Miracle-dz/UniFAR-FTUs huggingface dataset:
Natural Language Description Generation for Image using Deep Learning Architecture

Automatic natural description generation of an image is currently a challenging task. To generate a natural language description of the image, the system is implemented by combining with the techniques of computer vision and natural language processing. This paper presents different deep learning models for generating the natural language description of the image. Moreover, we discussed how the deep learning model, which works for the natural language description of an image, can be implemented. This deep learning model consists of Convolutional Neural Network (CNN) as well as Recurrent Neural Network (RNN). The CNN is used for extracting the features from the image and RNN is used for generating the natural language description. To implement the deep learning model in generating the natural language description of an image, we have applied the Flickr 8K dataset and we have also evaluated the performance of the model using the standard evaluation matrices. These experiments show that the model is frequently giving accurate natural language descriptions for an input image.
Comparative Analysis of Biometric Recognition Techniques

This research paper discusses a person’s different biometrics modalities that are used for personal recognition and identification system. Advantages and disadvantage have also been discussed for many biometrics modalities. Biometrics provide a reliable authentication to determine the identity of a person in contrast to IDs and passwords. There are lots of applications now a days which are using biometrics for their systems like ATMs, attendance systems, mobile phones, electronic banking, access to secure places etc. In this paper a survey of all biometrics is presented that are presently in use or under development or still in the research pipeline. Further, a comparative analysis of the biometric techniques is summarized in a Table which provides their cost, uniqueness, accuracy and their additional requirements. Furthermore, application, pros and cons are also discussed for each technique.
Research on privacy enhancement scheme of blockchain transactions

Blockchain is the supporting technology for cryptocurrencies, which can be used to record transaction information among users. However, the information on blockchain can be accessed and verified by any user. To some extent, it is effortless to acquire users' identities, transaction privacy information by means of transaction tracing technology and data analysis technology. In order to tackle the above problems, we proposed a transaction privacy enhancement scheme based on the computational Diffie‐Hellman problem with provable security. By leveraging a group signature scheme, the mixing peer can mix the transactions so that the transaction sender can be hidden into a mixing group. In addition, our solution improves the efficiency by utilizing a signature aggregation scheme and reducing the overhead of the signature verification. The security analysis shows that the proposed scheme is superior in privacy and the efficiency of signature verification.
Learning with Limited Data for Multilingual Reading Comprehension

This paper studies the problem of supporting question answering in a new language with limited training resources. As an extreme scenario, when no such resource exists, one can (1) transfer labels from another language, and (2) generate labels from unlabeled data, using translator and automatic labeling function respectively. However, these approaches inevitably introduce noises to the training data, due to translation or generation errors, which require a judicious use of data with varying confidence. To address this challenge, we propose a weakly-supervised framework that quantifies such noises from automatically generated labels, to deemphasize or fix noisy data in training. On reading comprehension task, we demonstrate the effectiveness of our model on low-resource languages with varying similarity to English, namely, Korean and French.
Geographic Routing Protocol for Ultrawideband WSNs

: Wireless Sensor Networks (WSNs) may exploit accurate localization capabilities of ultrawidband (UWB) transceivers to improve performance of high layer protocols. We analyze power consumption of a WSN willing to help both communication and positioning into the same UWB transceiver and implementing a geographic routing algorithm, either the Greedy Perimeter Stateless Routing (GPSR) or its energy aware version, the e-GPSR. Power consumption depends on beacon rate for positions updates, the number of hops to reach the destination and the number of neighbors per node. On the other hand, the beacon rate impacts the reliability of the neighbor lists; the number of hops impacts on the end-to-end latency; the number of neighbors, i.e. the network connectivity, impacts on routing performance. The presented analysis assesses, by means of both theoretical investigations and simulation results, the main trade-offs between power consumption and latency that can be applied to obtain the best possible performance.
U-Module: Better Parameters Initialization of Convolutional Neural Network for Medical Image Classification

In this paper, we present a novel U-module for better parameters initialization of convolutional neural network(CNN) on medical image classification tasks. CNN-based models such as VGG and Resnet have achieved great success on natural image classification tasks. However, these methods need a large number of training data to get good performance. In medical image field, the performance of the CNN-based models is always limited by the lack of annotated medical images. In this case, the parameters initialization plays an important role for the model performance. Addressing this problem, we design an up-sample structure with unsupervised loss function named U-module, which can be easily inserted into different CNN-based models. In addition, we propose a special training method for the CNN with our U-modules. Two challenging datasets of medical images are chosen for the evaluation, and the experiment results show consistent improvement on different models. Particularly, our method is even better than using the pre-train parameters on large natural image dataset.
Laconic Conditional Disclosure of Secrets and Applications

