Miracle-dz/UniFAR-FTUs huggingface dataset:
Constructions of Two-Dimensional Binary Z-Complementary Array Pairs

One-dimensional (1-D) Z-complementary pair (ZCP) is an extension of 1-D Golay complementary pair (GCP) where the autocorrelations of constituent sequences are complementary within a zero correlation zone (ZCZ). The concept of ZCZ can be applied to two-dimensional (2-D) Golay complementary array pair to obtain the 2-D Z-complementary array pair (ZCAP). In this paper, constructions of ZCAPs are proposed based on concatenations of 1-D ZCPs or existing 2-D ZCAPs. In addition, the construction of 1-D ZCPs based on Kronecker product is modified and extended to 2-D ZCAPs. One potential application of 2-D ZCAPs is 2-D synchronization.
Characteristics Study of Dance-charts on Rhythm-based Video Games

This paper studies the characteristics of dancecharts on rhythm-based video games while clustering the dancecharts with the track features. In the opinion of expert players, it is considered that the difficulty level of the rhythm-based games depends on multiple features for the song and chart. The difficulty levels for dance games has not well been studied though it has been already done for other genres of games. This paper designed the track features for dance games, which would be varied depending on characteristics of the track. Moreover, we conducted a dance-charts clustering by using the k-means method with the designed features. As a result of the clustering, it was confirmed that the clusters were composed mostly based on the step frequency and the complexity of rhythm. Also, it was found that the scores with the extremely characteristic features were separated from other scores and clustered as one class with themselves. The discussion in this paper indicated a possibility for the objective evaluation of the dance-charts.
Decentralized Optimal Power Flow in Distribution Networks Using Blockchain

The rapid development of distributed energy resources (DER) in the distribution grid calls for novel control and coordination solutions. Optimal management of DER will enable end-users to decrease their electricity costs and provide crucial services to grid operators. In this paper, a decentralized Optimal Power Flow (OPF) model is used to locally coordinate DER in distribution networks, while considering the network constraints, in a distributed, transparent and secure fashion. To achieve that, a consensus-based distributed optimization algorithm is developed using the general form Alternating Direction Method of Multipliers (ADMM). To enable transparent and verifiable management of the network, the paper provides a comprehensive procedure for the implementation of the decentralized OPF on a private blockchain-smart contracts platform. The performance of the proposed framework is tested using real data from a case study in a residential neighborhood in Amsterdam with different varieties of DER. The implementation procedure on a blockchain-smart contracts platform may be adopted in other problems that require a smart contract to act as a virtual aggregator.
Optimal Index Codes for Some Interlinked Cycle Structures with Outer Cycles

For index coding problems with special structure on the side-information graphs called Interlinked Cycle (IC) structures index codes have been proposed in the literature (C. Thapa, L. Ong, and S. Johnson, "Interlinked Cycles for Index Coding: Generalizing Cycles and Cliques", in IEEE Trans. Inf. Theory, vol. 63, no. 6, Jun. 2017 with a correction in "Interlinked Cycles for Index Coding: Generalizing Cycles and Cliques", in arxiv (arxiv:1603.00092v2 [cs.IT] 25 Feb 2018)). In this paper we consider a generalization of IC structures called IC structures with interlocked outer cycles. For IC structures with interlocked outer cycles we show that the optimal length (also known as the minrank of the index coding problem) depends on the maximum number of disjoint outer cycles. We give two sufficient conditions such that if any of these is satisfied then we provide explicit optimal index code construction. The conditions mentioned above are shown to be not necessary by an explicit example.
An IoT System for Monitoring and Data Collection of Residential Water End-Use Consumption

The paper presents a hardware/software platform that integrates IoT technologies for deploying a smart water network in a domestic environment. Low cost sensors and edge nodes have been deployed in a real pilot for monitoring water fixture use. A data collection platform, which can run in the Fog or in Cloud, allows for processing of raw data and monitoring. It makes available raw and intermediate data to offload the computation of complex Big Data analytic. The paper presents preliminary results according to the data available after two months of operation.
Improving TCP Performance and Reducing Self-Induced Congestion with Receive Window Modulation

We present a control module for software edge routers called Receive Window Modulation - RWM. Its main objective is to mitigate what we define as self-induced congestion: the result of traffic emission patterns at the source that cause buffering and packet losses in any of the intermediate routers along the path between the connection's endpoints. The controller modifies the receiver's TCP advertised window to match the computed bandwidth-delay product, based on the connection round-trip time estimation and the bandwidth locally available at the edge router. The implemented controller does not need any endpoint modification, allowing it to be deployed in corporate edge routers, increasing visibility and control capabilities. This scheme, when used in real-world experiments with loss-based congestion control algorithms such as CUBIC, is shown to optimize access link utilization and per-connection goodput, and to reduce latency variability and packet losses.
Design of CPW antenna for network applications

