Miracle-dz/UniFAR-FTUs huggingface dataset:
Architecture and Use Case for an IoT Deployment with SDN at the Edge and Dual Physical and Virtual Gateway

The flexibility and programmability offered by Software Defined Networking (SDN) emerge as an approach to resolve some of the main challenges that the Internet of Things (IoT) deployments expose. In the present article, we explore the application of SDN components onto IoT deployments, together with the decoupling of typical gateway functions in two realms; the physical one and the virtual at the edge. This disengagement is a novel approach necessary since the computation resources on the physical part of the infrastructure are limited. Thus, the more resource-consuming processes are transferred to the edge of the network, in the virtual counterpart of the gateway. Additionally, within this infrastructure, different network applications can be running at the top of the SDN controller, in the control plane, for example, QoS policies application, management of network parameters, etc. Providing a holistic view of the IoT deployment. Between its advantages, this work focus on the ease of creation of an IoT infrastructure with the gateway and SDN architecture, and the behaviour of the IoT system. For validation, a small setup scenario and a use case is created where heterogeneous IoT sensors communicate with the physical part of the gateway, implemented with a Raspberry Pis and this, with its virtual part at the edge of the network, so finally sent the data to an IoT platform (FIWARE-ORION) at the core network managed by a virtual Switch (OpenVSwitch) and a RYU-based SDN controller.
Music Matters: An empirical study on the effects of adaptive music on experienced and perceived player affect

Music is an important affective aspect of video games. We present the findings of an empirical study on the affective effects of adaptive uses of music in games. We find that adaptive music can significantly increase a players reported experienced feeling of tension, that players recognize and value music, and that player recognize and value adaptive music over linear music.
Small Swarm Search Robot System with Rigid-Bone Parachute Rapidly Deployable from Aerial Vehicles

In this research, the authors aim to construct a system of the small swarm search robot dropped from aerial vehicles to realize the efficient wide area exploration for rapid disaster response. The authors propose a jumping mechanism using an internally balanced magnet unit and a rigid bone parachute mechanism that can prevent entanglement and move effectively on rough terrains such as rubbles or narrow spaces. For this purpose, the platform of the robot requires small dimensions and mass to transport by aerial vehicles and enter confined spaces. However, if the diameter of the wheel is reduced, the height of the step that can be overcome decreases, and the mobility performance on a rough train would also decrease. For that reason, the authors propose a mechanism that can jump over the debris and a parachute mechanism that does not get caught by the debris, the environment, and itself. In this paper, the authors first show the basic concept and requirements of the swarm search robot. Next, the basic design of the mechanical structure and electrical system are shown, and the prototype of the two-wheeled robot is fabricated as a platform. Finally, experiments using the prototypes are conducted to verify the principle of the jumping mechanism and parachute mechanism and to discuss their effectiveness.
Learning Patent Speak: Investigating Domain-Specific Word Embeddings

A patent examiner needs domain-specific knowledge to classify a patent application according to its field of invention. Standardized classification schemes help to compare a patent application to previously granted patents and thereby check its novelty. Due to the large volume of patents, automatic patent classification would be highly beneficial to patent offices and other stakeholders in the patent domain. However, a challenge for the automation of this costly manual task is the patent-specific language use. To facilitate this task, we present domain-specific pre-trained word embeddings for the patent domain. We trained our model on a very large dataset of more than 5 million patents to learn the language use in this domain. We evaluated the quality of the resulting embeddings in the context of patent classification. To this end, we propose a deep learning approach based on gated recurrent units for automatic patent classification built on the trained word embeddings. Experiments on a standardized evaluation dataset show that our approach increases average precision for patent classification by 17 percent compared to state-of-the-art approaches.
A Hierarchical Approach for MARLÖ Challenge

Recently reinforcement learning has been showing remarkable performance in playing games. However, the majority of conventional approaches merely solve games with a single task. It is not yet well studied whether reinforcement learning is effective in games like Minecraft, where players are required to finish multiple different tasks while cooperating with other collaborators. In such games, AIs are confronted with dual challenges – finishing multiple tasks and building a multi-agent system. We propose a hierarchical approach with reinforcement learning policies to address the challenges. Experiments show that our approach performs well when dealing with multiple tasks and multiple agents simultaneously. Our approach got the second runner-up in MARLO Challenge, demonstrating its potential in tackling the challenges.
Real Time Attention Based Bidirectional Long Short-Term Memory Networks for Air Pollution Forecasting

