Miracle-dz/UniFAR-FTUs huggingface dataset:
The Impact of Preprocessing on Classification Performance in Convolutional Neural Networks for Turkish Text

The increase of text based content produced in electronic environment necessitated to classify these contents with advanced algorithms. For text classification, the text contents are reduced to a more structured form by being preprocessed. Many studies have examined how these preprocesses affect the performance of classification algorithms. The main question of this study is how the Convolutional Neural Network (CNN), which is noteworthy with its classification success, is influenced by text preprocessing in the text classification. In this study, the effect of text preprocessing is examined CNN classification performance on two different data sets with Turkish content. As a result of the study, it has been determined that the text preprocessing has a weak positive effect on CNN classification performance.
Drive Control of PMSM Considering Nonlinear Modeling Error in Over-modulation Region

In this paper, a single-mode overmodulation algorithm based on space vector pulse width modulation (SVPWM) is introduced. The nonlinear modeling error in the conventional single-mode overmodulation region has been investigated and an improvement is consequently proposed. Which uses the Fourier series expansion to achieve a precise linear and accurate control of the reference voltage and the fundamental component of inverter output voltage. The proposed method is then investigated on a permanent magnet synchronous machine (PMSM) drive control system modeled in Matlab/Simulink and shows better accuracy in voltage control.
NetClips: A Framework for Video Analytics in Sports Broadcast

This work presents an early stage big data application, NetClips, for automation in broadcast production in the area of content management, delivery, and consumption. NetClips acts as a cognitive engine to automatically annotate video clips with contextual sports content during match production and on archival contents. Ensemble of deep learning architectures and advanced machine vision techniques are used in NetClips to accurately recognize players, objects, actions, and game events in every clip created during a sport broadcast. This application also has a great impact on production workflow, as it will lift up the burden of concurrent tagging from operators list of duties.
Visual Tracking Based on Convolutional Neural Network

The tracking object is one of the important research directions in the field of computer vision and plays an important role in intelligent video monitoring. In this paper, a visual tracking method based on deep learning object detection is proposed. There are many functions required in human object tracking tasks, including the drive of hight-definition panoramic camera, real-time video streaming protocol of RTSP, object detection based on deep convolution neural network, ROI selection of interest area, dynamic object tracking of KF, and online video distribution of human coordinates through SOCKET communication.
CNN-SVM with Embedded Recurrent Structure for Social Emotion Prediction

The rapid development of the Internet has generated a large amount of online user-generated information. Automatic sentiment analysis of the user-generated information has great research and application prospects. Traditional sentiment analysis task mainly focuses on authors' emotions. Instead, our research aims at the emotions of readers invoked by news articles, which are called social emotions. In this paper, we propose a novel method, CNN-SVM with Embedded Recurrent Structure, for social emotion prediction. Specifically, we replace the fixed window convolutional layer in CNN with a bidirectional recurrent structure, that is, our model is a cascade of the bidirectional recurrent structure and a max-pooling layer. Then, the values of max-pooling layer are used as extracted features to predict social emotions with a SVM classifier. Experimental results show that our method outperforms the state-of-the-art methods in social emotion prediction by a significant margin.
Aspect Level Sentiment Classification with Memory Network Using Word Sentiment Vectors and a New Attention Mechanism AM-PPOSC

For aspect level sentiment classification, the attention mechanisms of memory network can capture the importance of each context word when inferring the sentiment polarity of an aspect. However, the existing attention mechanisms of memory network are either content-based or location-based while the part of speech (POS) of word is not used. Accordingly, this paper proposes a new attention mechanism called AM-PPOSC which uses the location, POS and content information. In addition, the common-used algorithms for learning word embedding typically only model the syntactic context of words but ignore the sentiment polarities of words. So, this paper proposes a method that based on TD-LSTM and SentiWordNet 3.0 to capture the sentiment polarities of words which represented as word sentiment vectors. Then, AM-PPOSC and word sentiment vectors are applied to memory network. Experiments on two datasets demonstrate that not only AM-PPOSC outperforms the original attention mechanisms of memory network when inferring the sentiment polarity of an aspect, but also word sentiment vectors can improve the performance of memory network. Furthermore, compared with feature-based SVM, LSTM and TD-LSTM, memory network with AM-PPOSC and word sentiment vectors achieves higher accuracies on two datasets.
A High Speed Dynamic StrongARM Latch Comparator

In this paper, a new design has been proposed for a high speed, low power StrongARM latch in 65nm CMOS technology. Latching speed improvements of 18% and 16% have been achieved in comparison to the conventional [4] and improved StrongARM [5], respectively, while the energy consumption has also been reduced.
Break up the Family: Protocols for Efficient Recall-Oriented Retrieval Under Legally-Necessitated Dual Constraints

