Miracle-dz/UniFAR-FTUs huggingface dataset:
An MSULBP Features Selection based on GA and Majority Voting Mechanism in Facial Expression Recognition

Because of excellent capability of description of local texture, local binary patterns (LBP) have been applied in many areas. Also, to extract individual features the efficacy of the uniform LBP has been validated. In this study, a proposed new facial expression recognition system based on Multi-Scale Uniform LBP (MSULBP) schema can fully utilize LBP information. In the previous works, the selection of the optimal subset of the extracted features has not been considered. But in the proposed algorithm, the MSULBP features were extracted from the original facial expression images. The best subset from MSULBP features was found by genetic algorithm (GA) and majority voting mechanism (MVM) and was represented as a histogram descriptor. Finally, support vector machines (SVM) classifier was used for facial expression classification. The experimental results on the popular Japanese female facial expression (JAFFE) database illustrate that the presented facial expression recognition method based on MSULBP obtains the best recognition accuracy rate. Additionally, experiments show that the MSULBP features are robust to lowresolution images, which is critical in real-world applications.
Psychological Gender Express via Mobile Social Network Activities: An Experimental Study on a Gay Network Data

It is well known that the social network users’ behavior can reflect their personal mental profiles. Therefore, in this paper, we conducted an experiment to study users’ gender identity through a mobile social network. Gender identity is the personal sense of one’s own gender, which can be the same or different from their sex assigned at birth, that is, one’s innermost concept of self as male, female, a blend of both, or neither. In general, a person’s psychological gender cannot be observed from the external characteristics of people. And even if some user’s attributes such as physical gender and age can be found in social networks, it is difficult to find out the psychological gender of users. Apart from the fact that it will be hidden as the user’s privacy, the main reason is that users’ behaviors in social networks have no direct relationship with psychological gender. In this paper, we choose a special mobile social network to get around these limitations. Specifically, this paper is based on a real-world large gay social network of more than 10 000 000 users. In this mobile social network, we can indirectly mark the psychological gender of users by their sexual orientation. We collect 1000 users, including their characters, followers, followees and the same data for their followers and followees. Then, we examine an extensive set of features that can be extracted from the data set and train some classifiers to automatically identify the user’s psychological gender. In the special mobile social network, a user’s psychological gender is related to his characteristics, which include his personal characteristics, dating preference, and so on. This paper mainly analyzes users’ personal characteristics, triads, and triadic evolution to infer their psychological gender. The experiments show that all these features are indeed effective in the classification, and even the features considered in previous studies have different implications with gender identity on other social networks. And it turns out that psychological gender as a kind of privacy can be leaked by these unintentional behaviors, and so we give some practical suggestions at the end of this paper to protect users’ privacy.
An Improved User Authentication Protocol for Hierarchical Wireless Sensor Networks using Elliptic Curve Cryptography

In wireless sensor network, most of the queries are issued at the base station or gateway node the network. However, there are some critical WSN applications where real-time data are needed. But data at base station may not be real-time because of communication delay or periodic nature of data collection. So, real-time data can be accessed from the sensor nodes directly on demand. Before allowing the user to access real-time data from the sensor node, authentication of user must be ensured. But user authentication in case of wireless sensor network is a very critical task, as sensor nodes are deployed in unattached environment and are prone to possible hostile network attacks. Any authentication protocol in WSN must be designed keeping the fact that sensor nodes have limited computing power, memory, energy and communication capabilities. In this thesis, an improved user authentication protocol based on Elliptic Curve Cryptography (ECC) has been introduced for hierarchical wireless sensor networks (HWSN). This thesis shows that the ECC based protocol is suitable for wireless sensor networks, where higher security is demanded. Besides this the proposed scheme provides mutual authentication and a secret session key for communication between the user and the cluster head. It also provides an option for addition or replacement of cluster head in the network whenever there is a need. Then a comparative study of the proposed scheme with various existing is presented.
Projet RAIVES (Recherche Automatique d'Informations Verbales Et Sonores) : vers l'extraction et la structuration de données radiophoniques sur Internet

