Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Improved DASH Adaptation in NDN: An Emulative Analysis

The Information-Centric Networking (ICN) paradigm is deemed to enable simpler and more efficient networking interaction by moving from a strict connection based relationship between client and server to an interest-based relationship between user and content. Hence, addressing shifts to content objects rather than any specific copy or location of the content. In order to achieve this new way of addressing the client uses the content name, which is propagated into the network. As a result, a higher efficiency is expected since ICN network nodes (e.g., routers) may reply to such requests using copies from their own caches, or different sources hosting a content object may be used in parallel. Thus, this concept promises better support for device mobility and implicit caching, and it provides inherent multicast support.However, the simplicity of this concept comes at a price. As established applications, such as adaptive bitrate video streaming, have been designed and optimized having a client-server networking environment in mind. In particular, the quality adaptation algorithms used by today's de-facto streaming standard Dynamic Adaptive Streaming over HTTP (DASH) uses bandwidth estimation techniques that are specifically designed for a client-server networking environment where all video segments are retrieved from the same host.This paper addresses issues related to adaptive video streaming in an ICN environment. It analyzes the video streaming behavior of state-of-the-art video quality adaptation algorithms such as PANDA and BOLA in emulated ICN environments. The analysis focuses on the impact ICN chunk-based throughput measurements and ICN caches have on quantitative measurements of Quality-of-Experience (QoE). The paper provides a detailed investigation of the chunk-based throughput estimation showing that it behaves fundamentally different due to ICN caching. Based on these results we provide extensions to existing adaptation algorithms (e.g., PANDA) that can significantly improve the QoE in ICN environments.
MORA: A Generative Approach to Extract Spatiotemporal Information Applied to Gesture Recognition

Gestures are related to a non-verbal language used on the interaction between subjects. Due to its applicability in several contexts, gesture recognition has been investigated by different researches, often investing on the capture of motion and appearance on videos. However, most of these methods do not properly explore the well-defined gesture temporal structure and are not suitable to deal with an increasing number of classes. Thus, we propose the Multi-Output Recurrent Autoencoders (MORA), an approach that relies on the representation of each gesture class independently. MORA employs a specific autoencoder model per class, composed by convolutional (3D) and a Gated Recurrent Unit (GRU) layer, what allows spatiotemporal information extraction and scalability in terms of number of classes. To validate MORA, experiments are conducted on SKIG and ChaLearn IsoGD datasets, for which the approach achieved accuracies comparable to state-of-the-art methods.
Modeling of Structure Landmark for Indoor Pedestrian Localization

The decreasing of accumulative error is a key issue for various multi-sensor fusion-based indoor localization systems that employ pedestrian dead reckoning (PDR) to improve their localization performance. Current studies mainly use activity-based map matching (AMM) to prevent the accumulative error. However, it is vulnerable to mismatch problems, which are usually caused by the randomness of human activities. This paper proposes a structure landmark map matching-based indoor localization approach. Structure landmarks refer to special spatial structures (e.g., intersections, corridors, or corners), which are visually salient in a local environment. These landmarks are visually recognizable in indoor spaces because of their distinct shapes. This paper integrates visual and inertial information to recognize the structure landmarks by using a Bayesian classifier. An algorithm is also proposed to realize indoor localization without prior knowledge of the initial location or the turning angles of people. This approach decreases the accumulative localization error of PDR by matching the detected structure landmarks to the ground-truth values. The experimental results showed that the identification accuracy of the structure landmark was about 90% and the matching accuracy was 92%. The mean off-line localization error was about 1.2 m. Compared with the AMM-based method, this approach is robust to the random turning activities of people and can realize indoor localization with a faster convergence speed.
Transform Learning Based Sparse Coding for LiDAR Data Denoising

In recent years, sparse coding (SC) has been exploited for light detection and ranging (LiDAR) data restoration, and promising results have been reported. However, such methods are usually time consuming, because much computational resource has been devoted to solving batches of <inline-formula><tex-math notation="LaTeX">$\ell _0$</tex-math></inline-formula> or <inline-formula><tex-math notation="LaTeX">$\ell _1$</tex-math></inline-formula>-norm optimization problems iteratively. More recently, fast SC method has been proposed to achieve nearly real-time performance at the expense of applicability. In this letter, we propose a transform learning based SC method for LiDAR data denoising. Moreover, we present a detailed evaluation for our series of SC methods with different models, and together with concluding remarks. Such remarks can be applied as a guide to apply appropriate SC model for specific application.
EGRET: Extortion Graph Exploration Techniques in the Bitcoin Network

