Miracle-dz/UniFAR-FTUs huggingface dataset:
Complex Unitary Recurrent Neural Networks using Scaled Cayley Transform

Recurrent neural networks (RNNs) have been successfully used on a wide range of sequential data problems. A well known difficulty in using RNNs is the vanishing or exploding gradient problem. Recently, there have been several different RNN architectures that try to mitigate this issue by maintaining an orthogonal or unitary recurrent weight matrix. One such architecture is the scaled Cayley orthogonal recurrent neural network (scoRNN) which parameterizes the orthogonal recurrent weight matrix through a scaled Cayley transform. This parametrization contains a diagonal scaling matrix consisting of positive or negative one entries that can not be optimized by gradient descent. Thus the scaling matrix is fixed before training and a hyperparameter is introduced to tune the matrix for each particular task. In this paper, we develop a unitary RNN architecture based on a complex scaled Cayley transform. Unlike the real orthogonal case, the transformation uses a diagonal scaling matrix consisting of entries on the complex unit circle which can be optimized using gradient descent and no longer requires the tuning of a hyperparameter. We also provide an analysis of a potential issue of the modReLU activiation function which is used in our work and several other unitary RNNs. In the experiments conducted, the scaled Cayley unitary recurrent neural network (scuRNN) achieves comparable or better results than scoRNN and other unitary RNNs without fixing the scaling matrix.
Design of All-Sinusoidal Single Phase On-Line UPS with AVR for Improving Sinusoidal Output Voltage

In order to generate dc voltage link from a back-up battery bank, a push-pull converter is employed. In order to produce regulated sinusoidal output, a full-bridge inverter is designed. Using sinusoidal PWM signals, the output total harmonic distortion is eliminated and therefore a simpler output filter would be necessary. Ripple, distortion or flicker of the voltage, grid frequency variation, and even a blackout would not affect the sinusoidal output voltage. In this scheme, a lower number of batterie decreases the weight, volume, and cost considerably.
Optimized Positioning of Relay Nodes Using Bat Algorithm in Heterogeneous Wireless Sensor Networks

In heterogeneous wireless sensor network (HWSNs), relay nodes are deployed to improve the network lifetime, connectivity and/or coverage of the network. The deployed relay nodes (RNs) can also perform data aggregation and disseminate the data to sink node, hence acting as cluster head. Therefore it is vital and challenging to identify optimal positions for placement of RNs. In this paper, we have studied four different metaheuristic algorithms for relay node placement problem. Also, in order to maintain the data latency, we have employed mobile sink for data collection purpose. Employing mobile sink(s) provide considerable benefits to the deployed Wireless Sensor Network(s) (WSNs) in terms of increased network lifetime, decreased energy consumption, decreased data latency and evenly distributed load on the sensors in the network. Three mobile sink traversal techniques are simulated for data collection purpose. The results obtained through extensive simulations demonstrate the superiority of bat algorithm for relay node placement.
Using Options to Accelerate Learning of New Tasks According to Human Preferences

Over the years, people need to incorporate a wider range of information and multiple objectives for their decision making. Nowadays, humans are dependent on computer systems to interpret and take proﬁt from the huge amount of available data on the Internet. Hence, varied services, such as location-based systems, must combine a huge quantity of raw data to give the desired response to the user. However, as humans have different preferences, the optimal answer is different for each user proﬁle, and few systems offer the service of solving tasks in a customized manner for each user. Reinforcement Learning (RL) has been used to autonomously train systems to solve (or assist on) decision-making tasks according to user preferences. However, the learning process is very slow and require many interactions with the environment. Therefore, we here propose to reuse knowledge from previous tasks to accelerate the learning process in a new task. Our proposal, called Multiobjective Options , accelerates learning while providing a customized solution according to the current user preferences. Our experiments in the Tourist World Domain show that our proposal learns faster and better than regular learning, and that the achieved solutions follow user preferences.
Boosting Model Performance through Differentially Private Model Aggregation

A key factor in developing high performing machine learning models is the availability of sufficiently large datasets. This work is motivated by applications arising in Software as a Service (SaaS) companies where there exist numerous similar yet disjoint datasets from multiple client companies. To overcome the challenges of insufficient data without explicitly aggregating the clients' datasets due to privacy concerns, one solution is to collect more data for each individual client, another is to privately aggregate information from models trained on each client's data. In this work, two approaches for private model aggregation are proposed that enable the transfer of knowledge from existing models trained on other companies' datasets to a new company with limited labeled data while protecting each client company's underlying individual sensitive information. The two proposed approaches are based on state-of-the-art private learning algorithms: Differentially Private Permutation-based Stochastic Gradient Descent and Approximate Minima Perturbation. We empirically show that by leveraging differentially private techniques, we can enable private model aggregation and augment data utility while providing provable mathematical guarantees on privacy. The proposed methods thus provide significant business value for SaaS companies and their clients, specifically as a solution for the cold-start problem.
Clone Detection in Model-Based Development Using Formal Methods to Enhance Performance in Software Development