Internet est devenu un vecteur important de la communication. Il permet la diffusion et l'echange d'un volume croissant de donnees. Il ne s'agit donc plus seulement de collecter des masses importantes " d'informations electroniques ", mais surtout de les repertorier, de les classer pour faciliter l'acces a l'information utile. Une information, aussi importante soit-elle, sur un site non repertorie, est meconnue. Il ne faut donc pas negliger la part du " Web invisible ". Le Web invisible peut se definir comme l'ensemble des informations non indexees, soit parce qu'elles ne sont pas repertoriees, soit parce que les pages les contenant sont dynamiques, soit encore parce que leur nature n'est pas ou difficilement indexable. En effet, la plupart des moteurs de recherche se basent sur une analyse textuelle du contenu des pages, mais ne peuvent prendre en compte le contenu des documents sonores ou visuels. Il faut donc fournir un ensemble d'elements descripteurs du contenu pour structurer les documents afin que l'information soit accessible aux moteurs de recherche. S'agissant de documents sonores, le but de notre projet est donc, d'une part, d'extraire ces informations et, d'autre part, de fournir une structuration des documents afin de faciliter l'acces au contenu. L'indexation par le contenu de documents sonores s'appuie sur des techniques utilisees en traitement automatique de la parole, mais doit etre distinguee de l'alignement automatique d'un texte sur un flux sonore ou encore de la reconnaissance automatique de la parole. Ce serait alors reduire le contenu d'un document sonore a sa seule composante verbale. Or, la composante non-verbale d'un document sonore est importante et correspond souvent a une structuration particuliere du document. Par exemple, dans le cas de documents radiophoniques, on voit l'alternance de parole et de musique, plus particulierement de jingles, pour annoncer les informations. Ainsi, nous pouvons considerer un ensemble de descripteurs du contenu d'un document radiophonique : segments de Parole/Musique, " sons cles ", langue, changements de locuteurs associes a une eventuelle identification de ces locuteurs, mots cles et themes. Cet ensemble peut etre bien entendu enrichi. Extraire l'ensemble des descripteurs est sans doute suffisant pour referencer un document sur Internet. Mais il est interessant d'aller plus loin et de donner acces a des parties precises du document. Chaque descripteur doit etre associe a un marqueur temporel qui donne acces directement a l'information. Cependant, l'ensemble des descripteurs appartenant a des niveaux de description differents, leur organisation n'est pas lineaire dans le temps : un meme locuteur peut parler en deux langues sur un meme segment de parole, ou encore sur un segment de parole dans une langue donnee, plusieurs locuteurs peuvent intervenir. Il faut donc aussi etre capable de fournir une structuration de l'information sur differents niveaux de representation.
Computational analysis of knowledge sharing in collaborative distance learning

The rapid advance of distance learning and networking technology has enabled universities and corporations to reach out and educate students across lime and space barriers. This technology supports structured, on-line learning activities, and provides facilities for assessment and collaboration. Structured collaboration, in the classroom, has proven itself a successful and uniquely powerful learning method. Online collaborative learners, however, do not enjoy the same benefits as face-to-face learners because the technology provides no guidance or direction during online discussion sessions. Integrating intelligent facilitation agents into collaborative distance learning environments may help bring the benefits of the supportive classroom closer to distance learners. In this dissertation, I describe a new approach to analyzing and supporting online peer interaction. The approach applies Hidden Markov Models, and Multidimensional Scaling with a threshold-based clustering method, to analyze and assess sequences of coded on-line student interaction. These analysis techniques were used to train a system to dynamically recognize when and why students may be experiencing breakdowns while sharing knowledge and learning from each other. I focus on knowledge sharing interaction because students bring a great deal of specialized knowledge and experiences to the group, and how they share and assimilate this knowledge shapes the collaboration and learning processes. The results of this research could be used to dynamically inform and assist an intelligent instructional agent in facilitating knowledge sharing interaction, and helping to improve the quality of online learning interaction.
A Distributed Intelligent Hungarian Algorithm for Workload Balance in Sensor-Cloud Systems Based on Urban Fog Computing

With the help of fog computing, urban computing and intelligence novel systems can be created to improve the urban environment and the quality of human life. Sensor-cloud systems based on urban fog computing (SCS-UFC) are new intelligent network systems, which combine a cloud platform with wireless sensor networks (WSNs) as well as fog nodes to provide functions such as sensing, computation, and storage of large-scale data. Since the sensor nodes in WSNs only have limited transmission capacity, they cannot transmit their data to the cloud platform directly. Therefore, fog nodes with stronger transmission capacity are deployed to relay the data from WSNs to the cloud platform. However, different fog nodes may be burdened with different workloads (i.e., amounts of data): usually, the fog nodes with heavier workloads mean longer transmission delay and more energy consumption. If a fog node exhausts its energy, it will die and then make the network cease to work. Therefore, it is necessary to balance the workload of all fog nodes so as to reduce transmission delay and energy consumption of the sensors. However, addressing the problem is challenging because each fog node only knows local information of its neighbors, and thus it is difficult to get a global optimization result by itself. In this paper, a distributed intelligent algorithm based on the Hungarian method is proposed. First, each fog node collects the information connected with its neighboring fog nodes that are located within its transmission range. Then, a new genetic algorithm is designed to find an approximate optimization solution. Finally, each fog node decides if it should forward parts of its workload to other fog nodes so that the workloads of all fog nodes are balanced. Simulation results show that our algorithm can achieve shorter delay and less energy consumption than existing works.
Service Migration in Fog Computing Enabled Cellular Networks to Support Real-Time Vehicular Communications