The novel CPW antenna design consists of monopole, which can used for High speed wide area or local area corporate networks and Video & CCTV surveillance networks and Long-range Rural connectivity. This antenna has been designed with design size parameters of 59.5(L) x 30 (W) x 1.6-(H) mm2, the dielectric constant of FR4 substrate is 4.4, and loss tangent of 0.002 is used. The resonant frequency range of this proposed antenna is 5.85 - 6.6 GHz with return loss above of -10dB achieved. The peak gain of this antenna is 5.5dBi and in addition the S, Z-parameters, VSWR, 2D & 3D radiation pattern are also derived. Good results are achieved after simulation.
Data Encoding Methods for Byzantine-Resilient Distributed Optimization

We consider distributed gradient computation, where both data and computation are distributed among m worker machines, t of which can be Byzantine adversaries, and a designated (master) node computes the model/parameter vector for generalized linear models, iteratively, using proximal gradient descent (PGD), of which gradient descent (GD) is a special case. The Byzantine adversaries can (collaboratively) deviate arbitrarily from their gradient computation. To solve this, we propose a method based on data encoding and (real) error correction to combat the adversarial behavior. We can tolerate up to $ \leq \left\lfloor {\frac{{m - 1}}{2}} \right\rfloor $ corrupt worker nodes, which is information-theoretically optimal. Our method does not assume any probability distribution on the data. We develop a sparse encoding scheme which enables computationally efficient data encoding. We demonstrate a trade-off between the number of adversaries tolerated and the resource requirement (storage and computational complexity). As an example, our scheme incurs a constant overhead (storage and computational complexity) over that required by the distributed PGD algorithm, without adversaries, for $t \leq \frac{m}{3}$ . Our encoding works as efficiently in the streaming data setting as it does in the offline setting, in which all the data is available beforehand.
Cost-Minimum Charger Placement for Wireless Power Transfer

As a promising technology to achieve perpetual operation of battery-powered wireless sensor devices, wireless power transfer has attracted much attention recently. In wireless power transfer, the charger enables the energy to be wirelessly transmitted to the rechargeable sensor devices that are hungry for energy. Previous works mainly focus on maximizing the charging utility or minimizing the charging delay. This paper concerns a more practical issue of placing wireless chargers, which aims at minimizing the deployment cost of chargers while satisfying the overall requirement for charging utility. We investigate the above cost-minimum charger placement problem under two typical scenarios in which omni chargers and directional chargers are used, respectively. To resolve this problem under the two charging models, we first prove its NP-hardness and then propose two approximation algorithms with proven performance guarantees. Finally, we conduct extensive simulation experiments to validate our designs, and the experimental results demonstrate that the proposed algorithms significantly outperform the baselines.
Action Spaces in Deep Reinforcement Learning to Mimic Human Input Devices

Enabling agents to generally play video games requires to implement a common action space that mimics human input devices like a gamepad. Such action spaces have to support concurrent discrete and continuous actions. To solve this problem, this work investigates three approaches to examine the application of concurrent discrete and continuous actions in Deep Reinforcement Learning (DRL). One approach implements a threshold to discretize a continuous action, while another one divides a continuous action into multiple discrete actions. The third approach creates a multiagent to combine both action kinds. These approaches are benchmarked by two novel environments. In the first environment (Shooting Birds) the goal of the agent is to accurately shoot birds by controlling a cross-hair. The second environment is a simplification of the game Beastly Rivals On-slaught, where the agent is in charge of its controlled character’s survival. Throughout multiple experiments, the bucket approach is recommended, because it is trained faster than the multiagent and is more stable than the threshold approach. Due to the contributions of this paper, consecutive work can start training agents using visual observations.
Capacity Analysis of MISO Channels with One-Bit Transceiver

In this paper, we analyze the information-theoretical limits of a multiple-input single-output (MISO) channel with one-bit transceiver. In particular, we present a capacity expression in a closed form when perfect channel state information (CSI) is available at both a transmitter and a receiver. One major finding is that the capacity-achieving transmission strategy is to uniformly use four multi-dimensional signal points. The four signal points are chosen as a function of the channel and the signal-to-noise ratio (SNR) from the channel input set constructed by a spatial lattice modulation (SLM) method. From our analysis and simulation results, we also demonstrate that the capacity loss caused by the use of one-bit DACs is sufficiently small throughout the entire SNR regime even with a few-bit CSIT feedback that suffices to achieve the capacity.
Deep Long Short-term Memory (LSTM) Network with Sliding-window Approach in Urban Thermal Analysis

