Miracle-dz/UniFAR-FTUs huggingface dataset:
Methods for facial expression recognition with applications in challenging situations

In recent years, facial expression recognition has become a useful scheme for computers to affectively understand the emotional state of human beings. Facial representation and facial expression recognition under unconstrained environments have been two critical issues for facial expression recognition systems. This thesis contributes to the research and development of facial expression recognition systems from two aspects: first, feature extraction for facial expression recognition, and second, applications to challenging conditions. Spatial and temporal feature extraction methods are introduced to provide effective and discriminative features for facial expression recognition. The thesis begins with a spatial feature extraction method. This descriptor exploits magnitude while it improves local quantized pattern using improved vector quantization. It also makes the statistical patterns domain-adaptive and compact. Then, the thesis discusses two spatiotemporal feature extraction methods. The first method uses monogenic signal analysis as a preprocessing stage and extracts spatiotemporal features using local binary pattern. The second method extracts sparse spatiotemporal features using sparse cuboids and spatiotemporal local binary pattern. Both methods increase the discriminative capability of local binary pattern in the temporal domain. Based on feature extraction methods, three practical conditions, including illumination variations, facial occlusion and pose changes, are studied for the applications of facial expression recognition. First, with near-infrared imaging technique, a discriminative component-based single feature descriptor is proposed to achieve a high degree of robustness and stability to illumination variations. Second, occlusion detection is proposed to dynamically detect the occluded face regions. A novel system is further designed for handling effectively facial occlusion. Lastly, multiview discriminative neighbor preserving embedding is developed to deal with pose change, which formulates multi-view facial expression recognition as a generalized eigenvalue problem. Experimental results on publicly available databases show that the effectiveness of the proposed approaches for the applications of facial expression recognition.
CADS innovative Corpus-driven Methodology for he Academic English Instruction of Transitional Linkers

The analysis and description of discourse based on corpora (CADS - Corpus-Assisted Discourse Studies) has experienced an important development during the last decade, essentially due to two main factors: (1) on the one hand the existence of new and more powerful corpus-analysis software and tools for language processing; (2) and, on the other, the realization that corpus methods can extend their application to larger fields of interest. In fact, with regards to L2 teaching, this methodology has promoted the design of specific tools, such as reference corpora, which, in turn, have shaped the ground for the description of, for instance, Academic English discursive conventions. Particularly, the knowledge of discursive and genre practices is paramount for university undergraduates who are, at the same time, acquiring proficiency in L2 and initiating themselves as academic professionals. For that reason, this study attempts to focus on the academic essay and tries to describe the differences in uses of Academic English between Native (corpus BAWE) and non- native speakers (corpus of Spanish speakers-NNS). Likewise, the paper raises issues concerning NNS proficiency assessment and, finally, intends to provide new pedagogical strategies that may facilitate the process of writing skills acquisition in Academic English. Ultimately, the data and results seem to corroborate the adequacy of a CADS approach in order to establish innovative pedagogical procedures in teaching L2 discursive competence. Palabras clave: Learner Corpus, Genre Analysis, Transitional Linkers, Academic English
Presenting Physical Things Digitally: New Collecting Practices

The motivations for collecting and the idiosyncrasies of physical and digital collections have been long studied. However, how they are presented in the digital space is an unresolved challenge. To help better understand this problem from a design perspective, we built Thinga.Me. Thinga.Me is a system which allows users to capture photographs of physical objects and then cut them out, place them into digital collections, and share them. By segmenting the object from the background the interface creates the illusion of a physical item, giving a sense of carrying your stuff with you in your pocket. Following two years of development, iteration and feedback, we discuss uses of the app and the implications it can have for changing the way we reflect on physical things in our lives. In particular, we focus on how digital collection are presented and displayed in a realistic way as a way of providing more meaning and helping shape users’ identities. Demonstrating the importance of visual design choices, our results lead to considerations on how to most appropriately display physical objects in the virtual world, whilst avoiding the uncanniness some might experience when interacting with skeuomorphic collections. Daniel Harrison1,2, Richard Banks2, Tim Regan2 and Martin Grayson2 1 UCL Interaction Centre, University College London, London, UK daniel.harrison@ucl.ac. uk 2 Microsoft Research, Cambridge, UK rbanks@microsoft.com, timregan@microsoft.com, martin.grayson@microsoft.com
Monitoring Tools for Large Scale Systems

