Miracle-dz/UniFAR-FTUs huggingface dataset:
Rank Aggregation from Pairwise Comparisons in the Presence of Adversarial Corruptions

Rank aggregation from pairwise preferences has widespread applications in recommendation systems and information retrieval. Given the enormous economic and societal impact of these applications, and the consequent incentives for malicious players to manipulate ranking outcomes in their favor, making rank aggregation algorithms robust to adversarial manipulations in data is a crucial challenge. In this paper, we initiate the study of robustness in rank aggregation under the popular Bradley-Terry-Luce (BTL) model for pairwise comparisons. We consider a setting where pair-wise comparisons are initially generated according to a BTL model, but a fraction of these comparisons are corrupted adversarially prior to being reported to us. We consider a strong contamination model, where an adversary having complete knowledge of the initial truthful data and the true BTL weights, can corrupt this data by inserting, deleting, or changing data points. The goal is to recover the true BTL weights given this corrupted data. We characterize the extent of corruption under which the true BTL weights are uniquely identiﬁable. We also provide a novel algorithm that provably ﬁlters out the adversarial corruption from data under reasonable conditions on data generation and corruption. We support our theory with experiments on both synthetic as well as real data, showing the resilience of our algo-rithm to a substantial degree of corruption and the vulnerability of existing approaches to even small amounts of corruption.
When Demands Evolve Larger and Noisier: Learning and Earning in a Growing Environment

We consider a single-product dynamic pricing problem under a specific non-stationary setting, where the underlying demand process grows over time in expectation and also possibly in the level of random fluctuation. The decision maker sequentially sets price in each time period and learns the unknown demand model, with the goal of maximizing expected cumulative revenue over a time horizon $T$. We prove matching upper and lower bounds on regret and provide near-optimal pricing policies, showing how the growth rate of random fluctuation over time affects the best achievable regret order and the near-optimal policy design. In the analysis, we show that whether the seller knows the length of time horizon $T$ in advance or not surprisingly render different optimal regret orders. We then extend the demand model such that the optimal price may vary with time and present a novel and near-optimal policy for the extended model. Finally, we consider an analogous non-stationary setting in the canonical multi-armed bandit problem, and points out that knowing or not knowing the length of time horizon $T$ render the same optimal regret order, in contrast to the non-stationary dynamic pricing problem.
Smart Electricity Measuring System

Recently, it is observed that the huge revenue loss in Electricity Department is due to the metering and billing system. Errors get introduced at every stage of billing like, human errors while noting down the meter reading, or error while processing the bills or faulty meters. As per few study reports, it is concluded that 40% of the electricity in India is unpaid. Around one-fourth of the power generated is either stolen or lost in transmission in the country. The solution to these problems is a smart electricity measuring system. The aim is to design a smart electricity measuring system with an android based payment system. The research work introduced in this article is described in three modules: a microcontroller based prepaid electricity use measuring system to calculate the power consumption; an android based application for meter reading and recharge; and GSM circuit based for Short Messaging Service to the user. This system will help the user to monitor the consumption of electricity, which will make the user aware of unnecessary consumption. Also, the balance amount can be checked, if low then recharge needs to be done, otherwise supply will be disconnected. Thus, in turn will reduce the manual work, automate the system and timely payment will be made to use electricity which will reduce revenue losses and theft.
Advanced Deep Learning-Based Computational Offloading for Multilevel Vehicular Edge-Cloud Computing Networks

The promise of low latency connectivity and efficient bandwidth utilization has driven the recent shift from vehicular cloud computing (VCC) towards vehicular edge computing (VEC). This paper presents an advanced deep learning-based computational offloading algorithm for multilevel vehicular edge-cloud computing networks. To conserve energy and guarantee the efficient utilization of shared resources among multiple vehicles, an integration model of computational offloading, and resource allocation is formulated as a binary optimization problem to minimize the total cost of the entire system in terms of time and energy. However, this problem is considered NP-hard and it is computationally prohibitive to solve this type of problem, particularly for large-scale vehicles, due to the curse-of-dimensionality problem. Therefore, an equivalent reinforcement learning form is generated and we propose a distributed deep learning algorithm to find the near-optimal computational offloading decisions in which a set of deep neural networks are used in parallel. Finally, simulation results show that the proposed algorithm can exhibit fast convergence and significantly reduce the overall consumption of an entire system compared to the benchmark solutions.
Linear Receiver Design for Time-Varying Poisson Molecular Communication Channels with Memory