The Bitcoin network is a complex network that records anonymous financial transactions while encapsulating the relationships among its pseudonymous users. This paper proposes graph mining techniques to explore the relationships among wallet addresses (pseudonyms for Bitcoin users) suspected to be involved in a given extortion racket, exploiting the anonymity of the Bitcoin network to collect and launder money. Starting around Bitcoin addresses of potential interest, neighborhood subgraphs are analyzed in terms of path length and confluence to detect suspicious Bitcoin flow and other wallet addresses controlled by the suspected perpetrators. We show with a dataset of the Ashley Madison blackmail campaign from August 2015 how the mechanisms can be used both to estimate the amount of money that was extorted by the suspected perpetrators under the specific blackmail campaign, and also estimate the amount of money handled by them during the same period of time.
Learning to Style-Aware Bayesian Personalized Ranking for Visual Recommendation

Recently, product images have been gaining the attention of recommender system researchers in the field of visual recommendation. This is because the visual appearance of products has a significant impact on consumers’ decisions. Extensive studies have been done to integrate the features extracted by convolutional neural networks directly into recommendations. This improves the performance of recommender systems. Style features, an important type of features, are rarely considered. Style features play a vital role in the visual recommendation as a user’s decision depends largely on whether the product fits his/her style. However, the representation of the conventional image features fails in capturing the styles of a product. To bridge this gap, we propose introducing style feature modeling, which is highly relevant with user preference, into the visual recommendation model. Furthermore, we propose incorporating the style features into collaborative learning to create awareness pertaining to the preferences of users. The experiments conducted on two public implicit feedback datasets demonstrate the effectiveness of our approach for the visual recommendation.
Exploring the Relationship Between Dimensionality Reduction and Private Data Release

It is important to facilitate data sharing between data owners and data analysts as data owners do not always have the ability to process and analyze data. For example, governments around the world are starting to release collected data to the public to leverage data analysis competence of the crowd. However, some privacy leakage incidents have made the public to rediscover the importance of privacy protection, leading to new privacy regulations. In existing researches dimensionality reduction has played an important role in private data release mechanisms to improve utility but its influence on privacy protection has never been examined. In this study, we perform a series of experiments and found that dimensionality reduction could provide similar privacy protection effects as K-anonymity mechanisms, and it could work as a preprocessor of K-anonymity process to it to reduce the generalization and suppression needed.
Calculation of Losses in Three-Core Submarine Cables for Fractional Frequency Transmission Operation

Fractional frequency transmission system (FFTS) has been proposed as a promising alternative to both 50/60 Hz HVAC and HVDC technology for offshore wind power applications. The potential advantages of FFTS are mainly the increasing transmission capability and the reduction of power losses. Research so far has focused on the system feasibility and its potential applications to wind power integration. Nevertheless, the issue of current rating and calculating cable losses under fractional frequency transmission operation (FFTO) is not covered adequately in the literature. In this paper, three-core submarine cables under FFTO are investigated. Typical current rating cases of both export and inter-array cables are examined and losses in conductors, sheaths and armor are evaluated. Calculations are made by means of Finite Element Analysis and ElectroMagnetic Transient Program, while results are also compared with the corresponding derived by IEC 60287 standard. Differences with conventional HVAC are discussed, revealing the benefits of FFTS in terms of losses as well as the implications imposed in the magnitude of the generated magnetic field.
Location-based Social Media Use in Families : Qualitative Outcomes from Participant Observations

This paper is about location-based social media use within families. An experiment was devised to study different family relationships and dynamics with respect to the use and application of location-based social media services. The methodology applied was two-fold: (1) an experiment using Google Latitude, a location social media app; and (2) semi-structured interviews. The data from the study is analyzed using qualitative analysis and compares the responses between siblings with that of parent-child relationship. The results of the study raise issues about (1) use and experience; (2) the disclosure of location; and (3) and the power to view location information. In the discussion uses and benefits of this technology are presented, as well as negative issues, trust and control factors. The main finding of the study is that location-based social media use while very beneficial in families has the propensity to propagate a power asymmetry, one individual over the other, although this power is not always displayed explicitly. While location-based social media is about "sharing" one’s location, the sharing is not always equitable, nor is it bidirectional.
Automated Optimization of Intersections Using a Genetic Algorithm

