Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and Implementation of Autonomous Parallel and Vertical Parking Mobile Vehicle

With the increase of autonomous vehicle popularity, autonomous parking approaches become more critical. In this study, the autonomous parking problem of a model car with Ackermann steering system is discussed and various algorithms have been tested for both vertical and parallel parking. We also propose a method for the problem of vertical parking. Algorithms have been tested in the simulation environment and on the designed vehicle model. It has been observed that the parking process is successful in real-world tests.
Intelligent Embedded Security control system for Maternity ward based on IoT and Face recognition

Internet of things (IoT) is an advancing technology for the creation of smart world that has the capacity to provide automation, safety and security through ubiquitous communication. The paper discusses various applications and works of contemporary authors. It has been observed that large number of sensors, devices, and smartphones are connected to the internet for providing personalized services like smart home, smart transportation, smart healthcare, etc. in this paper, Maternity ward security control system is proposed in which design and development of maternity ward is based on human face recognition and remote monitoring technology. The model regulates the entry of person according to identity and gender which will help in providing the security. Face detection and recognition algorithm is used for finding the identity and further wireless interface has been used for updating the database in case of new person and further alert message is sent. In future, the design is implemented and results will be analyzed in real time scenario.
Syllable based speech analysis fo affective robotics

This work presents a speech intonation processing method based on phonetic syllables and optimal pitch stylization and applied to dimensional and continuous emotion tracking. After presenting the automatic segmentation and stylization algorithms, experiments performed on emotional speech corpora show that results are competitive with the state of the art on dimensional emotion recognition and that continuous tracking is stable for the Activation axis. A real time porting of the analysis method is presented in a robotics setup where the behavior of the robot mimics the detected emotional state of a human user.
Semantic Driven Fielded Entity Retrieval

A common approach for knowledge-base entity search is to consider an entity as a document with multiple fields. Models that focus on matching query terms in different fields are popular choices for searching such entity representations. An instance of such a model is FSDM (Fielded Sequential Dependence Model). We propose to integrate field-level semantic features into FSDM. We use FSDM to retrieve a pool of documents, and then to use semantic field-level features to re-rank those documents. We propose to represent queries as bags of terms as well as bags of entities, and eventually, use their dense vector representation to compute semantic features based on query document similarity. Our proposed re-ranking approach achieves significant improvement in entity retrieval on the DBpedia-Entity (v2) dataset over existing FSDM model. Specifically, for all queries we achieve 2.5% and 1.2% significant improvement in NDCG@10 and NDCG@100, respectively.
An Introduction to Mean Field Games using probabilistic methods

This thesis is going to give a gentle introduction to Mean Field Games. It aims to produce a coherent text beginning for simple notions of deterministic control theory progressively to current Mean Field Games theory. The framework gradually extended form single agent stochastic control problems to multi agent stochastic differential mean field games. The concept of Nash Equilibrium is introduced to define a solution of the mean field game. To achieve considerable simplifications the number of agents goes to infinity and formulate this problem on the basis of McKean-Vlasov theory for interacting particle systems. Furthermore, the problem at infinity is being solved by a variation of the Stochastic Maximum Principle and Forward Backward Stochastic Differential Equations. To elaborate more the Aiyagari macroeconomic model in continuous time is presented using MFGs techniques
Applying Transfer Learning To Deep Learned Models For EEG Analysis

The introduction of deep learning and transfer learning techniques in fields such as computer vision allowed a leap forward in the accuracy of image classification tasks. Currently there is only limited use of such techniques in neuroscience. The challenge of using deep learning methods to successfully train models in neuroscience, lies in the complexity of the information that is processed, the availability of data and the cost of producing sufficient high quality annotations. Inspired by its application in computer vision, we introduce transfer learning on electrophysiological data to enable training a model with limited amounts of data. Our method was tested on the dataset of the BCI competition IV 2a and compared to the top results that were obtained using traditional machine learning techniques. Using our DL model we outperform the top result of the competition by 33%. We also explore transferability of knowledge between trained models over different experiments, called inter-experimental transfer learning. This reduces the amount of required data even further and is especially useful when few subjects are available. This method is able to outperform the standard deep learning methods used in the BCI competition IV 2b approaches by 18%. In this project we propose a method that can produce reliable electroencephalography (EEG) signal classification, based on modest amounts of training data through the use of transfer learning.
Discovery of recombining plasma inside the extended gamma-ray supernova remnant HB9