Driven by the increasing number of connected vehicles and related services, powerful communication and computation capabilities are needed for vehicular communications, especially for real-time and safety-related applications. A cellular network consists of radio access technologies, including the current long-term evolution (LTE), the LTE advanced, and the forthcoming 5th generation mobile communication systems. It covers large areas and has the ability to provide high data rate and low latency communication services to mobile users. It is considered the most promising access technology to support real-time vehicular communications. Meanwhile, fog is an emerging architecture for computing, storage, and networking, in which fog nodes can be deployed at base stations to deliver cloud services close to vehicular users. In fog computing-enabled cellular networks, mobility is one of the most critical challenges for vehicular communications to maintain the service continuity and to satisfy the stringent service requirements, especially when the computing and storage resources are limited at the fog nodes. Service migration, relocating services from one fog server to another in a dynamic manner, has been proposed as an effective solution to the mobility problem. To support service migration, both computation and communication techniques need to be considered. Given the importance of protocol design to support the mobility of the vehicles and maintain high network performance, in this paper, we investigate the service migration in the fog computing-enabled cellular networks. We propose a quality-of-service aware scheme based on the existing handover procedures to support the real-time vehicular services. A case study based on a realistic vehicle mobility pattern for Luxembourg scenario is carried out, where the proposed scheme, as well as the benchmarks, are compared by analyzing latency and reliability as well as migration cost.
Image-based Eye Pose and Reflection Analysis for Advanced Interaction Techniques and Scene Understanding

This work proposes a theory of the light transport at the corneal surface of the human eye including multiple eye poses. The theory is subsequently applied to solve two practical problems in scene reconstruction and interaction techniques. Related with these are the solutions to two general problems in scene reconstruction from multiple eye images. As the eyes are the interface between a human body and the visual information of the physical world, their movements also convey rich details for interpreting a person’s affective state, behavior, and relation with the environment. Despite having numerous applications in a variety of fields, current approaches to extract information from eyes are lacking, being intrusive, restricted to laboratory conditions, and not providing sophisticated ways to integrate the eye with its environment. Recently, the geometric relation between eye and camera, that can be obtained from a face image, has been formalized to analyze light reflections in the cornea of a single eye or in a pair of eyes to recover simple scene structure. Nevertheless, there exist no solutions for relating reflections among multiple eyes, probably imaged by different cameras, with the structure of the surrounding environment. This, however, is crucial to develop sophisticated strategies for geometric eye analysis in arbitrary environments. This study aims to provide a solution. The first practical problem lies in calibration to obtain display pose in display-camera setups. Understanding the combination of display and camera as a controlled system enables a range of interesting vision applications in non-professional environments, including object/face reconstruction and human–computer interaction, but attempting to do this in average homes has been difficult as current approaches require special hardware and tedious user interaction. This work proposes a novel calibration approach that simplifies this by building on the observation that a user is commonly located in front of the setup and that screen reflections in the cornea of the eye can be extracted from face images. Despite the difficult constraints, results obtained are feasible and should be sufficient for many applications involving non-intrusive calibration-free dynamic setups. The main question then becomes what accuracy can be expected for scene reconstruction from multiple eye images. For this discussion, significant factors that affect accuracy are identified among individual eye geometry, camera parameters and geometric relation in the setup. Comprehensive experimental evaluation shows that, due to common errors in eye image processing and an unknown shape for the individual eye, scene reconstruction results in a large error and cannot be applied directly. To compensate for this, an optimization strategy is developed that exploits geometry constraints within the system to jointly improve eye poses and scene structure. Results show that the method performs accurately and stably with respect to varying subjects, scene alignments, eye positions, and gaze directions.
Scaling up scientific computations by using map-reduce-like control flow on numa architectures

