Miracle-dz/UniFAR-FTUs huggingface dataset:
Wami Object Tracking Using L1 Tracker Integrated with a Deep Detector

We propose an object tracking method for Wide Area Motion Imagery (WAMI) video sequences, which models the tracking as a regularization problem through sparse representation of aerial video content. The proposed object tracker, Ll Dpct, applies particle filter tracking, and unlike the existing methods, it integrates a deep-learning-based object detector into the regularization scheme to improve the tracking performance. In order to enhance robustness to occlusion and scale changes, Ll Dpct monitors the state propagation, the level of sparsity as well as the representation capability of the model and receives feedback from the detector to update the observation model of the particle filter. Ll Dpct incrementally updates the dictionary of the sparse representation that enables us to efficiently represent the appearance changes of the object arising from illumination changes and high motion. Numerical results obtained on commonly used VIVID and UAV123 datasets denote that Ll Dpct significantly improves the object tracking performance in terms of precision rate and success rate compared to the state-of-the-art trackers.
Vehicle Re-Identification by Deep Feature Fusion Based on Joint Bayesian Criterion

Vehicle re-identification is a challenging task as the differences between vehicles of the same model are extremely small. In this paper, we propose to fuse deep features extracted by two different CNNs for vehicle re-identification. CNNs can extract discriminative features for classification tasks. Features extracted by different CNNs describe different aspects of the input image, and are complementary to each other. We propose a new loss function called the Joint Bayesian loss to fuse the different deep features. The proposed Joint Bayesian loss can minimize the intra-class variations and simultaneously maximize the inter-class variations of the fused features, and it is very fit for the vehicle re-identification. Experiments on a large-scale vehicle dataset demonstrate the effectiveness of the proposed method.
Witness-Based Location Proofs for Mobile Devices

Location-aware mobile applications are gaining popularity. This growth has caused the emergence of services that are offered to the users only when they are at specific locations. To implement valuable services, like a product sale, it is necessary to verify the presence of the user's device in a way which can be reliably trusted by the providers. This paper presents a system to support the creation of proofs that the user's device is at a claimed location. The system relies on different techniques for location estimation and on witness devices to testify to the presence of the user's device. A prototype was implemented and evaluated in regard to response times, accuracy of location estimates, and feasibility of proof exchanges. The results show that the solution is both practical and useful.
Dependence of Wages on the Duration of Training and Length of Service in the Russian Economy Sectors

The paper studies an influence of such characteristics of human capital as the duration of training and length of service on wages of workers in the Russian economy sectors. It is shown that in 2016, industries whose development under current conditions greatly depends on the human capital of the highest level as a rule do not attract specialists as the accumulation of human capital leads to a fairly low wage growth.
The lp Induced Norm and the Small-Gain Theorem for Discrete-time Stochastic Systems

This paper discusses $l_{p}$ induced norms and a small-gain theorem for discrete-time stochastic systems. The induced norm of systems generally depends on the associated input space of the systems. At the beginning, we consider a general stochastic $l_{p}$ space and introduce the primitive $l_{p}$ induced norm based on the space. However, the primitive $l_{p}$ induced norm can actually be confirmed not to reflect the nature of the randomness behind the stochastic systems, which leads us to overestimating its value. Hence, we take a proper subspace of the stochastic $l_{p}$ space and introduce another $l_{p}$ induced norm based on the subspace. We demonstrate with examples that the norm based on the subspace successfully reflects the nature of the system randomness. Then, we give the small-gain theorem based on the norm.
Rateless Code Profiles Design for Uplink C-RAN under Block Fading Channel

This paper considers a single user uplink transmission system based on rateless code in Cloud Radio Access Network (C-RAN) under block fading channel. The system consists of a single user, a cluster of remote radio heads (RRHs), and a building baseband unit (BBU) pool. The user encodes the messages with rateless code and sends them to RRHs. Each RRH quantizes the received signals and uploads them to the BBU pool through the high speed fronthaul link. BBU pool applies the belief propagation (BP) algorithm for joint decompression and decoding of the received signals. With only the knowledge of the statistics of the channel states, we design the universally good degree profile adopted at the user based on the extrinsic information transfer (EXIT) analysis for the decoding process at the BBU. The simulation results show that, compared with the conventional BEC degree profile, the rateless code with the optimized degree profile achieves a better performance in the metric of average system throughput.
Sentiment Analysis on Bangladesh Cricket with Support Vector Machine

