Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comprehensive Study on Enterprise Wi-Fi Access Points Power Consumption

Wi-Fi networks are becoming more and more ubiquitous and represent a substantial source of energy consumption around the globe, mainly when it comes to Access Points (APs). There has been some work done on the characterization of the power consumption of Wi-Fi APs and network interface cards (NICs), and the power usage of these devices under different configurations and standards but mostly using legacy standards. A detailed AP power consumption analysis, exploring the whole set of degrees of freedom and capabilities of these devices is lacking in the state of the art. In this paper, we present a thorough power consumption analysis, covering the configuration options available in enterprise Wi-Fi APs from the three major vendors on the market. The goal is to understand how the power consumption of an AP varies with the different configurations, and provide insights on the parameters that significantly affect the AP power consumption. The obtained experimental results confirm previous state-of-the-art conclusions but contradict some of the studies and results found in the literature, while updating results and conclusions taken in the past to the most recent standards, configurations, and data rates available today. The analysis provided herein is a valuable source of information for deriving new AP power consumption models and designing energy-efficient Wi-Fi networks.
Orbital Obstacle Avoidance Algorithm for Reliable and On-Line Mobile Robot Navigation

— This paper proposes an orbital obstacle avoidance algorithm which permits to obtain safe and smooth robot navigation in very cluttered environments. This algorithm uses specific reference frame which gives accurate indication on robot situation. The robot knows thus if it must avoid the obstacle in clockwise or counterclockwise direction. Moreover, it knows the moment to go into the orbit of the obstacle and the moment to go out. These orbital behaviors are performed using adaptive limit-cycle trajectories. The later with a specific conflicting situations module permit to prevent robot oscillations , local minima and dead ends. The proposed algorithm is embedded in a specific bottom-up control architecture with stability proof given according to Lyapunov synthesis. The overall proposed structure of control allows to decrease significantly the time to reach the target. In fact, according to the proposed algorithm, robot anticipates the collisions with obstacles according to smooth local trajectory modifications. A large number of simulations in different environments are performed to demonstrate the efficiency and the reliability of the proposed control architecture.
Adversarial Attacks On Vision Algorithms Using Deep Learning Features

Computer vision algorithms, such as those implementing object detection, are known to be susceptible to adversarial attacks. Small barely perceptible perturbations to the input can cause vision algorithms to incorrectly classify inputs that they would have otherwise classified correctly. A number of approaches have been recently investigated to generate such adversarial examples for deep neural networks. Many of these approaches either require grey-box access to the deep neural net being attacked or rely on adversarial transfer and grey-box access to a surrogate neural network. In this thesis, we present an approach to the synthesis of adversarial examples for computer vision algorithms that only requires black-box access to the algorithm being attacked. Our attack approach employs fuzzing with features derived from the layers of a convolutional neural network trained on adversarial examples from an unrelated dataset. Based on our experimental results, we believe that our validation approach will enable designers of cyber-physical systems and other high-assurance use-cases of vision algorithms to stress test their implementations.
Hybrid Mobile-App. on Multi-MEC Platforms in NFV Environment

In recent times, practicality of web applications has become more reliant upon big-data orientated materials such 4K videos, hi-def. resolution images, lossless audios and massive texts. Structured Query Languages (SQL) faces compatibility issues with large scale databases. Because of this data storage problem, NoSQL databases are used for storing big-data. NoSQL databases have been recently gaining traction with many options such MongoDB, CouchDB, Redis and Apache Cassandra. One of the major restrictions companies, enterprises and developers encounter during developing an application is multiplicative cost of building a native programing across different platforms. Besides, network Function Virtualization (NFV) plays a vital role for providing services for utilizing such applications in larger and more effective scale. Hence, in this paper, we discussed our main motivation behind selecting Iconic Framework, a hybrid system for rapid development real-time application based on Firebase in the NFV environment cooperating with Mobile Edge Computing (MEC). As a result, this approach provides comparatively flexible features.
LiDAR object detection based on optimized DBSCAN algorithm

In the process of obstacle detection based on LiDAR, the traditional DBSCAN clustering algorithm can’t achieve good clustering for both short-range and long-distance targets because of the uneven distribution of data density, resulting in missed detection or false detection. To solve the problem, this paper proposed an optimized DBSCAN algorithm which improves the adaptability under different distance by optimize the selection method of neighborhood radius. According to the distribution of the lines scanned by LiDAR, the distance between two adjacent scan lines is deter-mined and an improved neighborhood radius list is established. Then the neighborhood radius will be searched in the list based on the coordinated values of each scan point. Finally, linear interpolation method is used to obtain the corresponding neighborhood radius. The experimental results based on Ford dataset prove that compared with the traditional DBSCAN algorithm, the proposed algorithm can effectively improve the accuracy of obstacle detection and adapt to the target clustering operation under different distances. The positive detection rate of obstacle detection is increased by 17.52%.
Amino Acid Requirements of Gilthead Bream (Sparus aurata) Juveniles