In a Conditional Disclosure of Secrets (CDS) a verifier V wants to reveal a message m to a prover P conditioned on the fact that x is an accepting instance of some NP-language L. An honest prover (holding the corresponding witness w) always obtains the message m at the end of the interaction. On the other hand, if x ∉ L we require that no PPT P* can learn the message m. We introduce laconic CDS, a two round CDS protocol with optimal computational cost for the verifier V and optimal communication cost. More specifically, the verifier’s computation and overall communication grows with poly(|x|; λ; log(T)), where λ is the security parameter and T is the verification time for checking that x ∊ L (given w). We obtain constructions of laconic CDS under standard assumptions, such as CDH or LWE. Laconic CDS serves as a powerful tool for maliciousifying semi-honest protocols while preserving their computational and communication complexities. To substantiate this claim, we consider the setting of non-interactive secure computation: Alice wants to publish a short digest corresponding to a private large input x on her web page such that (possibly many) Bob, with a private input y, can send a short message to Alice allowing her to learn C(x; y) (where C is a public circuit). The protocol must be reusable in the sense that Bob can engage in arbitrarily many executions on the same digest. In this context we obtain the following new implications. 1) UC Secure Bob-optimized 2PC: We obtain a UC secure protocol where Bob’s computational cost and the communication cost of the protocol grows with poly(|x|; |y|; λ; d), where d is the depth of the computed circuit C. 2) Malicious Laconic Function Evaluation: Next, we move on to the setting where Alice’s input x is large. For this case, UC secure protocols must have communication cost growing with |x|. Thus, with the goal of achieving better efficiency, we consider a weaker notion of malicious security. For this setting, we obtain a protocol for which Bob’s computational cost and the communication cost of the protocol grows with poly(|y|; λ; d), where d is the depth of the computed circuit C.
Neighbourhood Context Embeddings in Deep Inverse Reinforcement Learning for Predicting Pedestrian Motion Over Long Time Horizons

Predicting crowd behaviour in the distant future has increased in prominence among the computer vision community as it provides intelligence and flexibility for autonomous systems, enabling the early detection of abnormal events and better and more natural interactions between humans and autonomous systems such as driverless vehicles and field robots. Despite the fact that Deep Inverse Reinforcement Learning (D-IRL) based modelling paradigms offer flexibility and robustness when anticipating human behaviour across long time horizons, compared to their supervised learning counterparts, no existing state-of-the-art D-IRL methods consider path planning in situations where there are multiple moving pedestrians in the environment. To address this, we present a novel recurrent neural network based method for embedding pedestrian dynamics in a D-IRL setting, where there are multiple moving agents. We propose to capture the motion of the pedestrian of interest as well as the motion of other pedestrians in the neighbourhood through Long-Short-Term Memory networks. The neighbourhood dynamics are encoded into a feature map, preserving the spatial integrity of the observed trajectories. Utilising the maximum-entropy based non-linear inverse reinforcement learning framework, we map these features to a reward map. We perform extensive evaluations on the publicly available Stanford Drone and SAIVT Multi-Spectral Trajectory datasets where the proposed method exhibits robustness towards lengthier predictions into the distant future, demonstrating the importance of capturing the dynamic evolution of the environment using the proposed embedding scheme.
Reading Like HER: Human Reading Inspired Extractive Summarization

In this work, we re-examine the problem of extractive text summarization for long documents. We observe that the process of extracting summarization of human can be divided into two stages: 1) a rough reading stage to look for sketched information, and 2) a subsequent careful reading stage to select key sentences to form the summary. By simulating such a two-stage process, we propose a novel approach for extractive summarization. We formulate the problem as a contextual-bandit problem and solve it with policy gradient. We adopt a convolutional neural network to encode gist of paragraphs for rough reading, and a decision making policy with an adapted termination mechanism for careful reading. Experiments on the CNN and DailyMail datasets show that our proposed method can provide high-quality summaries with varied length, and significantly outperform the state-of-the-art extractive methods in terms of ROUGE metrics.
Variational Hierarchical User-based Conversation Model

Generating appropriate conversation responses requires careful modeling of the utterances and speakers together. Some recent approaches to response generation model both the utterances and the speakers, but these approaches tend to generate responses that are overly tailored to the speakers. To overcome this limitation, we propose a new model with a stochastic variable designed to capture the speaker information and deliver it to the conversational context. An important part of this model is the network of speakers in which each speaker is connected to one or more conversational partner, and this network is then used to model the speakers better. To test whether our model generates more appropriate conversation responses, we build a new conversation corpus containing approximately 27,000 speakers and 770,000 conversations. With this corpus, we run experiments of generating conversational responses and compare our model with other state-of-the-art models. By automatic evaluation metrics and human evaluation, we show that our model outperforms other models in generating appropriate responses. An additional advantage of our model is that it generates better responses for various new user scenarios, for example when one of the speakers is a known user in our corpus but the partner is a new user. For replicability, we make available all our code and data.
Secure Quadrature Channel Modulation