This work investigates novel approaches for designing the linear filter and the threshold of the memory-less detector in the receiver of a diffusion based molecular communication system. We focus on the case where the transmitted symbols propagate through a Poisson channel with memory that introduces inter-symbol interference. Inspired by the theory of filtering under Poisson regime and considering all the possible mean values of the interference, we propose two new design methods for computing the coefficients of the filter. While in the development of the theory under Poisson regime the interference is not considered in the design of the filter, in our proposal we take it into account in the time-variation of the mean value. Since the proposed approach gives better performance for low transmission rate, another design based on linear programming is proposed for the high-rate case. For the design of the detector’s threshold we use an averaging method over the output of the receiving filter. The performance of the two proposed designs, in terms of bit error rate, is compared to that achieved by an approach which is based on the maximization of the signal-to-interference plus noise ratio (SINR). We show that our design achieves almost the same performance of the maximum SINR one with the advantage of lower computational cost to obtain receiver coefficient s and threshold.
Comparative Study of Clustering Approaches in Rumor Source Localization Algorithm in Social Networks

Social network usually consists of a set of people acting as nodes in which one node is connected to other nodes internally. These network nodes can be visualized and obtain a mathematical model of these relationships (edges) by analysing the social network. Large social networks can be divided into different communities which are tied together with strong and weak edges. This immensely populated and dense social network, however, is vulnerable to rumor dissemination among communities in the network. To detect the source of information, a two stage algorithm has been proposed by previous studies. This algorithm relies on clustering for detecting communities in the network. The main aim to improve this algorithm is by comparing the results of different community detection algorithms and executing the algorithm on a variety of datasets. The previously developed two-staged algorithm uses the Louvain clustering for this purpose. It has been proposed to replace the existing approach of clustering with a better performing algorithm. The results of these algorithms are demonstrated by computing various metrics such as average distance error (hop error) and the probability of correctly localizing the source. Finally, it has been demonstrated that the replacing Louvain approach with Girvan-Newman Algorithm in the source estimation algorithm improves its performance in terms of the accuracy of correctly estimating the source of rumor in a large-scale network.
Minutiae Selection using Reference Point for Fingerprint Data Interoperability and Identification

The most crucial features of fingerprint recognition are minutiae, which are friction ridge ending and bifurcation. Given an input fingerprint, minutiae are extracted and encoded into a template for exchange between two organizations. The number of minutiae is limited for the fixed size template, especially when embedding minutiae into a smart card. In this work, we propose a minutiae selection method that can choose a required number of most reliable minutiae from all extracted minutiae of the input fingerprint. These robust minutiae are selected based on distance from a reference point and a corresponding minutiae density. We benchmark the proposed method against three previous techniques with two fingerprint matching algorithms on the NIST SD14 database. The experimental results show that the proposed method achieves most of the best performance in terms of the rank-1 identification rate for both matching algorithms.
Multi-Task-Oriented Vehicular Crowdsensing: A Deep Learning Approach

With the popularity of drones and driverless cars, vehicular crowdsensing (VCS) becomes increasingly widely-used by taking advantage of their high-precision sensors and durability in harsh environments. Since abrupt sensing tasks usually cannot be prepared beforehand, we need a generic control logic fit-for-use all tasks which are similar in nature, but different in their own settings like Point-of-Interest (PoI) distributions. The objectives include to simultaneously maximize the data collection amount, geographic fairness, and minimize the energy consumption of all vehicles for all tasks, which usually cannot be explicitly expressed in a closed-form equation, thus not tractable as an optimization problem. In this paper, we propose a deep reinforcement learning (DRL)-based centralized control, distributed execution framework for multi-task-oriented VCS, called "DRL-MTVCS". It includes an asynchronous architecture with spatiotemporal state information modeling, multi-task-oriented value estimates by adaptive normalization, and auxiliary vehicle action exploration by pixel control. We compare with three baselines, and results show that DRL-MTVCS outperforms all others in terms of energy efficiency when varying different numbers of tasks, vehicles, charging stations and sensing range.
8:1 Radio Frequency High Power Combiner

