Miracle-dz/UniFAR-FTUs huggingface dataset:
Data-Driven Management of CDN Performance

Content Distribution Networks (CDNs) carry most of the web content, with the goals of offering good performance to users at a low cost. In this thesis, we introduce measurement and analysis techniques to help CDNs balance these goals. First, we allocate resources efficiently across the distributed edge servers by jointly minimizing network latency and cache misses. We propose a unified framework for CDNs to jointly solve the problems of placement, mapping, and disk allocation, while including the impact of cache misses. We evaluate our methods using request logs from a commercial CDN. We show that including the impact of cache misses in the post-mapping disk allocation enhances the performance significantly for a small increase in the cost. Still, there are other sources of performance problems on the end-to-end (e2e) path of the servers to clients, which takes us to the next part. Second, to detect and diagnose performance problems that cause poor experience for users, we propose a fine-grained instrumentation of the e2e path. In particular, we focus on the video delivery path because video is now the dominant application of the Internet. We deploy our instrumentation and diagnosis methods in a commercial content provider, enabling us to join the server-side, TCP statistics, and client-side measurements for the first time, and characterize the performance problems in a large set of videos. We uncover a wide range of problems, some of which were unknown before, and can only be discovered by an e2e instrumentation. While capable of diagnosing these problems, our tool is limited in how frequently it measures network. To remedy this, we propose our final solution. Finally, we dive deeper into diagnosing network problems by monitoring TCP connections directly in the network devices. Our tool can pinpoint if the performance of a TCP connection is hindered by the sender, receiver, or network. We deploy emerging programmable edge devices to implement our monitoring and diagnosis logic directly in the data plane, which runs at line-rate, without cooperation from iii servers. We infer fine-grained TCP metrics from the edge device (e.g, NIC), without imposing storage or monitoring overhead on the servers.
Dirichlet Latent Variable Hierarchical Recurrent Encoder-Decoder in Dialogue Generation

Variational encoder-decoders have achieved well-recognized performance in the dialogue generation task. Existing works simply assume the Gaussian priors of the latent variable, which are incapable of representing complex latent variables effectively. To address the issues, we propose to use the Dirichlet distribution with flexible structures to characterize the latent variables in place of the traditional Gaussian distribution, called Dirichlet Latent Variable Hierarchical Recurrent Encoder-Decoder model (Dir-VHRED). Based on which, we further find that there is redundancy among the dimensions of latent variable, and the lengths and sentence patterns of the responses can be strongly correlated to each dimension of the latent variable. Therefore, controllable responses can be generated through specifying the value of each dimension of the latent variable. Experimental results on benchmarks show that our proposed Dir-VHRED yields substantial improvements on negative log-likelihood, word-embedding-based and human evaluations.
Multi-Task Image Clustering through Correlation Propagation

Traditional image clustering algorithms deal with single-task clustering (STC) problem on a single domain. However, with the increasing number of related images on the Web, it is challenging for STCs to perform related image clustering tasks independently without considering the between-task relationship, which mainly consists of similar visual features and image patterns among tasks. Therefore, it is intuitive to resort to multi-task clustering (MTC) algorithms. However, most existing MTCs learn a shared feature subspace, which may lead to negative transfer when facing the image clustering tasks that are not strongly related. In this paper, we propose a novel multi-task image clustering algorithm, which performs multiple image clustering tasks simultaneously and propagates the task correlation to improve clustering performance. Specifically, we first extend the information bottleneck method to cluster tasks independently. The related and unrelated images between the pairwise clusters of different tasks are then discovered. Meanwhile, two corresponding types of correlations are propagated among the tasks, where only the positive correlation benefits the clustering of each task. A sequential and collaborative method is further designed to ensure an optimal solution. Moreover, we perform a theoretical analysis of the properties on correlation propagation and the convergence of our algorithm. The experimental results demonstrate that the proposed algorithm outperforms the state-of-the-art clustering methods.
Upgraded Optical Secret Sharing Strategy for QR Code Approach

: For Application such as data storage and high speed machine reading Quick Response (QR) code have been widely used. Access to the QR code is given to all those using it; therefore, encoding the information without using the cryptography or some protection algorithm is unsuitable in this paper we propose a visual secret sharing which divides the images into n-shares and decode the image with that n-shares. In Contrast with other techniques the share in our scheme are valid QR codes, by a standard QR reader that can be decoded with some specific meaning, thereby avoiding the raising issue with the potential attacker. By XOR-ing the qualified shares the secret message is recovered, this operation can be easily performed with the android smartphones or other QR code scanning devices. In proposed work, we implemented the AES Algorithm for encryption. To split the encrypted data into several shares, we proposed Division Algorithm.
Adding biological constraints to CNNs makes image classification more human-like and robust

