Miracle-dz/UniFAR-FTUs huggingface dataset:
Minimizing Cost of Regeneration at Regeneration Sites–A New Approach for Dynamic Lightpath Establishment in Translucent Optical Networks

Lightpaths in translucent optical networks have to undergo regeneration at certain nodes designated as regeneration sites to maintain its quality of transmission. Regenerating an optical signal necessitates optical-electronic-optical conversion and is thus a costly affair due to use of regeneration resources. Thus minimizing the cost of regeneration in such networks is vital. Minimizing cost of regeneration leads to two different problems; 1) the problem of minimizing the total cost of regeneration for the connections served and 2) the problem of minimizing the cost of regeneration at regeneration sites while serving the connections. The previous works on lightpath establishment in such networks have mostly addressed the first problem. In this paper we study the second problem i.e. the problem of minimizing the cost of regeneration at regeneration sites during dynamic establishment of lightpaths. As the problem is NP-Complete, we propose an Integer Linear Program for small networks and then propose two heuristic routing approaches for large networks. Time complexity analysis shows that our heuristic algorithms run in polynomial time. Extensive simulation experiments reveal that our approaches are not only efficient to address the problem but can also provide better blocking performance when regeneration resources are scarcely deployed at regeneration sites.
EmotionSense: Real-time Emotional Feedback from the Audience

With the explosion of automation impacting the whole world, understanding the human behavior and interfacing the man with the machine has become a predominant subject. The abstract goal of this project is to expose in real-time meaningful and compelling information regarding the emotions of an audience. We developed an application that enables a transmitter to monitor and better understand the emotional impact of the presentation, so that one can adapt the speech on the go, or take into consideration further improvements for better engaging the public’s attention.
Integration of Temporal Contextual Information for Robust Energy Disaggregation

An extension of the baseline non-intrusive load monitoring approach using temporal contextual information is presented. It uses contextual information in order to capture time-varying power consumption patterns. The proposed methodology was evaluated using datasets of different sampling frequency, number, and types of appliances. The overall energy disaggregation accuracy was improved across all evaluated datasets with the maximum observed improvement in terms of absolute increase of accuracy being equal to 5.7%.
Sensor and Actuator Attacks of Cyber-Physical Systems: A Study Based on Supervisory Control of Discrete Event Systems

Cyber-physical systems (CPS) consist of physical processes that are controlled or monitored by software and communication capabilities. Sensors and actuators are elements of CPS that can be vulnerable to attacks. Sensor attacks typically consist in corrupting or hiding information received from sensors, or creating nonexisting information. Actuator attacks typically consist in intercepting and modifying orders sent to actuators. Attacks may lead to serious situations that we will call failures. We adopt a discrete event system (DES) approach, where information received from sensors and actions realized by actuators are represented as discrete events. We first develop a model of the attacked system using supervisory control of DES. Then, we study the defense viewpoint, where we develop methods to detect attacks and avoid failures, and determine conditions of attack detectability and failure avoidability. Then, we study the attacker’s viewpoint, where we develop a strategy based on supervisory control of DES to make the attacker undetectable.
Code-First Model-Driven Engineering: On the Agile Adoption of MDE Tooling

Domain models are the most important asset in widely accepted software development approaches, like Domain-Driven Design (DDD), yet those models are still implicitly represented in programs. Model-Driven Engineering (MDE) regards those models as representable entities that are amenable to automated analysis and processing, facilitating quality assurance while increasing productivity in software development processes. Although this connection is not new, very few approaches facilitate adoption of MDE tooling without compromising existing value, their data. Moreover, switching to MDE tooling usually involves re-engineering core parts of an application, hindering backward compatibility and, thereby, continuous integration, while requiring an up-front investment in training in specialized modeling frameworks. In those approaches that overcome the previous problem, there is no clear indication - from a quantitative point of view - of the extent to which adopting state-of-the-art MDE practices and tooling is feasible or advantageous. In this work, we advocate a code-first approach to modeling through an approach for applying MDE techniques and tools to existing object-oriented software applications that fully preserves the semantics of the original application, which need not be modified. Our approach consists both of a semi-automated method for specifying explicit view models out of existing object-oriented applications and of a conservative extension mechanism that enables the use of such view models at run time, where view model queries are resolved on demand and view model updates are propagated incrementally to the original application. This mechanism enables an iterative, flexible application of MDE tooling to software applications, where metamodels and models do not exist explicitly. An evaluation of this extension mechanism, implemented for Java applications and for view models atop the Eclipse Modeling Framework (EMF), has been conducted with an industry-targeted benchmark for decision support systems, analyzing performance and scalability of the synchronization mechanism. Backward propagation of large updates over very large views is instant.
Ethics Issues in Artificial Intelligence