Power combiner used to combine the power from various solid-state amplifiers. A power combiner is essential for applications that require a larger power source for their efficient operation. Since taking inputs from multiple solid-state amplifiers is a tedious job and limits its overall power throughput. This paper proposes a design for 8:1 RF combiner, operating at radio frequency, which combines the power received at its eight input ports in one-step. The design approach is Circuit level combining based on N-way combining structure and radial combining and is analyzed using S-parameters. The simulated results show that this approach of designing 8:1 way power combiner provides a high degree of isolation, low return loss and low reflection loss with 90% efficiency and unsurpassed simplicity in design to fabricate.
Latency-aware VNF Chain Deployment with Efficient Resource Reuse at Network Edge

With the increasing demand of low-latency network services, mobile edge computing (MEC) emerges as a new paradigm, which provides server resources and processing capacities in close proximity to end users. Based on network function virtualization (NFV), network services can be flexibly provisioned as virtual network function (VNF) chains deployed at edge servers. However, due to the resource shortage at the network edge, how to efficiently deploy VNF chains with latency guarantees and resource efficiency remains as a challenging problem. In this work, we focus on jointly optimizing the resource utilization of both edge servers and physical links under the latency limitations. Specifically, we formulate the VNF chain deployment problem as a mixed integer linear programming (MILP) to minimize the total resource consumption. We design a novel two-stage latency-aware VNF deployment scheme: highlighted by a constrained depth-first search algorithm (CDFSA) for selecting paths, and a path-based greedy algorithm (PGA) for assigning VNFs by reusing as many VNFs as possible. We demonstrate that our proposed algorithm can efficiently achieve a near-optimal solution with a theoretically-proved worstcase performance bound. Extensive simulation results show that the proposed algorithm outperforms three previous heuristic algorithms.
Bayesian Link Adaptation under a BLER Target

The optimal modulation and coding scheme (MCS) for wireless transmission depends on the dynamic wireless channel state. Hence, wireless link adaptation relies on periodically reported channel quality index (CQI) values to select the optimal MCS for each transmission instance. However, to optimize link performance for a given wireless environment, current link adaptation techniques rely on tuning parameters such as a block error rate (BLER) target and algorithm adjustments that are difficult to optimize heuristically. Here, we propose BayesLA, a Bayesian link adaptation scheme that does not require careful parameter tuning for optimal link performance in diverse wireless environments. BayesLA, which is inspired by the Thompson Sampling approach widely used for online learning, efficiently learns the MCS success probabilities conditioned on the reported CQI values. Through numerical simulations for a Rayleigh fading wireless channel and a typical cellular link configuration, we demonstrate that BayesLA outperforms state-of-the-art outer loop link adaptation (OLLA) approach in terms of the realized link throughput for a given BLER target.
Computation Scheduling for Wireless Powered Mobile Edge Computing Networks

Mobile Edge Computing (MEC) and Wireless Power Transfer (WPT) are envisioned as two promising techniques to satisfy the increasing energy and computation requirements of latency-sensitive and computation-intensive applications installed on mobile devices. The integration of MEC and WPT introduces a novel paradigm named Wireless Powered Mobile Edge Computing (WP-MEC). In WP-MEC networks, edge devices located at the edge of radio access networks, such as access points and base stations, transmit radio frequency signals to power mobile devices and mobile devices can offload their intensive computation workloads to edge devices. In this paper, we study the Computation Completion Ratio Maximization Scheduling problem for WP-MEC networks with multiple edge devices, which is proved to be NP-hard. We jointly optimize the WPT time allocation and computation scheduling for mobile devices in a WP-MEC network to maximize the computation completion ratio of the WP-MEC network and propose approximation algorithms. The approximation ratio and computation complexity of the proposed algorithms are theoretically analyzed. Extensive simulations are conducted to verify the performance of the proposed algorithms.
QoS validation tests for Aircraft Access to SWIM by Ground Taxiing Experiments

The System Wide Information Management (SWIM) concept has been discussed for the standardization of the data format and exchange protocol internationally in order to share the necessary information efficiently and safely in the area of civil aviation. Therefore, aircraft access to SWIM (AAtS) is a key function to achieve rich information exchange between the aircraft and ground systems. However, it is difficult for current air-to-ground communication systems to satisfy the requirement of SWIM. In this paper, we present validation test for availability of Aeronautical Mobile Airport Communications System (AeroMACS) as a wireless media to achieve the AAtS in the airport. We carried out field taxiing experiments based on the AeroMACS and SWIM prototype systems at the Sendai airport. Furthermore, we conducted validation tests of Quality of Service (QoS) function of AeroMACS for accessing to ground SWIM systems. The results show the effectiveness of AeroMACS for AAtS.
A Soft Pneumatic Crawling Robot with Unbalanced Inflation*

