Miracle-dz/UniFAR-FTUs huggingface dataset:
Quality of Service Enhancement for Multimedia Applications Using Scalable Video Coding

Streaming of media data is the most rapid-growth network service. On the other end there are vast numbers of application users to access the service. Supporting end-to-end Quality of Service (QoS) is the real-time issue for the application administrators. For an application, safeguarding the QoS in streaming the video records from source to destination in a network is must. Representing client needs and reaching their desire for video service through application is essential as the clients expect greater administration quality. Henceforth the applications ought to be constantly reachable and responded at a quicker rate. This paper presents an approach for enhancing Quality of Service (QoS) over the network for streaming the media data in two phases: Firstly, Considering the type of resolution of the device to which the application user is connected. Secondly, the transmission capacity the client is associated. To preserve the Quality of Experience (QoE), the proposed solution aims at streaming only the file format the client device supports, taking into account the type of resolution the client device is having (this prevents the client to manually select the different file formats eg: Youtube). To achieve the streaming of different file formats Scalable Video Coding (SVC) technique is used which increases the QoS by preventing the packet loss and delay in the service. SVC is an expansion of (MPEG-4 AVC) video compression standard for video encoding. It is a standard that packs the video information which has the subsets (different formats) of video, this comprises of video designs (of various pixels). These are piled up in a single document and can be placed in the single server. Here we are decreasing the server overhead (different formats on different servers) by placing it on a single server and delivering the requested video recordings to the client at a faster rate. The experimental results shows that the application enables multiple devices to access the service at faster rate and accomplish with high quality video service without giving up the execution.
Sum-Rate Maximization of NOMA Systems Under Imperfect Successive Interference Cancellation

This letter addresses the sum-rate maximization for a downlink non-orthogonal multiple access (NOMA) system in the presence of imperfect successive interference cancellation (SIC). We assume that the NOMA users adopt improper Gaussian signaling (IGS), and hence derive new expressions of their rates under residual interference from imperfect SIC. We optimize the circularity coefficient of the IGS-based NOMA system to maximize its sum-rate subject to quality-of-service requirements. Compared to the NOMA with proper Gaussian signaling, simulation results show that the IGS-based NOMA system demonstrates considerable sum-rate performance gain under imperfect SIC.
An Overview of Vehicular Communications

The transport sector is commonly subordinate to several issues, such as traffic congestion and accidents. Despite this, in recent years, it is also evolving with regard to cooperation between vehicles. The fundamental objective of this trend is to increase road safety, attempting to anticipate the circumstances of potential danger. Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I) and Vehicle-to-Everything (V2X) technologies strive to give communication models that can be employed by vehicles in different application contexts. The resulting infrastructure is an ad-hoc mesh network whose nodes are not only vehicles but also all mobile devices equipped with wireless modules. The interaction between the multiple connected entities consists of information exchange through the adoption of suitable communication protocols. The main aim of the review carried out in this paper is to examine and assess the most relevant systems, applications, and communication protocols that will distinguish the future road infrastructures used by vehicles. The results of the investigation reveal the real benefits that technological cooperation can involve in road safety.
Investigation of an Electrically Excited Vernier Machine with a Concentrated Winding intended for Traction Applications

Low voltage traction applications demand high torques at low speeds and at a small design space. In order to achieve high torque densities, manufacturers often combine high-speed machines with mechanical gearboxes. In the past decade, flux modulation machines came up, offering low speeds and high torque densities by utilizing the magnetic gearing effect. Actually, the already investigated machines of this type are mostly permanent magnet or hybrid machines. However, the price and the field weakening capability rise the interest in electrically excited machines. Therefore, this paper explores the concept of an electrically excited Vernier machine and compares the benefits and drawbacks to an electrically excited synchronous machine. Furthermore, the investigated concept is compared to equivalent PM machine concepts.
Design of Circular Monopole Antenna with Enhance Bandwidth for GPS, Bluetooth, LTE, WLAN, Radiolocation and X-Band Application

A printed monopole circular patch triple band Multiresonating antenna is presented for GPS, LTE, BLUETOOTH, WLAN, RADIOLOCATION and X BAND application. It contains circular patch for WLAN, RADIOLOCATOIN AND X BAND application covering broad band from 5GHz to 12GHz. The first inverted L-strip on the top of circular patch is resonate from 2.36GHz to 2.78GHz for LTE and BLUETOOTH application and second inverted L strip at left side of circular patch add one narrow band of GPS application which at 1.5GHz.
A Neural Language Model with a Modified Attention Mechanism for Software Code

