Miracle-dz/UniFAR-FTUs huggingface dataset:
Taming Wild Faces: Web-Scale, Open-Universe Face Identification in Still and Video Imagery

With the increasing pervasiveness of digital cameras, the Internet, and social networking, there is a growing need to catalog and analyze large collections of photos and videos. In this dissertation, we explore unconstrained still-image and video-based face recognition in real-world scenarios, e.g. social photo sharing and movie trailers, where people of interest are recognized and all others are ignored. In such a scenario, we must obtain high precision in recognizing the known identities, while accurately rejecting those of no interest.
Sustainable Eco-Friendly Textile Sludge Management In Bangladesh : Construction And Validation Of Lab Scale Biogas Plant For Generation Of Biogas From Textile Sludge

Ready-Made Garments are backbone of economy of Bangladesh. About 80% of total exports are done by this sector. The export incomes were US $ 17.9 billion in the year 2010-11, US $ 19.0 billion in the year 2011-12, US $ 21.5 billion in the year 2012-13, US $ 24.5 in the year 2013-14 In Bangladesh, 1500 billion liter ground water is consumed every year by Textile sector. As a result, Textile sector of Bangladesh has been discharging 2 million m 3 effluents every day. For environmental pollution protection effluents are treated in various effluent treatment plants (ETPs). After effluent treatment a semisolid part is separated from Effluent Treatment Plants which is called sludge. A large quantity of sludge has created further problem for safe disposal. There are several sludge management options available. Considering the energy situation of Bangladesh biogas generation from Textile sludge is the suitable option of the country. A lab scale biogas plant was constructed in the Chemistry lab of Southeast University of Bangladesh. To validate the constructed lab scale biogas plant successive experiments were performed. First of all bio sludge obtained from Delta Knit Composite was digested in the digester tank of biogas plant. The maximum biogas obtained at third day of generation. Portable lab scale biogas plant is easy to carry and shifting from one place to another. Keywords— Textile Sludge , Sustainable, EcoFriendly, Biogas Generation
Two-Dimensional Immersive Cohort Analysis Supporting Personalised Medical Treatment

Genomic data are large and complex which are challenges to visualize them effectively on ordinary screens due to the limited display spaces. Large and high resolution displays could enable the capability to show more information at once for better comprehension from the visualization. This paper presents a two-dimensional interactive visualization system and supporting algorithm for multi-dimensional large genomic data analysis that can be used in both ordinary displays or immersive environments. We provide both view of the entire patient cohort in the similarity space and the genomic details currently for comparison among the patients. Through the similarity space and on the selected genes of interest, we are able to perceive the genetic similarity throughout the cohort. From the linked heat map visualisation of the selected genes, we apply hierarchical clustering on both the horizontal and vertical axes to group together the genetically similar patients. We demonstrate the effectiveness of the visualization with two case studies on pediatric cancer patients suffering from Acute Lymphoblastic Leukemia (ALL) and from Rhabdomyosarcoma (RMS)
Enabling Signal Space Diversity for MU-MIMO/OFDMA Cellular Systems That Employ Frequency Diversity

Signal space diversity (SSD) is a technique to obtain diversity without loss in spectral efficiency. Despite this, SSD has been minimally studied in multiuser scenarios. In this paper, SSD is operated in the uplink of orthogonal frequency division multiple access (OFDMA) cellular systems that exploit frequency diversity, use the optimum maximum-likelihood detector (MLD) and employ antenna arrays at the receivers. Thus, a multiuser multiple-input-multiple-output (MU-MIMO) system that works in presence of own-cell and co-cell interference (CCI) is taken into account. In order to diminish CCI, fractional frequency reuse (FFR) is used in the cellular system. For a feasible MLD implementation, a matrix structure of the received signals is obtained in order to use the sphere decoder algorithm. An exact closed-form expression to calculate the pairwise error probability between two multidimensional symbols is derived. From this, a bit error rate (BER) upper bound is found for single user scenarios. This BER expression is an accurate lower bound for high diversity orders in multiuser scenarios. The BER asymptotic analysis shows that our approach maintains the SSD multidimensional diversity, the spatial diversity of MIMO and the frequency diversity. This allows to overload the system by minimizing the BER and increasing the spectral efficiency, simultaneously.
Trinity: An Extensible Synthesis Framework for Data Science