The clock speed of current CPUs and RAM has stopped scaling with Moore's Law. Yet the scale of applications in science and engineering continues to increase. In order to address this scaling of applications, newer NUMA architectures are emerging. These include parallel disks, hybrid CPU-GPU, and many-core CPUs. Existing CPU-based algorithms, as well as legacy sequential code, need to be migrated to NUMA in order to stay on the curve of Moore's Law. Migrating applications from traditional sequential architectures to NUMA is difficult, because NUMA architectures penalize data access patterns and computation patterns that are often used by programs on traditional architectures. Specific inefficient operations on NUMA architectures include: random access in hash table lookups; tensor computations involving many small, independent matrix multiplications; collecting together the inputs for a task from multiple previous tasks; and slow access to remote memory. Specific issues for hybrid CPU-GPU architectures are: transferring data from CPU to limited GPU global memory (up to 6 GB for 512 cores, in comparison with up to 2 GB/core on the CPU), and the limited size of GPU on-chip cache (typically 768 KB today). This dissertation analyzes three real-world applications in computational science, each important to a particular community. The three applications are: the determinization and minimization of large finite state automata (important in permutation patterns), a port of the integral operator of the MADNESS scientific library from CPU-only architectures to hybrid CPU-GPUs (important in computational chemistry), and efficient multiplication of large permutations (important in computational group theory). In all three cases, traditional architectures were either too slow, or did not support problem sizes of sufficient scale. For each application, NUMA solutions based on adapting existing CPU-based algorithms are proposed. NUMA architectures were adopted for the sake of greater speed (through greater parallelism) and/or greater data storage. Two common techniques used across all three NUMA-based solutions are delayed data access (to allow for reordering of accesses) and delayed task execution (to hide the overhead of on-demand task launch). In hindsight, we observe that the usage of delayed operations leads to a map-reduce-like control flow in each of the three NUMA-based solution implementations.
Power constraint communication-aware task scheduling in reconfigurable multiprocessors

Heterogeneous multiprocessors with FPGA component have recently received a lot of attention due to its low cost and power consumption. However, most of existing works about task scheduling algorithm focus on minimization of system cost or power consumption. Actually, optimizing multiprocessor performance within a given power budget has recently received a lot of attention. Peak power consumption should be carefully controlled than directly improve computing performance. Furthermore, FPGA component in multiprocessors has essential parallelism ability to execute multiple tasks at same time using dynamic reconfigurable features. In this environment, tasks and communications should be carefully scheduled because their execution orders affect the performance of the whole chip. This paper presents an Integer Linear Programming (ILP) formulation that integrates the resource delay model and FPGA-component with pipelined scheduling and global power control. Moreover, to enhance the computation efficiency, a heuristic algorithm namely PCLS that integrates pipelined scheduling and global power control for heterogeneous multiprocessor architecture is proposed. Experiments show that our ILP method obtains the optimal results when task nodes are less than 35. Proposed PCLS heuristic algorithm achieves on average 10% higher makespan compare with DLS. For heavier synthetic task application, PCLS can provide only about 12% performance degradation under 70% power budgets based on different heterogeneous multiprocessor architectures.
Compensated magnetic insulators for extremely fast spin-orbitronics

The fast spin dynamics provide many opportunities for the future communication and memory technologies. One of the most promising examples is the domain wall (DW) racetrack memory. To achieve fast device performances, the high-speed motion of DWs is naturally demanded that leaves antiferromagnets (AFMs) and compensated ferrimagnets (FIMs) as the promising materials. While controlling and probing the dynamics of DWs in AFMs remains challenging, the fast motion of DWs with velocities around 1500 m/s has been demonstrated in metallic FIMs. The velocity of DWs in metallic FIMs is, however, suppressed by the magnetic damping of conduction electrons, which motivates us to explore how fast DWs can move in insulating FIMs where the conduction electron is absent. In this work, through synthesizing compensated FIM insulator Gd3Fe5O12 thin films with a perpendicular magnetic anisotropy, we demonstrate that the spin-orbit torque (SOT) induced motion of DWs along the Gd3Fe5O12/Pt racetrack can approach 6000 m/s. Our results show that the exceptionally fast motion of DWs can be achieved in compensated FIM insulators owing to small damping inherent to magnetic insulators, which could potentially facilitate the emerging ultrahigh-speed spintronic logics and racetrack memories by introducing insulating compensated FIMs as a new material platform.