Miracle-dz/UniFAR-FTUs huggingface dataset:
Product Maturity, Security and Software Engineering

The maturity of a product is defined by its capability to meet specified qualitative criteria at a given step in the design process. In this paper, we will focus on product maturity; a step beyond traditional process maturity as defined by Capability Maturity Model Integrated (CMMI)-like methods. Product maturity is measured through a series of high-level requirements, intensive formative evaluations and expert integration. A global philosophy is vital to guide complexity management throughout the design and development process. High-level requirements of the product must address the right function(s) as well as any security policy enforcement requirements. High-level requirements must be stable, correctly validated, clearly stated, well-written, and consistent. Scenario-based design should be promoted to appropriately test design solutions. The personnel resources assigned to the project are responsible for execution of the design. A knowledgeable technical manager must constantly enforce a global vision of the product and integrate possible partial visions throughout the design and development process. End-users should be included throughout the process to appropriately validate highlevel requirements. Technical coordination of the various actors is required to insure effective and correct integration and operation reliability insurance. Management effectiveness must be integrated also and reduce the distance between actors. Continuity of the team organization throughout process must be preserved; internal personnel turnover should be carefully controlled. Specific care should be devoted to selecting appropriate of design and evaluation methods, especially for innovative products. Finally, high-level requirements should be evaluated from the start and used to define incremental integration checkpoints to support continuous evaluation, i.e., not at the end of the development process.
Deep learning neural network for power system fault diagnosis

This paper deals with application of deep learning neural network for power system fault diagnosis. Deep learning is a more effective approach than traditional neural network to solve problems including availability of data, better local optimum, and diffusion of gradients. In the paper, data is extracted from power system dispatching department and preprocessed before training in the deep learning network. Then, processed data is put into auto-encoders and the hidden features are observed in different dimensions so that we can preliminarily judge about the fault. Afterwards, trained stacked auto-encoders (SAE) is used to initialize and train a deep learning neural network (DLNN). The hidden features are observed in different dimensions so that the fault is preliminarily judged. The classifier is the last part of the network to reflect the types and possibility of diagnosis. The method of data availability, preprocess, and modeling is proposed in the paper. The result of simulation proves the feasibility of the approach and the influence factors are shown in the paper.
Performance of active optics at the VLT

The active optics system of the ESO Very Large Telescope has now been in operation since May 1998. All results from the wavefront analyses as well as numerous other telescope and environmental data are continuously logged. This allows for an accurate assessment of the performance of the active optics system and yields statistical correlations between the wavefront data and other telescope or external parameters.
Document image understanding: integrating recovery and interpretation

Many document image understanding problems require a more comprehensive examination of document features than is typically deemed necessary for recognition tasks. We believe that these problems require a detailed analysis of stroke and sub-stroke features in the document image with the goal of obtaining information about the environment or process which created the document and establishing a context for understanding. We introduce the concept of recovery into the document domain. We provide a "stroke platform" representation which establishes a verifiable "link to the pixels" and demonstrate its usefulness for recovery tasks. This representation allows us to overcome many of the problems associated with the rapid, irreversible abstraction associated with traditional document processing methods and provides the basic framework for our analysis of handwritten documents. By obtaining a detailed description of the document and its properties, we are able to establish a context for analysis and validate assumptions about the domain. This dissertation presents our work on several document image understanding problems: (1) demonstrating the successful use of the stroke platform for the problem of interpreting and reconstructing junctions and endpoints, (2) exploring the effects of the handwriting process on the document by the development of a model for instrument grasp and a study of its effects on pressure features, (3) posing and providing an approach to the problem of recovering temporal information from static images of handwriting, (4) addressing various sub-tasks of the problem of processing form documents, and (5) extending the detailed analysis philosophy to demonstrate its feasibility in related document domains.
Automatic annotation of similes in literary texts

This thesis tackles the problem of the automatic recognition of similes in literary texts written in English or in French and proposes a framework to describe them from a stylistic perspective. For the purpose of this study, a simile has been defined as a syntactic structure that draws a parallel between at least two entities, lacks compositionality and is able to create an image in the receiver’s mind.Three main points differentiate the proposed approach from existing ones: it is strongly influenced by cognitive and linguistic theories on similes and comparisons, it takes into consideration a wide range of markers and it can adapt to diverse syntactic scenarios. Concretely speaking, it relies on three interconnected modules: - a syntactic module, which extracts potential simile candidates and identifies their components using grammatical roles and a set of handcrafted rules, - a semantic module which separates creative similes from both idiomatic similes and literal comparisons based on the salience of the ground and semantic similarity computed from data automatically retrieved from machine-readable dictionaries;- and an annotation module which makes use of the XML format and gives among others information on the type of comparisons (idiomatic, perceptual…) and on the semantic categories used.Finally, the two annotation tasks we designed show that the automatic detection of figuration in similes must take into consideration.Finally, the two annotation tasks we designed show that the automatic detection of figuration in similes must take into consideration a series of features among which salience, categorisation and the sentence syntax.
Image Threshold Segmentation Based on Improved Two-dimensional Renyi Entropy