With the rapid development and application of artificial intelligence (AI) in various fields, the threats and ethical issues to human beings are getting more attentions. Therefore, the study of the artificial intelligence ethics is increasingly important. This study focuses on the ethical issues in the development of artificial intelligence, the balance between artificial intelligence, human, and business ethics. Several AI ethical paradoxes will be provided and discussed in this paper. In the end, a framework of ethical design approach will be presented for future studies.
Modeling of Multivariate Systems using Vector Autoregression(VAR)

This study revolves around a group of variables which are coming under a single system. The multivariate systems are a group of individual components which works independently but makes the system as a whole. These variables tend to have an effect on the growth of each other within the system. This paper explores the relationship between such systems by developing a mathematical model. A Vector Autoregression (VAR) model is developed for a multivariate system, to analyze the relationship between the systems in the long and short run. The model is then used for forecasting the system. Using the Impulse Response analysis, a conclusion between the interactions of the variables is found. In the long term, the variables tend to be independent of the other variables as they become more stable. In short term, the variables have a dependence within them.
Trajectory based Deep Policy Search for Quadrupedal Walking

In this paper, we explore a specific form of deep reinforcement learning (D-RL) technique for quadrupedal walking—trajectory based policy search via deep policy networks. Existing approaches determine optimal policies for each time step, whereas we propose to determine an optimal policy for each walking step. We justify our approach based on the fact that animals including humans use “low” dimensional trajectories at the joint level to realize walking. We will construct these trajectories by using Bézier polynomials, with the coefficients being determined by a parameterized policy. In order to maintain smoothness of the trajectories during step transitions, hybrid invariance conditions are also applied. The action is computed at the beginning of every step, and a linear PD control law is applied to track at the individual joints. After each step, reward is computed, which is then used to update the new policy parameters for the next step. After learning an optimal policy, i.e., an optimal walking gait for each step, we then successfully play them in a custom built quadruped robot, Stoch 2, thereby validating our approach.
Detection of Car Abnormal Vibration using Machine Learning

One difficulty of car-sharing services is trouble among users related to damage of the automobile. With the rapid growth of car-sharing services, demand exists for a new approach for automatic detection of the occurrence of damage during crewless operation. In this study, we propose a method for automatic detection of abnormal vibration that damages an automobile that occurs driving and stopping. After using multiple piezoelectric sensors installed in the automobile to acquire vibration, we applied supervised learning to extract features from vibrations and to detect abnormal vibrations. The proposed method achieved recall of 0.96 for stopped automobiles and recall of 0.82 for driving automobiles.
A Study on Cyclic Performance Fluctuation of CUBIC TCP and TCP BBR Considering Estimated RTT and Bandwidth

Previous works showed that the throughputs of TCP BBR and CUBIC TCP fluctuated in a condition wherein they communicate concurrently. In this paper, we discuss the reason of this fluctuation. TCP BBR estimates the bottleneck link bandwidth (BtlBw) and the round-trip propagation delay time (RTprop) during communication. This then its congestion window size to the product of BtlBw and RTprop. We discuss the relationship between Btlbw, RTprop, and performance fluctuation. We evaluate performance fluctuation with constant Btlbw and RTprop and show that the fluctuation is relieved by constant RTprop.
A Novel Automatically Designed EBG Structure by Improved GA for Ultrawideband SSN Mitigation of System in Package

In this article, a novel automatically designed electromagnetic bandgap (EBG) structure is presented to prohibit the simultaneous switching noise (SSN) propagation of a system in package. The tool employed for the automatic EBG design is the improved genetic algorithm (GA), which has the advantages of rapid convergence and high precision by combining several improvements reasonably. Meanwhile, the equivalent circuit model and the dispersion diagram of the proposed structure are established to interpret the electromagnetic characteristics of the designed power distribution network (PDN). By removing some patches in a specific area determined by the GA, the surface impedance of PDN can be increased to reduce the SSN coupling. The SSN transmission can be suppressed from 0.37 to 20 GHz at a mitigation level of −50 dB. The measured results agree well with the simulated ones.
Analysis of Switch Losses in Cycloconverter-Type High Frequency Link Converter for Bidirectional Battery Chargers

