure the generalization of the classifiers and the replicability of the results. An average accuracy of 77.97% was obtained to recognize the four classes but increase up to 85.95% when noise recordings were left out of the classification process. Moreover, micro F1 scores of 89.96%, 79.23%, and 79.77% were obtained for normal rhythm, atrial fibrillation, and other arrhythmias, respectively. The imbalance of classes and the characteristic patterns of normal rhythm and atrial fibrillation were the main factors associated with these performances.
Skin Mimicked Sliding Triboelectric Sensor to Detect Caressing Touch for Prosthetic Users

In recent years, wearable triboelectric nanogenerators (TENGs) have attracted attention due to its highly flexible, scalable, and low-cost features. Here in this paper, the authors have designed a sliding touch sensor which mimics the human skin to detect caressing touch based on the principle of triboelectric effect using COMSOL Multiphysics simulation software. Various triboelectric materials like Polytetrafluoroethylene(PTFE),Polyvinylchioride(PVC) and Polymethyl methacrylate(PMMA) were selected based on the triboelectric series for the comparative study on the effect of these materials on the proposed structure. The study of the device is carried out considering the voltage and charge variation obtained during the caressing of finger over the rectangular block of the polymer.
A Real-Time Speech Enhancement Algorithm Based on Convolutional Recurrent Network and Wiener Filter

Major breakthroughs have been made in speech enhancement with the introduction of deep learning. However, the noise reduction performance under the lower signal-to-noise ratio (SNR) conditions and the noise generalization ability of the model are still to be improved. In this paper, we propose a novel real-time monaural speech enhancement algorithm by combining the convolutional recurrent network (CRN) and Wiener filter. The CRN includes a convolutional encoder-decoder (CED) and a gated recurrent unit (GRU), and the Wiener filter gain function is optimized according to the output of the CRN. The proposed CRN-Wiener model adopts a causal system and achieves a high parameter efficiency, which results in a real-time speech enhancement system. The experimental results show that the proposed system obviously outperforms the baselines under the lower SNR conditions. Moreover, it achieves a stronger noise generalization performance for both the unmatched noises and the untrained SNRs.
Stock Price Prediction using Machine Learning and Sentiment Analysis

The stock market is a very dynamic market where nothing is as stable as a rock but as the technology is upgrading there are many ways and methods one can try to learn this dynamic change and be prepared accordingly. This paper focuses on such different methods of dynamically learning the market and its trends. We have used three different models for this paper and have also performed sentiment analysis on the tweets regarding the company or the stock, the model with the least error is the ideal and the most preferred method for prediction. The results of this classification have given a clear and insightful idea about the random ups and downs of the market and also a new approach for investors so that they know where they can bet their money. The ARIMA model is giving the best accuracy for every stock.
Impact of Inverter-Based Resources on Memory-Polarized Distance and Directional Protective Relay Elements

Distance and directional protective relay elements use memory polarization to ensure proper operation in case of close-in faults where the short-circuit voltage may be too small to be accurately measured. A side advantage of memory polarization is an increase in the resistive reach of distance mho characteristics. The proper operation of memory polarization is based on the following assumptions: (i) the amplitude of source impedance behind the relay is predictable and consistent, allowing the additional resistive reach to be accurately calculated and (ii) voltage phase angle does not significantly change during a fault, allowing the phase angle of the short-circuit voltage to be estimated by that of the memory voltage (i.e., the pre-fault voltage). While these assumptions are valid in a traditional Synchronous Generator (SG)-dominated power system, they may no longer be valid when Inverter-Based Resources (IBRs) displace a large amount of SGs, leading to potential misoperation of the memory-polarized elements. The paper studies these misoperation problems. Specifically, conducted simulations on a multi-Wind Park (WP) transmission test system show a case where WPs cause a variable expansion of a memory-polarized distance mho circle, thus leading to unintentional operation of the element. In another case, WPs cause a significant shift in the phase angle of short-circuit voltage, leading to an incorrect directionality decision. The objective is to identify such potential protection challenges and the cause thereof as a first step towards developing future solutions to ensure effective protection under IBRs.
Simulation of Low Power DVCC Based LNA for Wireless Receiver

The use of a low-voltage PMOS bulk-driven cascade current mirror (P MOS BDCCM) to realize LNA using CMOS differential voltage current conveyor (DVCC) based on low power and free from trans-conductance variation is presented in this research paper. Just one current mirror is used in this circuit, which uses four DVCCs as active elements and two capacitors and five resistors as passive elements. The use of this active component simplifies and improves the deployment process. The circuit is consuming Low Power of 1.24mW with the Voltage gain of 2.21 dB for the input voltage of 1V and seems to operate in 1- 10 GHz. The circuit's functionality is checked using Tanner simulator version 16 for a 35µm CMOS process model, and the transfer function is realized using MATLAB R2017a, yielding a variety of simulation results. To show the outcomes, simulation results are included.
Compact Ambient RF Energy Harvesting CPW Fed Antenna for WLAN