Operating computing systems, file systems, and associated networks at unprecedented scale offer unique challenges for fault monitoring, performance monitoring and problem diagnosis. Conventional system monitoring tools are insufficient to process the increasingly large and diverse volume of performance and status log data produced by the world’s largest systems. In addition to the large data volume, the wide variety of systems employed by the largest computing facilities present diverse information from multiple sources, further complicating analysis efforts. At leadership scale, new tool development is required to acquire, condense, correlate, and present status and performance data to systems staff for timely evaluation. This paper details a set of system monitoring tools developed by the authors and utilized by systems staff at Oak Ridge National Laboratory’s Leadership Computing Facility, which includes the Cray XT5 Jaguar. These tools include utilities to correlate I/O performance and event data with specific systems, resources, and jobs. Where possible, existing utilities are incorporated to reduce development effort and increase community participation. Future work may include additional integration among tools and implementation of fault-prediction tools.
Software tools for modeling and simulation of on-chip communication architectures

In multiprocessor based systems-on-chips (SoCs), optimizing the communication architecture is often as important as, if not more than, optimizing the computation architecture. Designers need to make important choices related to the on-chip communication architectures, i.e. Network-on-Chips (NoCs). These choices are often not self-evident. Designers need to understand the interplay between the application, the Processor Element (PE) architecture and the NoC to make the right decisions. At the same time, there is a tremendous pressure on designers to accelerate the “time-to-market” of their design without sacrificing the design quality. Achieving this requires a set of design automation tools which can model, simulate and verify the components of the system design before the silicon is available. Thus, a major challenge in designing the NoC is to build an efficient and accurate model for simulation and performance evaluation. The contributions of this dissertation are twofold. First, a new methodology based on modular design and object-oriented modeling techniques is proposed. This methodology is based on an object-oriented class hierarchy for the NoC and a dedicated NoC module library organized according to this hierarchical structure. Second, a retargetable simulation framework is developed where SoC designs can be constructed easily and evaluated efficiently and faithfully. The modeling and simulation platform is based on a formal concurrency model, the Operation State Machine (OSM). The NoC models are constructed faithfully by explicitly modeling both the operation concurrency and the microarchitecture concurrency. Coupled with existing PE models, this framework is capable of synthesizing a multiprocessor cycle-accurate SoC simulator from a system-level description. The case studies include a router-based packet-switching on-chip communication network and an industry-standard on-chip bus architecture. Experiment results show that this framework can significantly reduce the design turnaround time and improve design reuse in the early stages of SoC design.
Full waveform inversion algorithm using time imaging methods

Summary We propose a fast Full Waveform Inversion (FWI) method that is based on the Pre-Stack Time Migration (PSTM) algorithm. The method is fast compared to conventional FWI techniques in forward and inverse iterations; however, since we are doing time migration, we are limited to models with moderate complexity.
Fuzzy Model Reference Learning Control for Smart Lights

This research is motivated by the world’s fast-growing demand for energy savings. Due to the increasing cost of fossil fuels (e.g., oil, coal, natural gas), research has been conducted to effectively reduce the electricity consumption in office buildings by means of employing smart lighting. This thesis investigates the implementation of an adaptive and nonadaptive fuzzy control for a smart light experimental testbed. The objective is to accurately regulate the light level across the experimental testbed to a desired voltage reference value, and to test the performance of the fuzzy controllers under cross-illumination effects, and bulb and sensor failures. As an initial approach, a decentralized (i.e., no communication between controllers) nonadaptive fuzzy controller is implemented and applied to the experimental testbed. This approach is convenient for this type of experimental testbed where a mathematical model of the plant is not available and heuristic information about how to control the system is sufficient. The nonadaptive fuzzy controller, when properly tuned, is able to achieve uniform lighting across the entire testbed floor in most of the tested situations but it fails whenever an on/off light bulb failure is introduced. In order to attain uniform lighting for complex failures, a fuzzy model reference learning controller (i.e., adaptive fuzzy) is developed for the experimental testbed, and this algorithm proves to be able to adapt to uncertainties such as disturbances and failures via a learning mechanism. ii To my parents, José and Ana, and to my sister and brother.
Design and Speedup of an Indoor Walkthrough System

This paper describes issues in designing a practical walkthrough system for indoor environments. We propose several new rendering speedup techniques implemented in our walkthrough system. Tests have been carried out to benchmark the speedup brought about by different techniques. One of these techniques, called dynamic visibility, which is an extended view frustum algorithm with object space information integrated. It proves to be more efficient than existing standard visibility preprocessing methods without using pre-computed data.
Internet evolved : from secondary to primary competitive intelligence data