On-board chargers with bi-directional power flow capability should display high power density to make them suitable for vehicle-to-grid applications. Selecting an optimal power topology and evaluating power losses in various power device candidates are important steps in the design and development of battery chargers. This paper presents an analysis of switch power losses in a 3.3kW cyclo-converter type high-frequency link converter with silicon carbide MOSFET. Although power losses are caused by many passive and active components, power losses in semiconductors are typically the dominant factors influencing the estimation the power conversion efficiency. In this respect, efficiency can be improved to achieve high power density by adopting the ac-ac power conversion architecture and by using wide bandgap devices. A theoretical power loss model for the proposed system is derived, and power losses in various power devices are estimated. In addition, the power losses in cyclo-converter type high- frequency link converter proves its superiority over the conventional dual-active-bridge converter, one of the popular circuit topologies for galvanic- isolated battery chargers. The analysis results for the power loss models are validated through PSIM thermal simulations.
JND-based Perceptual Rate Distortion Optimization for AV1 Encoder

AV1 is the next-generation open video coding format, and it can achieve significant coding efficiency with novel coding tools. It supports Lagrangian rate distortion optimization (RDO) method to optimize the coding performance. However, the distortion and the Lagrangian multiplier used in RDO ignore the characteristics of human visual system (HVS), which leads to insufficiency for perceptual video coding. To solve this problem, a perceptual RDO scheme based on the Just Noticeable Distortion (JND) threshold of HVS is proposed. The JND for each pixel is first measured according to three perceptual features: luminance adaptation, masking effects and structure sensitivity. Based on the observation that the regions with smaller distortion visibility thresholds are more sensitive to HVS, a JND-based Lagrangian multiplier is derived to adaptively adjust the rate-distortion (RD) performance for each coding block. Experiments demonstrate that the proposed method can achieve an average SSIM-based −3.93% BD-Rate saving compared with the original AV1 encoder, which effectively improve the coding performance.
A Randomized Low Latency Resource Sharing Algorithm for Fog Computing

In this paper, we propose and report a study of a low latency resource sharing protocol for Fog Computing. The protocol has its root in the power-of-random choices family of randomization protocol. The protocol, dubbed $LL_{g}(T)$ is designed to cope with a not homogeneous set of nodes and dealing with a communication latency comparable with the task execution, a characteristic of time-constrained applications supported by this service delivery model. The protocol allows to determine when a task can be moved from the origin fog node that receives the task to another node, where it can be executed faster. This task handoff is controlled via a threshold T. The remote node is selected uniformly at random.
Design and Additive Microfabrication of a Two-Pole 287-GHz Waveguide Bandpass Filter

This letter presents the design and fabrication of a 287-GHz resonator and a bandpass filter. The fabrication is conducted using an additive microfabrication process, based on successive photolithography and metal electroplating steps. This process has allowed the fabrication of a single-cavity resonator and a 3.5% relative bandwidth two-pole bandpass filter. The cavity has a measured unloaded <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> of 137, and the filter has a measured loss of 4.6 dB and matching better than 20 dB in very good agreement with E-M simulation results.
Crowdsourced Report Generation via Bug Screenshot Understanding

Quality control is a challenge of crowdsourcing, especially in software testing. As some unprofessional workers involved, low-quality yieldings may hinder crowdsourced testing from satisfying requesters' requirements. Therefore, it is in demand to assist crowdworkers to raise bug report quality. In this paper, we propose a novel auxiliary method, namely CroReG, to generate crowdsourcing bug reports by analyzing bug screenshots uploaded by crowdworkers with image understanding techniques. The preliminary experiment results show that CroReG can effectively generate bug reports containing accurate screenshot captions and providing positive guidance for crowdworkers.
On the Role of Trust in Child-Robot Interaction*

In child-robot interaction, the element of trust towards the robot is critical. This is particularly important the first time the child meets the robot, as the trust gained during this interaction can play a decisive role in future interactions. We present an in-the-wild study where Polish kindergartners interacted with a Pepper robot. The videos of this study were analyzed for the issues of trust, anthropomorphization, and reaction to malfunction, with the assumption that the last two factors influence the children’s trust towards Pepper. Our results reveal children’s interest in the robot performing tasks specific for humans, highlight the importance of the conversation scenario and the need for an extended library of answers provided by the robot about its abilities or origin and show how children tend to provoke the robot.
Effects of Antenna Orientation in Ultra Wideband Indoor Positioning System

This paper presents an investigation on the effect of antenna orientations to the positioning performance of Ultra wideband (UWB) real time location system. The main objective of this study is to estimate the performance degradation when tag’s antenna orientation is not placed in the optimal orientation. The ranging accuracy and localization performance in the 3 dimensions (3D) positioning systems are considered. The two way ranging (TWR) method is used to determine the distance between transmitter and receiver, while the 3D trilateration algorithm is used to estimate the target location. Experimental results showed that the vertical antenna orientation of both transmitters and receivers provided the highest ranging accuracy and the highest positioning performance as recommended by the manufacturer. Specifically, the vertical antenna orientation has the average error of measured range about 0.18 meters and provides the localization error in 3D less than 0.84 meters. On the other hand, the worst performance, which caused 52.27 % more error, was when both transmitters and receivers were in horizontal orientation.
RefBot: Intelligent Software Refactoring Bot