The language model, which has its roots in statistical natural language processing, has been shown to successfully capture the predictable regularities of source code, and help with many software tasks, such as code suggestion, code porting, and bug detection. However, learning long-range dependencies is still a big challenge at present for modeling programming language. In this paper, we explore deep learning techniques to strengthen it, with a novel neural language model for software code (NLM4Code) proposed. This model is mainly constructed by a recurrent neural network and augmented with a modified key-value-predict attention mechanism, which can effectively facilitate learning context dependencies of source code in a simpler way. To evaluate the effectiveness, five language models are selected as the baselines on a dataset of 2.03M lines of Java code. The experimental results show NLM4Code is much better than traditional n-gram models and outperforms a state-of-the-art method by 3.12% in perplexity index.
Rumor Detection on Twitter with Hierarchical Attention Neural Networks

Social media has become an integral part of peoples agenda life, however social media also plays a vital role in communication and information dissemination, making it an ideal platform for spreading rumors. Automatically debunking rumors at their stage of diffusion is known as early rumor detection. However, automatic rumor detection is technically very difficult and uncertain. In this work, we try to build a model to learn extracted features from tweets content by concentrating their posts and generate more meaningful representations to identify different type of rumors. This paper presents a deep hierarchical attention model on the basis of recurrent neural networks (RNN) for rumor representation learning and classification. Extensive experiments on real datasets which collected from social media web sites (mainly on twitter) demonstrate that the deep hierarchical attention based RNN model outperforms state-of-the-arts that rely on hand-crafted features.
Effect of Non-Symmetric Loss on Quantum Reading Using a Quasi-Bell State

Quantum reading is one of application protocols of entanglement. This protocol uses entanglement for reading of a digital memory such as a CD or DVD. O. Hirota showed that in a noiseless environment, quantum reading using a quasi-Bell state that has maximum entanglement is error free. Moreover, K. Kato and O. Hirota considered equal losses for amplitudes of modes A and B of this state in determining energy-loss effects. However, in real settings, the loss associated with the amplitude of mode B is greater than that of mode A. Therefore, we consider non-symmetric loss and its effect on quantum reading using a quasi-Bell state. In this paper, we show the analytical solution of the error probability in this case, and investigate the amplitude of mode A that minimizes the error probability when the amplitude of mode B is fixed. Moreover, we consider the robustness against alignment mismatch.
Super Resolution Channel Estimation with Spread Spectrum Signal and Atomic Norm Minimization

One of the important tasks in wireless communications is propagation delay estimation. It has been found that the channel estimation accuracy of compressed sensing is superior to the existing method based on signal parameter estimation through MUltiple SIgnal Classification (MUSIC) and Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT). However, there is a problem in the method that multipath delays may not be resolved if they span between the grids. This problem leads to inaccurate channel estimation. In this paper, we describe the Atomic Norm Minimization (ANM) that enables super resolution channel estimation. In additions, we propose ANM by using Spread Spectrum (SS) signal. In this study, we show new experiments using Gold code for Global Positioning System (GPS). We have also added a new experiment to check the performance of the ANM with SS signal.
Ultra High Resolution Jitter Measurement Method for Ethernet Based Networks

This document presents a new approach to network jitter measurement and analysis in asynchronous data networks such as Ethernet. The developed monitoring device is capable to analyze an incoming stream speed of 1 Gb/s with the resolution up to 8 ns. The system architecture supports speeds up to 100 Gb/s networks. The presented architecture can provide several statistical functions such as measuring a network jitter by Interarrival Histograms method providing the mean value and peak-to-peak value as well. The architecture was implemented and tested on Xilinx Kintex UltraScale FPGA chip using Avnet AES-KU040-DB-G development board.
Multi-Object Tracking with Multiple Birth, Death, and Spawn Scenarios Using A Randomized Hypothesis Generation Technique (R-FISST)

In multi-object tracking one may encounter situations were at any time step the number of possible hypotheses is too large to generate exhaustively. These situations generally occur when there are multiple ambiguous measurement returns that can be associated to many objects. This paper contains a newly developed approach that keeps the aforementioned situations computationally tractable. Utilizing a hypothesis level derivation of the Finite Set Statistics (FISST) Bayesian recursions for multi-object tracking we are able to propose a randomized method called randomized FISST (R-FISST). Like our previous methods, this approach utilizes Markov Chain Monte Carlo (MCMC) methods to sample highly probable hypotheses, however, the newly developed (R-FISST) can account for hypotheses containing multiple births and death within the MCMC sampling. This alleviates the burden of having to exhaustively enumerate all birth and death hypotheses and makes the method more equipped to handle spawn scenarios. We test our method on Space Situational Awareness (SSA) scenarios with spawn events.
EMG dataset augmentation approaches for improving the multi-DOF wrist movement regression accuracy and robustness