Primary data is an important source ofinformation for Competitive Intelligence. Traditionally, it has been collected from interviews with stakeholders, talks at conferences and other means of direct interpersonal communication. The role of the Internet in the data collection – if it was used at all – was that of a provider of supplementary secondary data. Here, this approach is challenged and, using three examples of Social Media, it is shown that the Internet can and does provide valuable primary information to the Competitive Intelligence professional. Accordingly, a case is made for a shift of focus in the data collection process.
EMOTION AND SARCASM IDENTIFICATION OF POSTS FROM FACEBOOK DATA USING A HYBRID APPROACH

Facebook has become the most important source of news and people’s feedback and opinion about almost every daily topic. Facebook represents one of the largest and most dynamic datasets of user generated content. Facebook posts can express opinions on different topics. With this massive amount of information in Facebook, there has to be an automatic tool that can categorize these information based on emotions. The proposed system is to develop a prototype that help to come to an inference about the emotions of the posts namely anger, surprise, happy, fear, sorrow, trust, anticipation and disgust with three sentic levels in each. This helps in better understanding of the posts when compared to the approaches which senses the polarity of the posts and gives just their sentiments i.e., positive, negative or neutral. The posts handling these emotions might be sarcastic too. When detecting sarcasm in social media posts, the various features that are especially inherent to Facebook must be considered with importance.
Sentiment-Specific Word Embeddings For Effectiveness Of Word Contexts And Exploit Sentiment

We recommend picking up information of assessment extraordinary expression embeddings named conclusion embeddings on this paper. Existing expression installing considering calculations normally handiest utilize the settings of words however disregard the notion of writings. It is detailed for opinion assessment in light of the fact that the expressions with comparable settings however opposite slant extremity, including great and terrible, are mapped to neighboring expression vectors. We adapt to this trouble by encoding supposition certainties of writings (e.G., sentences and expressions) together with settings of expressions in assumption embeddings. By consolidating setting and slant degree confirms, the nearest relates in supposition installing region are semantically comparable and it favors words with a similar assumption extremity. Keeping in mind the end goal to investigate feeling embeddings effectively, we build up various neural systems with fitting misfortune capacities, and gather colossal messages naturally with assessment alarms like emojis as the instruction information. Supposition embeddings can be clearly utilized as expression highlights for a spread of slant assessment obligations without trademark designing. We rehearse opinion embeddings to word-degree notion investigation, sentence organize notion class, and building supposition vocabularies. Trial results demonstrate that slant embeddings constantly outflank setting construct embeddings with respect to a few benchmark datasets of these obligations. This artworks gives experiences on the design of neural systems for picking up learning of task specific expression embeddings in various home grown dialect preparing commitments.
Klasifikasi Data Trafik Internet Menggunakan Metode Bayes Network (Studi Kasus Jaringan Internet Universitas Semarang)

Pemakaian internet merupakan kebutuhan yang penting yang mendukung kinerja dan aktivitas di kampus. Bagian yang terpenting dari infrastrutur internet yang difasilitasi oleh kampus adalah tersedianya bandwidth yang cukup untuk kelancaran trafik data melalui internet. Metode klasifikasi menggunakan Bayes Network ini memanfaatkan metode klasifikasi yang dimiliki oleh data mining untuk diterapkan pada data trafik jaringan internet. Penelitian ini bertujuan untuk mengklasifikasi data pemakaian internet sehingga dari klasifikasi tersebut dapat diketahui destination network, protocol dan lebar bandwidth yang banyak diakses pada waktu tertentu. Data trafik internet diambil melalui software Wireshark . Sedangkan pengolahan data dan proses pengklasifikasian data trafik internet diolah dengan Weka
Academy of Reading® Impact on Student Achievement in Extended Learning Program

The purpose of this casual-comparative study was to assess the effectiveness of Academy of READING® (AOR) on eighth grade Response to Intervention students using third nine-week benchmark scaled score and Criterion Reference Competency Test scaled score. The data collected from a public middle-school in rural Georgia revealed how student achievement, gender, and socioeconomic status were impacted by AOR. AOR participants, the treatment group, received 45 minutes of research-based computer intervention while the control, nonparticipating AOR, did not receive computer-based instruction during extended learning. This study compared differences in the mean scaled scores for at-risk students using an independent samples t-test. The findings for this research study indicated AOR participants’ third nine-week reading benchmark scores were slightly higher than non-participating AOR. No significant differences were revealed between third nine-week reading benchmark based on gender. High SES AOR participants scored slightly higher than low SES AOR participants although the sample size was small. Non-participating AOR participants’ student achievement outcomes were marginally higher than AOR participants on the CRCT. The researcher concludes that Academy of READING® did not impact student achievement. Furthermore, the researcher recommends that this study be replicated for a longer period with students from different ethnicities, more diverse economic population, and provide more feedback from students and teachers.
Determining and Avoiding Wormhole Attack in MANET