In this study, we show that when standard convolutional neural networks (CNNs) are trained end-to-end on datasets containing low-level and spatially high-frequency features, they are susceptible to learning these potentially idiosyncratic features if they are predictive of the output class. Such features are extremely unlikely to play a major role in human object recognition, where instead a strong preference for shape is observed. Through a series of empirical studies, we show that standard CNNs cannot overcome this reliance on non-shape features merely by making training more ecologically plausible or using standard regular-isation methods. However, we show that these problems can be ameliorated by forgoing end-to-end learning and processing images initially with Gabor ﬁlters, in a manner that more closely resembles biological vision.
On the Problem of Standardisation in Time-Domain Photometry

Abstract This presentation addressed some aspects of photometric standardisation and calibration that have a very significant effect on the accuracy of long time-baseline photometry. The difficulties were illustrated by examples of combinations of vintage photographic magnitudes with photomultiplier and CCD photometry, and with photometry from space. The case studies involved variability on time-scales of hours, years, decades and centuries. The examples went beyond classical problems of combining incongruent and ambiguous passbands, non-linear detectors and poor standardisation.
Multi-task Learning for Natural Language Generation in Task-Oriented Dialogue

In task-oriented dialogues, Natural Language Generation (NLG) is the final yet crucial step to produce user-facing system utterances. The result of NLG is directly related to the perceived quality and usability of a dialogue system. While most existing systems provide semantically correct responses given goals to present, they struggle to match the variation and fluency in the human language. In this paper, we propose a novel multi-task learning framework, NLG-LM, for natural language generation. In addition to generating high-quality responses conveying the required information, it also explicitly targets for naturalness in generated responses via an unconditioned language model. This can significantly improve the learning of style and variation in human language. Empirical results show that this multi-task learning framework outperforms previous models across multiple datasets. For example, it improves the previous best BLEU score on the E2E-NLG dataset by 2.2%, and on the Laptop dataset by 6.1%.
Recognizing Chinese Texts with 3D Convolutional Neural Network

In this paper, we propose a deep learning system to localize and recognize Chinese texts in scenes with signage and road marks through 3D convolutional neural network. The proposed system adopts YOLO for detecting target location and exploits 3D convolutional neural network for recognizing the contents. The proposed design outperforms the existing designs based on LSTM and achieves real-time processing performance, which is feasible to be implemented on embedded platforms. The proposed system reaches over 90% accuracy in recognizing Chinese texts on bird’s-eye viewing road marks in a self-driving vehicle equipped with a fisheye camera. In addition, this system can achieve 20 fps execution speed with NVIDIA DIGITS DevBox with 1080Ti GPU, which is fast enough for autonomous driving applications.
Observing the Impact of Science Education on Undesirable Behaviors of Students in the Agent-based Simulation Environment

Agent-based modeling is a new computational model for social sciences. Computational models are formulated as computer programs which represent the processes that exist in the social world. When simulations of these computational models for social sciences are performed, it is possible to gather information about real systems' processes and predict future outcomes of the processes. The agent-based model includes a set of agents that represent real actors in a real system in the simulated environment. The simulated environment represents the environment in which the agents contain their resources and perform their actions. Agents interact with other agents and entities in the environment while they try to achieve their individual goals. In the present study, it is aimed to imitate the school environment in a simulation environment by using the Repast Simphony 2.4 simulation tool and observe the impact of science education on undesirable behaviors of students represented by agents in the simulation environment.
Evaluating the short‐term effect of cross‐market discounts in purchases using neural networks: A case in retail sector

Promotional tools such as cross‐market discounts have been increasingly used as a means to increase customer satisfaction and sales. This paper aims to assess whether the implementation of a cross‐market discount campaign by a retailing company encouraged customers to increase their purchases level. It contributes to the literature by using neural networks to detect novelties in a real context involving cross‐market discounts. Besides the computation of point predictions, the methodology proposed involves the estimation of neural networks prediction intervals. Sales predictions are compared with the observed values in order to detect significant changes in customers' spending. The use of neural networks is validated through the comparison with the forecasting estimates of support vector regression, regression trees, and linear regression. The results reveal that the promotional campaign under analysis did not significantly impact the sales of the rewarded customers.
An Iterative Sure-Let Deconvolution Algorithm Based on BM3D Denoiser