In this demo paper, we introduce Trinity , a general-purpose framework that can be used to quickly build domain-speciﬁc program synthesizers for automating many tedious tasks that arise in data science. We illustrate how Trinity can be used by three diﬀerent users: First, we show how end-users can use Trinity ’s built-in synthesizers to automate data wrangling tasks. Second, we show how advanced users can easily extend existing synthesizers to support additional functionalities. Third, we show how synthesis experts can change the underlying search engine in Trinity . Overall, this paper is intended to demonstrate how users can quickly use, modify, and extend the Trinity framework with the goal of automating many tasks that are considered to be the “janitor” work of data science.
Making an RDBMS Data Scientist Friendly: Advanced In-database Interactive Analytics with Visualization Support

We are currently witnessing the rapid evolution and adoption of various data science frameworks that function external to the database. Any support from conventional RDBMS implementations for data science applications has been limited to procedural paradigms such as user-defined functions (UDFs) that lack exploratory programming support. Therefore, the current status quo is that during the exploratory phase, data scientists usually use the database system as the “data storage” layer of the data science framework, whereby the majority of computation and analysis is performed outside the database, e.g., at the client node. We demonstrate AIDA, an in-database framework for data scientists. AIDA allows users to write interactive Python code using a development environment such as a Jupyter notebook. The actual execution itself takes place inside the database (neardata), where a server component of AIDA, that resides inside the embedded Python interpreter of the RDBMS, manages the data sets and computations. The demonstration will also show the visualization capabilities of AIDA where the progress of computation can be observed through live updates. Our evaluations show that AIDA performs several times faster compared to contemporary external data science frameworks, but is much easier to use for exploratory development compared to database UDFs. PVLDB Reference Format: Joseph Vinish D’silva, Florestan De Moor, Bettina Kemme. Making an RDBMS Data Scientist Friendly: Advanced In-database Interactive Analytics with Visualization Support. PVLDB, 12(12): 1930-1933, 2019. DOI: https://doi.org/10.14778/3352063.3352102
Hypocoercivity properties of adaptive Langevin dynamics

Adaptive Langevin dynamics is a method for sampling the Boltzmann-Gibbs distribution at prescribed temperature in cases where the potential gradient is subject to stochastic perturbation of unknown magnitude. The method replaces the friction in underdamped Langevin dynamics with a dynamical variable, updated according to a negative feedback loop control law as in the Nos\'e-Hoover thermostat. Using a hypocoercivity analysis we show that the law of Adaptive Langevin dynamics converges exponentially rapidly to the stationary distribution, with a rate that can be quantified in terms of the key parameters of the dynamics. This allows us in particular to obtain a central limit theorem with respect to the time averages computed along a stochastic path. Our theoretical findings are illustrated by numerical simulations involving classification of the MNIST data set of handwritten digits using Bayesian logistic regression.
Flare & Lantern: Efficiently Swapping Horses Midstream

Running machine learning (ML) workloads at scale is as much a data management problem as a model engineering problem. Big performance challenges exist when data management systems invoke ML classi ﬁ ers as user-de ﬁ ned functions (UDFs) or when stand-alone ML frameworks interact with data stores for data loading and pre-processing (ETL). In particular, UDFs can be precompiled or simply a black box for the data management system and the data layout may be completely di ﬀ erent from the native layout, thus adding overheads at the boundaries. In this demo, we will show how bottlenecks between existing systems can be eliminated when their engines are designed around runtime compilation and native code generation, which is the case for many state-of-the-art relational engines as well as ML frameworks. We demonstrate an integration of Flare (an accelerator for Spark SQL), and Lantern (an accelerator for TensorFlow and PyTorch) that results in a highly optimized end-to-end compiled data path, switching between SQL and ML processing with negligible overhead.
Feature Extraction and Classification of Multispectral Imagery by Using Convolutional Neural Network

