Miracle-dz/UniFAR-FTUs huggingface dataset:
Classification of Sentimental Reviews Using Natural Language Processing Concepts and Machine Learning Techniques

Natural language processing (NLP) is the hypothetically motivated scope of computational strategies for representing and analyzing naturally occurring text at many levels of textual analysis for the goal of attaining automatic language processing system for multiple tasks and applications. One of the most import applications of natural language processing from industry perspective is sentiment analysis. Sentiment analysis is the most eminent branch of NLP because of its capability to classify any textual document to either as positive or negative polarity. With the proliferation of World Wide Web, huge textual unstructured data in form of tweets, messages, articles, social networking discussions, reviews of products and movies are available so as to extract right information from the large pool. Thus, a need is felt to analyze this data to bring out some hidden facts based on the intention of the author of the text. The intention can be either criticism (negative) of product and movie review or it can be admiration (positive). Although, The intention can vary from strongly positive to positive and strongly negative to negative. This thesis completely focuses on classification of movie reviews in either as positive or negative review using machine learning techniques like Support Vector Machine(SVM), K-Nearest Neighbor(KNN) and Naive Bayes (NB) classifier. Further, a N-gram Model has been proposed where the documents are classified based on unigram, bigram and trigram composition of words in a sentence. Two dataset are considered for this study; one is a labeled polarity dataset where each movie review is either labeled as positive or negative and other one is IMDb movie reviews dataset. Finally, the prediction accuracy of above mentioned machine learning algorithms in different manipulations of same dataset is studied and a comparative analysis has been made for critical examination.
Shortest Path Approximate Algorithm for Complex Network Analysis

The tremendous scale of the social networks mined from Internet is the main obstacle of a social network analysis application. The bottleneck of many network analysis algorithms is the extortionate computational complexity of calculating the shortest path. Real-World networks usually exhibit the same topological features as complex networks such as the "scale-free" and etc,which indicate the intrinsic laws of the shortest paths in complex networks. Based on the topological features of real-world networks,a novel shortest path approximate algorithm which uses an existent short path passing through some local center nodes to estimate the shortest path in complex networks,is proposed. This paper illustrates the advantage and feasibility of incorporating the proposed algorithm within the network properties,which suggests a new idea for complex social network analysis. The proposed algorithm has been evaluated both on synthetic network stage and real world network stage. Experimental results show that the proposed algorithm can largely reduce the computational complexity and remain highly effective in complex networks.
An Easy Stack-Analogy On-Chip Memory Dynamic Allocation Compilation Technique

Many-core CPUs prefer selecting SPM,and a non-cached on-chip SRAM acts as the fast memory in these memory hierarchies owing to the area and power consumption. These memory hierarchies need software to explicitly manage data placement and data transfer. This article proposes an easy stack-analogy on-chip memory dynamic deallocation approach applied to program memory-access-sequence optimizations with many-core memory hierarchies. It introduces an ILP solver to develop a method when the sum of the array variable sizes in each program phase is larger than the on-chip memory size. The experimental results show that this approach can improve significantly the performance.
Differential evolution for neural networks learning enhancement.

Evolutionary computation is the name given to a collection of algorithms based on the evolution of a population toward a solution of a certain problem. These algorithms can be used successfully in many applications requiring the optimization of a certain multi-dimensional function. These algorithms have widely been used to optimize the learning mechanism of classifiers, particularly on Artificial Neural Network (ANN) Classifier. Major disadvantages of ANN classifier are due to its sluggish convergence and always being trapped at the local minima. To overcome this problem, Differential Evolution (DE) has been used to determine optimal value for ANN parameters such as learning rate and momentum rate and also for weight optimization. In ANN, there are many elements need to be considered, and these include the number of input nodes, hidden nodes, output nodes, learning rate, momentum rate, bias parameter, minimum error and activation/transfer functions. These elements will affect the speed of neural network learning. DE has been applied successfully to improve ANN learning from previous studies. However, there are still some issues on DE approach such as longer training time to produce the output and the usage of complex functions in selection, crossover and mutation calculation. In this study, DE is chosen and applied to feed forward neural network to enhance the learning process and the network learning is validated in terms of convergence rate and classification accuracy. Three programs have developed; Differential Evolution Neural Network (DENN), Genetic Algorithm Neural Network (GANN) and Particle Swarm Optimization with Neural Network (PSONN) to probe the impact of these methods on ANN learning using various datasets. The results have revealed that DENN has given quite promising results in terms of convergence rate and smaller errors compared to PSONN and GANN.
A SURVEY ON MACHINE LEARNING IN WIRELESS SENSOR NETWORKS