We present the results from the Suzaku X-ray Imaging Spectrometer observation of the mixed-morphology supernova remnant (SNR) HB9 (G160.9+2.6). We discovered recombining plasma (RP) in the western Suzaku observation region and the spectra here are well described by a model having collisional ionization equilibrium (CIE) and RP components. On the other hand, the X-ray spectra from the eastern Suzaku observation region are best reproduced by the CIE and non-equilibrium ionization model. We discuss possible scenarios to explain the origin of the RP emission based on the observational properties and concluded that the rarefaction scenario is a possible explanation for the existence of RP. In addition, the gamma-ray emission morphology and spectrum within the energy range of 0.2–300 GeV are investigated using 10 yr of data from the Fermi Large Area Telescope (LAT). The gamma-ray morphology of HB9 is best described by the spatial template of radio continuum emission. The spectrum is well fit to a log-parabola function and its detection significance was found to be 25σ. Moreover, a new gamma-ray point source located just outside the south-east region of the SNR’s shell was detected with a significance of 6σ. We also investigated the archival H i and CO data and detected an expanding shell structure in the velocity range of $-10.5$ and $+1.8$ km s−1 that is coinciding with a region of gamma-ray enhancement at the southern rim of the HB9 shell.
Broad-band spectral study of X-ray transient MAXI J1820+070 using Swift/XRT and NuSTAR

We report on a NuSTAR and Swift/XRT observation of the newly discovered X-ray transient MAXI J1820+070. Swift/XRT and NuSTAR have concurrently observed the newly detected source on 2018 March 14. We have simultaneously fitted the broad-band spectra obtained from Swift/XRT and NuSTAR. The observed joint spectra in the energy range 0.6–78.0 keV are well modeled with a weak disc black-body emission, dominant thermal Comptonization, and relativistic reflection fraction. We have detected a fluorescent Iron-Kα line relativistically broadened and a Compton hump at ∼30 keV. We constrain the inner disc radius as well as the disc inclination angle, and their values are found to be 4.1$^{+0.8}_{-0.6}$RISCO (where RISCO ≡ radius of the innermost stable circular orbit) or 5.1$^{+1.0}_{-0.7}$ rg (where rg ≡ gravitational radius) and 29.8$^{+3.0}_{-2.7}$°, respectively. The best-fitting broad-band spectra suggest that the source was in the hard state and evolving. The source emission is best described by weak thermal emission along with strong thermal Comptonization from a relatively cold, optically thick, geometrically thin and ionized accretion disc. X-ray spectral modeling helps us to understand the accretion and ejection properties in the vicinity of the compact object.
Measurements of single-state and state-ensemble lifetimes of high-lying Rb Rydberg levels

We demonstrate a hybrid method based on field ionization and state-selective de-excitation capable of measuring the lifetimes of high-lying Rydberg states. For nS Rydberg states of Rb atoms with principal quantum number $60\leq n\leq88$, we measure both the individual target state lifetimes and those of the ensemble of Rydberg states populated via black-body radiation-induced transitions. We find good overall agreement with numerical calculations of the expected lifetimes in both cases. However, for the target state lifetimes, we find a local deviation towards shorter lifetimes for states around $n=72$, which we interpret as a signature of a modified black-body spectrum in the finite volume in which our experiments take place.
CSSegNet: Fine-Grained Cardiac Structures Segmentation Using Dilated Pyramid Pooling in U-net

Cardiac structure segmentation plays an important role in medical analysis procedures. Images' blurred boundaries issue always limits the segmentation performance. To address this difficult problem, we presented a novel network structure which embedded dilated pyramid pooling block in the skip connections between networks' encoding and decoding stage. A dilated pyramid pooling block is made up of convolutions and pooling operations with different vision scopes. Equipped the model with such module, it could be endowed with multi-scales vision ability. Together combining with other techniques, it included a multi-scales initial features extraction and a multi-resolutions' prediction aggregation module. As for backbone feature extraction network, we referred to the basic idea of Xception network which benefited from separable convolutions. Evaluated on the Post 2017 MICCAI-ACDC challenge phase data, our proposed model could achieve state-of-the-art performance in left ventricle (LVC) cavities and right ventricle cavities (RVC) segmentation tasks. Results revealed that our method has advantages on both geometrical (Dice coefficient, Hausdorff distance) and clinical evaluation (Ejection Fraction, Volume), which represent closer boundaries and more statistically significant separately.
The Sensitivity of Counterfactual Fairness to Unmeasured Confounding

