Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning for Domain Adaption: Engagement Recognition

Engagement is a key indicator of the quality of learning experience, and one that plays a major role in developing intelligent educational interfaces. Any such interface requires the ability to recognise the level of engagement in order to respond appropriately; however, there is very little existing data to learn from, and new data is expensive and difficult to acquire. This paper presents a deep learning model to improve engagement recognition from face images captured ‘in the wild’ that overcomes the data sparsity challenge by pre-training on readily available basic facial expression data, before training on specialised engagement data. In the first of two steps, a state-of-the-art facial expression recognition model is trained to provide a rich face representation using deep learning. In the second step, we use the model’s weights to initialize our deep learning based model to recognize engagement; we term this the Transfer model. We train the model on our new engagement recognition (ER) dataset with 4627 engaged and disengaged samples. We find that our Transfer architecture outperforms standard deep learning architectures that we apply for the first time to engagement recognition, as well as approaches using HOG features and SVMs. The model achieves a classification accuracy of 72.38%, which is 6.1% better than the best baseline Omid Mohamad Nezami E-mail: omid.mohamad-nezami@hdr.mq.edu.au Len Hamey E-mail: len.hamey@mq.edu.au Deborah Richards E-mail: deborah.richards@mq.edu.au Mark Dras E-mail: mark.dras@mq.edu.au Department of Computing, Macquarie University, Sydney, Australia model on the test set of the ER dataset. Using the F1 measure and the area under the ROC curve, our Transfer model achieves 73.90% and 73.74%, exceeding the best baseline model by 3.49% and 5.33% respectively.
Space-Efficient Non-Interactive Verification Enhancing Arbitrary Secret Sharing Schemes By Adding Cheater Detection Capabilities

Secret sharing deals with the problem of securely distributing confidential information among a certain number of users, in such a way that only some subsets of them are able to jointly decrypt it. Several schemes and variants of s ecret sharing have been proposed, from the seminal schemes of Sham ir and Blakley, which are based respectively on polynomial int erpolation, and hyperplanes intersection, to the newest appr oaches closely involving number theory, such as the ones based on the Chinese Remainder Theorem (Mignotte and Asmuth-Bloom) . One important issue in the design of a secret sharing protoco l is its robustness against cheaters: common solutions propo sed in literature rely on checking consistency of the secret infor mation after reconstruction from more than one group of users, or on adding helpful data to the shares in order to detect and/or identify mistrustful behavior. We present new verification algorithms providing arbitrary secret sharing schemes with cheater detection capabilities, and prove their space efficiency with regard t o other schemes appeared in the literature. We also introduce, in on e of our schemes, the Exponentiating Polynomial Root Problem (EPRP), which is believed to be NP-Intermediate and therefo re difficult.
DeepMasterPrint: Generating Fingerprints for Presentation Attacks

We present two related methods for creating MasterPrints, synthetic fingerprints that are capable of spoofing multiple people's fingerprints. These methods achieve results that advance the state-of-the-art for single MasterPrint attack accuracy while being the first methods capable of creating MasterPrints at the image level. Both of the methods presented in this paper start with training a Generative Adversarial Network (GAN) on a set of real fingerprint images. The generator network is then used to search for fingerprints that maximize the probability of matching with most subjects in a dataset. The first method uses evolutionary search in the space of latent variables, and the second method uses gradient-based optimization. The unique combination of evolution and GANs is able to design a MasterPrint that a commercial fingerprint system matches to 23% of all users in a strict security setting, and 77% of all users at a looser security setting.
Network capacity enhancement with various link rewiring strategies

The structure of the network has a great impact on its traffic dynamics. Most of the real world networks follow heterogeneous structure and exhibit scale-free feature. In scale-free network, a new node prefer to connect with hub nodes and the network capacity is curtailed by smaller degree nodes. Therefore, we propose rewiring a fraction of links in the network, to improve the network transport efficiency. In this paper, we discuss various link rewiring strategies and perform simulations on scale-free networks, confirming the effectiveness of these strategies. The rewiring strategies actually reduce the centrality of the nodes having higher betweenness centrality. After the link rewiring process, the degree distribution of the network remains the same. This work is beneficial for the enhancement of network performance.
How Important is Syntactic Parsing Accuracy? An Empirical Evaluation on Sentiment Analysis