The sensors in Wireless Sensor networks gather data about the objects they are used to sense . However these sensors are limited in their performance by constraints of energy and bandwidth. Machine Learning techniques can help them in overcoming such constraints. This survey paper surveys machine learning techniques which have been used to improve the working efficiency of such networks especially with respect to the above two constraints. The emphasis is on Applications and the paper also discusses emerging directions for further research work in this area.
Federal enterprise architecture framework

To deal with problems of evaluation,construction,selection and usage for the Federal Enterprise Architecture Framework(FEAF),steps and approaches of systemic studies were discussed.Firstly,FEAF and its methodology,usage guide and reference model were introduced.Then,the ISO standard of Generalized Enterprise Reference Architecture and Methodology(GERAM) was adopted to study FEAF and mappings between them.The analysis was conducted from several aspects which included: lifecycle,modeling frameworks,modeling languages,methodologies,reference models,generic modeling concepts,enterprise modeling tools,and enterprise modules.Finally,advantages and disadvantages of FEAF were summarized.
Guide to Industrial Control Systems (ICS) Security

This document provides guidance on how to secure Industrial Control Systems (ICS), including Supervisory Control and Data Acquisition (SCADA) systems, Distributed Control Systems (DCS), and other control system configurations such as Programmable Logic Controllers (PLC), while addressing their unique performance, reliability, and safety requirements. The document provides an overview of ICS and typical system topologies, identifies typical threats and vulnerabilities to these systems, and provides recommended security countermeasures to mitigate the associated risks.
Improving random forest algorithm through automatic programming

Random Forest is a successful ensemble prediction technique that exploits the power of many decision trees and judicious randomization to generate accurate predictive models. Recently, it has become one of the main current directions in Machine learning research. In this thesis, we aimed to investigate the possibility of improving the Random Forest using automatic programming, specially the Automatic Design of Algorithms Through Evolution (ADATE) system. To achieve the goal, we first studied the Random Forest algorithm from the perspective of a member in the family of ensemble learning methods. Based on this knowledge, we conducted two experiments using the ADATE system. In the first experiment, we attempted to improve the combination of base classifiers. The second experiment concentrated on improving the way in which base classifiers are generated. Although we did not succeed in our first experiment, the second experiment brought us good results. Experiments with 19 benchmark data sets showed that the best model we got achieves up to 14.3% improvement in performance (in total) compared with the original one.
Crowbar Resistance Setting and its Influence on DFIG Low Voltage Based on Characteristics

In the case of low voltage fault cases , doubly fed induction generator ( DFIG ) with Crowbar protection circuit to realize low voltage ride through (LVRT) , and the choice of crowbar resistance of unit LVRT effects is very large . In this paper, from the DFIG wind power system steady state and transient mathematical model of voltage drop down, using space vector analysis method and Laplace transform, deduced the time domain transient current expressions of voltage drop Put forward the method for calculating the maximum fault current and the rotor side crowbar resistance setting method, solve the crowbar protection circuit for tilting rotor current and DC bus over-voltage problems. Numerical example and simulation shows that , The proposed method, effectively suppress the transient component of fault current, significantly i mproved the automation level of the wind power system.
Computational methods for fluid dynamics

This text develops and applies the techniques used to solve problems in fluid mechanics on computers and describes in detail those most often used in practice. It includes advanced techniques in computational fluid dynamics, such as direct and large-eddy simulation of turbulence, multigrid methods, parallel computing, moving grids, structured, block-structured and unstructured boundary-fitted grids, and free surface flows.
Influences of Prefresh and Early Fresh Body Condition Score Changes on Some Holstein Dairy Performance Traits