In one trial 360 gilthead bream fingerlings received two different feed mixtures containing two different levels of protein (diet A 400 and B 360 g? kg? 1). The average initial live weight was about 2.2 ± 0.1 g fish? 1 in each replicate of all treatments. Sixty fish per tank were stocked randomly in 6 fiberglass tanks (1 m3 each). Fish were fed with experimental diets at rate 8% of the body weight daily. The Amino Acids (AA) content was analyzed in the diets, whole fish and muscle at the end of the growth period (60 days). The results indicated that at the end of experiment gilthead bream fingerlings had reached an average live weight 18.5 and 16.2 g fish? 1 for fish fed diet A and B respectively. Also the increase of protein levels in the feed to 40% caused a significant increase in the content of Indispensable Amino Acid (IAA) and dispensable amino acid (as %v of whole fish and muscle of wet weight). Two methods were subsequently used to estimate the quantitative IAA requirements of gilthead bream fry based on the hypothesis that (a) the dietary requirement pattern of IAA reflected the tissue pattern and (b) the rate of daily deposition in the fish could be equated with the dietary requirement as percentage of 100 g? 1 diet. The quantitative IAA requirement (%) of gilthead bream (Sparus aurata) diets was as follow: Lysine 2.27, Methionine 1.09, Therionine 0.61, Leucine 2.4, Isoleucine 0.47, Histidine1.6, Arginine 1.39, Phenylalanine 1.43, Valine 1.22 and Tryptophane 0.42.
Conceptual ClusteringAnalysis in Data Mining: A Study

Clustering on unsupervised learning handles with instances, which are not classified already and not having class attribute with them. Applying algorithms is to find useful but items on unknown classes. Approach of unsupervised learning is about instances are automatically making into meaningful groups basing on its similarity. This paper we study about the basic clustering methods in data mining on unsupervised learning such as ensembles distributed clustering and its algorithms.
A Practical Approach for Objects Identification in Rgb Colored Image using Segmentation in Matlab Simulation

: Object Analysis is one of the great challenges of computer vision. The RGB color models are an additive coloration version in wherein red, green and blue light are added collectively in various way to reproduce a large array of colors. The primary goal of the RGB color model is for sensing, representation and show image in electronics systems like televisions and computers, though it‟s been used in conventional images. There are general phases that define the flaws the working on these Techniques. The proposed work has been explained the concept of the Image Segmentation means to analysis object in RGB colored and generate performance parameter based on the results or output. The analysis of the different objects and quality of image can be implement using image segmentation process.
Machine Learning based Protein Sequence to (un)Structure Mapping and Interaction Prediction

xix Introduction . 1 1.1 Thesis Overview . 4 1.2.1 Statement of Research Problem . 4 1.1.1 Contribution of The Thesis . 6 1.2 Technical Results of the Thesis . 7 1.2.1 An Optimized SVM for Protein Disorder Prediction . 7 1.2.2 Extraction of Energy Scores from Protein Sequence . 8 1.2. 3 A Reinforced Regression for Accessible Surface Area Prediction . 8 1.2. 4 A Stacked Model for Peptide-Binding Residue Prediction . 9 1.3 Thesis Organization . 9 1.4 Related Publications . 10 DisPredict: A Predictor of Disordered Protein . 13 2.1 Background and Motivations . 14 Intrinsically Disordered Proteins  Types and Characteristics . 16 Abundance of Protein Disorder . 19 Protein Disorderedness and Functions . 20 Role of in silico Disorder Prediction . 21 2.2 Review of Disorder Prediction . 22 2.2.1 Predictors based on Machine Learning . 22 2.2.2 Predictors based on Physicochemical Properties . 24 2.2. 3 Predictors based on Meta-approach . 24 2.2. 4 Our Contributions . 25
Energy Saving Techniques Comparison for Green Computing in Cloud Server