Approximately 95% of the world's population live in places with unsafe air which leads to serious health hazards for people worldwide. Monitoring and preserving air quality has become one of the most essential activities in many industrial and urban areas today. In view of increasingly alarming environ-mental pollution problems due to rapid urbanization, ambient air pollution prediction is becoming extremely important. This paper presents an online real time air pollution prediction system at five prominent locations in Delhi utilizing the past historic air quality and meteorological data. We propose a novel end to end sequential modelling framework to predict the air quality by estimating the concentration levels for various pollutants (nitrogen dioxide (NO_2), particulate matter (PM_2.5 and PM_10) and thereby classifying the threat level for them in the next 24 hours. The problem of air pollution prediction system becomes extremely challenging in the presence of unreliable and missing data entries. We also quantify the variations in the pollution concentrations on seasonal basis. We exhaustively evaluate and demonstrate that the proposed approach outperforms several baseline methods for forecasting air pollutants concentration based on data sources obtained in Delhi.
Software-Defined Network Application-Aware Controller for Internet-of-Things

In the following paper, we propose implementation of Application-Aware Controller (AAC) for Software-Defined Network (SDN) that integrates Internet-of-Things-devices (IoT-devices). The main goal of the investigation is to establish communication mechanisms for SDN and its main components and simplify overall complexity of the system by moving part of the logic into AAC-component. The presented approach leverages capability of SDN-controller to work on the application level and to process data according to the application logic. Generally speaking, AAC acts like a transparent network node that contains program logic. We have considered several scenarios for IoT-network and have devised solutions for communication organization in the cases that are based on usage of flow tables and packets generation. The proposed communication scheme has been evaluated using well-known tools for SDN-investigation and proved feasibility of the scheme.
Software-Defined Wide Area Network (SD-WAN): Architecture, Advances and Opportunities

Emerging applications and operational scenarios raise strict requirements for long-distance data transmission, driving network operators to design wide area networks from a new perspective. Software-defined wide area network, i.e., SD-WAN, has been regarded as the promising architecture of next-generation wide area network. To demystify software-defined wide area network, we revisit the status and challenges of legacy wide area network. We briefly introduce the architecture of software-defined wide area network. In the order from bottom to top, we survey the representative advances in each layer of software-defined wide area network. As SD-WAN based multi-objective networking has been widely discussed to provide high-quality and complicated services, we explore the opportunities and challenges brought by new techniques and network protocols.
EMPTCP: An ECN Based Approach to Detect Shared Bottleneck in MPTCP

The major challenge of Real Time Protocol is to balance efficiency and fairness over limited bandwidth. MPTCP has proved to be effective for multimedia and real time networks. Ideally, an MPTCP sender should couple the subflows sharing the bottleneck link to provide TCP friendliness. However, existing shared bottleneck detection scheme either utilize end-to-end delay without consideration of multiple bottleneck scenario, or identify subflows on switch at the expense of operation overhead. In this paper, we propose a lightweight yet accurate approach, EMPTCP, to detect shared bottleneck. EMPTCP uses the widely deployed ECN scheme to capture the real congestion state of shared bottleneck, while at the same time can be transparently utilized by various enhanced MPTCP protocols. Through theory analysis, simulation test and real network experiment, we show that EMPTCP achieves higher than 90% accuracy in shared bottleneck detection, thus improving the network efficiency and fairness.
Evolution Analysis of Iterative BICM Receivers with Expectation Propagation over ISI Channels

This paper investigates the dynamic behaviour of doubly iterative bit-interleaved coded modulation (BICM) receivers based on expectation propagation (EP). When implemented in the frequency domain, for single-carrier (SC) systems, such receivers achieve attractive performance-complexity trade-offs in quasi-static wideband channels. With this category of receivers, conventional binary extrinsic information transfer (EXIT) functions are subject to a great number of parameters, including channel realizations, constellation and inner iteration parameters. Hence, this paper proposes a novel extrinsic information evolution analysis method which simplifies the receiver’s EXIT function into independent inner transfer functions. The core idea is to track state-evolution dynamics of EP through numerically stable extrinsic variance/information transfer (EXVIT) functions. Numerical results attest to the accuracy of this method for tracking the asymptotic receiver behaviour.
Towards theoretically-founded learning-based denoising