Once large-scale disasters occur like earthquake and tunami, it becomes very important task to secure safe evacuation and rescue routes. In this task, land cover classification can be performed on satellite images using remote sensing technology. But, there is a problem that the accuracy of these classification is still low in order to be practical in large-scale disasters. Therefore, we decided to create a detailed situation map right after the disaster by overlaying the cover classification result on the map information before the disaster. As a first step for this purpose, we propose a method to improve the classification accuracy. In this paper, we employ a convolutional neural network(CNN) for feature extraction. Generally, in classification tasks, most of deep learning models employ the softmax activation. But, [1] shows that by simply replacing softmax with linear SVMs, it gives significant gain. So, we employ the RBF(Radial Basis Function) SVM for classification. From our experimental results, we demonstrated that classification method using SVM is about 4(%) more accurate than softmax used in general classification method using CNN.
Radioxenon monitoring for verification of the Comprehensive nuclear-Test-Ban Treaty

This project is about developing and testing data mining tec hniques to verify worldwide compliance of the global ban on nuclear tests. The dataset consists of Ra dioxenon measurements from five CTBTO monitoring sites. The key features of this dataset include i mbalanced representation of the two classes. We experiment with several classifier types like J48 with ada boosting, single hidden layer neural networks, support vector machine and decision trees. We experiment wi th three methods for handling data imbalance: the Smote method for SVMs, changing the probabilty cuto ff f r classification and randomly upsampling and downsampling the classes.
Comparative study of the uncertainty of stereoscopic micro-PIV , wavefront-deformation micro-PTV , and standard micro-PIV

In this paper a comparative study is presented to assess the uncertainty of two methods for 3C3D velocimetry in microscopic flows: stereoscopic micro-particle image velocimetry (S-μPIV) and wavefrontdeformation, or astigmatism, micro-particle tracking velocimetry (WD-μPTV). First, the main parameters affecting both methods’ measurement uncertainty are identified, described and quantified. Second, the test case of flow over a backward-facing step is analyzed using both methods. For comparison standard 2D-2C μPIV measurements and numerical flow simulations are shown as well. Advantages and disadvantages of both methods are discussed.
Contribution of vertical and horizontal components of ground reaction forces on global motor moment during a golf swing : a preliminary study

The swing is a crucial movement for golf performance. During their swing, golfers apply forces on the ground through their feet at the origin of the clubhead acceleration. Most of the studies that investigated ground reaction forces during a golf swing focused on the vertical component. However horizontal forces (mediolateral and anteroposterior components) were shown to be different between golfers and to play a role in the generation of the global moment creating the clubhead acceleration (McNitt-Gray et al. 2013). The aim of this study was to evaluate the respective contribution of vertical and horizontal components of the ground reaction forces to the global motor moment generated during the swing.
FLUID FLOW A FLASHING CYCLONE SEPARATOR

In geothermal steam-gatheringsystems for electric power generation, are used to provide a turbine working fluid which is dry or slightly superheatedsteam (water vapour). This paper describes the steam flow patterns and pressure distribution within a typical cyclone separator which is operating at saturation (boiling or flashing)conditions. Themass and volume fractions of the gas (steam) and liquid phases which must be separated are for a typical two-phase flow example. The steam fraction occupies most of the separator vessel volume; analysis of the motion of this steam phase allows depiction of the stream surfaceswithin the flow and an estimate of the pressure drop across the separator. Solutionsof a set of simplified conservation equationsare found by analytical and numerical methods.
DYNAMIC ENVIRONMENT DESIGN WITH NEW HARDWARE /SOFTWARE INTELLIGENT TECHNIQUE