This study was performed to investigate the effect of body condition change from the prefresh to early fresh periods on the subsequent milk production performance in Holstein dairy cows. For this purpose, 89 Holstein cows in a private high yielding dairy enterprise (average milk yield 8000 kg/season) were included in this study . Body condition score (BCS) was evaluated at the prefresh period (260–270 day) post last insemination, as well as one month postpartum. Evaluations of BCS were performed via, two evaluators on a scale of 1–5 points and the average BCS value for each cow was detected. BCS loss was estimated via, subtract of postpartum BCS value from the prefresh one. Data of the daily, initial, 305-day, and actual milk yield were collected and recorded. The results of this study revealed that, fatty cows during their prefresh phase (BCS 4.5–5.0 points) had high dairy performance, as these cows depicted the highest values of daily milk yield ( 37.58 kg ), initial milk yield ( 43.38 kg ), 305–day milk yield ( 11461.00 kg ) and actual milk yield (12229.38 kg ) . Moreover, the cows with a postpartum BCS of 3.5–4.0 points recorded the highest significant daily milk yield ( 37.93 kg ), 305-day milk yield ( 11566.67 kg ), initial milk yield ( 49.00 kg ) and actual milk yield ( 14945.00 kg ). The cow losing from (–0.5)–(–1.0) point BCS has the highest significant daily and 305-day milk yield. In conclusion, BCS significantly influenced the milk performance traits of Holstein cows both at prefresh and postpartum period. For higher performance, the cows should be with somewhat heavy BCS at prefresh and with medium BCS postpartum. The dairy Holstein cows could loss about 0.5–0.75 BCS unit during their early fresh phase with efficient milk production. Further research is needed to prove this presumption. Keywords: body condition score, Holstein, prefresh, postpartum, milk yield Cite this Article Dawod A, Abdel-Hamid TM, Ramadan S. Influences of Prefresh and Early Fresh Body Condition Score Changes on Some Holstein Dairy Performance Traits. Research & Reviews: Journal of Dairy Science and Technology . 2015; 4(2): 12–18p.
Partial Referential Integrity in SQL Databases

The goal of data integrity is to maintain and ensure the accuracy and consistency of data over its life-cycle. It is a critical aspect of the design, implementation and usage of any system which stores, processes, or retrieves data. Edgar Codd, the Turing award winning inventor of the relational model of data, proposed domain, entity, and referential integrity as the three most basic classes of constraints that a database system enforces to help achieve data integrity. In particular, referential integrity ensures that data in some part of the database correctly references some other part in the database. Despite their fundamental importance to data processing, referential integrity constraints have not been well-studied for real-world databases that conform to the industry standard SQL. In such databases, incomplete information is represented by null marker occurrences. The SQL standard defines three semantics that can be applied when referential integrity is verified for SQL data: Full, simple and partial semantics. Surprisingly, commercial and open-source database management systems only offer built-in support for simple semantics. This gap between the SQL standard and its implementations can have a large negative impact on the accuracy and consistency of real-world data, and therefore severely limit the insight from data analysis, and the profit from data-driven decision making. The thesis addresses this long-standing gap between the industry standard and its implementations in actual database systems. My research develops techniques that aim to lift the partial semantics of referential integrity constraints to a first-class citizen of database systems. In particular, first techniques are developed that allow database systems to i) discover, ii) enforce, and iii) validate partial referential integrity constraints efficiently. Furthermore, some applications are identified that highlight the benefit of partial referential integrity to core data processing tasks such as queries, updates, and repairs. My findings can be used to improve daily database practice.
Software and Hardware Development of A Water Quality Detection Microcontroller System

This paper presents an online system to detect water quality, controlled by C51 microprocessor, and elucidates the hardware structure of the control system. It introduces the structure and the design method of the software, and emphasizes on how to modularize the program of display. Compared with traditional ways of water quality detection, explains the superiority and application value of this system.
Large-Scale Text Similarity Computing with Spark

Text understanding is a hot research in Natural Language Processing and Information Retrieval. In recent years, it has received wide attention and research. In the era of big data, Understanding text in large-scale datasets is a challenge. Although the earliest systems designed for these workloads, such as MapReduce, gave users a powerful, but low-level, procedural programming interface. So, MapReduce doesn’t compose well for lager text applications. Recently, Spark, an in-memory cluster-computing platform, has been proposed. It has emerged as a popular framework for large-scale data processing and analytics. It provides a general-purpose efficient cluster computing engine and simpler for the end users. In this work, we consider using Vector Space Model (VSM) and TF-IDF weighting schema and feature hashing feature extraction techniques in order to solve the problem of large-scale text data similarity computing by Spark. As a result, Experimental results that using Spark in order to solve document similarity computation problems as soon as quickly by 20Newsgroups. In additions, It is more benefit from document classification and clustering of machine learning tasks.
Dynamic controller design for an IVCIM