Recently, the plug-and-play priors (PPP) have been a popular technique for image reconstruction. Based on the basic iterative thresholding scheme, we in this paper propose a new iterative SURE-LET deconvolution algorithm with a plug-in BM3D denoiser. To optimize the deconvolution process, we linearly parametrize the thresholding function by using multiple BM3D denoisers as elementary functions. The key contributions of our approach are: (1) the linear combination of several BM3D denoisers with different (but fixed) parameters, which avoids the manual adjustment of a single non-linear parameter; (2) linear parametrization makes the minimization of Stein’s unbiased risk estimate (SURE) finally boil down to solving a linear system of equations, leading to a very fast and exact optimization during each iteration. In particular, the SURE of BM3D denoiser is approximately evaluated by finite-difference Monte-Carlo technique. Experiments show that the proposed algorithm, in average, achieves better deconvolution performance than other state-of-the-art methods, both numerically and visually.
Redcoat: A Collaborative Annotation Tool for Hierarchical Entity Typing

We introduce Redcoat, a web-based annotation tool that supports collaborative hierarchical entity typing. As an annotation tool, Redcoat also facilitates knowledge elicitation by allowing the creation and continuous refinement of concept hierarchies during annotation. It aims to minimise not only annotation time but the time it takes for project creators to set up and distribute projects to annotators. Projects created using the web-based interface can be rapidly distributed to a list of email addresses. Redcoat handles the propagation of documents amongst annotators and automatically scales the annotation workload depending on the number of active annotators. In this paper we discuss these key features and outline Redcoat’s system architecture. We also highlight Redcoat’s unique benefits over existing annotation tools via a qualitative comparison.
Informative Frame Classification of Endoscopic Videos Using Convolutional Neural Networks and Hidden Markov Models

The goal of endoscopic analysis is to find abnormal lesions and determine further therapy from the obtained information. For example, in case of Barrett’s esophagus, the objective of endoscopy is to timely detect dysplastic lesions, before endoscopic resection is no longer possible. However, the procedure produces a variety of non-informative frames and lesions can be missed due to poor video quality. Especially when analyzing entire endoscopic videos made by non-expert endoscopists, informative frame classification is crucial to e.g. video quality grading. This analysis involves classification problems such as polyp detection or dysplasia detection in Barrett’s Esophagus. This work concentrates on the design of an automated indication of informativeness of video frames. We propose an algorithm consisting of state-of-the-art deep learning techniques, to initialize frame-based classification, followed by a hidden Markov model to incorporate temporal information and control consistent decision making. Results from the performed experiments show that the proposed model improves on the state-of-the-art with an F1-score of 91%, and a substantial increase in sensitivity of 10%, thereby indicating improved labeling consistency. Additionally, the algorithm is capable of processing 261 frames per second, which is multiple times faster compared to other informative frame classification algorithms, thus enabling real-time computation.
A Generative Algorithm for Modeling Social Networks with Trait Spaces

A method for the reliable generation of random networks that model known social networks is becoming increasingly desirable as a tool in the study of how these networks are structured and how they change over time. This paper will present an algorithm capable of growing large directed networks that are designed to model a range of observed social networks with regard to several key measures. Our algorithm is loosely based on the Barab´asi-Albert algorithm for scale-free graph generation. However, our model includes many additional parameters that play key roles in social networks, including a means of assigning attributes to individuals in the network, which allows for the exploration of networks in which there is a certain degree of diversity. In doing so, we have produced an algorithm that is not only intuitive in its implementation, but also extremely ﬂexible and easily adaptable to a variety of situations. Furthermore, the algorithm exhibits structural traits present in social networks but not produced by the Barab´asi-Albert model. We will discuss directed and undirected versions of the algorithm, show how each version performs with regard to several key metrics of the network, examine how the algorithm compares to observed real-world networks, and discuss extensions of the model that could further enrich its modeling capabilities.
Crosstalk Noise Aware System For WDM-Based Optical Network on Chip

Network on chip (NoC) is presented as a promising solution to face off the growing up of the data exchange in the multiprocessor system-on-chip (MPSoC). However, the traditional NoC faces two main problems: the bandwidth and the energy consumption. To face off these problems, a new technology in MPSoC, namely, optical network-on-chip (ONoC) has been introduced which it uses the optical communication to guaranty a high performance in communication between cores. In addition, wavelength division multiplexing (WDM) is exploited in ONoC to reach a high rate of bandwidth. Nevertheless, the transparency nature of the ONoC components induce crosstalk noise to the optical signals, which it has a direct effect to the signal-to-noise ratio (SNR) then decrease the performance of the ONoC. In this paper, we proposed a new system to control these impairments in the network in order to detect and monitor crosstalk noise in WDM-based ONoC. Furthermore, the crosstalk monitoring system is a distributed hardware system designed and test with the different optical components according the various network topology used in ONoC. The register-transfer level (RTL) hardware design and implementation of this system can result in high reliability, scalability and efficiency with running time less than 20 ms.
Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks

Graph convolutional networks (GCNs) have recently received wide attentions, due to their successful applications in different graph tasks and different domains. Training GCNs for a large graph, however, is still a challenge. Original full-batch GCN training requires calculating the representation of all the nodes in the graph per GCN layer, which brings in high computation and memory costs. To alleviate this issue, several sampling-based methods are proposed to train GCNs on a subset of nodes. Among them, the node-wise neighbor-sampling method recursively samples a fixed number of neighbor nodes, and thus its computation cost suffers from exponential growing neighbor size across layers; while the layer-wise importance-sampling method discards the neighbor-dependent constraints, and thus the nodes sampled across layer suffer from sparse connection problem. To deal with the above two problems, we propose a new effective sampling algorithm called LAyer-Dependent ImportancE Sampling (LADIES). Based on the sampled nodes in the upper layer, LADIES selects nodes that are in the neighborhood of these nodes and uses the constructed bipartite graph to compute the importance probability. Then, it samples a fixed number of nodes according to the probability for the whole layer, and recursively conducts such procedure per layer to construct the whole computation graph. We prove theoretically and experimentally, that our proposed sampling algorithm outperforms the previous sampling methods regarding both time and memory. Furthermore, LADIES is shown to have better generalization accuracy than original full-batch GCN, due to its stochastic nature.
Automatic Pause Detection during P300 Web Browsing

Brain-computer interfaces (BCI) have been investigated for more than 40 years. P300based BCIs can nowadays control very complex applications such as spelling applications and even web browsers. To do this in a practical way, it is essential to have the possibility to pause the command selection of the BCI. We implemented and evaluated an automatic pause detection system for P300-based BCIs based on artifact detection with an inverse filtering method. Experimental results of an offline study (9 healthy participants) demonstrate the feasibility of the proposed approach and its high performance.
Intelligent Control Sytem for Car Muffler

Muffler is sub-system attached on car and used to control exhausting sound from machine. Conventional muffler using some material to filter exhaust. With conventional muffler engine spends extra energy to push exhaust gasses trough the filter. Of these, the filtering processes produce mechanical friction, and more energy consumption. These condition decreased rehabilitee and performance from car. Active Noise Control (ANC) is sound field modification that can reduce or eliminate unwanted sound by electro-acoustical means. But ANC works best for sound fields that spatially simple. Computational intelligent are interesting achievement at control and computer theory. Some research is about neurofuzzy that has capability to control complicated system like sound field (produced by car machine). Combining ANC with neurofuzzy will attenuate noise as we desire. Key words: Noise, Control, Muffler, Neurofuzzy
Twin Auxilary Classifiers GAN

Conditional generative models enjoy significant progress over the past few years. One of the popular conditional models is Auxiliary Classifier GAN (AC-GAN) that generates highly discriminative images by extending the loss function of GAN with an auxiliary classifier. However, the diversity of the generated samples by AC-GAN tends to decrease as the number of classes increases. In this paper, we identify the source of low diversity issue theoretically and propose a practical solution to the problem. We show that the auxiliary classifier in AC-GAN imposes perfect separability, which is disadvantageous when the supports of the class distributions have significant overlap. To address the issue, we propose Twin Auxiliary Classifiers Generative Adversarial Net (TAC-GAN) that adds a new player that interacts with other players (the generator and the discriminator) in GAN. Theoretically, we demonstrate that our TAC-GAN can effectively minimize the divergence between generated and real data distributions. Extensive experimental results show that our TAC-GAN can successfully replicate the true data distributions on simulated data, and significantly improves the diversity of class-conditional image generation on real datasets.
Radar data simulation using deep generative networks

: Due to the high cost of real experiments, radar data simulation plays an important role in radar applications. However, the accuracy and the calculation speed of existing simulation methods is limited by the model error and the heavy calculation of electromagnetic simulation. Here, a radar data simulation method based on deep generative network (DGN) is proposed. DGN is generative model involving deep network as the representation tool, in which the model is trained with labelled data. When the training phase is finished, the generative model can generate data samples which are similar to the real samples. The performance of the DGN is evaluated on the ground-based radar dataset, and the results show that the distribution of the generated radar data is similar to the training radar data.