The visualization of urban thermal analysis is a prevalent topic in the establishment of smart cities. With the popularity of mobile devices, large volumes of data about people and their locations are gradually accumulating at mobile base stations. In this study, we used such call details records (CDR) and long short-term memory (LSTM) networks—a kind of recurrent neural network (RNN) —to predict the future traffic of a base station. By implementing gate mechanism, the LSTM can solve the problem of exploding and vanishing gradients of ordinary RNNs. We use a sliding-window approach to transform the problem of time series forecasting into a supervised learning problem. Then, we use the proposed deep LSTM network to model the traffic of base stations, which enables the prediction of future traffic and the generation of the heat map of a city. The method we presented can decrease the root mean square error (RMSE) of the predicted access time down to 23.34 minutes per hour per base station.
Multi-Beam On-Demand Power Allocation MAC Protocol for MIMO Terahertz Communication Networks

Terahertz (THz) communication is envisioned as one of the key technologies to achieve Terabit-per-second (Tbps) wireless data transmission speed. However, the communication distance in THz networks is constrained by the severe path loss in THz frequencies and the limited transmission power, which results in the network deployment issue. In recent years, researchers have shown a growing interest in the utilization of adaptive transmission power as well as multi-connectivity THz communication. In this paper, a multi-beam on-demand power allocation medium access control (MAC) protocol for Multiple-Input Multiple-Output (MIMO) THz communication in distributed wireless networks is presented. The proposed dual-band protocol relies on a multi-beam antenna structure, which utilizes the limited transmission power to achieve multiple concurrent THz connections by applying the proposed distance-aware power allocation scheme and redesigning the frame formats. The simulation results demonstrate the relationship between the number of beams with SNR threshold and average node distance. The results concluded by comparing the total throughput of the proposed multi-beam network with classical single-beam network show that the proposed protocol maximize the THz channel utilization and achievable throughput.
Mining Trajectory Data and Identifying Patterns for Taxi Movement Trips

In past years, trajectory data generated from Automatic Identification System (AIS) networks and taxi GPS devices increased significantly. There is a high demand for analyzing this data and extracting the knowledge from it. Large-scale taxi trajectory data is represented by a sequence of timestamped geographical locations, this sequence starts with the origin point and ends with the destination point. Applying data mining techniques such as clustering on trajectory data can provide useful information about the movement patterns and the behavior of people. Thus, can enhance the transportation management services in terms of urban planning and environment issues. In this paper, we propose a methodology which extracts movement patterns of taxi trips in Porto, Portugal. we cluster taxi trips using Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm, each point in the trip is a pair of coordinates which consists of longitude and latitude values.
Shannon Capacity is Achievable for a Large Class of Interactive Markovian Protocols

We address the problem of simulating a binary interactive protocol over a pair of binary symmetric channels with crossover probability ε. We are interested in the achievable rates of reliable simulation, i.e., in characterizing the smallest possible blowup in communications such that a vanishing error probability in the protocol length can be attained. We analyze the family of Mth-order Markovian protocols in which the transmission at every time depends only on the last M bits of the protocol. For M =1 (first-order Markovian) we prove that all protocols can be simulated at Shannon’s capacity. For M > 1 we characterize large classes of protocols that can be simulated at Shannon’s capacity.
Modular Architecture with Power Optimizers for Photovoltaic Systems

Solutions that increase efficiency in renewable energy are important for environmental sustainability. In this way, dealing with photovoltaic generation a new architecture constituted by power optimizer for photovoltaic systems emerges. Power optimizer is a DC/DC converter, allocated to each one or two photovoltaic modules, that tries to reduce losses in photovoltaic systems, increasing the photovoltaic power generation. Thus, this paper presents the architecture used in a photovoltaic system with power optimizer and a performance simulation for application in a residence and a photovoltaic plant. Thus, it is possible to measure the gain provided by power optimizer in simulated systems. As a result, the application of the photovoltaic system in residence provided better performance than the application in the photovoltaic plant, due to the greater frequency of shading in the photovoltaic modules. Therefore, power optimizer for photovoltaic systems is a solution that can be considered, mainly with the reduction of the cost of power electronics in the future.
Generating Knowledge Maps for Songs and Users in Music Market with Probabilistic Topic Model

With the explosive growth of the quantity of digital music and music service users, a huge number of data has been yielding in the music market. However, it becomes more challenging for the public to orientate a song as well as find its relationship with others in the ocean of music. Besides, since users are the target of the music market, it is also important to show their relationships within and their connections with songs. Showing songs and users by knowledge maps based on data including both music and user sides will be useful for analyzing songs, users as well as the whole music market. To achieve this goal, we extract effective representations of songs and users by applying a probabilistic topic model to the real-world user-song-listening records and generate knowledge maps for songs and users. Moreover, we propose a way of matching two maps together to show the connection between both. Analyses show that this data visualization method is of practical use and value in the music market.
Message Transmission over Classical Quantum Channels with a Jammer with Side Information, Correlation as Resource and Common Randomness Generating