Denoising a stationary process (Xi)i∈ℤ corrupted by additive white Gaussian noise (Zi)i∈ℤ, i.e., recovering Xn from Yn = Xn + Zn, is a classic and fundamental problem in information theory and statistical signal processing. Theoretically-founded and computationally-efficient denoising algorithms which are applicable to general sources are yet to be found. In a Bayesian setup, given the distribution of Xn, a minimum mean square error (MMSE) denoiser computes E[Xn|Yn]. However, for general sources, computing E[Xn|Yn] is computationally very challenging, if not infeasible. In this paper, starting from a Bayesian setup, a novel denoiser, namely, quantized maximum a posteriori (Q-MAP) denoiser, is proposed and its asymptotic performance is analyzed. Both for memoryless sources, and for structured first-order Markov sources, it is shown that, asymptotically, as σ2 (noise variance) converges to zero, $\frac{1}{{{\sigma ^2}}}{\text{E}}\left[ {{{\left( {{X_i} - \hat X_i^{{\text{Q - MAP}}}} \right)}^2}} \right]$ converges to the information dimension of the source. For the studied memoryless sources, this limit is known to be optimal. A key advantage of the Q-MAP denoiser is that, unlike a MMSE denoiser, it highlights the key properties of the source distribution that are to be used in its denoising. This naturally leads to a learning-based denoising algorithm. Using ImageNet database for training, initial simulation results exploring the performance of such a learning-based denoiser in image denoising are presented.
Simultaneous Localization and Mapping for Exploration with Stochastic Cloning EKF

While exploring unknown territory on search and rescue missions, fusing multiple sensors is vital for the precise on-line localization of mobile robots. The Extended Kalman filter (EKF) with stochastic cloning is well suited for this purpose and allows to directly integrate multiple absolute and relative state measurements. The latter measure differences between a past state and the current state, thus introducing correlations. These inter-dependencies are modeled by stochastic cloning, which performs a state augmentation by cloning the respective state estimates connected by a relative measurement. Different approaches to feed back information from absolute updates to the cloned state are presented and compared to a SLAM algorithm based on factor graph optimization.
Towards Procedurally Generated Languages for Non-playable Characters in Video Games

Non-playable characters (NPCs) enhance a player’s immersion in a video game. Communications among NPCs create atmosphere and, in some games, are a core element of the gameplay. Yet, the majority of games manually script inter-NPC communications creating only an illusion of such exchanges. Doing so is laborious and results in fixed interactions which may not respond to the player’s actions or changes in the environment. In this paper we propose procedural content generation (PCG) for emergent inter-NPC languages. Indeed, recent research demonstrated that deep neural networks can be trained to develop an artificial language to communicate with each other. The work used a fixed, handcrafted network architecture identical for both the sending and receiving agents. We extend the work by using neuroevolution for both the sender and the receiver. In doing so we evolve both the architecture and weights of the agents and show that they successfully develop novel languages to communicate among themselves.
Spectrum usage model for smart spectrum

This paper focuses on a spectrum usage model in the time domain in the context of dynamic spectrum access (DSA). To achieve a sophisticated dynamic spectrum access, understanding the spectrum usage is an important task. We focus on duty cycle (DC) as a feature quantity of spectrum usage in the time domain and observed DC (O-DC) obtained from long-term spectrum measurement results is used for the modeling. In fact, O-DC has stochastic and deterministic behaviors and we have been investigated modeling for both behaviors. O-DC is stochastic behavior and a mixture distribution based modeling has been considered for the model of stochastic behavior. We employ nonparametric Bayesian model (NPBM) in which the number of distributions is also an adjustable parameter. Statistics of O-DC, such as mean of O-DC, has a deterministic behavior in time domain. Specifically, the deterministic behavior is determined by the common daily habits, such as mean of O-DC during is night is low, but it is high during daytime. We show the validity of the stochastic and deterministic model for O-DC based on long-term spectrum measurement results.
Asymmetry Between Main Channels of a Multilevel Buck Converter - Operation in Steady State

The goal of this paper is to examine a special case of asymmetrical operation of a four level, five channel resonant Buck converter in discontinuous current conduction mode ($DCM$) among steady state conditions. Having two input ($p$ and $n$) and four output ($p1,p2, n1, n2$) channels, the converter can operate in symmetrical mode when the input and output voltages are both symmetrical. Asymmetry can be caused by the unevenness of any parameters inside the channels or among them. This paper focuses on the output asymmetry appearing between main channels $p$ and $n$, the case of asymmetrical output voltages, loads, and powers are examined. Steady state analysis and control strategy is proposed that are supported by both simulation and experimental results. The proposed control strategy can be used when applying the converter in feeding multilevel inverters.
Being correct eventually almost surely

We study the problem of predicting upper bounds on the next draw of an unknown probability distribution after observing a sample generated by it. The unknown distribution is modeled as belonging to a class ${\mathcal{P}}$ of distributions over natural numbers. The goal is to err only finitely many times even though the game proceeds over an infinite horizon, and though there is no upper bound on what the next sample can be. If a universal prediction scheme exists that makes only finitely many errors regardless of what model in ${\mathcal{P}}$ generated the data, we say ${\mathcal{P}}$ is eventually almost surely (e.a.s.) predictable. In this paper, we fully characterize when ${\mathcal{P}}$ can be e.a. s. -predictable.
Towards Deep Learning-Based Detection Scheme with Raw ECG Signal for Wearable Telehealth Systems