This paper presented a new image threshold segmentation method based on improved two-dimensional Renyi entropy.First,two-dimensional histogram was build according to gray value and gradient value of the pixels,on this basis,calculated two-dimensional Renyi entropy of target and Background region,at last,we got segmentation threshold by maximizing the Renyi entropy function.It can handle more types of images and get more accurate shape of the image edge that pixels gradient information in combination with parameter of Renyi entropy which is adjustable.
Bio-inspired optical flow vision sensors for visual guidance of autonomous robots

Visual perception the ability to interpret the environment by sensing light is an impressive ability of biological organisms. Vision allows animals and human beings to explore and to interact with their environment. It is a daily task biological organisms seem to perform with very little effort. Even smallest insects with a body size in the range of millimeters depend on vision as a main source of feedback for their behavior control which includes impressive flight and landing skills. Despite the efforts of many researchers in mimicking these skills in artificial systems, biological vision systems are still more powerful, versatile and efficient than any artificial systems developed so far. Nature has evolved integrated and compact solutions where the vision system is welladjusted to the properties of the sensory-motor control system and the physical body of the biological organism properties that artificial systems still lack. Optical flow is a major visual sensor cue used by animals in navigating their environment. Optical flow is due to the relative motion of the vision system and the objects in its environment. It encodes information about the shape and position of objects as well as the self-motion of the animal. This information is used extensively in particular by insects such as flies and bees in flight control. Because of the impressive properties of biological systems, various groups have designed and implemented sensors in analog Very Large Scale Integrated (aVLSI) technology that mimic biological optical flow processing on various levels of abstraction. In parallel with recent efforts to construct miniature aerial platforms, optic flow information from camera sensors together with gyroscope outputs are used on such platforms to demonstrate properties such as autonomous flight. At the EPFL Laboratory of Intelligent Systems, Jean-Christophe Zufferey and others developed a 10-gram indoor airplane and demonstrated autonomous wall avoidance. The airplane uses an off-the-shelf one-dimensional image sensor from which optical flow caused by the relative motion of objects on the left and right side of the airplane is extracted. Optical flow is computed on a microcontroller that is also running the control
Compressed Sensing Based Traffic Prediction For 5G HetNet IoT Video Streaming

Nowadays, IoT video applications are in a sharp rise, various real-time video streaming of video surveillance systems transmitted via Internet are widely investigated. The real-time video surveillance can actively monitor and detect the abnormal events in time. In 5G HetNets, we specifically develop a compressed sensing based linear predictor to predict the traffic load at the next moment. The results justify that our proposed method can forecast the traffic load and improve system performance.
An Efficient Algorithm for Influence Blocking Maximization based on Community Detection

Popularity of online social network services makes it a suitable platform for rapid information diffusion ranging from positive to negatives information. Although the positive diffused information may welcomed by people, the negative information such as rumor, hate and misinformation content should be blocked. However, blocking inappropriate, unwanted and contamination diffusion are not trivial. In particular, in this paper, we study the notion of competing negative and positive campaigns in a social network by addressing the influence blocking maximization (IBM) problem to minimize the bad effect of misinformation. IBM problem can be defined as finding a subset of nodes to promote the positive influence under Multi-campaign Independent Cascade Model as diffusion model to minimize the number of nodes that adopt the negative influence at the end of both propagation processes. In this regard, we proposed a community based algorithm called FC_IBM algorithm using fuzzy clustering and centrality measures for finding a good candidate subset of nodes for diffusion of positive information in order to minimizing the IBM problem. The experimental results on well-known network datasets showed that the proposed algorithm not only outperforms the baseline algorithms with respect to efficiency but also with respect to the final number of positive nodes.
Deep Residual Convolutional Neural Network for Protein-Protein Interaction Extraction