In Mobile Adhoc network is accountable for path establishment from source to destination using multi-hop routing. It uses wireless medium i.e., radio signal for transmission. In MANET each node acts as a router and end node. i.e., it receives the data packet and then processes it and forwards it to another node along a path. MANET is vulnerable to different security attacks because of open and dynamic nature with a minute security measures. Wormhole attack is a tunneling attack and is one of the server attacks among all security attacks which involve two or more malicious nodes that are on either side of tunnel. Attacker attracts data packet from neighbors and transmits via tunnel and passes it to another attacker on other side. The wormhole attack is the most strongest and is difficult to detect. In this proposed paper, we did a change in AODV protocol that detects and avoids wormhole attack in MANET and make MANET secure for transmission.
Near Language Identification Using NooJ

In this work we took a linguistic knowledge aware approach tailored for a specific pair of languages. We use NooJ as a core part of a system designed for automatic identification of near languages, Croatian and Serbian in particular. We use several levels of NooJ processing capabilities. First, we apply specially designed lexical transducers for the detection of the typical morphological spots in language. Then we apply the syntactic grammars for the detection of verb da verb syntagmas, characteristic for Serbian language. Finally, we measure discrepancies between texts provided by text processing. The output is generated according to predefined voting principle using AutoHotkey program. Our results show high F1 measures for language identification of Croatian and Serbian texts.
Foundations for behavioral higher-order contracts

Contracts are a popular mechanism for enhancing the interface of components. In the world of first-order functions, programmers embrace contracts because they write them in a familiar language and easily understand them as a pair of a pre-condition and a post-condition. In a higher-order world, contracts offer the same expressiveness to programmers but their meaning subtly differs from the familiar first-order notion. For instance, it is unclear what the behavior of dependent contracts for higher-order functions or of contracts for mutable data should be. As a consequence, it is difficult to design monitoring systems for such higher-order worlds. In response to this problem, this dissertation investigates complete monitors, a formal framework for deciding if a contract system is correct. The intuition behind the framework is that a correct contract system should: 1) mediate the exchange of values between contracted components 2) and blame correctly in case of contract violations. The framework reveals flaws in the semantics for dependent contracts from the literature and suggests a natural fix. In addition, this dissertation demonstrates the usefulness of the framework for language design with a language with contracts for mutable data and a language that mixes typed and untyped imperative programs. The final contribution is the provably correct design of a novel form of contracts, dubbed options contracts, that mix contract checking with random and probabilistic checking.
An Effect Evaluation of Automatic Driving Control for Passing through Intersections

This paper reports on effect evaluation, which focuses on intersections, as a part of a study to clarify the effects of automatic driving control. While intersections are essential for vehicle traffic, they cause several traffic problems such as accidents and congestion. Although many technologies have been developed to solve these problems, they remain imperfect. Recently, there have been many attempts to realize automatic driving control. Automatic driving control is expected to remove the uncertainty of manual driving and enhance safety and efficiency. Intersections are no exception: automatic driving technologies are expected to decrease traffic congestion and yield an energy-saving effect while reducing accidents at intersections. However, previous studies have not sufficiently clarified the magnitude attainable by these effects or what types of automatic driving technologies are needed to achieve them. This study therefore evaluates the effects on traffic flow of automatic driving control for passing though intersections. In this paper, first, the background surrounding automatic driving technologies is described to clarify the context of this study. Next, related works that have examined the effects of automatic driving control, such as automatic driving control for passing through intersections, are introduced. Then, vehicle behavior models designed for effect evaluation by simulation are explained. Finally, evaluation using the simulated vehicle behavior models is reported. The experimental evaluation verifies that automatic driving control for passing through intersections yields much greater effect in decreasing traffic congestion and energy saving than conventional traffic control using traffic signals.
360World A mobile app for creating and sharing 360° images

This report details the construction of 360World, an Android based prototype for a social media platform, the purpose of which is facilitating the capture and location focused sharing of 360 panorama pictures. The project encompasses the development of an Android application, a database for storing user information and the interface between these, as well as the con guration of hosting servers. The application makes use of algorithms from the OpenCV code library which are used to stitch multiple captured images into a single 360 panorama image; an image which can then be shared with the help of features that are typical for social media platforms, namely users and friends. 360World also has an innovative map-based way of visualising user pro les. Social media as an industry has been on the rise since its inception, and picture sharing is one of its major features; 360 pictures, however, are still relatively fringe. In this project, we explore one possible way 360 pictures could be utilised in a social media setting. The project resulted in a working prototype with which users can create, and share 360 panoramic images via map-based image presentation.