Syntactic parsing, the process of obtaining the internal structure of sentences in natural languages, is a crucial task for artificial intelligence applications that need to extract meaning from natural language text or speech. Sentiment analysis is one example of application for which parsing has recently proven useful. In recent years, there have been significant advances in the accuracy of parsing algorithms. In this article, we perform an empirical, task-oriented evaluation to determine how parsing accuracy influences the performance of a state-of-the-art sentiment analysis system that determines the polarity of sentences from their parse trees. In particular, we evaluate the system using four well-known dependency parsers, including both current models with state-of-the-art accuracy and more innacurate models which, however, require less computational resources. The experiments show that all of the parsers produce similarly good results in the sentiment analysis task, without their accuracy having any relevant influence on the results. Since parsing is currently a task with a relatively high computational cost that varies strongly between algorithms, this suggests that sentiment analysis researchers and users should prioritize speed over accuracy when choosing a parser; and parsing researchers should investigate models that improve speed further, even at some cost to accuracy.
Task learning of a task robot in real space by using a learning system in virtual space

Reinforced learning by which a robot acquires control rules by trial and error has attracted considerable attention. However, it is quite difficult for robots to acquire control rules by reinforcement learning in the real space because many learning trials are needed to arrive at the control rules; the robot itself may lose control, or there may be safety problems with the control objects. In this paper we propose a method in which a robot in the real space learns a virtual task, after which the task is transferred from the virtual to the real space. The robot eventually acquires the task in a real environment. We show that a real robot can acquire a task in a virtual space with an input device, using the example of an inverted pendulum. Next, we verify that the acquired task in the virtual space can be applied to a real‐world task. We emphasize the utilization of the virtual space to effectively obtain the real‐world task. © 2010 Wiley Periodicals, Inc. Electr Eng Jpn, 172(1): 38–47, 2010; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20968
Proceedings of the 1st ACM international conference on Multimedia information retrieval

Welcome to the 1st International ACM Conference on Multimedia Information Retrieval (MIR2008) held in Vancouver, Canada from October 30-31st, 2008. The goal of MIR is to illuminate new paradigms, theories, and insights in the area of multimedia information retrieval. Topics of special interest included exploration of media archives; interfaces for multimedia exploration; indexing and search of multimedia data: digital life experience analysis and retrieval; video surveillance browsing and retrieval; learning and relevance feedback in multimedia retrieval; and diverse applications in culture, society, and science. In the past decade there have been a wide variety of relatively small workshop activities from several ACM Special Interest Groups (SIGs) which were related to multimedia retrieval. The workshops delved into diverse questions involving digital life and lifebits, video surveillance and analysis of human activity, exploration of media archives, etc. This year we have attempted to cluster the disparate activities to create this conference. Last year, the combined MIR related workshop activities from ACM SIGs had less than 120 full paper submissions. However, this year we received 308 unique paper submissions of which 262 full papers were selected by the organizing committee for the double-blind peer review process. Based upon the peer reviews of the program committee, 21 percent or 56 papers were accepted for the research track of the conference including both oral and poster presentations. Many conferences do not have a best paper award because comparing two (or more) excellent papers could involve the old "apples vs. oranges" questions. Is a great theory paper better than a great application paper? To avoid such subjective issues we decided it would be relevant to take note of the papers which were considered to be excellent or outstanding by the program committee. This means that there could potentially be many excellent papers, each in a different way and that we would not have to select one as the best paper. This year we decided to archive as a citation the papers which received two or more outstanding ratings from the program committee. This was not a competitive process, nor an award, but more of a record of which papers the program committee considered to be exceptional. Furthermore, future generations will be able to look back on this proceedings and study how the papers were perceived by history.
Inter-Pin Skew Compensation Scheme for 3.2-Gb/s/pin Parallel Interface

An inter-pin skew compensation scheme is proposed, which minimizes the inter-pin skew of parallel interface induced by unequal trace length and loading of printed circuit board (PCB). The proposed scheme measures the inter-pin skew and compensates during power-up with simple hardware. The proposed scheme is applied to 3.2-Gb/s/pin DDR4 SDRAM and implemented in a 0.18 m CMOS process. The inter-pin skew is compensated in 324-cycles of 400-㎒ clock and the skew is compensated to be less than 24-ps.
Follow the Signs for Robust Stochastic Optimization

Stochastic noise on gradients is now a common feature in machine learning. It complicates the design of optimization algorithms, and its effect can be unintuitive: We show that in some settings, particularly those of low signal-to-noise ratio, it can be helpful to discard all but the signs of stochastic gradient elements. In fact, we argue that three popular existing methods already approximate this very paradigm. We devise novel stochastic optimization algorithms that explicitly follow stochastic sign estimates while appropriately accounting for their uncertainty. These methods favorably compare to the state of the art on a number of benchmark problems.
Learning Representations from Product Titles for Modeling Large-scale Transaction Logs