Visual simulation has always played a significant role in the development of environmental relation and design. Designers often compose their design into the image of surrounding environment and create still image to have a peek look at the finished product. This research intends to incorporate Robotics of Lego and camera-matching technology used in filming industry to achieve more vivid motion composition simulation through self-assembled hardware and script, in order to help designers observe the relationship of the design and the environment in a more efficient manner.
Personalized Travel Recommendation Based on Sentiment-Aware Multimodal Topic Model

In this paper, we try to solve the personalized travel recommendation problem by exploiting the multi-modal data available from the real world social media, and a probabilistic graph model so called Sentiment-aware Multi-modal Topic Model (SMTM) is proposed to mine the latent semantics of the multi-modal data on the online travel website. Distinguished from previous approaches, our proposed approach try to mine the topics from tourist and attraction domains separately for disclosing semantics for tourist topics and attraction themes. In addition, we analyze tourist’s sentiments on attractions to further obtain the tourist’s attitude over attractions and recommend the attraction with proper sentiment on the related attraction themes accordingly. Based on the proposed SMTM model, the documents in tourist domain and in attraction domain can be compared with each other after they were projected into the mutual topic space, and this latent space projection scheme can be further applied to two personalized traveling recommendations, that is, the single platform traveling recommendation and the inter-platform traveling recommendation. Evaluation results based on the real world online travel website have shown the improved performance of our method.
Flame analysis using image processing techniques

This paper presents image processing techniques with the use of fuzzy logic and neural network approach to perform flame analysis. Flame diagnostic is important in the industry to extract relevant information from flame images. Experiment test is carried out in a model industrial burner with different flow rates. Flame features such as luminous and spectral parameters are extracted using image processing and Fast Fourier Transform (FFT). Flame images are acquired using FLIR infrared camera. Non-linearities such as thermal acoustic oscillations and background noise affect the stability of flame. Flame velocity is one of the important characteristics that determines stability of flame. In this paper, an image processing method is proposed to determine flame velocity. Power spectral density (PSD) graph is a good tool for vibration analysis where flame stability can be approximated. However, a more intelligent diagnostic system is needed to automatically determine flame stability. In this paper, flame features of different flow rates are compared and analyzed. The selected flame features are used as inputs to the proposed fuzzy inference system to determine flame stability. Neural network is used to test the performance of the fuzzy inference system.
Matrix Elements for the Morse Potential Using Ladder Operators

The exact solution of problems with anharmonic potentials using algebraic methods [l-51 allows for approximations in more complex collision problems [2-51 involving anharmonic interactions between molecular fragments. In the case of the Morse potential, the original quantum solution [6] was found by transforming SchriMinger's equation to a Whittaker equation, equivalent to the radial part of the Coulomb problem [7]. Using this transformation, we can construct two different operator algebras associated with the Morse oscillator. The so(2,l) algebra contains ladder operaton that shift the potential sang& for a fmed energy level. The so(3) algebra, on the other hand, includes ladder operators that allow us to shift energy levels while having a fmed well depth. With these tools we have calculated matrix elements of some simple operators, as well as their recursion relations, in a straightforward fashion [ 11. A different change of variable is used to transform [2] the Morse problem into the radial equation for a two-dimensional harmonic oscillator (HO). In this case, the energy eigenvalue of the Morse oscillator appears as the coefficient of the centrifugal term in the equivalent HO. It thus proves most convenient to introduce an additional angular variable [ 1,2] in order to transform this augmented system into the HO equation in two-dimensions. The advantage of this procedure consists in being able to work with simpler commutation relations, namely those of the harmonic oscillator. We immediately notice that not all of the allowed quantum states of the 2-dimensional HO cornspond to physical states of the original Morse potential. Nevertheless, in order to calculate matrix elements for the physical problem at hand, we can use the HO algebra, replace the appropriate physical quantum numbers at the end, and finally
Search and rescue under the forest canopy using multiple UAVs