Traffic jams in large cities, in addition to having a very high economic cost, cause an increase in emissions generated by vehicles over the same route being driven under normal conditions. In recent years, there has been a rapid evolution in the technologies applied to the field of autonomous vehicles. There are currently commercial solutions for assisted driving and semi-autonomous driving systems, with very favorable forecasts for reaching a completely autonomous vehicle scenario in the coming decades. This new environment generates opportunities and challenges to reduce congestion in scenarios with autonomous or semi-autonomous vehicles. This paper focuses on the automatic optimization of the passage of vehicles through intersections. The intersections are one of the most conflict-generating elements in a traffic network. This type of conflicts arises because the intersections must manage multiple traffic flows with different priorities and preferences, often leading to traffic jams. The problem has been addressed by proposing three mechanisms to model any type of intersection, to calculate the roads with fewer points of conflict between their inputs and outputs, and to optimize the arrival rate of vehicles using a Genetic Algorithm to achieve the maximum performance of the intersection. To validate this solution, a cellular automata simulator has been developed, which can be adapted to both autonomous and conventional vehicle scenarios and can provide realistic results when certain conditions are met. The results obtained have been compared with other traditional solutions (priority and traffic lights) using microscopic traffic simulations, and with those obtained in other studies showing the advantages of the proposed system. The proposed systems achieve a throughput improvement between 9.21% and 36.98% compared with the traditional solutions.
Omnidirectional inductive wireless charging of a 3D receiver cube inside a box

Omnidirectional inductive wireless charging of a $6\times 6\times 6 cm^3$ 3D receiver cube with 6 planar coils inside a $47\times 47\times 47 cm^3$ box with 4 planar emitting coils is studied. The power delivered to the cube in the central position of the box is in the 1 W - 2.4 W range with 6 - 7% transfer efficiency. In this position, the power is insensitive to the pitch angular orientation. When moving the 3D receiver cube toward the corners of the box, there is no sudden drop of transmitted power. However, the delivered power near the corners increases or decreases in an acceptable range depending on the orientations of the magnetic field of the emitters. Perspectives of this work, when replacing the manufacturing of coils based on PCBs technology by 3D Molded Interconnect Device technology, are briefly discussed.
Optimizing the Self-Amplitude Modulation of Different 2-D Saturable Absorbers for Ultrafast Mode-Locked Fiber Lasers

Passive mode locking of erbium-doped fiber lasers (EDFLs) started by different two-dimensional (2-D) saturable absorbers (SAs) is demonstrated and compared. The self-amplitude modulation (SAM) coefficients of four 2-D SAs, including graphene, MoS<sub>2</sub>, and n-/p-type Bi<sub>2</sub>Te<sub>3</sub>, are also estimated to compare their mode-locking performance at first stage. By analyzing the nonlinear absorption of those 2-D SAs, graphene possesses the largest SAM coefficient <inline-formula><tex-math notation="LaTeX">$(\gamma _{{\rm{Graphene}}})$</tex-math></inline-formula> of 8.7x10<sup>–4</sup> better than other 2-D SAs (<inline-formula><tex-math notation="LaTeX">$\gamma _{{\rm MoS}2}= 5.5\times 10^{\hbox{--} 4}$</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">$\gamma _{{\rm n-Bi2Te}3}= 7.2\times 10^{-4}$</tex-math></inline-formula>, and <inline-formula><tex-math notation="LaTeX">$\gamma _{{\rm p-Bi2Te}3}= 7.9\times 10^{\hbox{--} 4}$</tex-math></inline-formula>). When operating the EDFL under low-gain condition, the mode-locked EDFL pulsewidth is mainly correlated with the SAM coefficient of SA. All of these 2-D SAs can generate the sub-700 fs EDFL pulsewidth at first mode-locking stage. In particular, graphene can produce the narrowest EDFL pulse with 615-fs pulsewidth because of its largest SAM coefficient to start up the mode locking. When inducing the soliton mode locking by mutually compensating the group-delay dispersion and self-phase modulation, the pulsewidth can be further compressed to sub-400 fs with enlarging pumping power. The graphene mode-locked EDFL effectively compresses its soliton pulsewidth to 343 fs with the widest spectral linewidth of 7.86 nm among all cases. All the obtained EDFL pulses exhibit similar compression ratio of ∼0.55.
Semantic Image Inpainting Vsing Self-Learning Encoder-Decoder and Adversarial Loss