Knowledge extracted from the protein–protein interaction (PPI) network can help researchers reveal the molecular mechanisms of biological processes. With the rapid growth in the volume of the biomedical literature, manually detecting and annotating PPIs from raw literature has become increasingly difficult. Hence, automatically extracting PPIs by machine learning methods from raw literature has gained significance in the biomedical research. In this paper, we propose a novel PPI extraction method based on the residual convolutional neural network (CNN). This is the first time that the residual CNN is applied to the PPI extraction task. In addition, the previous state-of-the-art PPI extraction models heavily rely on parsing results from natural language processing tools, such as dependence parsers. Our model does not rely on any parsing tools. We evaluated our model based on five benchmark PPI extraction corpora, AIMed, BioInfer, HPRD50, IEPA, and LLL. The experimental results showed that our model achieved the best results compared with the previous kernel-based and CNN-based PPI extraction models. Compared with the previous recurrent neural network-based PPI extraction models, our model achieved better or comparable performance.
Content Caching Strategy for Edge and Cloud Cooperation Computing

With the wide application of the Internet of Things, the number of network edge devices is increasing rapidly, resulting in huge network traffic that brings huge challenges to the current network. Aiming at the problem of heavy network load, researchers proposed some caching strategies. However, current strategies have some limitations. These existing caching strategies are usually global within the whole network. Most of them are to reduce the network delay and allow users to obtain content more quickly and easily. This paper proposes a network caching strategy based on edge and cloud coordination (ECC). The strategy divides the caching network into two parts (core and edge) to discuss different caching strategies. By choosing reasonable caching strategies in the core network and the edge network to implement different caching goals for different areas. Besides, the paper sets up PN nodes for coordinating and optimizing the cache resources between the edge and the core. Experimental results show that ECC has significant advantages in Server Load Reduction Ratio, Average Hop Reduction Ratio and Cache Redundancy compared with existing methods.
Data Analytics and Machine Learning in Wide Area Surveillance Systems

Modern surveillance networks are able to provide trajectories of all kind of vessels and aircrafts within worldwide or at least extended environment. Most widely used are Automatic Dependent Surveillance – Broadcast (ADS-B) and (Satellite-) Automatic Identification System (AIS) used within air and maritime surveillance. Both of them are cooperative systems. Besides these systems, sensor networks based on ground installations or mounted on airborne and space-based platforms deliver object trajectories independent of any cooperation. Examples include GMTI radar-based systems operating on UAV platforms and coastal or air traffic control sensor network installations. These surveillance systems provide mid- and long-term trajectories. The challenging part is the related situational awareness and the estimation of the intent of the tracked objects. New technologies include activity-based intelligence and the determination of patterns of life. An approach for these technologies can be found in the advanced analysis of those trajectories, which are extracted by the mentioned surveillance systems. Trajectories are partitioned into specific segments of interest using cluster algorithms. This helps to decode their pattern of life based on unsupervised machine learning. Trajectories are aggregated into different routes with dedicated representatives. Calculated probabilities indicate the frequentation of these routes. This allows predictive analytics and the identification of anomalous behaviour. Finally, these new data analytic techniques have to be integrated in existing near real time surveillance systems. This requires specific system architectures as well as a completely new software and hardware landscape. So, trajectory-based Machine Learning is embedded in local or global clouds and uses dedicated mechanisms for distributed and parallel processing.
Embedding Multiple-Step-Ahead Traffic Prediction in Network Energy Efficiency Problem

Adaptive Link Rate (ALR) is widely used to save energy consumption of network by adjusting the link rate according to the carried traffic through a network-level optimization of the flow allocation process. Existing ALR solution is mainly reactive, in which link speed is changed only when new traffic demand is requested. Also, they focus on energy consumption, and do not consider the cost of changes in the network (e.g., change in traffic routes, and link rates). Once bandwidth has been allocated for a demand, the link rate remains constant during the entire session. Therefore, this solution may result in sub-optimal schemes and requires multiple re-optimizations as traffic flows are fluctuating during the session, hence reducing the overall network performance. In this paper, we improve the ALR with a multiple-step-ahead method to optimize link rates based on forecasting traffic demand predictively. We formulate the proposed Predictive ALR (PALR) as an Integer Linear Programming (ILP) model and then design a heuristic simulated annealing (SA) -based algorithm to solve it. Our experimental results show our approach provides energy saving while it decreases on average 18% of link state transition and 11% of the flow reroutings compared to the original ALR.
On Stability Analysis of Non-Smooth Systems with Density Functions