Clone detection is gaining more attention due to its advantages of software maintenance. Clone detection helps in code optimization (code present in multiple locations can be updated and optimized once), bug detection (discovering bugs that are copied to multiple locations in the code), and analysis of re-used code in software systems. Importantly, model-based software development is gaining more popularity due to its reduced production time and cost. There are several approaches to detect clones in the code level, but few methods to detect clones at the model level. These methods use syntactic based analysis of models to detect clones at the model level. In this paper, we present a novel approach to detect clones at model-based levels using semantic based analysis. Our method is based on model checking which involves mathematical based analysis. Our method is tested with control flow based models and yields good results in detection of model clones.
Iterative Learning Control Based Robust Distributed Algorithm for Non-Holonomic Mobile Robots Formation

This paper presents an iterative learning control-based robust distributed algorithm on the formation issue for a group of differential-drive mobile robots. The fundamental robustness problem in practical application involving initial state shifts, disturbances, noises, and communication time-delays are considered. The distributed algorithm is proposed for each robot in directed network, which is based on the iterative learning rule with both predictive and current learning terms. It is shown that the convergence of formation tracking objective can be guaranteed under a matrix norm condition by using the two-dimensional analysis approach. Numerical simulation and experiment are both given to validate the effectiveness of the proposed algorithm.
Faulted Branch Location in Distribution Networks Based on the Analysis of High- Frequency Transients

Electric Power Distribution Systems (EPDS) are steadily growing in both size and complexity, and thereby faults on distribution feeders tend to affect more customers and take more time to be located and fixed. Hence, fast and accurate fault location is of primordial importance in minimizing the economic loses for both customers and utilities. Some methods such as those based on the apparent impedance analysis are able to accurately identify the fault distance from the local terminal. However, as the distribution systems have multiple branches and monitoring infrastructure is usually limited to the substation, the location of the faulted branch is a problem not solved yet. This paper proposes the transient analysis of the fault-generated traveling wave in order to identify the faulted branch of radial distribution feeders. The method is based on the correlation of theoretical frequencies calculated for each wave propagation path and characteristic frequencies inferred from the transient spectrum. A frequency-dependent modal transformation matrix is formulated by modifying the Carson's equations thus allowing to accurately model the propagation of high-frequency transients in unbalanced distribution networks. Aiming to verify the performance of the proposed methodology, simulations were carried out considering several fault scenarios in the IEEE 13 nodes distribution system.
A Health Monitoring Application to Support Informal Caregivers of People with Cognitive Impairment

Informal caregivers of people with cognitive impairment are often concerned about the health and wellbeing of their loved ones, leading to frequent care-related visits, check-ups, and conversations. Results from focus groups we held in daily care showed a strong need for support in terms of information about the health and wellbeing of the person with cognitive impairment (PwCI). The health monitoring application developed in this study, called HELMA, aims to offer a solution by monitoring the health and wellbeing of the PwCI in the long term and informing the informal caregivers. HELMA is an eHealth application that uses short frequent online monitoring questions to provide informal caregivers with information about the following health and wellbeing domains: physical; mental; social; and environmental. The next step is to evaluate HELMA on a large scale with end-users in daily practice.
Real-time Patient Localization in Urgent Care: System Design and Hardware Perspective

The real-time location systems (RTLS) take recently important part in many location-aware systems especially in indoor objects localization tasks. In the health-care environment, bed tracking, patient monitoring or critical equipment tracking are important applications of such systems. The paper deals with currently developed RTLS based on the Infra-Red (IR) light. We develop the system primarily for University Hospital of Ostrava however the general structure of the system a hardware components can be used in other systems. In the article we introduce one part of the localization system – the Anchor. This paper also presents an energy assessment and testing of the power consumption of anchor performance. The result section brings major conclusions and possible directions for future work.
Bridging Network Embedding and Graph Summarization