In the legal domain, the primary objective of eDiscovery is to retrieve, from within a document population, as many individual relevant documents as possible given the expenditure of a reasonable review effort. The production of relevant documents to opposing counsel, however, is typically made on a family basis (an email and all its attachments), such that the entire family is produced as a collective unit. Counsel’s ethical responsibility to prevent the disclosure of privileged information necessitates review of every document in every family being produced. Consequently, it is standard industry practice to batch entire families for review together. This work examines the overall review ordering and efficiency of alternative techniques for managing review leading to the production of relevant document families while fully protecting privilege. Batching, document sequencing, and therefore, training of the core supervised machine learning (AI) algorithm differ among each protocol, and lead to different overall efficiencies. Our empirical results support two conclusions. First, break up the family. In every instance, broken family retrieval protocols are more efficient than full family protocols. Second, a carefully designed and implemented dual phase workflow incorporating an initial, expedited relevance review can be more efficient than a single phase workflow, even if some documents are reviewed twice.
Correlation-based network analysis for biomarkers in obesity

Background: Obesity is associated with chronic activation of the immune system and an altered gut microbiome, leading to increased risk of chronic disease development. As yet, no biomarkers have been found to distinguish individuals at greater risk of obesity-related disease. The aim of this study was to explore a correlation-based network approach to find existing patterns of immune-microbiome interactions in obesity.Results: The current study performed correlation-based network analysis on five different datasets obtained from 11 obese and 12 healthy weight men: anthropometric measures, metabolic measures, immune cell abundance, serum cytokine concentration, and gut microbial composition. The obese cohort had a denser network (total number of edges, n=237) compared to the healthy network (n= 190). Within the obese network, immune cell abundance was found to be correlated to biomarkers from all four other datasets while in the healthy network, immune cell abundance was only correlated with serum cytokine concentration and gut microbial composition. Neutrophils within the obese immune cell abundance group were correlated with the most number of other biomarkers. Two different types of neutrophil measurements were taken, an abundance measure from immune cell gene expression and a whole blood count, with correlations to 10 and 6 other biomarkers, respectively. From the combined number of 16 biomarkers, 4 biomarkers were correlated between the two measurements: Anaerostipes abundance, Blautia abundance, EscherichialShigella abundance and Flavonifractor abundance.Conclusion: The obesity-related dysregulation of immune biomarkers was suggested by the high connectivity of immune cells in the obese network compared to the healthy network. Our study also revealed the importance of integrated analysis to uncover immune-microbiome interactions in obesity that are likely to have been missed in univariate analysis.
Medical Ultrasound Image: A Pre-Processing Approach Towards Reconstruction

Almost all despeckling schemes have worked on removing speckle noise from the medical ultrasound image after reconstruction; in the final stage when the image is displayed on the screen. In this paper we use the principle component analysis approach (PCA) and Improved PCA (IPCA) by using a nonlinear soft thresholding technique to remove speckle noise from the medical ultrasound image before reconstruction. Wiener filter and NLmeans filter are used as benchmark for performance comparison with PCA and IPCA. Performance comparison is done between despeckling the image before decimation and after decimation. Despeckling the image before decimation has removed the speckle noise more efficiently than despeckling it after decimation and maintained the texture of the original image. Wiener filter and NLmeans were found less efficient than PCA or IPCA in removing speckle noise. IPCA has provided better visual and numerical results than PCA and that is mainly in terms of edge detection and peak signal to noise ratio.
A New Recommendation Approach Based on Probabilistic Soft Clustering Methods: A Scientific Documentation Case Study

Recommender system (RS) clustering is an important issue, both for the improvement of the collaborative filtering (CF) accuracy and to obtain analytical information from their high sparse datasets. RS items and users usually share features belonging to different clusters, e.g., a musical-comedy movie. Soft clustering, therefore, is the CF clustering’s most natural approach. In this paper, we propose a new prediction approach for probabilistic soft clustering methods. In addition, we put to test a not traditional scientific documentation CF dataset: SD4AI, and we compare results with the MovieLens baseline. Not traditional CF datasets have challenging features, such as not regular rating frequency distributions, broad range of rating values, and a particularly high sparsity. The results show the suitability of using soft-clustering approaches, where their probabilistic overlapping parameters find optimum values when balanced hard/soft clustering is used. This paper opens some promising lines of research, such as RSs’ use in the scientific documentation field, the Internet of Things-based datasets processing, and design of new model-based soft clustering methods.
An Optimal Control Method of Long Tunnel Ventilation Based on Variable Domain Fuzzy Control