Causal approaches to fairness have seen substantial recent interest, both from the machine learning community and from wider parties interested in ethical prediction algorithms. In no small part, this has been due to the fact that causal models allow one to simultaneously leverage data and expert knowledge to remove discriminatory effects from predictions. However, one of the primary assumptions in causal modeling is that you know the causal graph. This introduces a new opportunity for bias, caused by misspecifying the causal model. One common way for misspecification to occur is via unmeasured confounding: the true causal effect between variables is partially described by unobserved quantities. In this work we design tools to assess the sensitivity of fairness measures to this confounding for the popular class of non-linear additive noise models (ANMs). Specifically, we give a procedure for computing the maximum difference between two counterfactually fair predictors, where one has become biased due to confounding. For the case of bivariate confounding our technique can be swiftly computed via a sequence of closed-form updates. For multivariate confounding we give an algorithm that can be efficiently solved via automatic differentiation. We demonstrate our new sensitivity analysis tools in real-world fairness scenarios to assess the bias arising from confounding.
PAGAN: Video Affect Annotation Made Easy

How could we gather affect annotations in a rapid, unobtrusive, and accessible fashion? How could we still make sure that these annotations are reliable enough for data-hungry affect modelling methods? This paper addresses these questions by introducing PAGAN, an accessible, general-purpose, online platform for crowdsourcing affect labels in videos. The design of PAGAN overcomes the accessibility limitations of existing annotation tools, which often require advanced technical skills or even the on-site involvement of the researcher. Such limitations often yield affective corpora that are restricted in size, scope and use, as the applicability of modern data-demanding machine learning methods is rather limited. The description of PAGAN is accompanied by an exploratory study which compares the reliability of three continuous annotation tools currently supported by the platform. Our key results reveal higher inter-rater agreement when annotation traces are processed in a relative manner and collected via unbounded labelling.
On Slicing Sorted Integer Sequences

Representing sorted integer sequences in small space is a central problem for large-scale retrieval systems such as Web search engines. Efficient query resolution, e.g., intersection or random access, is achieved by carefully partitioning the sequences. In this work we describe and compare two different partitioning paradigms: partitioning by cardinality and partitioning by universe. Although the ideas behind such paradigms have been known in the coding and algorithmic community since many years, inverted index compression has extensively adopted the former paradigm, whereas the latter has received only little attention. As a result, an experimental comparison between these two is missing for the setting of inverted index compression. We also propose and implement a solution that recursively slices the universe of representation of a sequence to achieve compact storage and attain to fast query execution. Albeit larger than some state-of-the-art representations, this slicing approach substantially improves the performance of list intersections and unions while operating in compressed space, thus offering an excellent space/time trade-off for the problem.
The increasing importance of Ashi Waza, in high level competition. (Their Biomechanics, and small changes in the form)

In this paper we are interested, both: at the description of Biomechanics of the Ashi Waza techniques, with precision of few of them, members of the Japanese, so called, Ko Waza group; and at the biomechanical analysis of the small changes in the applicative form of them. These small changes, from the basic movement defined by Kodokan and show in thousand books, probably born from practical competitive situations. They are today become the usual applicative form, not only present among the various National Federations, but also in Japan itself among Universities dojos and the Kodokan. Among the Ko Waza techniques, we will select for our analysis only the following: Ko Uchi Gari , Ko Soto Gari, Ko Uchi Barai, Ko Soto Barai, Ko Soto Gake , Ko Uchi Gake That are considered by Japanese followers different throwing techniques, as the different names show us. But from the Biomechanical point of view all these Kodokan throws are based on the same physical principle and practically on the same movements.
A Semantic Approach for User-Brand Targeting in On-Line Social Networks

We propose a general framework for the recommendation of possible customers (users) to advertisers (e.g., brands) based on the comparison between On-line Social Network profiles. In particular, we represent both user and brand profiles as trees where nodes correspond to categories and sub-categories in the associated On-line Social Network. When categories involve posts and comments, the comparison is based on word embedding, and this allows to take into account the similarity between topics popular in the brand profile and user preferences. Results on real datasets show that our approach is successfull in identifying the most suitable set of users to be used as target for a given advertisement campaign.
OrthoNet: Multilayer Network Data Clustering

