Rethinking Algerian Visibility and Invisibility in Ali au Pays des Merveilles

This article examines Djouhra Abouda and Alain Bonnamy’s experimental documentary Ali au pays des merveilles (1975) and discusses how the filmmakers expose Algerian workers’ living conditions in the 1970s France, a promised land where racism and exclusion persist. This study analyses the visibility and invisibility of the Algerian labour by first discussing the exclusion of Algerian migrants on the basis of their racial identity and their social status, in light of thinking related to French republican identification. The author then examines the interrelations between the Algerian labour and the commodities produced by their labour, as well as the glamorous spectacle associated with the commodities. Finally, the article reflects on the reflexive archaeology of the image that questions the power and limits of archives, interrogating the entanglements of French colonial history in Algeria. The article argues that Abouda and Bonnamy’s stylistic devices are in line with those of the Third Cinema, providing an alternative that allows post-colonial sensibilities to challenge the official discourse and the self-claiming “universal” but indeed Eurocentric aesthetics

Supporting Stylized Language Models Using Multi-Modality Features

As AI and machine learning systems become more common in our everyday lives, there is an increased desire to construct systems that are able to seamlessly interact and communicate with humans. This typically means creating systems that are able to communicate with humans via natural language. Given the variance of natural language, this can be a very challenging task. In this thesis, I explored the topic of humanlike language generation in the context of stylized language generation. Stylized language generation involves producing some text that exhibits a specific, desired style. In this dissertation, I specifically explored the use of multi-modality features as a means to provide sufficient information to produce high-quality stylized text output. I also explored how these multi-modality features can be used to identify and explain errors in the generated output. Finally, I constructed an automated language evaluation metric that can evaluate stylized language models

IVFS: Simple and Efficient Feature Selection for High Dimensional Topology Preservation

Feature selection is an important tool to deal with high dimensional data. In unsupervised case, many popular algorithms aim at maintaining the structure of the original data. In this paper, we propose a simple and effective feature selection algorithm to enhance sample similarity preservation through a new perspective, topology preservation, which is represented by persistent diagrams from the context of computational topology. This method is designed upon a unified feature selection framework called IVFS, which is inspired by random subset method. The scheme is flexible and can handle cases where the problem is analytically intractable. The proposed algorithm is able to well preserve the pairwise distances, as well as topological patterns, of the full data. We demonstrate that our algorithm can provide satisfactory performance under a sharp sub-sampling rate, which supports efficient implementation of our proposed method to large scale datasets. Extensive experiments validate the effectiveness of the proposed feature selection scheme

Bibliometric analysis of social commerce research

Recently, social commerce has attracted the attention from both academics and practitioners and became a significant emerging research area. In this paper, bibliometric analysis has been applied to identify the characteristics and the developments of social commerce research. Based on the definition, we conduct a systematic review of social commerce research by synthesizing 1900 publications published between 2003 and 2018 in Web of Science. The 1900 publications cover 4033 authors, 724 journals, 79 countries or territories, and 1648 institutions. Furthermore,‘Computers in Human Behavior’ is the key journal publishing on social commerce research, and the USA, China and England are the countries that dominate the publication production. It can be concluded that there is much collaborative research in the social commerce domain as multi-authored publications make up the majority of all publications. In addition, three main research areas can be distinguished based on LLR (log-likelihood ratio): (1) the development trend of social commerce, (2) the relationship between customers and vendors, and (3) consumer trust in the context of social shopping. We believe that this review can provide some guidelines for future research

RESEARCH ON CORRELATION FILTERS OF VISUAL TRACKING ALGORITHMS Information Technology 2017

This thesis focuses mainly on the visual tracking algorithm, the Kernelized Correlation Filters (KCF) algorithm and Discriminative Scale Space Tracker (DSST) algorithm. They are widely applied in many fields. Moreover, the visual tracking framework with KCF and DSST outperforms in the perspective of tracking speed and accuracy, which has drawn increasing attention. Although these target tracking algorithms achieve long-term and accurate tracking of the target, there are still many problems in the practical application en-vironment such as stability, adaptability and real-time performance. In view of these problems, some improved methods are proposed. Aiming at the problem that the detection module in the algorithm needs to detect the lack of accuracy of the fast-moving object, a Kalman filter is used to estimate the approximate appearance area of the target in the current frame. This approximate area is taken as the target de-tection area of the algorithm. Although the speed of the algorithm has a certain impact, but the accuracy of the algorithm has a certain degree of improvement In this thesis, the Kalman filter is proposed to be utilized in the visual tracking framework with KCF, which is more robust to movements of the target area. Fur-thermore, the simulation results with test beds based on Matlab and OpenCV 3.3 show that the proposed framework outperforms the conventional KCF and DSST-based visual tracking framework. Experiments show that the two algorithms have their own advantages in the matching rate, the matching speed and the number of frames successfully tracked. And the improved algorithms are more effective than the original ones

Coupled Analysis of the Motion and Mooring Loads of a Spar "CONSTITUTION"

A truss spar, named as 'Constitution' was installed in Gulf of Mexico located at 90.58' 4.8" West Longitude and 27.17'31.9" North Latitude. Since its installation in October 2006, it has weathered multiple hurricanes. After the installation, British Maritime Technology (BMT) installed an Environmental Platform Response Monitoring System (EPRMS). The EPRMS is an integrated system collecting myriad of data that include the significant wave height and peak period of waves, the magnitude and direction of current and wind in the vicinity of the truss spar, its six-degree of freedom (6-D) motions, and tensions in its mooring lines and Top-Tension Risers. With the permission from Anadarko Petroleum Corporation (APC), these data are available to the Ocean Engineering Program at Texas A&M University (TAMU). In this study, the coupled dynamic analysis of the spar interacting with the mooring and riser systems will be performed using a numerical code, named as 'COUPLE'. 'COUPLE' was developed and is continuously expanded and improved by his former and current graduate students and Professor Jun Zhang at TAMU for the computation of the interaction between a floating structure and its mooring line/riser/tendon system in time domain. The main purpose of this study is to exam the accuracy and efficiency of 'COUPLE' in computing offshore structure motions and mooring line tensions and discuss the main issues of the computation. The numerical results will be compared with the corresponding ones obtained using another commercial software, 'Orcaflex', and the corresponding field measurement during Hurricane Ike which occurred on 12th September of 2008 and a winter storm on 9th November of 2009. The satisfactory agreement between the numerical prediction made using 'COUPLE' and field measurement are observed and presented. The results of the comparisons between 'COUPLE' with 'Orcaflex' and field measurements in this study have verified the accuracy and efficiency of 'COUPLE' in computing offshore structure motions and mooring line tensions due to its nonlinear hybrid wave model which could better estimate the second-order difference-frequency wave loading

Driver-centric Risk Object Identification

A massive number of traffic fatalities are due to driver errors. To reduce fatalities, developing intelligent driving systems assisting drivers to identify potential risks is in urgent need. Risky situations are generally defined based on collision prediction in existing research. However, collisions are only one type of risk in traffic scenarios. We believe a more generic definition is required. In this work, we propose a novel driver-centric definition of risk, i.e., risky objects influence driver behavior. Based on this definition, a new task called risk object identification is introduced. We formulate the task as a cause-effect problem and present a novel two-stage risk object identification framework, taking inspiration from models of situation awareness and causal inference. A driver-centric Risk Object Identification (ROI) dataset is curated to evaluate the proposed system. We demonstrate state-of-the-art risk object identification performance compared with strong baselines on the ROI dataset. In addition, we conduct extensive ablative studies to justify our design choices.Comment: Submitted to TPAM