In order to make a comfortable and safe driving environment for passers in the tunnel, designers need to reduce CO pollutants and visibility indicators to an appropriate level. Road tunnel ventilation is an uncertainty and large time delay system, and it's very difficult to obtain an exact mathematical model. In addition, the traditional fuzzy control (FLC) method is often unable to reduce the concentration of pollutants effectively, which also caused unnecessary waste of ventilation fans. In order to solve the above problems, an optimized fuzzy control is proposed in the article. The FLC was optimally redesigned using the particle swarm algorithm, which is stochastic global search method. This control method is realized by establish the mathematical model of tunnel air volume and analysis the performance indicators in the tunnel. In this way, the fuzzy controller input and the output field are dynamically adjusted to improve the tunnel environment parameters. The simulation and experiment for this new fuzzy control law to be applied in road tunnel ventilation system are conducted. The simulation and experiment showed that the improved method is better and it also cost less energy compared to the traditional fuzzy control method. What's more, this new method is more suitable for fast response of signal transmission in the road tunnel.
Overlapping Community Detection in Multi-view Brain Network

Community detection in multi-view brain network is a significant research topic. Many efforts have been made on developing multi-view network community detection approaches. However, most of them can only reveal non-overlapping community structure, and the task of discovering overlapping community structure in multi-view brain network remains largely unsolved. In this paper, we propose a novel approach for Overlapping Community Detection in Multi-view Brain Network (oComm). The main idea is to design a network generative model and a node-wise cross-view consistency model for respectively measuring the within-view community quality and characterizing the cross-view community consistency. Some experiments have been conducted to confirm the effectiveness of the proposed method.
A Proposal of Scalable and Performing Implementation of Algorithms forAnomaly and Community Detection

This paper presents a study of various standard anomalies detection techniques in internet networks, using machine learning algorithms, classification algorithms, and graph techniques working on SQL/Mapreduce and SQL-Graph implementation platforms. This approach shows its efficiency for the study of large datasets. Firstly, several algorithmic approaches and possible implementations have been studied and experimentally tested to see how the SQL-MapReduce and SQL-Graph implementation can process large-scale data. Secondly the performance and scalability of the algorithms for large volumes of data have been compared to choose the most appropriate for typical anomaly detection. The application scope is very broad, such as spam detection, crime detection, mafia or terrorism community detection, network intrusion detection, malignant tumors detection in healthcare, fraud detection on banking transactions, identity theft detection etc. The main problem we will address on this paper is the processing performance on large scale data using an implementation of Massively Parallel Processing (MPP), SQL-MapReduce, and SQl-Graph.
Antenna selection and power allocation for energy efficient MIMO systems

In this paper, we investigate a new solution for the energy efficiency (EE) maximization and power allocation problem in point-to-point multiple-input multiple-output (MIMO) spatial multiplexing schemes. Different from conventional energy-efficient optimization approaches that require iterative numerical algorithms, we derive an optimal solution in a closed form, which provides an insight upon the relation between the optimum EE and system parameters such as circuit power and channel conditions. In addition, using the proposed closed form function, we present an upper bound on the optimum EE in terms of the full active transmit antennas parameters. Based on the derived upper bound, we also propose a new antenna selection algorithm which achieves almost the same performance as the optimum solution with much reduced complexity.
Transient and quasi-steady state simulation of Czochralski silicon single crystal growth

In this paper, we established a transient heat transfer model for a Czochralski furnace and compared it with steady-state simulations at different crystal heights. Both simulations showed the same qualitative results in Equal diameter stage with a temperature difference of less than 3K. However, in the shoulder-changing and tailing stages of the diameter change, the temperature, stress distribution, etc. show a qualitative difference between the transient and quasi-steady-state simulations. Therefore, dynamic research is better for strong transient phases with sharp changes in diameter.
A modular hybrid approach to employee timetabling

We consider a classical employee timetabling problem: a set of employees with various skill levels, qualifications, workload and availability distributions has to be assigned to a set of shifts, each requiring a prescribed number of qualified employees and spanning a given time period. The novelty of our approach is a hybrid combination of the methods proposed in bibliography, such that we leverage the advantages of known methods while minimizing their disadvantages. Thus, we first apply the generalized local hill-climbing with randomized neighborhoods to quickly reach the vicinity of local optima, and then use the tabu search to explore more of the search space around those solutions. Our experiments show that the resulting hybrid technique performs better than the comparable approaches presented in bibliography due to the hybrid nature of the technique. In addition, we propose a modular design that utilizes dependency injection to compose the the search algorithm. Together with careful modeling, this approach allows for constant-time evaluation of each possible step in the neighborhood and for an easy evaluation of different hybrid combinations that can be combined and parameterized at runtime.
DyBED: An Efficient Algorithm for Updating Betweenness Centrality in Directed Dynamic Graphs