Images are exposed to deterioration over years due to many factors. These factors may include, but not limited to, environmental factors, chemical processing, improper storage, etc. Image inpainting has gained significant attention from researchers to recover the deteriorated parts in images. In this paper, two new techniques for image inpainting techniques using Deep Convolution Neural Networks (CNN) are proposed. In the first technique, a self-tuned Encoder-Decoder architecture based on a Fully Convolution Network (FCN) is used to generate different sized blocks from non-deteriorated image dataset with L2 being used as a loss measure. On the other hand, the second technique is a two-step technique inspired from Context Encoders. In the first step, Context Encoders are trained on non-deteriorated image dataset to select blocks from training images with minimum L2 loss. In the second step, the selected block is applied to Generative Adversarial Networks (GAN) in order to improve the quality of the recovered image. Several simulation examples were made to proof that the performance self-tuned Encoder-Decoder and GAN is the same. Simulations have also shown that the proposed methods have superior performance in recovering missing regions in deteriorated images over other state-of-art techniques. Paris Street View dataset was used for training and validation to validate our results.
Detecting Spam Tweets using Character N-gram Features

Twitter popularity made it an important and instantaneous source of news and trending events around the world. It has attracted the attention of spammers who post malicious content embedded in tweets and in their profile pages. Spammers use different and evolving techniques to evade traditional security mechanisms, and that creates the need to develop robust solutions that adapt with these techniques. In this paper, we propose using a low-level character n-grams feature that avoids the use of tokenizers or any language dependent tools. Using a publicly available dataset, we evaluate the performance of multiple ma-chine learning classifiers with different representations of the proposed feature. Our experiments show that our approach is an enhancement over the approaches that use word n-grams from tweet tokens. We also show that our technique can detect spam tweets with low latency which is crucial in a real-time environment like twitter.
Evaluation of Proinflammatory Cytokines in Obese vs Non-obese Patients with Metabolic Syndrome

Background: Obesity is one of the most common yet neglected public health problems in both the developed and developing countries. Metabolic syndrome (MS) is a multiplex of risk factor for the development of type 2 diabetes (T2D) and cardiovascular disease (CVD) and it reflects the clustering of multiple risk factors resulting from obesity and insulin resistance. Despite its predominance in obese individuals, MS does occur in non-obese individuals. Many individuals characterised as normal weight as per their body mass index (BMI), have increased visceral adiposity thereby leading to an unfavourable inflammatory cytokine profile. There are limited studies from India with respect to inflammatory cytokines in obesity and MS in general and non-obese patients with MS in particular. Materials and Methods: An observational cross-sectional study was carried out in patients with MS with or without obesity. Anthropometric parameters such as height, weight and waist girth were measured and BMI was calculated. Serum levels of TNF-α, IL-6 and adiponectin were measured by using the enzyme-linked immunosorbent assay. Results: A significant proportion of individuals categorised as normal weight had an increased waist circumference which correlated with BMI, acanthosis nigricans (AN) and fatty liver. There was no statistically significant difference in the cytokine levels in obese and non-obese patients with MS; similarly among non-obese patients with MS, cytokine levels were comparable in patients with or without abdominal obesity. However, triglycerides inversely correlated with adiponectin levels and there was no significant correlation between the cytokines and other parameters of MS. Conclusion: There was no significant difference in various metabolic and inflammatory parameters between obese and non-obese patients with MS. Even in non-obese group, there were no differences in metabolic and inflammatory markers between individuals with or without abdominal obesity. This finding indicates that apart from adipose tissue, other factors are also responsible for the development of MS and its associated proinflammatory profile. There could be a significant contribution of genetic and epigenetic factors which needs to be further explored.
A single-phase two-level rectifier modeling method based on improved Hefner model

The traditional rectifier modeling method does not take into consideration the transient working process of the insulated gate bipolar transistor (IGBT). This paper first proposes a single-phase two-level rectifier modeling method based on improved Hefner model. On the premise of maintaining accuracy, complexity of the Hefner model and the tedious of the iterative process of the system equation are improved to make it more suitable for rectifier topology. Secondly, the improved Hefner model is applied to single-phase two-level rectifier to complete the overall model. Finally, Matlab/simulink is used as a platform to verify the correctness of the improved model and the rectifier model. The proposed model in this paper can reflect the complete working process of IGBT, which is more in line with the actual working conditions.
Unified Cross-Platform Profiling of Parallel C++ Applications

To address the great variety of available parallel hardware architectures (CPUs, GPUs, etc.), high-performance applications increasingly demand cross-platform portability. While unified programming models like OpenCL or SYCL provide the ultimate portability of code, the profiling of applications in the development process is still done by using different platform-specific tools of the corresponding hardware vendors. We design and implement a unified, cross-platform profiling interface by extending the PACXX framework for unified programming in C++. With our profiling interface, a single tool is used to profile parallel C++ applications across different target platforms. We illustrate and evaluate our uniform profiler using an example application of matrix multiplication for CPU and GPU architectures.