Benefiting from the powerful learning capacity of deep learning (DP), a general model for predicting 3-DOF wrist movements could achieve good prediction accuracy for those subjects even not involved in the training. This paper tends to verify this assumption. Since the quantity of training dataset in DP largely influences the model's performance, a limited dataset collected from a number of subjects should be extended first through some data-augmentation approaches. In this paper, we first summarized the possible mistakes happen in the EMG data-collection procedures. Then, according to these mistakes, we designed six data-augmentation approaches to expend our EMG dataset. Through experiments, we found the prediction accuracy can be improved when some approaches are introduced during training. As well, the model robustness could also be improved when the same mistakes occur in the predicting process. With regards to those approaches, placing all electrodes in opposite direction and random switching two channels have significant positive effect on both accuracy and robustness. These two data-augmentation approaches are highly advocated in the pre-processing of the training data for DP-based prediction models.
Distributed Area Partitioning for Multi-UAVs in Cooperative Monitoring Task

In resource and task allocation problems, reasonable allocation can make full use of the UAVs(Unmanned Aerial Vehicles) and bring massive improvement in efficiency. Specifically, the region distribution policies are studied in this paper. In order to make the workload match the ability of the UAVs, the environment is expected to be divided into several subregions of certain sizes. And the group of UAVs should always keep monitoring the whole region no matter how many UAVs are left, which means that the distribution of the area is supposed to react timely even when some UAVs quit the task. Additionally, for the convenience of single UAV's monitoring, the region of one partition is expected not to be too scattered. Therefore, both the area and the shape of the partition are considered in the partition strategies. In this paper, a power diagram based area partitioning method is proposed to rationalize both the allocation of the areas and the shape of each partition. By adjusting the weights of the partitions and the positions of the generators iteratively, the areas and shapes of the partitions can reach an expected and stable state finally. Simulation results are provided to verify the effectiveness and feasibility of the proposed method.
Pseudo-Locomotion Design with a Quadrotor-Assisted Biped Robot

Real-world mechanical characters play an important role in our daily lives and provide rich experiences. This study presents a novel approach to develop a biped robot that has the ability to display walking appearance with dynamic mobility, regardless of its environment. We utilize a quadrotor as an overhead tether to provide mobility and stability. Using the thrust of the rotors, we developed a biped robot for entertainment purpose. It is able to move agilely using novel foot trajectory generation. This method focuses on the velocity of the robot's foot that is in contact with the ground. The control policy is designed to generate in the robot's legs such that it reduces the velocity of the foot in contact with the ground to zero. The policy used for the foot trajectory generation is trained by deep reinforcement learning with a physics-based simulator.
Statistical API Completion Based on Code Relevance Mining

While Application Programming Interface (API) enables an easy and flexible software development process, selecting a best-fit API is often non-straightforward in practice due to misunderstanding on the API specification or a complex programming context etc. Consequently, the API selection has always been time-consuming and error-prone. In recent years, API recommendation systems have been introduced to help developers choose an API automatically, e.g., Eclipse and IntelliJ can generate an internal or user-defined API on the fly. Other research leveraged language models to capture the regularity in API usage and further guide the completion of APIs. While existing approaches provided a general support for API usage, they suffer from the lack of semantic awareness (e.g., Eclipse) and code relevance (e.g., language model based methods). To overcome these limitations, we proposed Crmac in this paper. The key insight of Crmac is a combination of a cache language model which learns code regularity from both open-source projects and local projects, as well as a relevance mining engine that identifies similar code to enable a weighted language model training. In our empirical evaluation, Crmac overwhelmed n-gram approaches, with an improvement of 5.28% in terms of top 10 accuracy. Moreover, over 79% APIs were correctly predicted in the top 10 guesses of Crmac.
Explainable Structuring and Discovery of Relevant Cases for Exploration of High-Dimensional Data

Data described by numerous features create a challenge for domain experts as it is difficult to manipulate, explore and visualize them. With the increased number of features, a phenomenon called "curse of dimensionality" arises: sparsity increases and distance metrics are less relevant as most elements of the dataset become equidistant. The result is a loss of efficiency for traditional machine learning algorithms. Moreover, many state-of-the-art approaches act as black-boxes from a user point of view and are unable to provide explanations for their results. We propose an instance-based method to structure datasets around important elements called exemplars . The similarity measure used by our approach is less sensitive to high-dimensional spaces, and provides both explainable and interpretable results: important properties for decision-making tools such as recommender systems. The described algorithm relies on exemplar theory to provide a data exploration tool suited to the reasoning used by experts of various fields. We apply our method to synthetic as well as real-world datasets and compare the results to recommendations made using a nearest neighbor approach.
SYSTEMIC MESENCHYMAL STEM CELL TRANSPLANTATION FOR THE TREATMENT OF SKELETAL MUSCLE TRAUMA

