Miracle-dz/UniFAR-FTUs huggingface dataset:
Double-layered embedding based wet-paper-code adaptive steganography

In order to enhance the statistical security of a data hiding system,the factors that influenced the security of a steganography were analyzed.Three ways to reduce the statistical distortion of the stego-image were found: Increasing embedding efficiency,controlling modifying amplitude and choosing embedding position adaptively.According to the three ways,a new adaptive steganography based on double-layered embedding method was proposed.The new scheme chose pixels under strong noise background as message carrier and embedded secrets in their least significant and second least significant bit-planes.The experimental results on uncompressed images show that the proposed steganography outperforms the prior algorithm for resisting blind steganalysis.
A Memetic Algorithm for the Partition Graph Coloring Problem

The partition graph coloring problem (PGCP) belongs to the class generalized network design problems (GNDPs). This class is obtained by considering classical network design problems on a clustered graph where the original problem’s feasibility constraints are expressed in terms of the clusters, i.e., node sets instead of individual nodes. Formally, the PGCP is defined on an undirected graph G = (V,E) where vertex set V is partitioned into p mutually exclusive nonempty clusters V1, . , Vp with V = V1∪ . ∪Vp and Vi∩Vj = ∅ ∀i, j ∈ {1, . , p}, i 6= j. The goal is to find a subset V ∗ ⊆ V that contains exactly one vertex for each cluster and a coloring for V ∗ so that in the graph induced by V ∗ two adjacent vertices have different colors and the total number of used colors is a minimum. An example for the PGCP is shown in Figure 1. Li and Simha [6] introduced this NP-hard problem which was motivated by the wavelength routing and assignment in an optical network. In this context several approaches have been proposed: construction heuristics [6], a tabu search algorithm [9], a branch-and-cut [3] and a branch-and-price [5]. Finally, Demange et al. [2] analyzes the complexity for some special graph classes.
Investigating the Relationship between Volitional Strategies and Academic Achievement in a Flipped Learning Environment

Abstract This study was conducted to determine whether there is a relationship between academic volitional strategies and academic achievement in a flipped learning environment. Academic achievement entails an environment where students exert self-discipline on classroom tasks. By using volitional strategies, learners can enhance engagement on academic tasks and complete assignments more efficiently. Undergraduate students enrolled in a teacher education programme at a higher education institution in Turkey participated in the research. Academic Volitional Strategies Inventory (AVSI), with α = . 87 internal consistency, was applied to a group of students. The obtained data were explored through Pearson correlation. The results showed a statistically significant positive relationship between AVSI scores and academic achievements (r =.39, p=<.05). Hence, volitional skills and academic achievements act in concert in a teacher education programme. Key words: correlational study; flipped learning; higher education; self-regulated learning; teacher education. --- Sažetak Ova studija provedena je kako bi se utvrdilo postojanje veze između akademskih voljnih strategija i akademskog postignuca u 'obrnutoj ucionici'. Akademsko postignuce podrazumijeva okolinu u kojoj studenti pokazuju samodisciplinu na nastavnim zadatcima. Studenti mogu povecati angažman tijekom akademskih zadataka koristeci se voljnim strategijama te ih ucinkovitije izvrsiti. Sudionici istraživanja bili su dodiplomski studenti upisani u program obrazovanja buducih ucitelja na instituciji visokog obrazovanja u Turskoj. Na toj skupini studenata primijenjen je Upitnik akademskih voljnih strategija (AVSI) unutarnje konzistentnosti α = ,8. Dobiveni podatci istraženi su Pearsonovom korelacijom. Rezultati pokazuju statisticki pozitivan odnos između AVSI rezultata i akademskih postignuca (r =,39, p=<,05). Stoga voljne vjestine i akademska postignuca djeluju zajedno u programu obrazovanja ucitelja. Kljucne rijeci: korelacijska studija; obrnuto ucenje; visoko obrazovanje; obrazovanje ucitelja; samoregulirano ucenje
Multiple-Channel Local Binary Fitting Model for Medical Image Segmentation

This study proposes an innovative M-L (Multiple-channel local binary fitting) model for medical image segmentation. Designed to improve upon existing image segmentation models, the M-L model introduces a regional limit function to the multi-band active contour model to enable multilayer image segmentation. The Gaussian kernel function is used to improve the previous model's robustness, necessitating the use of a new initialization curve which enhances the accuracy of segmentation results. Compared to existing image segmentation methods, the proposed M-L model improves numerical stability and efficiency through the introduction of a new penalty term and an increased step length. This simulation experiment verifies the advantages of the new M-L model for improved medical image segmentation, including increased efficiency and usability of the model.
RLMViz: Interpreting Deep Reinforcement Learning Memory