We present a multi-robot system for GPS-denied search and rescue under the forest canopy. Forests are particularly challenging environments for collaborative exploration and mapping, in large part due to the existence of severe perceptual aliasing which hinders reliable loop closure detection for mutual localization and map fusion. Our proposed system features unmanned aerial vehicles (UAVs) that perform onboard sensing, estimation, and planning. When communication is available, each UAV transmits compressed tree-based submaps to a central ground station for collaborative simultaneous localization and mapping (CSLAM). To overcome high measurement noise and perceptual aliasing, we use the local configuration of a group of trees as a distinctive feature for robust loop closure detection. Furthermore, we propose a novel procedure based on cycle consistent multiway matching to recover from incorrect pairwise data associations. The returned global data association is guaranteed to be cycle consistent, and is shown to improve both precision and recall compared with the input pairwise associations. The proposed multi-UAV system is validated both in simulation and during real-world collaborative exploration missions at NASA Langley Research Center.
Mapping Consumer Sentiment Toward Wireless Services Using Geospatial Twitter Data

Hyper-dense wireless network deployment is one of the popular solutions to meeting high capacity requirement for 5G delivery. However, current operator understanding of consumer satisfaction comes from call centers and base station quality-of-service (QoS) reports with poor geographic accuracy. The dramatic increase in geo-tagged social media posts adds a new potential to understand consumer satisfaction towards target-specific quality-of-experience (QoE) topics. In our paper, we focus on evaluating users’ opinion on wireless service-related topics by applying natural language processing (NLP) to geo-tagged Twitter data. Current generalized sentiment detection methods with generalized NLP corpora are not topic specific. Here, we develop a novel wireless service topic-specific sentiment framework, yielding higher targeting accuracy than generalized NLP frameworks. To do so, we first annotate a new sentiment corpus called SignalSentiWord (SSW) and compare its performance with two other popular corpus libraries, AFINN and SentiWordNet. We then apply three established machine learning methods, namely: Naïve Bayes (NB), Support Vector Machine (SVM), and Recurrent Neural Network (RNN) to build our topic-specific sentiment classifier. Furthermore, we discuss the capability of SSW to filter noisy and high-frequency irrelevant words to improve the performance of machine learning algorithms. Finally, the real-world testing results show that our proposed SSW improves the performance of NLP significantly.
IMPLEMENTATION OF THE HUMAN EYE PUPIL DETECTION USING INTENSITY VALUES WITH EDGE DETECTION

: Eye tracking refers to measure gaze positions and movement to reveal what individuals are looking at. Thanks to the advances of eye tracking technology, there are growing numbers of research focus in using eye tracking to study human behavior. In order to improve the accuracy of the eye gaze tracking technology, this paper presents a novel pupil detection algorithm based on intensity level with canny edge detection technique. Field programmable logic array (FPGA) based hardware implementation of the proposed technique is presented, which can be used in iris localization system on FPGA based platforms for iris recognition application.
Visual Analysis of Formula One Races

In this paper we describe an interactive web-based visual analysis tool for Formula one races. It first provides an overview about all races on a yearly basis in a calendar-like representation. From this starting point, races can be selected and visually inspected in detail. We support a dynamic race position diagram as well as a more detailed lap times line plot for showing the drivers’ lap times in comparison. Many interaction techniques are supported like selections, filtering, highlighting, color coding, or details-on-demand. We illustrate the usefulness of our visualization tool by applying it to a Formula one dataset while we describe the different dynamic visual racing patterns for a number of selected races and drivers.
MATLAB / SIMULINK MODEL OF A SYSTEM FOR DETERMINING THE ANGLE OF INTERNAL SYNCHRONOUS GENERATORS

This paper deals with a Matlab / Simulink original system for determining the internal angle of high power synchronous generators,when they operate under electrical charge. Knowledge the load angle value at steady state and dynamic operation conditions have a great significance for the generator operating conditions. The system presented in this paper provides the user simulation models of synchronous generators in Matlab / Simulink and a friendly graphical interface for viewing and reading results.