The electrocardiogram (ECG) signal, as one of the most important vital signs, can provide indications of many heart-related diseases. Nonetheless, in the case of telehealth context, the automated analysis and accurate detection of ECG signals remain unsolved issues, because the poor data quality collected by the wearable devices and unprofessional users further increases the complexity of hand-crafted feature extraction, ultimately affecting the efficiency of feature extraction and the detection accuracy. To address this issue and improve the detection accuracy, in this paper we present a novel detection scheme with the raw ECG signal in wearable telehealth system. Our systembenefits from the concept of big data, sensing and pervasive computing and the emerging deep learning technology. In particular, a Deep Heartbeat Classification (DHC) scheme is proposed to analyze the ECG signal for arrhythmia detection. Distinct from existing solutions, the detection model in DHC can be trained directly on the raw ECG signal without hand-crafted feature extraction. A cloud-based prototypical system is also designed and implemented with the functions of data acquisition, wireless transmission, back-end data management, and ECG detection. The experimental results demonstrate that our prototypical system is feasible and effective in real-world practice, and extensive experimentation based on the MIT-BIH database demonstrates that the proposed DHC scheme outperforms baseline schemes.
Improved TFIDF weighting techniques in document Retrieval

In information retrieval, documents are usually retrieved using lexical matching which matches where words in a user's query with words found in a set of documents. A significant model used in information retrieval is the vector space model where these words are represented as a vector in space and are assigned weights using a favorite weighting technique called TFIDF (Term Frequency Inverse Document Frequency). In this thesis, we have devised three new weighting techniques to improve the TFIDF weighting technique. The first technique is Dispersed Words Weight Augmentation (DWWA) which gives more weight to the words distributed in most of the document’s paragraphs; we consider that those words are more significant than words found in few paragraphs. The second technique is called Title Weight Augmentation (TWA) which gives more weight to the words found in the document’s title and first paragraphs. The third technique is called First Ranked Words Weight Augmentation (FRWWA) which increments further the weight of the most frequent words in a document. We tested the three techniques, and we found more relevant documents were retrieved in our system.
Toward Computation Offloading in Edge Computing: A Survey

The explosive growth of massive data generation from Internet of Things in industrial, agricultural and scientific communities has led to a rapid increase for data analytics in cloud data centers. The ubiquitous and pervasive demand for near-data processing urges the edge computing paradigm in recent years. Edge computing is promising for less network backbone bandwidth usage and thus less data center side processing pressure, as well as enhanced service responsiveness and data privacy protection. Computation offloading plays a crucial role in edge computing in terms of network packets transmission and system responsiveness through dynamic task partitioning between cloud data centers and edge servers and edge devices. In this paper a thorough literature review is conducted to reveal the state-of-the-art of computation offloading in edge computing. Various aspects of computation offloading, including energy consumption minimization, Quality of Services guarantee, and Quality of Experiences enhancement are surveyed. Moreover, resource scheduling approaches, gaming and tradeoffing among system performance and overheads for computation offloading decision making are also reviewed.
The Design Of A Clicker Game for Text Labelling

Games for text annotation / labelling are becoming more common, but it’s difficult to find a mechanics that fits. In this work we discuss a clicker game that can support text annotation. We believe this type of game is uniquely suited to addressing some of the challenges faced by games featuring text annotation as a core task.
Towards an Algebraic Network Information Theory: Distributed Lossy Computation of Linear Functions

Consider the important special case of the K-user distributed source coding problem where the decoder only wishes to recover one or more linear combinations of the sources. The work of Körner and Marton demonstrated that, in some cases, the optimal rate region is attained by random linear codes, and strictly improves upon the best-known achievable rate region established via random i.i. d. codes. Recent efforts have sought to develop a framework for characterizing the achievable rate region for nested linear codes via joint typicality encoding and decoding. Here, we make further progress along this direction by proposing an achievable rate region for simultaneous joint typicality decoding of nested linear codes. Our approach generalizes the results of Körner and Marton to computing an arbitrary number of linear combinations and to the lossy computation setting.
Control of Traffic Streams with the Multi-Rate Token Bucket

The method of adaptive formation of flows of network traffic and the method of tuning control systems with indirect feedback with the control parameters and structure of the shaper are presented. Mathematical models of computer networks are analyzed, including models of traffic flow management processes. Recommendations on the choice of parameters of formers and their management system (necessary order of system management, structure of formers, measuring devices of parameters of packages, etc.) are developed depending on the intensity of data flows, their statistical descriptions and network structure. The study of the characteristic properties of the process is carried out by the formation of the flow, which must be taken into account when choosing the parameters and structure of the control system multistage motion shaper with variable parameters of the load windows.