We present RLMViz, a visual analytics interface to interpret the internal memory of an agent (e.g., a robot) trained using deep reinforcement learning. This memory is composed of large temporal vectors updated before each action of the agent moving in an environment. This memory is not trivial to understand, and is referred to as a black box, which only inputs (images) and outputs (actions) are understood, but not its inner workings. Using RLMViz, experts can form hypothesis on this memory and derive rules based on the agent's decisions to interpret them, and gain an understanding towards why errors have been made and improve future training process. We report on the main features of RLMViz which are memory navigation and contextualization techniques using time-lines juxtapositions. We also present our early findings using the VizDoom simulator, a standard benchmark for DRL navigation scenarios.
Generating multitasking Ada programs from high-level specifications

A method for automatic generation of large-scale multitasking systems in an Ada programming environment is presented. The method allows the human designers to independently develop each functional tasks and independently develop the system integration. Thus individual task developers are allowed to have completely independent naming spaces. A dataflow graph in intertask communications is specified through a separate language: CSL (Configuration Specification Language). It is processed by a component of the MODEL system called Configurator. The Configurator conducts verification and validation of the overall data flow graph and produces an Ada shell to control the execution and communication of the functional tasks. It also produces an optimized implementation for the intertask (possibly interprocessor) communications. Individual functional tasks can also be generated automatically using the MODEL compiler. <<ETX>>
Probabilistic Models for the Semantic Web

Recently, there has been an increasing interest in formalisms for representing uncertain information on the Semantic Web. This interest is triggered by the observation that knowledge on the web is not always crisp and we have to be able to deal with incomplete, inconsistent and vague information. The treatment of this kind of information requires new approaches for knowledge representation and reasoning on the web as existing Semantic Web languages are based on classical logic which is known to be inadequate for representing uncertainty in many cases. While different general approaches for extending Semantic Web languages with the ability to represent uncertainty are explored, we focus our attention on probabilistic approaches. We survey existing proposals for extending semantic web languages or formalisms underlying Semantic Web languages in terms of their expressive power, reasoning capabilities as well as their suitability for supporting typical tasks associated with the Semantic Web.
Utilizing JUSUR in an information systems course

The aims of this paper are to report the perceptions of both faculty and students when JUSUR, a web based Learning Management System, is used to supplement the teaching inside and outside the classroom during one academic subject; and to compare the experience of the instructor who had previously taught the same course using a course website to support traditional face-to-face methods with interactive web-based technology. Qualitative online evaluation at the end of the course in which the JUSUR component was employed shows that students have positive attitudes about web-based learning management system. This paper discusses the pros and cons of integrating JUSUR into a course and identifies factors that other Instructors may consider before using such an e-learning tool.
A Technical Infrastructure to Integrate Dynamics AX ERP and CRM into University Curriculum

Enterprise Resource Planning and Customer Relationship Management are becoming important topics at the university level, and are increasingly receiving course-level attention in the curriculum. In fact, the Information Systems Body of Knowledge specifically identifies Enterprise Architecture as an Information Systems-specific knowledge area. The revised Information Systems Curriculum Guide from 2010, sponsored by the Association of Information Systems and the Association of Computing Machinery, suggest Enterprise Architecture as a required course with Enterprise Systems and Business Process Management as suggested electives. Implementing the aforementioned courses into the curriculum poses challenges such as providing necessary resources, overcoming institutional constraints, and the lack of hardware architecture for advanced systems such as Microsoft Dynamics AX. This work addresses three critical issues. First, we provide a suggested technical architecture built upon the Windows Server family and Dynamics AX which may be used to implement ERP and CRM, based on Dynamics AX, into the classroom. Second, we demonstrate connectivity between an installation of Dynamics AX 2012 R3 and CRM 2011 in the cloud using the Microsoft Connector for Dynamics. Finally, we suggest a sample scenario and case for implementing ERP and CRM concepts into the university curriculum.
A Combination of Clustering Techniques and Fuzzy Control in 2D Polygon Determination for the Terrain Splitting and Mapping Problem

Recently, the Terrain Splitting and Mapping problem [11] has been considered as one of some current striking trends in GIS researches. This problem is very important in making 3D WebGIS systems and handling large terrain data. In fact, these have been some algorithms dealing with it based on Conditional partitioning approaches [11], Greedy strategies [12], and Heuristic Optimization [17]. However, these algorithms only count for major parameters when minimizing the memory space saving percents such as the error parameter between blocks, the number of processors, and the number of polygons in 2PVD. A minor parameter that can bring much effect to outputted results is the shapes of polygons in 2PVD. Depending on which shapes of polygons are, the answers of whether a solution may exist for this terrain or which algorithm is suitable can be quickly determined. In this paper, we will present a method to determine the shapes of polygons in 2PVD by combining clustering techniques and fuzzy control. The final result of our method is a value showing which algorithm in the literatures [11], [12], [17] is the most suitable for the inputted map. Some experiments in the last section have shown the efficiency of our method for various kinds of polygons in 2PVD.
Classification of Human Emotions using Multiwavelet Transform based Features and Random Forest Technique