An important reason behind the prevalence of node representation learning is their superiority in downstream machine learning tasks on graphs. However, storing the vector-based node representation of massive real-world graphs often requires space that is orders of magnitude larger. To alleviate this issue, we introduce the problem of latent network summarization that is complementary to the problem of network embedding, and propose a general framework called Multi-LENS. Instead of deriving dense node-wise representations, the goal of latent network summarization is to summarize the structural properties of the graph in a latent space with dimensionality that is independent of the nodes or edges in the input graph. The size-independent graph summary given by Multi-LENS consists of (i) a set of relational aggregators with their compositions (relational functions) that captures structural features of multi-order node-centric subgraphs, and (ii) the low-rank approximations to matrices that incorporate captured structural features. In addition, Multi-LENS is able to derive node embeddings on the fly from this latent summary due to its inductive properties. Multi-LENS bridges advantages brought by both embeddings and graph summarization, and applies to graphs with or without directionality, weights, attributes or labels. Extensive experiments on both synthetic and real-world graphs show that Multi-LENS achieves 2 - 89% improvement in AUC for link prediction, while requiring less than 79x space compared to existing representation learning approaches. We also show the effectiveness of Multi-LENS summaries in anomaly and event detection on two real-world graphs.
A 21-Gb/s Dual-Channel Voltage-Mode Transmitter With Stacked NRZ and PAM4 Drivers

This paper describes a voltage-mode transmitter that stacks independent non-return-to-zero (NRZ) and four-level pulse amplitude modulation (PAM4) drivers for dual channels. The stacked structure can improve the transmitter’s power efficiency owing to its current-recycling capabilities. The PAM4 voltage-mode driver with voltage regulators, implemented to increase data rates, generates multi-level signals. The proposed dynamic load control inside the PAM4 driver minimizes the voltage variation in the output of the regulators, which is caused by the variations of the data patterns. A prototype chip was fabricated using a 28-nm CMOS process. The stacked 7-Gb/s NRZ and 14-Gb/s PAM4 transmitters have achieved a full aggregate transmission rate of 21 Gb/s while consuming 12.6 mW from a 1-V power supply. When transmitting 500-mVpp signals with the proposed transmitter through 2-cm FR4 channels, the eye heights obtained in the NRZ and PAM4 channels were 312 and 92 mV, respectively.
Universal Marginalizer for Amortised Inference and Embedding of Generative Models

Probabilistic graphical models are powerful tools which allow us to formalise our knowledge about the world and reason about its inherent uncertainty. There exist a considerable number of methods for performing inference in probabilistic graphical models; however, they can be computationally costly due to significant time burden and/or storage requirements; or they lack theoretical guarantees of convergence and accuracy when applied to large scale graphical models. To this end, we propose the Universal Marginaliser Importance Sampler (UM-IS) -- a hybrid inference scheme that combines the flexibility of a deep neural network trained on samples from the model and inherits the asymptotic guarantees of importance sampling. We show how combining samples drawn from the graphical model with an appropriate masking function allows us to train a single neural network to approximate any of the corresponding conditional marginal distributions, and thus amortise the cost of inference. We also show that the graph embeddings can be applied for tasks such as: clustering, classification and interpretation of relationships between the nodes. Finally, we benchmark the method on a large graph (>1000 nodes), showing that UM-IS outperforms sampling-based methods by a large margin while being computationally efficient.
Two-Phase Interleaved SEPIC MPPT Using Coupled Inductors in Continuous Conduction Mode

A SEPIC (single-ended primary inductance converter) DC-DC converter is capable of operating in either step-up or step-down modes; therefore, it is widely used in power electronic applications where output voltage may be above or below the converter input voltage. In this study, a two-phase interleaved SEPIC converter with coupled inductors is designed by considering that operating in continuous conduction mode (CCM), and it is employed in DSP-based maximum power point tracking (MPPT) for photovoltaic (PV) system applications. MPPT process is realized the reference voltage (Vref) generated from MPPT algorithm is directly applied the PI controller. The peak-current-mode controller is implemented to control each inductor current according to reference inductor current obtained from PI controller. The designed system has been examined for sudden solar irradiation and load changes. Experimental results show that the proposed converter is very efficient in the MPPT process and has a fast-dynamic response.
Towards Solving Imperfect Recall Games

Imperfect recall games represent dynamic interactions where players can forget previously known information, such as the exact history of played actions. Opposed to perfect recall games, where the players remember all information, imperfect recall games allow a concise representation of strategies. However, most of the existing algorithmic results are negative for imperfect recall games and many theoretical and computational results do not translate from perfect recall games. The goal of this paper is to (1) summarize the existing results regarding imperfect recall games and (2) extend these results to a restricted subclass of imperfect recall games termed A-loss recall games . Finally, (3) we emphasize the impact of these theoretical results on algorithms that compute (approximately) optimal strategies in extensive-form games and show that they cannot be easily extended to imperfect recall games.
[Research Paper] POI: Skew-Aware Parallel Race Detection