Objectives: Skeletal muscle trauma leads to severe functional deficits. Present therapeutic treatments are unsatisfying and insufficient posttraumatic regeneration is a problem in trauma and orthopaedic surgery. Mesenchymal stem cell (MSC) therapy is a promising tool in the regeneration of muscle function after severe trauma. Our group showed increased contraction forces compared to a non-treated control group 3 weeks after MSC transplantation (TX) into a skeletal muscle trauma. In addition we demonstrated a dose-response relationship of the amount of MSC and force enhancement. We furthermore investigated the fate of the transplanted MSC labelled with very small iron oxide particles using 7 Tesla-MRI. Histological analysis revealed fusion events between existing myofibers but only to a low amount. The increase of muscle force can not be explained by these events only. Before further steps are taken the impact of paracrine effects and the homing to the site of trauma of the MSC has to be evaluated. Experimental studies about the functional regeneration of traumatized skeletal muscule after systemic MSC-TX do not exist. Methods: 36 female SD-rats received open crush trauma of the left soleus muscle. One week after trauma 2.5 x 106 autologous MSC, harvested from tibial biopsies, were transplanted intraarterially (i.a., femoral arte-ria, group 1) or intravenously (i.v., tail vein, group 2) (n=18). Control animals received saline (i.a.: group 3; i.v.: group 4) (n=18). Histological analysis and biomechanical evaluation by in vivo muscle force measurement was performed 3 weeks after TX. Results: Twitch stimulation of the healthy right soleus muscles resulted in a contraction force of 0.52±0.14 N. Forces of tetanic contraction in the uninjured muscles reached 0.98±0.27 N. The i.a. MSC-TX improved the muscle force of the injured soleus significantly compared to control (twitch: 82,4%, p=0.02, tetany: 61.6%, p=0.02). Contraction forces of muscles treated i.v. (MSC vs. saline) showed no significant difference. The histological analysis showed no differences in the amount of fibrotic tissue. Conclusions: The presented study demonstrates the effect of systemic MSC-TX in the treatment of severe skeletal muscle injuries. Interestingly, the functional regeneration could only be increased by i.a. application. The entrapment of MSC in the lungs and the dilution effect in the circulation, when injecting the MSC i.v. could be the reason. For possible future therapeutic approaches a systemic application is considered to be favourable compared to local injections due to the better distribution of the cells in the target muscle.
Three-Dimensional Mapping of High-Level Music Features for Music Browsing

The increased availability of musical content comes with the need of novel paradigms for recommendation, browsing and retrieval from large music libraries. Most music players and streaming services propose a paradigm based on content listing of meta-data information, which provides little insight on the music content. In services with huge catalogs of songs, a more informative paradigm is needed. In this work we propose a framework for music browsing based on the navigation into a three-dimensional (3-D) space, where musical items are placed as a 3-D mapping of their high-level semantic descriptors. We conducted a survey to guide the design of the framework and the implementation choices. We rely on state-of-the-art techniques from Music Information Retrieval to automatically extract the high-level descriptors from a low-level representation of the musical signal. The framework is validated by means of a subjective evaluation from 33 users, who give positive feedbacks and highlight promising future developments especially in virtual reality field.
Exploring Everyday Sharing Practices of Smart Speakers

Smart devices like mobile phones, tablets, and smart watches are designed under the assumption that they will be used by a single user. In contrast, many other devices such as smart thermostats and smart speakers are inherently sharable. This paper presents prelimi-nary results (based on a data set of 15 participants) from an ongoing multi-methods study (using diary study and semi-structured inter-views) that aims to gain a nuanced understanding of motivators and constraints of sharing such smart devices, which are cumulatively referred to as Internet of Things. Specifically, this paper illuminates the purposes and practices of sharing smart speakers and discusses two influential factors that shape these practices. Finally, we discuss the implications of our findings and provide guidelines for the design of future smart conversational speakers.
Exploring User-Controlled Hybrid Recommendation in a Conference Context

Ahybrid recommender system fuses multiple data sources to deliver recommendations. One challenge of this approach is to match the changing user preferences with a list of static recommendations. In this paper, we present two user-controllable hybrid recommender interfaces, Relevance Tuner (for people recommendation) and Paper Tuner (for paper recommendation), which offer a set of sliders to tune the multiple relevance sources on the final recommendation ranking on-the-fly. We deployed the user interfaces to a real-world international academic conference with a field study. The result of the log analysis showed the conference attendees did adopt the interface in exploring the hybrid recommendations. The finding provided evidence in supporting the proposed controllable interface can be deployed to a broader set of conference context.