Network data appears in very diverse applications, like biological, social, or sensor networks. Clustering of network nodes into categories or communities has thus become a very common task in machine learning and data mining. Network data comes with some information about the network edges. In some cases, this network information can even be given with multiple views or layers, each one representing a different type of relationship between the network nodes. Increasingly often, network nodes also carry a feature vector. We propose in this paper to extend the node clustering problem, that commonly considers only the network information, to a problem where both the network information and the node features are considered together for learning a clustering-friendly representation of the feature space. Specifically, we design a generic two-step algorithm for multilayer network data clustering. The first step aggregates the different layers of network information into a graph representation given by the geometric mean of the network Laplacian matrices. The second step uses a neural net to learn a feature embedding that is consistent with the structure given by the network layers. We propose a novel algorithm for efficiently training the neural net via gradient descent, which encourages the neural net outputs to span the leading eigenvectors of the aggregated Laplacian matrix, in order to capture the pairwise interactions on the network, and provide a clustering-friendly representation of the feature space. We demonstrate with an extensive set of experiments on synthetic and real datasets that our method leads to a significant improvement w.r. t. state-of-the-art multilayer graph clustering algorithms, as it judiciously combines nodes features and network information in the node embedding algorithms.
Multi-Cue Vehicle Detection for Semantic Video Compression In Georegistered Aerial Videos

Detection of moving objects such as vehicles in videos acquired from an airborne camera is very useful for video analytics applications. Using fast low power algorithms for onboard moving object detection would also provide region of interest-based semantic information for scene content aware image compression. This would enable more efficient and flexible communication link utilization in lowbandwidth airborne cloud computing networks. Despite recent advances in both UAV or drone platforms and imaging sensor technologies, vehicle detection from aerial video remains challenging due to small object sizes, platform motion and camera jitter, obscurations, scene complexity and degraded imaging conditions. This paper proposes an efficient moving vehicle detection pipeline which synergistically fuses both appearance and motion-based detections in a complementary manner using deep learning combined with flux tensor spatio-temporal filtering. Our proposed multi-cue pipeline is able to detect moving vehicles with high precision and recall, while filtering out false positives such as parked vehicles, through intelligent fusion. Experimental results show that incorporating contextual information of moving vehicles enables high semantic compression ratios of over 100:1 with high image fidelity, for better utilization of limited bandwidth air-to-ground network links.
Virtual-Pod-Assisted Resource Assignment in Elastic All-Optical Switching Intra-Datacenter Networks

We propose efficient resource assignment schemes based on virtual-pod (V-Pod) and node migration in elastic all-optical switching intra-datacenter networks (intra-DCNs). Simulation results show that the proposed schemes can significantly reduce the service request blocking probability and improve computing and storage (C/S) resource utilization.
Optimistic Bull or Pessimistic Bear: Adaptive Deep Reinforcement Learning for Stock Portfolio Allocation

Portfolio allocation is crucial for investment companies. However, getting the best strategy in a complex and dynamic stock market is challenging. In this paper, we propose a novel Adaptive Deep Deterministic Reinforcement Learning scheme (Adaptive DDPG) for the portfolio allocation task, which incorporates optimistic or pessimistic deep reinforcement learning that is reflected in the influence from prediction errors. Dow Jones 30 component stocks are selected as our trading stocks and their daily prices are used as the training and testing data. We train the Adaptive DDPG agent and obtain a trading strategy. The Adaptive DDPG's performance is compared with the vanilla DDPG, Dow Jones Industrial Average index and the traditional min-variance and mean-variance portfolio allocation strategies. Adaptive DDPG outperforms the baselines in terms of the investment return and the Sharpe ratio.
An Integrated Image Filter for Enhancing Change Detection Results

Change detection is a fundamental task in computer vision. Despite the fact that significant advances have been made, most of the change detection methods fail to work well in challenging scenes due to ubiquitous noise and interferences. Nowadays, post-processing methods aiming to enhance the binary change detection results still fall short of the requirements on universality for distinctive scenes, applicability for different types of detection methods, accuracy, and real-time performance. Inspired by the nature of image filtering, which separates noise from pixel observations and recovers the real structure of patches, we consider utilizing image filters to enhance the detection masks. In this paper, we present an integrated filter that comprises a weighted guided image filter and a weighted spatiotemporal tree filter. The spatiotemporal tree filter leverages the global spatiotemporal information of adjacent video frames, and meanwhile, the guided filter carries out local window filtering of pixels. The main contributions are as follows. First, the proposed filter can make full use of the information of the same object in consecutive frames to improve its current detection mask by computations on a spatiotemporal minimum spanning tree. Second, the integrated filter possesses both advantages of local filtering and global filtering; it not only has good edge-preserving property but also can handle heavily textured and colorful foreground regions. Third, unlike some popular enhancement methods [Markov random field (MRF) and conditional random field (CRF)] that require either a priori background probabilities or a posteriori foreground probabilities to improve the coarse detection masks, our method is a versatile enhancement filter that can be applied after many different types of change detection methods and is particularly suitable for video sequences. The experiments demonstrate that our method is suitable to be applied after a wide range of change detection methods, and it also works effectively on various scenes. In comparison, it evidently outperforms seven popular enhancement approaches.