The adoption of refactoring techniques for continuous integration received much less attention from the research community comparing to root-canal refactoring to fix the quality issues in the whole system. Several recent empirical studies show that developers, in practice, are applying refactoring incrementally when they are fixing bugs or adding new features. There is an urgent need for refactoring tools that can support continuous integration and some recent development processes such as DevOps that are based on rapid releases. Furthermore, several studies show that manual refactoring is expensive and existing automated refactoring tools are challenging to configure and integrate into the development pipelines with significant disruption cost. In this paper, we propose, for the first time, an intelligent software refactoring bot, called RefBot. Integrated into the version control system (e.g. GitHub), our bot continuously monitors the software repository, and it is triggered by any "open" or "merge" action on pull requests. The bot analyzes the files changed during that pull request to identify refactoring opportunities using a set of quality attributes then it will find the best sequence of refactorings to fix the quality issues if any. The bot recommends all these refactorings through an automatically generated pull-request. The developer can review the recommendations and their impacts in a detailed report and select the code changes that he wants to keep or ignore. After this review, the developer can close and approve the merge of the bot's pull request. We quantitatively and qualitatively evaluated the performance and effectiveness of RefBot by a survey conducted with experienced developers who used the bot on both open source and industry projects
Compiler-Directed Parallelism Scaling Framework for Performance Constrained Energy Optimization

Evolution of semiconductor manufacturing technology leads to the rising trend of leakage current and the end of Dennard scaling. At the dark silicon era, aggressive power gating scheme with quantitative management on power-gated hardware resources is required. This paper proposes a novel approach —parallelism scaling — to control static energy on power-gated parallel hardware. This work presents performance-constrained optimization method to power off the greatest possible amount of hardware. As a first trial, this paper examines the idea on VLIW-style architecture exploiting instruction-level parallelism. This paper establishes a theoretical foundation to realize parallelism scaling. The mathematical programming theory includes (1) topological model to control the granularity of program partitioning, (2) optimal partitioning on well-structured control flow graph in polynomial time, and (3) decision support for parallelism through item packing model guided by energy density. Evaluation conducted on EEMBC Denbench benchmark suite shows at least 15% to 53% saving on static energy compared to non-power-gated architecture. Compared to the state-of-art resource management scheme, our approach saves about 20% to 30% energy to meet the same performance demand. The evaluation reveals noteworthy opportunity to save static energy for future dark-silicon architecture design.
Design of a Multi-purpose Surface-EMG Readout System for Draft Control Applications

This paper presents current work on the design of a multi-purpose surface-electromyography (EMG) readout system that can be used in various draft controlled applications of low-accuracy (e.g. rehabilitation environments for stroke patients, ambient assisted living for elders, remote control of robotic arm). The proposed system interfaces six low-cost and low-power EMG channels exhibiting $\pm \mathbf{1.5}\mathbf{mV}$ sensitivity and 400mV output range. The ADC converters of a microcontroller (i.e. Arduino) platform are used to fetch the analog signal from the read-out system. Digital data is verified after ADC conversion using oscilloscope instruments. The microcontroller is programmed to filter the analog signal and identify its rough changes based on continuously adjustable thresholds. The measured data are transmitted in a flexible digital coded format via the microcontroller's network connectivity hardware to the associated control receiver, which in turn decodes it and performs the associated operation as required by the draft-controlled environment. The current consumption ranges from 330uA to 2.9mA per channel, depending on the type of operational amplifier used.
A Density-Based Congestion Avoidance Protocol for Strict Beaconing Requirements in VANETs

Accident prevention in roads and highways is one of the most important aspects of vehicular networks. In a Vehicular Ad Hoc Network (VANET), vehicles are equipped with a device that enables wireless communication between them. In VANETs, vehicles usually build network topology by periodic exchange of messages known as beacons. These messages should include the vehicle's position, speed and heading. Safety applications in VANETs define strict requirements to guarantee their proper functionality, among them, beacon transmission rates that can vary between 10 to 50 Hz. However, high beacon transmission rates can lead to a problem known as broadcast storm, which may impair correct operation of safety applications in high vehicle density scenarios. During a broadcast storm, numerous messages are transmitted simultaneously, which increases collisions and decreases packet delivery ratio. In this work, we propose a Density-based Congestion Avoidance Protocol (DCAP) to adapt beacon transmission rate in VANETs and minimize broadcast storm occurrence. Experimental results show that even in high vehicle density scenarios, DCAP improves on other protocols by 8% and 50% regarding packet delivery ratio, while still assuring coverage of approximately 100%.