Shopping basket data analysis is significant in understanding the shopping behaviors of customers. Existing models such as association rules are poor at modeling products which have short purchase histories and cannot be applied to new products (the cold-start problem). In this paper, we propose BASTEXT, an efficient model of shopping baskets and the texts associated with the products (e.g., product titles). The model's goal is to learn the product representations from the textual contents, that can capture the relationships between the products in the baskets. Given the products already in a basket, a classifier identifies whether a potential product is relevant to the basket or not, based on their vector representations. This enables us to learn high-quality representations of the products. The experiments demonstrate that BASTEXT can efficiently model millions of baskets, and that it outperforms the state-of-the-art methods in the next product recommendation task. Besides, we will also show that BASTEXT is a strong baseline for keyword-based product search.
Maximum Likelihood based Direct Position Estimation for Mobile Stations in Dense Multipath

The problem of direct position estimation in dense-multipath mobile scenarios is addressed. A low-complexity, fully-adaptive algorithm is proposed, based on the maximum likelihood approach. The processing is done exclusively on-board at the mobile node by exploiting the training sequence of a narrowband radio signal; thus, an antenna array is required only on the mobile node, as opposed to MIMO approaches where multiple antennas are considered at both sides. The proposed algorithm is able to estimate via adaptive beamforming (with spatial smoothing for coherence decorrelation) the optimal projection matrix that makes the received signal orthogonal to the multipath subspace; in addition, it tracks line-of-sight associations over the trajectory, hence achieving an integration gain. A simpler variant of the algorithm is also devised, which does not require any training sequence, at the price of some accuracy loss. The performance assessment shows that the proposed algorithms are very effective in (dense) multipath conditions, significantly outperforming natural competitors also when the number of antennas and snapshots is kept at the theoretical minimum.
Survey of Bayesian Networks Applications on Unmanned Intelligent Autonomous Vehicles

This article review the applications of Bayesian networks on Unmanned Intelligent Autonomous Vehicles (UIAV) from the decision making point of view, which represents the final step for fully autonomous unmanned vehicles (currently under discussion). Until now when it comes to make high level decisions for unmanned autonomous vehicles (UAV) the humans have the last word. Based on the works exposed in this article and current analysis, the modules of a general decision making framework and its variables are inferred. Many efforts have been made in the labs showing Bayesian networks as a promising computer model for decision making. Remains for the future to test Bayesian networks models in real situations. Besides the applications, Bayesian networks fundaments are introduced as elements to consider when we try to develop (UIAVs) with the potential of achieving high level judgements.
Comparing Titles vs. Full-text for Multi-Label Classification of Scientific Papers and News Articles

Until today there has been no systematic comparison of how far document classification can be conducted using just the titles of the documents. However, methods using only the titles are very important since automated processing of titles has no legal barriers. Copyright laws often hinder automated document classification on full-text and even abstracts. In this paper, we compare established methods like Bayes, Rocchio, kNN, SVM, and logistic regression as well as recent methods like Learning to Rank and neural networks to the multi-label document classification problem. We demonstrate that classifications solely using the documents' titles can be very good and very close to the classification results using full-text. We use two established news corpora and two scientific document collections. The experiments are large-scale in terms of documents per corpus (up to 100,000) as well as number of labels (up to 10,000). The best method on title data is a modern variant of neural networks. For three datasets, the difference to full-text is very small. For one dataset, a stacking of logistic regression and decision trees performs slightly better than neural networks. Furthermore, we observe that the best methods on titles are even better than several state-of-the-art methods on full-text.
Fast Approximate Nearest Neighbor Search With Navigating Spreading-out Graphs

Approximate nearest neighbor search (ANNS) is a fundamental problem in many tasks of machine learning and computer vision. An ANNS algorithm is required to be efficient on both memory and search performance. Recently, several graph based methods have achieved revolutionary performance on public data sets, but their indices are usually complicated and of large size. In this paper, we present a novel search algorithm based on a Navigating Spreading-out Graph (NSG). All the search begins with the navigating vertex and iteratively examines the neighbors' neighbors for closer neighbors on NSG. An NSG contains only a navigating vertex and a spreading-out graph, in which the angles among any two connected edges are ensured to be no smaller than $60^{\circ}$. We propose an efficient algorithm to build the NSG, and the max degree of resulting NSG is very small, thus it's quite memory-efficient. Extensive experiments on public datasets show that our approach outperforms the state-of-art algorithms significantly on both index size and search performance.
Computationally Efficient Cascaded Training for Deep Unrolled Network in CT Imaging