Objective: To extract statistical features from EEG signal for human emotion recognition. Methods: Emotions, the “inner” state of a person play a vital role in analysing the state of mind. In this paper, a new method for human emotion recognition using Multi Wavelet Transform (MWT) and random forest (ensemble technique) for classification of human emotions from EEG signals has been proposed. The experiment is conducted on EEG database for emotions i.e., DEAP data set. In present work, the data set used contains EEG recording from 4 participants (S01, S02, S03, S04) from 15 channel (FP1, FP2, F3, P3, F4, T7, T8, P4, O1, PZ, PO3, O2, P7, CP2, and C4) having 40 trails each. The paper explore the capability of proposed features namely Mean, Standard deviation, Variance, Shannon entropy, Hjorth parameters and Band power. Findings: The feature set obtained using Multi-Wavelet are then used as input for MLP, KNN, MC-SVM with Puk kernel function and Random Forest (ensemble) classifier for the classification of emotions. The classification accuracy for different emotion state happy 99.8%, sad 98.3%, exciting 95%, hate 96.4% are obtained by the proposed method. Overall 98.1% for classification of emotions (happy, sad, exciting, hate) from EEG signals. Application: The findings are helpful in monitoring alertness, cognitive engagement of patients with impaired motor functions; recognition of emotions; investigating sleep disorders and physiology. In future, efficient feature extraction algorithm using different multiwavelet functions and with a different set of features which include the temporal dynamics of brain signals in the human cognitive system can be considered.
Dynamic voltage and frequency scaling for energy-efficient system design

Demand for low power consumption in battery-powered computer systems has risen sharply. This is due to the fact that extending the service lifetime of these systems by reducing their power dissipation is a key customer requirement. Dynamic voltage and frequency scaling (DVFS) techniques have proven to be a highly effective in achieving low power consumption in various computer systems. The key idea behind DVFS technique is to adaptively scale the supply voltage level of the CPU so as to provide “just-enough” circuit speed to process the system workload while meeting total computation time and/or throughput constraints, and thereby, reduce the energy dissipation. This thesis presents intra-process DVFS techniques targeted toward both non real-time and real-time applications running on embedded system platforms. To enhance the amount of the CPU energy saving by DVFS, a technique called “workload decomposition” is proposed whereby the workload of a target program is decomposed in two parts: on-chip and off-chip. The on-chip workload signifies the CPU clock cycles that are required to execute instructions inside the CPU, whereas the off-chip workload captures the number of external memory access clock cycles that are required to perform external memory transactions. When combined with a DVFS technique to minimize the energy consumption, this workload decomposition method results in higher energy savings for memory-intensive applications. Notice that on-chip and off-chip workload cause different amount of power dissipation because on-chip workload requires the CPU to be executed while off-chip workload requires a proper system component such as memory. So, if the task workload is decomposed into on-chip and off-chip component, the system energy variation according to the CPU frequency can be predicted very accurately, which enables the development of a more effective DVFS approach for the system energy reduction. The proposed techniques have been implemented on two real computing systems: (1) the XScale-based embedded system platform built at USC and (2) the PXA255-processor based BitsyX system from ADS Inc. Energy savings with the proposed DVFS policies have been obtained by performing current measurements on real hardware.
Scenario-Based Extreme Flood Risk of Residential Buildings and Household Properties in Shanghai

Extreme flooding usually causes huge losses of residential buildings and household properties, which is critical to flood risk analysis and flood resilience building in Shanghai. We developed a scenario-based multidisciplinary approach to analyze the exposure, losses and risks of residential buildings and household properties, and their spatial patterns at the neighborhood committee level in Shanghai, based on extreme storm flood scenarios of 1/200, 1/500, 1/1000 and 1/5000-year. Our findings show that the inundation area of the residential buildings caused by a 1/200-year storm flood reaches 24.9 km2, and the total loss of residential buildings and household properties is 29.7 billion CNY (Chinese Yuan) (or 4.4 billion USD), while the inundation area of residential buildings and the total loss increases up to 162.4 km2 and 366.0 billion CNY (or 54.2 billion USD), respectively for a 1/5000-year storm flood. The estimated average annual loss (AAL) of residential buildings and household properties for Shanghai is 590 million CNY/year (or 87.4 million USD/year), with several hot spots distributed around the main urban area and on the bank of the Hangzhou Bay. Among sixteen districts, Pudong has the highest exposure and annual expected loss, while the inner city is also subject to extreme flooding with an AAL up to near half of the total. An analysis of flood risk in each of 209 subdistricts/towns finds that those most vulnerable to storm flooding are concentrated in Pudong, Jiading, Baoshan Districts and the inner city. Our work can provide meaningful information for risk-sensitive urban planning and resilience building in Shanghai. The methodology can also be used for risk analysis in other coastal cities facing the threat of storm flooding.
QOS aggregation algorithms in hierarchical ATM networks