While social platform and news portal play a big role in Internet today, it also becomes the valuable medium for public opinions. We want to perform sentiment analysis in these public opinions. Many works have been done on sentiment analysis in different sectors for English language. But works in Bengali language are limited to only Bengali corpus and micro-blogging. So we have targeted a special sector which is Bangladesh Cricket where people express their opinions in their native Bengali languages on social medias in every moment. So we have prepared a dataset of three sentiment classes about Bangladesh Cricket from real people sentiments. We have processed our dataset by removing unnecessary words from the Bengali texts. Then we have used TF-IDF Vectorizer for vectorization and the classifier Support Vector Machine to classify our data.
Online Music Performance Tracking Using Parallel Dynamic Time Warping

Resource allocation is a critical issue in the implementation of a portable, low-latency and efficient system for interactive experience. To address this, we propose the parallel dynamic time warping (PDTW), that utilizes multi-thread computation on a modern multi-core system, for online audio-to-audio music alignment. We also discuss the evaluation methodology that benchmarks the trade-offs among latency, accuracy of alignment, and computing resource. The proposed system utilizes multiple DTW alignment processes and parallel computing technique to reduce the processing time, as well as to improve both the alignment accuracy and robustness to tempo variation. Evaluation on several datasets with artificial time stretching exhibits the capacity of the system in enriched concert experience.
Snapshot Distillation: Teacher-Student Optimization in One Generation

Optimizing a deep neural network is a fundamental task in computer vision, yet direct training methods often suffer from over-fitting. Teacher-student optimization aims at providing complementary cues from a model trained previously, but these approaches are often considerably slow due to the pipeline of training a few generations in sequence, i.e., time complexity is increased by several times. This paper presents snapshot distillation (SD), the first framework which enables teacher-student optimization in one generation. The idea of SD is very simple: instead of borrowing supervision signals from previous generations, we extract such information from earlier epochs in the same generation, meanwhile make sure that the difference between teacher and student is sufficiently large so as to prevent under-fitting. To achieve this goal, we implement SD in a cyclic learning rate policy, in which the last snapshot of each cycle is used as the teacher for all iterations in the next cycle, and the teacher signal is smoothed to provide richer information. In standard image classification benchmarks such as CIFAR100 and ILSVRC2012, SD achieves consistent accuracy gain without heavy computational overheads. We also verify that models pre-trained with SD transfers well to object detection and semantic segmentation in the PascalVOC dataset.
Basic Thermal Field Analysis of a Small Turbo-Jet Engine

Information about temperature distribution in the thermal field can serve as one of the major parameters for turbojet engine diagnostics and can contribute to extended technical lifetime of the engine. The aim of the submitted paper is to perform a basic analysis of the thermal field distribution during various operational modes of a small turbojet engine. As the object for thermal field investigation, a small turbojet engine iSTC-21v, installed in a laboratory, was used. The thermal field has been captured by a thermographic camera. The article presents an analysis of temperature distribution in various nodes of the small turbojet engine, defining critical parts of the engine during its operation, which can be further analyzed for diagnostic purposes. It is expected that this analysis can also be applicable for high power jet engines.
Classification and Detection of Skin Tones Using Big Data Machine Learning Algorithms Under Rapidly Varying Illuminating Conditions

Skin tone detection is a perceptual symptotic interspatial computational analysis in pixel segmentation extraction from an image to identify the skin components from non-skin background. In a country like India, skin tone detection is a very complex task due to presence of wide variety of skin tones, further; modeling an algorithm under different environmental conditions is even more complex. The main aim of this paper is to overcome the drawbacks of existing algorithms in acquiring accuracy. We used Big Data Analysis and Big Data Machine learning techniques on a complete set of data collected from more than 800 images of different persons/group under different illuminating conditions. In this paper we propose a real time skin tone detection algorithm under different illuminating conditions and compare its performance parameters like True Positive Rate (TPR). , False Positive Rate (FPR) and False Negative Rate (FNR). , accuracy, F-score, precision and recall with existing skin tone detection algorithms. The proposed algorithm outperformed the existing algorithms.
Comparing neural‐ and N‐gram‐based language models for word segmentation

Word segmentation is the task of inserting or deleting word boundary characters in order to separate character sequences that correspond to words in some language. In this article we propose an approach based on a beam search algorithm and a language model working at the byte/character level, the latter component implemented either as an n‐gram model or a recurrent neural network. The resulting system analyzes the text input with no word boundaries one token at a time, which can be a character or a byte, and uses the information gathered by the language model to determine if a boundary must be placed in the current position or not. Our aim is to use this system in a preprocessing step for a microtext normalization system. This means that it needs to effectively cope with the data sparsity present on this kind of texts. We also strove to surpass the performance of two readily available word segmentation systems: The well‐known and accessible Word Breaker by Microsoft, and the Python module WordSegment by Grant Jenks. The results show that we have met our objectives, and we hope to continue to improve both the precision and the efficiency of our system in the future.
Analysis of Stock Market using Streaming data Framework