In this paper we analyze the capacity of a special model for arbitrarily varying classical-quantum channels when the sender and the receiver use a weak resource. In this model a jammer has side information about the channel input. We determine the correlation assisted capacity. As an application, we determine the correlation assisted common randomness capacity with informed jammer. We also analyze these both capacities when only a small amount of correlation is available.
Aggregated Residual Dilation-Based Feature Pyramid Network for Object Detection

In order to tackle the issue of multi-scale object detection, recent detectors usually adopt hierarchical feature pyramids which are generated by naive combinations of top-down features and lateral features. Considering the limited effective receptive fields of the methods for top-down features augmentation, the generated regions are only associated with the fixed areas of the coarser features. Meanwhile, noisy features are introduced by irrelevant regions inevitably since the finer features in relation to rigid coarser regions. Thus, the pyramidal features with strong semantics are difficult to be obtained via simply enlarging the top-down features. In this paper, we present the Aggregated Residual Dilation based Feature Pyramid Network (ARDFPN) to exploit the inherent correlation of regions in feature pyramid. The network is designed by stacking a building block that aggregates a set of dilated convolutions with the same topology. We show that carefully adding additional transformation stages into feature pyramid enables a potential way for further multi-scale feature generation. As an intuitive extension of Feature Pyramid Network (FPN), we conduct an exhaustive study to evaluate the model performance by replacing FPN with the proposed ARDFPN in both object detection and instance segmentation tasks. With Residual network in Faster R-CNN and Mask R-CNN framework, ARDFPN outperforms the prevalent detection module — FPN on the challenging COCO dataset without bells and whistles. In particular, ARDFPN exhibits a superior performance, especially for the small and middle objects.
Goodbye, motherboard. Bare chiplets bonded to silicon will make computers smaller and more powerful: Hello, silicon-interconnect fabric

THE NEED TO MAKE some hardware systems tinier and tinier and others bigger and bigger has been driving innovations in electronics for a long time. The former can be seen in the progression from laptops to smartphones to smart watches to hearables and other "invisible" electronics. The latter defines today's commercial data centers-megawatt-devouring monsters that fill purpose-built warehouses around the world. Interestingly, the same technology is limiting progress in both arenas, though for different reasons. • The culprit, we contend, is the printed circuit board. And the solution is to get rid of it. • Our research shows that the printed circuit board could be replaced with the same material that makes up the chips that are attached to it, namely silicon. Such a move would lead to smaller, lighter-weight systems for wearables and other size-constrained gadgets, and also to incredibly powerful high-performance computers that would pack dozens of servers' worth of computing capability onto a dinner-plate-size wafer of silicon.
Maximum Likelihood Tensor Decomposition of Markov Decision Process

Model reduction is critical to reinforcement learning in high dimensions. In this paper, we study how to learn a reduced model of an unknown Markov decision process (MDP) from empirical trajectories. We focus on estimating the tensor decomposition of an unknown MDP, where the factor matrices correspond to the state and action features. For this purpose, we develop a tensor-rank-constrained maximum likelihood estimator and prove statistical upper bounds of the Kullback-Leiber divergence error and the ℓ2 error between the estimated model and true model. An information-theoretic lower bound for the estimation error is also provided and it nearly matches the upper bound. This estimator provides a statistically efficient approach for model compression and feature extraction in reinforcement learning.
Exploiting Social Media for Recommending New Locations

Social media represent one of the main sources of information concerning human dynamics within an urban context, allowing to enhance the comprehension of people behaviour, including human mobility regularities. The paper presents an approach to recommend new unseen locations to social media users exploiting historic mobility data, social features of users and geographic characteristics of locations. The location recommendation problem is formulated as a ranking task so that the recommended locations to be visited will be ranked at the highest position in the prediction set. A ranking function that exploits usersâ€™ similarity in visiting locations and in travelling along mobility paths is used to predict places the user could like. The experimental results obtained by using a real-world dataset of tweets show that the proposed method is effective in recommending unseen locations achieving remarkable precision and recall rates.
Fusion of Airspace Surveillance Systems Data

This document proposed and explored a fusion data model of primary radar systems and Identification Friend or Foe (IFF) systems of an airspace control system, in which coordinates calculated by data of primary radar systems for air objects for which there are no response signals of IFF systems are entered into the air object form, and if there are response signals from the IFF systems, the coordinates of the air object that are included in the air object form are estimated based on the weight fusion of the air objects coordinates calculated according to the data of both primary radar systems and IFF systems, which allows improving the quality of information support for decisions makers in the airspace use monitoring system.