Multithreaded programs are prone to dataraces. Dataraces are known to be hard to detect and reproduce by manual effort, although they often have detrimental effects on program reliability. Automated techniques are thus demanded for detecting dataraces efficiently and precisely. There have been proposed a lot of datarace detectors so far, among which dynamic ones are promising because of their precision. However, existing dynamic race detectors incur high race-checking overheads. Even a state-of-the-art dynamic race detector, called Parallel FastTrack, fails to efficiently detect races under certain conditions, despite its attempt to parallelize race detection for efficiency. In this paper, we propose an efficient and precise parallel race detector. For our proposal, we first experimentally reveal that the load-distribution policy of Parallel FastTrack tends to skew race-checking loads to a few detection threads. We then present a simple but effective technique, called POI, for balancing race-checking loads among detection threads. POI takes race-checking loads of each detection thread into account and reduces the load skew by making each detection thread manage almost the same number of memory addresses to be checked. Experiments on several real multithreaded data-processing applications show that POI succeeded in reducing, on average, about 37% of race detection overheads, which the load-distribution policy of Parallel FastTrack would impose.
IoT Security Model and Performance Evaluation: A Blockchain Approach

It is a research hotspot that using blockchain technology to solve the security problems of the Internet of Things (IoT). Although many related ideas have been proposed, there are very few literatures with theoretical and data support. This paper focuses on the research of model construction and performance evaluation. First, an IoT security model is established based on blockchain and InterPlanetary File System (IPFS). In this model, many security risks of traditional IoT architectures can be avoided, and system performance is significantly improved in distributed large capacity storage, concurrency and query. Secondly, the performance of the proposed model is evaluated through the average latency and throughput, which are meaningful for further research and optimization of this direction. Analysis and test results demonstrate the effectiveness of the blockchain-based security model.
A New Compact Wide-Band Antenna Design for Satellite Communications with Dual Band-Notches for WiMAX and WLAN Using Particle Swarm Optimization

In this work, a new design of compact wide-band antenna with dual notch-band characteristics is proposed using Particle Swarm Optimization (PSO). The proposed antenna design is compact in size (32 mm x 38 mm x 1 mm) with a beveled ground plane for wide-band performance. In order to prevent interference problems from nearby communication systems like WiMAX (3.3-3.8 GHz) and WLAN (5.1-5.8 GHz) technologies, an open C-shaped stub is embedded in the radiating patch and a pair of U-shaped parasitic strips is inserted beside the feed line. Simulation results show that the proposed antenna design has a very wide bandwidth covering the entire UWB, X-band, and Ku band frequency range (3 to 20 GHz), along with notch-bands in the WiMAX (3.3–3.8 GHz) and (5.1–5.8 GHz) frequencies. Also, the results show good gain flatness, good impedance matching, and omnidirectional radiation patterns at almost all frequency bands of operation (3 to 20 GHz). This reveals the effectiveness of the proposed antenna design in the applications of wide-band communication systems such as satellite communications.
Analysis of the Amplitude and Phase-Manipulated Signals of Automation Devices via Bluetooth Technology

A problem relating to metering of electrical parameters is concerned, including complex modulated signals on the modern technical systems. Modern technical devices in the railroads of the Russian Federation as well as difficulties around integration of the advanced solutions existing already are described. A goal of the Russian railroads to replace all basic metering electrical instruments with a single flexible one is described, which permits to make standard electrical measurements as well as measurement, display, and analysis of the modulated coded signals, including those in power circuits, with a capability of transmitting the data from a monitoring system. The authors have developed approaches to data pickup and analysis, which have permitted to design a flexible measuring instrument that meets all the safety requirements for electrical measurements on the traffic safety systems; this approach is capable of ensuring transmission and further processing of the measurements under a Bluetooth protocol to a mobile device. The proposed engineering solutions demonstrate that the instrument can be used to measure and analyze almost all electrical values and coded signals on, though are not limited to the automation systems.
Interaction Modeling Based on Segmenting Two Persons Motions Using Coupled GP-HSMM

Humans interact with one another daily and learn from their experiences via observation and interaction. To create robots that can coexist with humans, it is important that they learn how to appropriately interact in the human community. In this paper, we propose a novel model, the coupled Gaussian process hidden semi-Markov model (GP-HSMM), which enables robots to learn rules of interaction between two persons by observing them in an unsupervised manner. The continuous motions of the persons are segmented into discrete actions based on GP-HSMM, and the relationships between the actions are extracted. Moreover, all corresponding actions are not simultaneously conducted by two persons during actual interaction. Thus, the coupled GP-HSMM accounts for these lags. We conduct experiments using the motion data of interaction games. Experimental results showed that the coupled GP-HSMM can estimate actions, lags between them and their relationships.