A dynamic output feedback (DOF) controller design is proposed for an indirect vector-controlled induction motor (IVCIM). The proposed controller is an alternative to the conventional proportional integral (PI) one and can be used for improving the dynamic performance of the IVCIM system. A sequential design method is used in which, first, the inner-loop controllers are designed. Then, the designed inner-loop controller is employed to design the outer speed-loop controller. The procedure incorporates the iron loss dynamics of the induction motor model to fetch the benefit of better performance than conventional controller designs neglecting the same. The DOF controller is designed in an H ∞ control framework by solving the iterative linear matrix inequality that makes stator currents and speed response to be robust against load disturbances and parameter variations. A first-order dynamic controller is tested to highlight its superiority over the PI controller. Comparison between the proposed dynamic and conventional PI controller is shown through several experimental results. It depicts enhanced performance by the proposed controller and suitability to industrial use.
Energy Cost Minimization by Joint Radio and NFV Resource Allocation: E2E QoS Framework

In this paper, we propose an end to end radio and network function virtualization (NFV) resource allocation for next generation networks providing different types of services with different requirements in terms of latency and data rate. We consider both the access and core parts of the network, and formulate a novel optimization problem whose aim is to perform the radio resource allocation jointly with virtual network function (VNF) embedding, scheduling, and resource allocation such that the network cost, defined as the consumed energy and the number of utilized network servers, is minimized. The proposed optimization problem is non-convex, NP-hard, and mathematically intractable, and hence, we adopt the alternative search method (ASM) to decouple the main problem into low complex subproblems. Moreover, we propose a novel heuristic algorithm for NFV resource allocation by proposing a novel admission control algorithm. Then, we compare the performance of the proposed algorithm with a greedy-based solution in terms of the acceptance ratio and the number of active servers. Our simulation results show that the proposed heuristic algorithm outperforms the conventional ones by approximately 8%
News Cover Assessment via Multi-task Learning

Online personalized news product needs a suitable cover for the article. The news cover demands to be with high image quality, and draw readers' attention at same time, which is extraordinary challenging due to the subjectivity of the task. In this paper, we assess the news cover from image clarity and object salience perspective. We propose an end-to-end multi-task learning network for image clarity assessment and semantic segmentation simultaneously, the results of which can be guided for news cover assessment. The proposed network is based on a modified DeepLabv3+ model. The network backbone is used for multiple scale spatial features exaction, followed by two branches for image clarity assessment and semantic segmentation, respectively. The experiment results show that the proposed model is able to capture important content in images and performs better than single-task learning baselines on our proposed game content based CIA dataset.
AVDNet: A Small-Sized Vehicle Detection Network for Aerial Visual Data

Detection of small-sized targets in aerial views is a challenging task due to the smallness of vehicle size, complex background, and monotonic object appearances. In this letter, we propose a one-stage vehicle detection network (AVDNet) to robustly detect small-sized vehicles in aerial scenes. In AVDNet, we introduced ConvRes residual blocks at multiple scales to alleviate the problem of vanishing features for smaller objects caused because of the inclusion of deeper convolutional layers. These residual blocks, along with enlarged output feature map, ensure the robust representation of the salient features for small-sized objects. Furthermore, we proposed a recurrent-feature aware visualization (RFAV) technique to analyze the network behavior. We also created a new airborne image data set (ABD) by annotating 1396 new objects in 79 aerial images for our experiments. The effectiveness of AVDNet is validated on VEDAI, DLR-3K, DOTA, and the combined (VEDAI, DLR-3K, DOTA, and ABD) data set. Experimental results demonstrate the significant performance improvement of the proposed method over state-of-the-art detection techniques in terms of mAP, computation, and space complexity.
Relation Network for Multilabel Aerial Image Classification

Multilabel classification plays a momentous role in perceiving intricate contents of an aerial image and triggers several related studies over the last years. However, most of them deploy few efforts in exploiting label relations, while such dependencies are crucial for making accurate predictions. Although an long short term memory (LSTM) layer can be introduced to modeling such label dependencies in a chain propagation manner, the efficiency might be questioned when certain labels are improperly inferred. To address this, we propose a novel aerial image multilabel classification network, attention-aware label relational reasoning network. Particularly, our network consists of three elemental modules: 1) a label-wise feature parcel learning module; 2) an attentional region extraction module; and 3) a label relational inference module. To be more specific, the label-wise feature parcel learning module is designed for extracting high-level label-specific features. The attentional region extraction module aims at localizing discriminative regions in these features without region proposal generation, yielding attentional label-specific features. The label relational inference module finally predicts label existences using label relations reasoned from outputs of the previous module. The proposed network is characterized by its capacities of extracting discriminative label-wise features and reasoning about label relations naturally and interpretably. In our experiments, we evaluate the proposed model on two multilabel aerial image data sets, of which one is newly produced. Quantitative and qualitative results on these two data sets demonstrate the effectiveness of our model. To facilitate progress in the multilabel aerial image classification, our produced data set will be made publicly available.