A compact coplanar waveguide with slotted symmetrical M-shape microstrip patch antenna has been presented in this paper to perform wireless radio frequency (RF) energy harvesting in Wireless Sensor Network (WSN) applications at a center frequency of 5.8 GHz. Non-uniform or irregular M-shape microstrip line antenna design has been proposed to improve the gain of microstrip patch antenna with a maximum gain of about 2.04 dB and it also increases the current distribution along the edges of the microstrip patch and across the feed line. Omni-directional radiation pattern has been obtained with this proposed antenna at 5.8 GHz for ambient radio frequency energy harvesting applications. In the RF energy harvesting system, this antenna works as a receiver in order to obtain the radio frequency from the RF energy transmitters and further convert it to DC voltage by using the following circuit. The antenna size is minimized up to $25\times 30$ mm2 with a return loss of −26.79dB in this article.
Photochemistry of aromatic hydrocarbons: implications for ozone and secondary organic aerosol formation

ion from the methyl group and major OH addition to the aromatic ring (about 90% ) [Calvert et al., 2002; Atkinson et al., 1997; Seinfeld and Pandis, 1998]. Under atmospheric condition the OH-toluene adduct reacts with O2 either by O2 addition to form (primary) peroxy radicals or by H-abstraction to yield phenolic compounds. The latter channel has been shown to be relatively minor, accounting for about 16% of the total products. The fate of the peroxy radicals is governed by competition between reaction with NO to form alkoxy radicals and cyclization to form bicyclic radicals. The bicyclic radicals can undergo unimolecular rearrangement to form epoxide radicals or bimolecular reaction with O2 to form (secondary) bicyclic peroxy radicals. The mechanistic complexity of the toluene oxidation further arises from multiple isomeric pathways at each reaction stage. Initial OH addition to toluene results in four distinct structural OHtoluene adduct isomers (A1 to A4). Subsequent reactions of OH-toluene adducts with O2 and cyclization of aromatic peroxy radicals also incur additional isomeric branching [Garcia-Cruz et al., 2000]. Schemes 1 and 2 illustrate the likely degradation pathways of toluene oxidation following OH addition to the ortho and para positions. For the ortho OH-toluene adduct, for example, O2 addition to the aromatic ring occurs at C1, C3, or C5 on the ring, forming three possible structural isomers of the peroxy radicals. The next step involves formation of bicyclic radicals with an O-O bridge across the benzene ring, forming five possible isomers. The bicyclic radicals may isomerize to form eight possible epoxide radicals.
Optimizing global processing time in the detection of patterns related to suicide in social networks

There is a suicide every 40 seconds in the world and it is the third cause of death for young people between 15 and 19 years old worldwide. For every suicide, many more attempt it, which is why suicide prevention remains an universal challenge and has been recognized by the World Health Organization (WHO) as a public health priority. Experts say that one of the best ways to prevent suicide is for people who are going through this urge to take their own lives to listen to people who are close to them and social networks such as Twitter or Facebook are in a unique position to help these people connect in real time in difficult situations that people with these suicidal tendencies are going through, but also represents a potential risk to receive information that could later prove harmful, either by stressing the same information or by taking some suicidal ideas. In this research we propose a model to optimize the global time processing in the detection of patterns related to suicide in the social network Twitter. Our results show that the proposed model can be a good alternative when it comes to optimizing the response time in this type of problems.
Intelligent Fertilization System Based on Image Recognition

In order to solve the problem that the normal fertilization robot cannot feed different species of plants in one time, an intelligent fertilization robot based on image recognitions was designed. Based on the customers' requirements, the system we designed can supply the machine ability of normal plants classification and auto fertilization after verifying the plants. In this paper, the Intelligent Fertilization System Based on Image Recognition will be introduced but base on the data leakage of dataset, it requires the plants to be pictured in the same background.
Constrained Truth Discovery (Extended Abstract)

Aggregating the information provided by multiple data sources, which is also known as information integration , plays an important role in data analytics. Since there often exists recording errors, intentional errors, conflicts and outdated data across different data sources, finding the true attribute values of each entity is a fundamental task of crucial importance [3] . The process to fulfill this task is called truth discovery , which has been extensively studied in the literature.
Cross-View Matching for Vehicle Localization by Learning Geographically Local Representations

Cross-view matching aims to learn a shared image representation between ground-level images and satellite or aerial images at the same locations. In robotic vehicles, matching a camera image to a database of geo-referenced aerial imagery can serve as a method for self-localization. However, existing work on cross-view matching only aims at global localization, and overlooks the easily accessible rough location estimates from GNSS or temporal filtering. We argue that the availability of coarse location estimates at test time should already be considered during training. We adopt a simple but effective adaptation to the common triplet loss, resulting in an image representation that is more discriminative within the geographically local neighborhood, without any modifications to a baseline deep neural network. Experiments on the CVACT dataset confirm that the improvements generalize across spatial regions. On a new benchmark constructed from the Oxford RobotCar dataset, we also show generalization across recording days within the same region. Finally, we validate that improvements on these image-retrieval benchmarks also translate to a real-world localization task. Using a particle filter to fuse the cross-view matching scores of a vehicle's camera stream with real GPS measurements, our learned geographically local representation reduces the mean localization error by 17% compared to the standard global representation learned by the current state-of-the-art.