Dose reduction in computed tomography (CT) has been of great research interest for decades with the endeavor to reduce the health risk related to radiation. Promising results have been achieved by the recent application of deep learning to image reconstruction algorithms. Unrolled neural networks have reached state-of-the-art performance by learning the image reconstruction algorithm end-to-end. However, it suffers from huge memory consumption and long training time, which made it hard to scale to 3D data with current hardware. In this paper, we proposed an unrolled neural network for image reconstruction which can be trained step-by-step instead of end-to-end. Multiple cascades of image domain network were trained sequentially and connected with iterations which enforced data fidelity. Local image patches could be utilized for the neural network training, which made it fully scalable to 3D CT data. The proposed method was validated with both simulated and real data and demonstrated competing performance against the end-to-end networks.
TTMF: A Triple Trustworthiness Measurement Frame for Knowledge Graphs

The Knowledge graph (KG) uses the triples to describe the facts in the real world. It has been widely used in intelligent analysis and understanding of big data. In constructing a KG, especially in the process of automation building, some noises and errors are inevitably introduced or much knowledges is missed. However, learning tasks based on the KG and its underlying applications both assume that the knowledge in the KG is completely correct and inevitably bring about potential errors. Therefore, in this paper, we establish a unified knowledge graph triple trustworthiness measurement framework to calculate the confidence values for the triples that quantify its semantic correctness and the true degree of the facts expressed. It can be used not only to detect and eliminate errors in the KG but also to identify new triples to improve the KG. The framework is a crisscrossing neural network structure. It synthesizes the internal semantic information in the triples and the global inference information of the KG to achieve the trustworthiness measurement and fusion in the three levels of entity-level, relationship-level, and KG-global-level. We conducted experiments on the common dataset FB15K (from Freebase) and analyzed the validity of the model's output confidence values. We also tested the framework in the knowledge graph error detection or completion tasks. The experimental results showed that compared with other models, our model achieved significant and consistent improvements on the above tasks, further confirming the capabilities of our model.
How consistent are our discourse annotations? Insights from mapping RST-DT and PDTB annotations

Discourse-annotated corpora are an important resource for the community. However, these corpora are often annotated according to different frameworks, making comparison of the annotations difficult. This is unfortunate, since mapping the existing annotations would result in more (training) data for researchers in automatic discourse relation processing and researchers in linguistics and psycholinguistics. In this article, we present an effort to map two large corpora onto each other: the Penn Discourse Treebank and the Rhetorical Structure Theory Discourse Treebank. We first propose a method for aligning the discourse segments, and then evaluate the observed against the expected mappings for explicit and implicit relations separately. We find that while agreement on explicit relations is reasonable, agreement between the frameworks on implicit relations is astonishingly low. We identify sources of systematic discrepancies between the two annotation schemes; many of the differences in annotation can be traced back to different operationalizations and goals of the PDTB and RST frameworks. We discuss the consequences of these discrepancies for future annotation, and the usability of the mapped data for theoretical studies and the training of automatic discourse relation labellers.
Data processing of the ASG-EUPOS network test campaign

An idea to establish Active Geodetic Network in Pol and as permanent GNSS network is the consequence of more than ten years efforts to imple ent this technique into the geodetic, navigation and other wide range of professional and private applications. Starting in the beginning of 1990’s with the first polish GPS recei vers and first two permanent IGS/EPN stations, in 1992, with the GPS EUREF-POL campaign the unification of Polish and European reference networks and the introduction of ETRS’89 geodetic system in Poland has been completed. This network was re-surveyed in Sep tember 2001 with the same set of 11 stations. In 1994-95 POLREF densification network w as established and measured to provide the ETRF reference frame for geodetic, surveying an d mapping applications as well as to verify the existing classical national horizontal g eodetic networks. Along with the EUVN’97 campaign, performed in 1997 for unification of the European vertical datum, all stations of these three networks in Poland constrain the fundam ental Polish national GPS network which was densified in the next few years. The first, tes t permanent network with GPS automatic services in Poland was established in Upper Silesia by the end of 2002 as ASG-PL pilot project which, during next few years, was connected with Malopolska Precise Positioning System (MPPS). ASG-EUPOS system as a multifunctiona l precise satellite positioning system is a country-wide realization of the concept of GNSS real-time and postprocessing services for precise positioning and navigation, fu lly agreed with EUPOS requirements and standards. Due to the fact that the reference stati ons of the ASG-EUPOS system will create the geodetic control (reference) network and by mea ns of it’s services provide ETRS’89 system on the whole Polish territory, calibration o f all ASG-EUPOS stations had to be done before starting it in fully operational mode. This paper describes the measurement, data processing and results of the calibration campaign.