In this paper, a physical layer security (PLS) scheme based on quadrature channel modulation (QCM) called secure QCM (SQCM) is proposed for time division duplex (TDD) multi-input multi-output (MIMO) media based modulation (MBM) systems to resist eavesdropping. By exploiting the abundant legitimate channel realizations generated by the radio frequency (RF) mirrors, secure mappings between the original information bits and the transmit patterns are designed, where the transmit patterns include the transmit antenna activation pattern (AAP), mirror activation pattern (MAP) and symbol modulation pattern. The secure mappings used by the transmitter change for each transmission and are unavailable at the eavesdropper due to the independent fading of the legitimate link and the eavesdropping link. Based on the secure mappings, the proposed SQCM conveys the source information bits securely through the complex modulation symbol and instantaneous channel realizations by using a single RF chain. The ergodic secrecy rate is analyzed and a closed-form approximation is derived. Simulation results show that the closed-form approximation coincides with the experimental ergodic secrecy rate, and the achievable ergodic secrecy rate is not influenced by the number of eavesdropping antennas or the channel advantage of the eavesdropping link. Compared to the secure MBM scheme using the traditional artificial noise (AN) technique, the proposed SQCM scheme shows significant performance gain without consuming jamming power when the eavesdropper has more antennas than the legitimate receiver. Compared to the precoded MBM and probabilistic optimized MBM schemes exploiting the eavesdropper's CSI, the proposed SQCM scheme has better secrecy performance in all SNR region and medium-to-high SNR regions respectively without requiring the eavesdropper's CSI.
Interval and Continuous Exercise Elicit Equivalent Post- Exercise Hypotension Despite Differences in Baroreflex Sensitivity and Heart Rate Variability

Equi-caloric bouts of interval (INT: 5x 2:2 min at 85 and 40% VO2max) and continuous (21 minutes at 60% VO2max) exercise were performed by 13 older prehypertensive males on separate days, at equivalent times of day, to assess the influence of exercise mode on postexercise hypotension (PEH). Cardiovascular measures were collected for 30 min pre and 60 min post-exercise. PEH as measured by mean post-exercise systolic blood pressure (SBP) decrease, area under the SBP curve, and minimum SBP achieved, was equivalent after both conditions. SV was significantly reduced and HR was significantly elevated post-exercise after both conditions. No significant reductions in CO or TPR were observed. INT exercise provided a larger perturbation to the autonomic nervous system as measured by Baroreflex sensitivity and Heart Rate Variability. The responses elicited by acute INT exercise, with repeated exposure, may lead to greater improvements in blood pressure regulation than those associated with continuous
Adaptive Parameterization for Neural Dialogue Generation

Neural conversation systems generate responses based on the sequence-to-sequence (SEQ2SEQ) paradigm. Typically, the model is equipped with a single set of learned parameters to generate responses for given input contexts. When confronting diverse conversations, its adaptability is rather limited and the model is hence prone to generate generic responses. In this work, we propose an Adaptive Neural Dialogue generation model, AdaND, which manages various conversations with conversation-specific parameterization. For each conversation, the model generates parameters of the encoder-decoder by referring to the input context. In particular, we propose two adaptive parameterization mechanisms: a context-aware and a topic-aware parameterization mechanism. The context-aware parameterization directly generates the parameters by capturing local semantics of the given context. The topic-aware parameterization enables parameter sharing among conversations with similar topics by first inferring the latent topics of the given context and then generating the parameters with respect to the distributional topics. Extensive experiments conducted on a large-scale real-world conversational dataset show that our model achieves superior performance in terms of both quantitative metrics and human evaluations.
Echocardiographic predictors for recurrence of ischemic mitral regurgitation after restrictive annuloplasty

and poster presentations Changes in QRS Duration and Morphology Immediately After and At Long-Term FollowUp after Transcatheter Aortic Valve Implantation With the Self-Expanding and Balloon-Expandable Prosthesis. P Houthuizen, RMA van der Boon, LAFM Van Garsse, M Urena, TT Poels, N van Mieghem, BRG Brueren, J Rodés-Cabau, FW Prinzen, P de Jaegere. Poster presentation. AHA, 2013. Incidence and mortality of left bundle branch block induced by surgical aortic valve replacement. TT Poels, P Houthuizen, LAFM Van Garsse, FW Prinzen, JG Maessen and AHM van Straten. Abstract presentation. Najaarsvergadering NVT, 2012. Utrecht, The Netherlands. Blood Transfusion And The Risk Of Acute Kidney Injury Following Transcatheter Aortic Valve Implantation. RJ Nuis, J Rodes-Cabau, JM Sinning, L Van Garsse, J Kefer, J Bosmans, A Dager, N Van Mieghem, M Urena, N Werner, G Nickenig, J Maessen, P Astarci, L Benitez, E Dumont, R Van Domburg, P De Jaegere. Poster presentation. TCT, 2012.
Toward a combined GIS-ABM approach for simulation of pilgrims’ movement during Hajj days in Mekkah