- The IT industry has been revolutionized in the past few decades. Cloud Computing companies (Google, Yahoo, Gaikai, ONLIVE, Amazon and eBay) use large data centers which are comprised of virtual computers that are placed globally and require a lot of power cost to maintain. Demand for energy consumption is increasing day by day in IT firms. Therefore, Cloud Computing companies face challenges towards the maintenance of power costs. Energy consumption is dependent upon several factors, e.g., service level agreement, virtual machine selection techniques, optimization policies, workload types etc. We address a solution for the energy saving problem by enabling dynamic voltage and frequency scaling technique for gaming data centers. The dynamic voltage and frequency scaling technique is compared against non-power aware and static threshold detection techniques. This helps service providers to meet the quality of service and quality of experience constraints by meeting service level agreements. The CloudSim platform is used for implementation of the scenario in which game traces are used as a workload for testing the technique. Selection of better techniques can help gaming servers to save energy cost and maintain a better quality of service for users placed globally. The novelty of the work provides an opportunity to investigate which technique behaves better, i.e., dynamic, static or non-power aware. The results demonstrate that less energy is consumed by implementing a dynamic voltage and frequency approach in comparison with static threshold consolidation or non-power aware technique.
Fabrication of Mg-Zn-Y-based quasi- crystal by pouring melt into different cooling media

Mg-Zn-Y-based quasi-crystal (QC) alloys were fabricated by pouring melts into different cooling media. The effects of different cooling rates on the QC morphology, size, volume fraction and micro-hardness were studied. Multi-component Mg-Zn-Y-based QC alloys were synthesized based on amorphous design principle. QC morphology transformation and its influencing factors were analyzed. Micro/nano spherical quasi-crystals (QCs) were fabricated through a wedge-shaped copper mould and their forming mechanism were discussed by atoms cluster theory, optimum cooling rate theory and the known crucial criteria. The results of research show that with the cooling rate reduced, the solidified morphology of QC phase changes from near-spherical, micro petals (1 to 2 μm) to big petals (20 μm) and finally grows up to bulk pentagon or hexagon (200 to 400 μm). Multi-component micro-spherical QCs possess higher value of micro-hardness than petal-like QCs with the same components, and also higher than ternary micro-Mg-Zn-Y QCs. The fine master alloys containing micro-QCs (0.4 μm) and nano-QCs (about 300 nm and 40 nm) have been fabricated correspondingly on the middle and the tip of wedge-shaped castings. A morphology evolution schematic diagram of Mg-Zn-Y-based QCs is included in this paper.
A Case Study of Writing to Learn to Program: Codebook Implementation and Analysis

Dr. Jean Mohammadi-Aragh is an assistant professor in the Department of Electrical and Computer Engineering at Mississippi State University. Dr. Mohammadi-Aragh investigates the use of digital systems to measure and support engineering education, specifically through learning analytics and the pedagogical uses of digital systems. She also investigates fundamental questions critical to improving undergraduate engineering degree pathways. She earned her Ph.D. in Engineering Education from Virginia Tech. In 2013, Dr. Mohammadi-Aragh was honored as a promising new engineering education researcher when she was selected as an ASEE Educational Research and Methods Division Apprentice Faculty.
DEVELOPMENT OF A MACHINE VISION SYSTEM FOR STRAWBERRY POWDERY MILDEW DISEASE DETECTION

Strawberry plants have been facing a significant proportion of diseases during cultivation, scattered throughout the field, emphasizing the need for proper diseases management. Powdery mildew is one of the major fungal strawberry disease which is typically responsible for approximately 30-70% loss of yields. Disease inspection is limited by the human visual capabilities because of the microscopic preliminary symptoms. As powdery mildew disease scrutiny is still determined by human naked eyes due to lack of technological development for plant disease detection task, machine vision systems seem to be well adapted. The aim of this study was to develop a machine vision based artificial cloud lighting condition system for detecting strawberry powdery mildew leaf disease. The artificial cloud lighting condition system was developed consisting of custom software, two μEye colour cameras, a black cloth cover, real time kinematics-global positioning system and a ruggedized laptop computer and mounted on a mobile platform. The custom software was developed in C# programming language. The colour cooccurrence matrix based texture analysis was used to extract image features and discriminant analysis (quadratic) for classification. The study proposed mobile platform of artificial cloud lighting condition for image acquisition is beneficial. It showed higher detection accuracies of 95.26%, 95.45% and 95.37% for recall, precision and F-measure, respectively compared to 81.54%, 72% and 75.95% of recall, precision and F-measure, respectively with acquired images at natural cloud lighting condition. The feature selection results suggested the PM_GHSI feature model was best fit for this study. This study also revealed that the image acquisition speed (1.5 km
Modeling Labial Coarticulation with Bidirectional Gated Recurrent Networks and Transfer Learning