This study discusses a condition of the stability with density functions, which is known as a dual theorem of the Lyapunov stability theorem, for non-smooth dynamical systems. In the previous study, the stability analysis can be applied only for smooth systems. The aim of this study is to relax the smoothness conditions to develop the analysis for non-smooth dynamical systems, which would appear in global stabilization problems of nonlinear systems and the analysis of the finite-time stability. To this end, we develop an extension of a density function approach for non-smooth systems. We focus on a class of non-smooth systems and we show the stability analysis of non-smooth systems for the class of the systems with density functions. We also provide numerical examples to show the validness of the presented results.
High-Efficiency Ultra-Broadband Multi-Tip Edge Couplers for Integration of Distributed Feedback Laser With Silicon-on-Insulator Waveguide

A high-efficiency ultra-broadband multi-tip edge coupler based on a silicon-on-insulator platform for direct coupling with the elliptic beam of a distributed feedback laser was developed. The device is composed of a multi-tip section and a combiner section with extra offset regions to reduce the mode mismatch caused by the structural discontinuity which results from a limitation of the fabrication process that creates an inevitable gap width at the junction between the two sections. The widths and the spacing of the tips for the multi-tip section and the extra offset region for the combiner section are fine-tuned by using the particle swarm optimization method to reduce the mode mismatch. A high overall coupling efficiency up to 90.68% (0.4249 dB) at 1550 nm can be achieved for the optimized 90-μm-long four-tip edge coupler. The device can be operated over a broad spectral range of 1260–1675 nm with less than 1 dB extra loss. With its high-efficiency ultra-broadband operation and small device footprint, the proposed device is promising for laser-to-chip edge coupling in silicon photonics.
Pro Android Media: Developing Graphics, Music, Video, and Rich Media Apps for Smartphones and Tablets

Mobile devices haveevolved to focus onrich media production and consumption. Developers of mobile applications are able to create applications that allow people to play, capture, andshare media in a variety of new ways on mobile devices. The popularity of Android has soared in part because the platform offers developers a rich set of capabilities including access to media capturing and playback functions. Pro Android Mediaprovides concise and clear instruction on how to utilize the media APIs made available through Android to create dynamic apps. It takes youfrom a simple means to gain access to the camera to complex video capture and sharing examples. It also covers sound, graphics, painting, and moreeverything you need to make your app come "alive. " After reading this book, the app you create willshowcase the best of multimedia that Android has to offer. What youll learn Develop graphics, music, video and rich media apps for Android smartphones and tablets Build touchscreen input features into Android apps that allow users to draw, paint,and do other creative forms of input. Turn the Android smartphone into a full fledged media player How to integrate and use location based services and media related web service APIs Who this book is for This book is aimed primarily at the growing market of Android developers. It is written in such a way that it may be used by those who are familiar with Android, but have no experience developing applications that deal with images, audio, or video. Table of Contents Introduction to Android Imaging Building Custom Camera Applications Image Editing and Processing Graphics and Touch Events Introduction to Audio on Android Background and Networked Audio Audio Capture Audio Synthesis and Analysis Introduction to Video Advanced Video Video Capture Media Consumption and Publishing Using Web Services
Concurrent Gain and Bandwidth Improvement of a Patch Antenna with a Hybrid Particle Swarm Optimization Algorithm

This paper demonstrates that antenna design and performance can be prominently improved by linking commerically available software with the Hybrid Particle Swarm (HPSO) optimization algorithm. To demostrate this technique, multiple parasitic elements were added to a conventional patch antenna and optimized for bandwidth and realized gain. HPSO is implimented with Python and a commercially available full-wave software, ANSYS High Frequency Structure Simulator (HFSS). The simulated and measured results show that antenna performance is substantiably improved in both bandwith and realized gain, with an increase of 10% in −10 dB bandwidth and approximately 15% in realized gain.
A New Weighted Indoor Positioning Algorithm Based On the Physical Distance and Clustering

The weighted K-nearest neighbor (WKNN) algorithm is one of the most frequently used algorithms for indoor positioning. However, the traditional WKNN algorithm select the k points only based on their received signal strength (RSS), and the algorithm weights the reference points’ coordinates by the RSS, which is not accurate enough because of the exponential relationship between RSS and physical distance. Therefore, in order to improve the positioning accuracy of the traditional location algorithm, this paper proposes a new algorithm based on clustering and the physical distance of the RSS. Experiments were conducted in an office building and results demonstrate that the proposed algorithm is better than a series of indoor positioning algorithm. This proposed algorithm is based on the WKNN algorithm and the Kmeans algorithm.