We propose a new private network-to-network interface (PNNI) aggregation algorithms for hierarchical ATM networks. Standard PNNI routing provides a scalable hierarchical routing capability to support a large ATM network, and its hierarchy mechanism can decrease the network complexity significantly. However, in order to support effective QoS-based routing across foreign subnetworks, the topology aggregation mechanism, representing the lower level network topology to higher level, becomes a key issue. PNNI specification defines general notions of this aggregation mechanism, leaving enough opportunities for subnetwork administrators to choose the amount of aggregated information that they advertise. So far, no feasible PNNI compliant algorithms have been proposed. We propose a new three step aggregation scheme to solve the problem. We evaluated the proposed scheme, based on large-scale simulations with different network topologies, traffic scenarios, and performance metrics. The proposed scheme delivers good network performance while significantly reducing the network complexity.
Prolonging Network Life Time and Running Multi-Sequence Applications in Wireless Sensor Networks

III ACKNOWLEDGMENTS . V PUBLICATIONS FROM THIS THESIS . VI TABLE OF CONTENTS . VII LIST OF FIGURES . X LIST OF TABLES . XII GLOSSARY . XIII CERTIFICATE OF AUTHORSHIP . XVI
Guide des analyses en pédologie : 3e édition revue et augmentée

Aider a bien choisir ses analyses de sols, a maitriser les modes d'expression des resultats et a les interpreter finement, telle est l'ambition de ce guide mis a jour. Indispensable a tous ceux qui ont des sols a decrire et a caracteriser.
Survey of Image Retrieval via Visual Attention & Saliency Feature

— In the real world applications such as landmark search, copy protection, fake image detection, partial duplicate image retrieval is very important. In the internet era users regularly upload images which are partially duplicate images on the various domains. The partial image is only part of whole image, and the various kind of transformation involves scale, resolution, illumination, viewpoint. This technique is demanded by various real world applications and thus has attracted towards this research. In object based image retrieval methods usually use the whole image as the query. This technique has been compared with the text retrieval system by using the bag of visual words (BOV)[3]. There is lots of background noise in the images and impossible to perform interaction operation on the large scale database of the images. Two observations are notable as a user point of view. First, people show various objects or region through the images which are shared on the web, we also expect that the returned result also focus on the major parts or objects. Regions of interest are only found in salient region of the retrieval. Second, and the similar region in the returned result also identical to the salient region of the images. To filter out the non salient region from the image, which able to eliminate the background noise we introduce visual attention analysis technique. We also want to generate saliency region which having the expected visual contents.
Heart Biometrics: Theory, Methods and Applications

Automatic and accurate identity validation is becoming increasingly critical in several aspects of our every day lives such as in financial transactions, access control, traveling, healthcare and other. Traditional strategies to automatic identity recognition include items such as PIN numbers, tokens, passwords and ID cards. Despite the wide deployment of such tactics, the authenticating means is either entity or knowledge-based which rises concerns with regard to their ease of acquisition and use from unauthorized third parties. According to the latest The US Federal Commission Report, Frebruary 2010 (n.d.), in 2009 identity theft was the number one complaint category ( a total of 721,418 cases of consumer complaints). As identity theft can take different forms, credit card fraud was the most prominent (17%), followed by falsification of government documents (16%), utilities fraud (15%), employment fraud (13%) and other. Among these cases, true-identity theft constitutes only a small portion of the complaints, while ID falsification appears to be the greatest threat. Unfortunately, the technology for forgery advances without analogous counterfeit improvements. Biometric recognition was introduced as a more secure means of identity establishment. Biometric features are characteristics of the human body that are unique for every individual and that can be used to establish his/her identity in a population. These characteristics can be either physiological or behavioral. For instance, the face, the iris and the fingerprints are physiological features of the body with identifying information. Examples of behavioral features include the keystroke dynamics, the gait and the voice. The fact that biometric features are directly linked with the users presents an extraordinary opportunity to bridge the security gaps caused by traditional recognition strategies. Biometric features are difficult to steal or counterfeit when compared to PIN numbers or passwords. In addition, the convenience by which a biometric feature can be presentedmakes the respective systemsmore accessible and easy to use. However, biometric recognition has a drawback that rises from the nature of the authenticating modality. As opposed to static PIN numbers or passwords, biometric recognition may present false rejection since usually no two readings of the same biometric feature are identical. Anatomical, psychological or even environmental factors affect the appearance of the biometric feature at a particular instance. For instance, faces may be presented to the recognizers under various expressions, different lighting settings or with 10