In this study, we investigate how to learn labial coarticula-tion to generate a sparse representation of the face from speech. To do so, we experiment a sequential deep learning model, bidi-rectional gated recurrent networks, which have reached nice result in addressing the articulatory inversion problem and so should be able to handle coarticulation effects. As acquiring audiovisual corpora is expensive and time-consuming, we designed our solution to counteract the lack of data. Firstly, we have used phonetic information (phoneme label and respective duration) as input to ensure speaker independence, and in second hand, we have experimented around pretraining strategies to reach acceptable performances. We demonstrate how a careful initialization of the last layers of the network can greatly ease the training and help to handle coarticulation effect. This initialization relies on dimensionality reduction strategies, allowing injecting knowledge of useful latent representation of the visual data into the network. We focused on two data-driven tools (PCA and autoencoder) and one hand-crafted latent space coming from animation community, blendshapes decomposition. We have trained and evaluated the model with a corpus consisting of 4 hours of French speech, and we have gotten an average RMSE close to 1.3mm.
Semantic Segmentation of Remote Sensing Images via Stepwise-Refined Large-Kernel Deconvolutional Networks

Deep CNN based semantic segmentation has been developed for several years and many models are proposed. However, most of them are designed for natural scene images such as PASCAL VOC, and cannot perform very well on remote sensing images, in which objects are much smaller and more densely distributed than those in natural scene images. In this paper, we demonstrate the importance of high-resolution feature maps and the problem of large dilated convolutional kernels in semantic segmentation of remote sensing images. Furthermore, we propose a Stepwise-Refined Large-Kernel Deconvolutional Network with a focus on small and densely-distributed objects such as houses and buildings, or long and narrow ones such as roads and rivers. Experiments on a public available ISPRS Vaihingen Challenge Dataset and our self-compiled Fujian Dataset show that our model outperforms the state-of-the-art models in semantic segmentation of remote sensing images.
Research on Neural Machine Translation Model

In neural machine translation (NMT), cyclic neural networks, especially long-term and short-term memory networks and gated recurrent neural networks, have been regarded as the latest methods for sequence modeling and transduction problems for a long time, such as language modeling and machine translation. When the cyclic neural network is running, the sequence information is processed one by one, strictly following the order from left to right or from right to left, processing one word at a time, and parallel operation cannot be realized, resulting in slow running speed. With the rapid development of neural machine translation (NMT) network architecture, cyclic neural network has been effectively replaced by convolution network and self - attention. Convolution neural network has replaced the divine circulation neural network due to its parallel computation of convolution. The Transformer model replaces the long-term and short-term memory network with a complete self-attention structure, and abandons the traditional encoder and decoder model which must combine the inherent mode of convolutional neural network or circular neural network and only uses the self-attention mechanism. Although the biggest innovation of Transformer architecture is to use full self - attention, there are several other factors, such as multi-head attention and residual connection. The model flexibly combine several common building blocks in the Transformer architecture with the cyclic neural network. By borrowing the framework of the Transformer architecture without using full self - attention, experiments show that the cyclic model can be very close to the performance of the Transformer Our model achieved 26.7 BLEU in the WMT 2014 English to German translation task and 37.8 BLEU in the WMT 2014 English to French translation task. Using these two scores alone is very close to the score of the Transformer architecture using full attention, so even if the cyclic neural network is used instead of full self - attention, it can perform well on the data set.
An Improved Vision-Based Lane Departure Warning System under High Speed Driving Condition

Recently, Lane departure warning system has attracted great attention as it contributes to vehicle active safety. In this paper, a vision-based lane departure warning system under high-speed driving is proposed. The system consists of two functional parts: lane markings detection and vehicle departure identification. The Hough Transform is applied to detect lane boundaries, which is a most effective detection method with high reliability. Based on the road line, a method using several Euclidean-distance-related parameters to calculate vehicle’s position and its deviating status is proposed, which addresses the problem of efficient detection of lane departure under high-speed driving condition. The algorithm is tested and verified in various real driving conditions and proved a reliable and steady performance.
Ensemble of deep learning, visual and acoustic features for music genre classification

ABSTRACT In this work, we present an ensemble for automated music genre classification that fuses acoustic and visual (both handcrafted and nonhandcrafted) features extracted from audio files. These features are evaluated, compared and fused in a final ensemble shown to produce better classification accuracy than other state-of-the-art approaches on the Latin Music Database, ISMIR 2004, and the GTZAN genre collection. To the best of our knowledge, this paper reports the largest test comparing the combination of different descriptors (including a wavelet convolutional scattering network, which has been tested here for the first time as an input for texture descriptors) and different matrix representations. Superior performance is obtained without ad hoc parameter optimisation; that is to say, the same ensemble of classifiers and parameter settings are used on all tested data-sets. To demonstrate generalisability, our approach is also assessed on the tasks of bird species recognition using vocalisation and whale detection data-sets. All MATLAB source code is available.
Prototype Application as the Implementation of Software as a Service

Cloud computing is an internet-based computing process, and Software as a Service is a software in the form of services that are part of Cloud Computing. Cloud computing as a form of market will grow rapidly, can be used as a means of marketing and supply services or goods. This application become a meeting point of the supplier of goods/services with consumer goods / services.