It has been proved that the decisions in stock market exchange may bring influence on the investors, financial institutions, banking sectors etc. The stock market is a highly composite system in addition often concealed with mystery, it is therefore, very difficult to analyze all the impacting factors before making a decision. In this research, we have tried to design a stock market prediction model which is considers different parameters of a particular stock. Analysis is performed after obtaining the stock scores. This analysis involves visualization of stock scores in the form of various plots and prediction of the scores using a time series model known as ARIMA (auto regressive moving average). The results shows that the time series model performed a descent prediction of the market scores with considerably high accuracy. Each factor was studied independently to find out its association with market performance. Furthermore the results suggests that behavior of market can be predicted using machine learning techniques.
The Utilization of Drones for Smart Governance Implementation: Tax Extensification Strategy for Transfer of Ownership in Real Estate Sales at Cibinong Primary Tax Office

The low realization of tax revenue from the transfer of building ownership rights in the business of buying and selling real estate in the working area of Cibinong Primary Tax Office is caused by abundant leakage in tax revenue as well as tax evasion by various parties, particularly Property Taxpayers. This study aims to analyze the strategies and implementation strategies in increasing tax revenue from the business of buying and selling real estate in the working area of Cibinong Primary Tax Office. This study applies quantitative approach with post-positivism paradigm. The data collection technique applied is in-depth interview. The findings of the study show that the strategy applied to increase tax revenue from the business of buying and selling real estate is tax extensification and intensification. The strategy implementation was done by Cibinong Primary Tax Office. In practice, Cibinong Primary Tax Office has constraints on the size of the working area, so the drones become one of the special strategies that can handle one of these obstacles. The results of aerial photographs produced by the drones are used as spatial data for the tax extensification and intensification activities of Cibinong Primary Tax Office. Other than that, the utilization of drone applied as one of the government's effort in implementing smart governance that can handle tax evasion issues.
Therapeutic Targeting of HIV Reservoirs: How to Give T Cells a New Direction

HIV cannot be cured by current antiretroviral therapy (ART) because it persists in a transcriptionally silent form in long-lived CD4+ cells. Leading efforts to develop a functional cure have prioritized latency reversal to expose infected cells to immune surveillance, coupled with enhancement of the natural cytolytic function of immune effectors, or “kick and kill. ” The most clinically advanced approach to improving the kill is therapeutic immunization, which aims to augment or re-focus HIV-specific cytolytic T cell responses. However, no vaccine strategy has enabled sustained virological control after ART withdrawal. Novel approaches are needed to overcome the limitations of natural adaptive immune responses, which relate to their specificity, potency, durability, and access to tissue reservoirs. Adoptive T cell therapy to treat HIV infection was first attempted over two decades ago, without success. Since then, progress in the field of cancer immunotherapy, together with recognition of the similarities in tumor microenvironments and HIV reservoirs has reignited interest in the application of T cell therapies to HIV eradication. Advances in engineering of chimeric antigen receptor (CAR)-transduced T cells have led to improved potency, persistence and latterly, resistance to HIV infection. Immune retargeting platforms have incorporated non-neutralizing and broadly neutralizing antibodies to generate Bispecific T cell Engagers (BiTEs) and Dual-Affinity Re-Targeting proteins (DARTs). T cell receptor engineering has enabled the development of the first bispecific Immune-mobilizing monoclonal T Cell receptors Against Viruses (ImmTAV) molecules. Here, we review the potential for these agents to provide a better “kill” and the challenges ahead for clinical development.
Person Re-identification Using Two-Stage Convolutional Neural Network

Person re-identification is a fundamental task in automated video surveillance and has been an area of intensive research in the past few years. Several person re-identification methods based on deep learning have been proposed and achieved remarkable performance. However, extraction of more useful spatial and temporal information from input images and design of a more effective approach to match the same persons are still challenging. In this paper, we present a novel Two-Stage Convolution Neural Network (TSCNN), which effectively extracts the spatio-temporal feature with two-stream network in two directions, and matches the person with a novel convolutional neural network. Extensive experiments are conducted on three public benchmarks, i.e., iLIDS-VID, PRID2011 and MARS datasets. The experimental results demonstrate that the performance of our TSCNN is better in comparison with the state-of-the-art methods. The code of TSCNN is available at https://github.com/zyoohv/TSCNN.
Improved DNA-Binding Protein Identification by Incorporating Evolutionary Information Into the Chou’s PseAAC