The safety of pilgrims is one the most important issues faced by authorities to make sure that Hajj rituals are carried out in the best condition. Management of pilgrims crowd’s movement as well as their transportation among Holy places during Hajj days are two main key aspects to take into consideration to ensure their safety. The Hajj authorities in the Kingdom of Saudi Arabia develop each year a set of plans and schedules to ensure the safety and efficiency of pilgrims’ movement. However, these plans and schedules must be tested and evaluated before their final adoption and implementation. To do so, it is necessary, first, to develop a powerful Spatial Data Infrastructure(SDI) that intends to provide useful and relevant spatial information and spatial analysis to support decision making for pilgrims crowd’s management. Then, it is necessary to perform realistic simulations of the proposed plans and schedules to assess their safety and efficiency. In this paper, we propose an approach that combines Geographic Information Systems(GIS) and Agent Based Modeling(ABM) for simulation of pilgrims’ movement during Hajj days. Five plans for the movement of 66000 pilgrims were simulated; 3 plans for moving from camps entrances to gathering points and 2 plans for moving from camps entrances to Jamarat gates. The simulations carried out allow assessing the safety of the proposed plans based on the density of pilgrims per meter square. In addition, the efficiency of these plans was estimated in accordance with the time needed for all pilgrims to reach their destination in each scenario.
Research on Purifying Paired Feature Point Based on RANSAC Algorithm

Aiming at the problem that the matching precision of feature points in ORB algorithm is not high enough, an improved feature point matching purification algorithm was proposed. After detecting the feature points of the images, the BF algorithm was used to calculate the Hamming distance between the feature points. Then filtering some wrong matching points through two-way matching, the preliminary matching point pairs were obtained, and then the appropriate distance threshold was set to further filter the matching point pairs, finally using the random sample consensus (RANSAC) algorithm to purify. The experimental results show that the improved feature point matching purification algorithm can improve the matching accuracy under the condition of ensuring the real-time performance of the algorithm.
Parasitic GAN for Semi-Supervised Brain Tumor Segmentation

In semantic segmentation, researchers face the shortage of pixel-level annotated data. And it is particularly severe in the medical images. On the other hand, the unlabeled data are abundantly produced in the diagnosis routine. In the paper, we introduced the Parasitic GAN for the brain tumor segmentation to exploit the unlabeled data more efficiently. Parasitic GAN is composed of three parts: the segmentor ${\mathcal{S}}$, the generator ${\mathcal{G}}$, and the discriminator ${\mathcal{D}}$. With the label maps produced by the segmentor and the supplementary label maps synthesized by the generator, the discriminator could learn a more precise boundary of ground truth. Thus, the segmentor benefits from the adversarial learning mechanism and the extra supervision provided by the discriminator. This parasitic relationship between the segmentor and the generative adversarial network (${\mathcal{G}}$ and ${\mathcal{D}}$) restricts the fitness ability of the segmentor and improves its generalization capacity. In practice, it definitely improved the performance of segmentor in brain tumor segmentation tasks, increasing the dice score 0.010-0.035.
Integrating Sign Prediction With Behavior Prediction for Signed Heterogeneous Information Networks

People often use online social networks not only to express attitudes towards others, but also to make decisions, which forms signed heterogeneous information networks. Both sign prediction and behavior prediction can provide useful information for networks analysis, each of which has been a hot topic. However, existing methods for sign prediction mainly rely on the features from labeled links but ignore users’ behavior and the features from unlabeled links, which often leads to dumb results. Similarly, inferring users’ behavior without considering links’ signs is dull as well. In order to solve this issue, in this paper, we present a novel model called SPBP to integrate Sign Prediction with Behavior Prediction in the context of signed heterogeneous information networks. It simultaneously captures users’ social links (including both labeled links and unlabeled links) and users’ behavior to improve the accuracy of prediction. First, due to the lack of labeled links in main stream social networks, we propose correlation estimation methods to estimate social correlation and behavioral correlation between users respectively. Then we encode structural balance-based features and status-based features according to social psychology theories. With the extracted features, we propose a sign prediction algorithm based on transfer learning to use knowledge extracted from related source networks to train the target network, which can effectively make up for the incompleteness of target samples. Finally, we propose a behavior prediction algorithm based on the predicted signs of links. Extensive experiments conducted on real-world signed heterogeneous information networks, Epinions, Slashdot and Wiki-RfA, demonstrate that SPBP can effectively solve both the sign prediction problem and the behavior problem.