An important index widely used to analyze social and information networks is betweenness centrality. In this paper, given a dynamic and directed graph G and a vertex r in G, we present the DyBED algorithm that updates the (approximate) betweenness centrality of r, when an update operation (vertex/edge insertion/deletion) occurs in G. Our algorithm first during pre-processing computes two subsets of the vertex set of G, called $\mathcal{R}\mathcal{F}\left( r \right)$ and $\mathcal{R}\mathcal{T}\left( r \right)$. The Cartesian product of these two sets defines the sample space of our algorithm. In other words, each sample is a pair, whose first element belongs to $\mathcal{R}\mathcal{F}\left( r \right)$ and second element belongs to $\mathcal{R}\mathcal{T}\left( r \right)$. Then after each update operation, DyBED updates the sets $\mathcal{R}\mathcal{F}\left( r \right)$ and $\mathcal{R}\mathcal{T}\left( r \right)$, the sampled pairs, the information stored for each sample and accordingly, the betweenness centrality of r. We theoretically and empirically evaluate DyBED and show that it yields significant improvement over existing work. In particular, our extensive experiments reveal that DyBED is orders of magnitude faster than most efficient existing algorithms.
An Interface to Heterogeneous Data Sources Based on the Mediator/Wrapper Architecture in the Hadoop Ecosystem

Heterogeneity of data sources is a challenge for Big Data applications. This contribution presents an approach based on the conceptual foundation of the mediator/wrapper architecture where source data is transformed into a global schema and transferred to one or more data sinks with Apache Kafka. Given appropriate wrappers, arbitrary data sources and sinks can be connected with one another in a flexible fashion. The design and implementation of a prototype for a data set from a metagenomics study of are described.
Coach-Trainee: A New Methodology for the Correction of Predefined Motions

This paper presents a new methodology which focuses on the use of Inverse Kinematics to constraint predefined motions while retaining the most of their initial flow of movement. A general correction method is first presented which emphasizes on the need of two distinct entities called the COACH and the TRAINEE , then a case study of providing floor support to a kinematic model of walking is developed and some general issues for motion specification are considered. keywords : Inverse Kinematics , motion correction , motion blending .
Breast Cancer Classification with Electronic Medical Records Using Hierarchical Attention Bidirectional Networks

Precise breast cancer classification can aid the clinicians in making decision of diagnosis and therapy. The existing works mainly utilized medical images for breast cancer classification, ignoring the value of Electronic Medical Records (EMR). In this study, we investigate the problem of breast cancer classification with EMR data and propose a new breast cancer classification model based on Hierarchical Attention Bidirectional Networks (HA-BiRNN). Our proposed model supports the integration of the features of the diagnosis reports with the features of the context information in EMR. Specially, our proposed model first uses three HA-BiRNNs to extract features from one type of report in a hierarchical way. In each HA-BiRNN, a hierarchical neural network structure, consisting in two encoder layers of BiRNN (Bidirectional Recurrent Neural Networks), mirrors the hierarchical structure of diagnosis reports, and a hierarchical attention mechanism, consisting of two level attentions, attends to important elements within diagnosis report with word-level attention and sentence-level attention. Secondly, to learn the feature of context information, our proposed model uses two additional BiRNNs. Finally, we add one more recurrent neural network layer for classifying breast cancer based on patients’ features generated by combing the reports’ vectors. We evaluate our method on the real world breast cancer clinical reports, and results show that our method achieves higher performance on breast cancer classification.
Spark-uDAPL: Cost-Saving Big Data Analytics on Microsoft Azure Cloud with RDMA Networks*

Efficient Big Data analytics on Cloud Computing systems is still full of challenges. One of the biggest hurdles is the unsatisfactory performance offered by underlying virtualized I/O devices such as networks. To address this issue, the modern cloud resource providers (e.g., Microsoft Azure) have deployed high-performance networks, such as Remote Direct Memory Access (RDMA) capable networks in their clouds. However, in this paper, we find that by far, the RDMA networks on Microsoft Azure cannot support either IPoIB or native standard Verbs-based RDMA protocols. Instead, applications need to use the uDAPL (i.e., user Direct Access Programming Library) interface to enable RDMA communication on Azure Cloud, which makes impossible for modern Big Data stacks to leverage these high-performance networks as none of them can support the uDAPL interface yet. To address this issue, we first design an efficient uDAPL-based communication library with the best combinations of uDAPL communication operations. Then, we adapt the designed uDAPL library into the Hadoop RPC ping-pong message passing engine and the Spark Shuffle engine for bulk data transferring. Through our designs, we can improve the performance of Big Data analytics workloads with Hadoop RPC and Spark on RDMA-enabled Azure VMs by up to 90% and 82%, respectively, and save users’ cloud resource renting cost by 4.24x. To the best of our knowledge, this is the first work to design a uDAPL-based RDMA communication engine for Big Data analytics stacks (e.g., Spark).