DNA-binding proteins play critical roles in various cellular biological processes, such as gene expression and transcription. However, the experimental methods to identify these proteins like ChIP-sequencing are expensive and time-consuming, which presents the need for in silico methods, especially machine learning-based methods. In recent years, the accuracy of machine learning-based DNA-binding protein prediction has been increasing significantly. However, there are still some critical problems to be solved like how to convert protein sequences into an appropriate discrete model or vector. In this paper, we propose a novel feature construction method based on a position-specific scoring matrix (PSSM) named K-PSSM-Composition. The proposed features can efficiently capture the information about 20 amino acid residues and the local information of a given sequence during the evolutionary process. We perform a recursive feature elimination to extract the optimal set of features, which are used to train the support vector machine model for predicting DNA-binding proteins. We evaluate and compare our proposed predictor with other advanced predictors via two standard benchmark data sets. The proposed method achieves the accuracy values of 89.77% and 88.71% for the jackknife test and independent test respectively, outperforming the compared methods. This finding demonstrates the efficacy and effectiveness of the proposed method in predicting the DNA-binding proteins. The source code and data are available at https://github.com/Excelsior511/DNA-Binding-Proteins.
Deletion of 3p13 is a late event linked to progression of TMPRSS2:ERG fusion prostate cancer

Introduction Deletion of 3p13 is one of the most common alterations in prostate cancer preferentially occurring in tumors with TMPRSS2:ERG fusion. The cause for the striking association between 3p13 loss and ERG fusion is unknown. Methods Here, we made use of a preexisting heterogeneity prostate cancer tissue microarray including ten tissue spots from ten different tumor areas of 317 cancers to examine the spatial distribution of 3p13 deletions (determined by fluorescence in situ hybridization) in prostate cancer areas with and without ERG overexpression (determined by immunohistochemistry). Results 3p13 deletions were found in 61 of 299 (20.4%) and ERG positivity in 174 of 317 (54.9%) interpretable cancers. The likelihood of 3p13 loss was twice as high in ERG-positive cancers (39/152, 25.7%) than in ERG-negative cancers (17/124, 13.7%, P=0.010). At least three tissue spots were interpretable for 3p13 deletion status in 279 cancers: only these were used for heterogeneity assessment. Among these tumors, 58 (20.8%) had a 3p13 deletion and 221 (79.2%) were undeleted. The majority of 3p13-deleted cancers showed marked intratumoral heterogeneity. Areas with and without 3p13 loss were found in 50 (18%) of 279 cancers with three or more interpretable tissue spots, while only eight (3%) tumors had a homogeneous 3p13 loss. Comparison with ERG data revealed that ERG fusion usually precede 3p13 deletions. In total, 26 (66.7%) of 39 cancers with ERG and 3p13 alteration had only focal 3p13 deletions in an otherwise ERG-positive background. In contrast, none of the cancers showed a pattern that would be consistent with 3p13 deletion preceding ERG fusion. Conclusion Our study identifies 3p13 deletion as a highly heterogeneous alteration in prostate cancer preferentially developing at rather late stages of progression in TMPRSS2:ERG fusion-positive tumors.
Algorithm for pace pulses detection in a single lead ECG: Performance in case of EMG artifacts

The paper presents results from testing a pace pulse detection algorithm in case of EMG artifacts. The study is performed over a large dataset containing 624 ECG+EMG signals acquired with high amplitude and time resolution. Two operating frequencies are studied – 16 kHz and 32 kHz. PPV of the algorithm decreases considerably in case of EMG noise – about 10% for 32 kHz (89.32% vs. 98.95%) and about 22% for 16 kHz (74.70% vs. 96.75%). The observed Se variation is below 0.5% (99.44% vs. 99.27% for 32 kHz, 97.30% vs. 97.14% for 16 kHz).
A Simple Torque Estimator for In-Service Efficiency Determination of Inverter Fed Induction Motors

This work deals with air-gap torque estimation in inverter fed induction motors. Torque estimation is one of the main functions needed to successful implementation of energy consumption and efficiency estimation methods used today. Those methods are essential to monitoring electric motors and processes in mining and metals industries. Given that some of the drives have voltage and current sensors already installed in them for condition monitoring, it is very attractive to use measurement data available to perform torque and efficiency estimation. The goal is to implement these functions without process interruption and/or requiring minimum changes on the drive configuration. Most of the torque estimation algorithms require two or three voltage and current measurement to be implemented. Aiming to reduce the number of sensors and obtain a lower cost setup, a solution requiring data acquisition of the current and voltage of only one phase suited to be used in algorithms of efficiency estimation of in-service induction motors was presented in a previous paper. It was investigated in it the performance of the method when applied to induction motors directly connected to the mains. In this paper, the performance of the method is additionally investigated, but, at this time, the induction motor is supplied by means of a PWM frequency inverter. The effects of unbalance in voltage supply are described and analyzed. Simulation and experimental results are used to check the validity and performance of the proposed technique.