Soft pneumatic robots have gained research interests in many fields, showing an advantage of compliance and safeness to human-robotic interaction and various environments. In this paper, a multi/dual-chamber soft crawling robot based on unbalanced pneumatic actuation is presented, which requires only simple design and control to realize the forward locomotion and steering. The design is composed of three parts: the main part of the crawling robot is made of hyper-elastic silicone rubber; an unstretchable fabric is attached outside of part of the robot to form an exterior constraint; two chambers are mounted on the head as steering module. Experiments validate the design in principle and an adequate locomotion performance of the robot. Due to the simple, low-cost structure and its flexible actuation, such crawling robots can be used to perform pipeline exploration, search and rescue missions.
Voltage Sag Mitigation by using Photovoltaic based Dynamic Voltage Restorer in Barak Valley Power System

In the distribution and sub-transmission system, the main power quality challenge is voltage sag. To counteract it, diverse solutions have been searched to compensate it so that financial losses in the industries can be minimized. To avoid such type of negative impact in sensitive loads where commercial factories are connected, dynamic voltage restorer (DVR) have been used. The DVR works in a series reactive power compensation principle and is located in series with the load bus and retains the capability to produce counter action quickly in opposition to the voltage sag. This paper explained series reactive power injection using photovoltaic (PV) based DVR. The recommended approach is to develop an analytical aspect which is tested in a real-time Barrak Valley sub-transmission/distribution power system to validate this approach. Some percentage of voltage sag can be exterminated by supplying reactive power. The proposed scheme of DVR incorporates a PV system connected to the DC-DC-AC converter.
May the odds — or your personality — be in your favor: Probability of observing a favorable outcome, Honesty-Humility, and dishonest behavior

In the light of the potential negative consequences of dishonest behaviors for individuals and societies, researchers from different disciplines have aimed to investigate situation and person factors shaping the occurrence and extent of such behaviors. The present study investigates the roles of a situation factor, the baseline probability of observing a favorable outcome, and a person factor, trait Honesty-Humility from the HEXACO Model of Personality, in shaping dishonest behavior. Next to main effects, a person-situation interaction between these factors was tested. Across three studies with 5,297 participants overall, we find that a higher baseline probability of observing a favorable outcome and lower levels in Honesty-Humility are linked to more dishonest behavior, whereas there was no strong evidence for an interaction between these factors. By testing the assumed effects in two different cheating paradigms, this study additionally allows to disentangle previously found effects of (a) the distance between an observed and the favorable outcome and (b) the baseline probability of observing a favorable outcome.
RF-Mirror: Mitigating Mutual Coupling Interference in Two-Tag Array Labeled RFID Systems

Recent RFID systems start attaching a tag array consisting of two or more tags on an object to deal with polarization mismatch and RF phase periodicity for battery-free sensing and localization. The multi-tag solution can also provide target orientation estimation. However, when these tags are closely spaced apart, mutual coupling will be induced, producing the unexpected changes in reported RSSI and RF phase. In this paper, we present RF-Mirror that enables compensating the distortion in a two-tag array labeled RFID system. The system would output the accurate difference in tag-to-antenna distances between two tags, which is a fundamental parameter in previous works for use. Firstly, we model the backscatter signal of a responding tag in a two-tag scenario, and then formulate novel RSSI- and RF phase-distance models with coupling terms. Secondly, we design an algorithm to characterize the coupling effect on tag gain by fusing RSSI and RF phase. Thirdly, we design a decoupling algorithm based on an observation that tag mutual coupling is independent of the position of a tag array relative to a reader antenna. Our experiments show the effectiveness of our models and RF-Mirror achieves the decoupling error of 0.197 cm in calculating the tag-to-antenna distance difference.
MagStroke: A Magnetic Based Virtual Keyboard for Off-the-Shelf Smart Devices

We present MagStroke, a system that provides a more flexible mechanism for providing textual input to smart devices with small screens. The key idea is that MagStroke leverages the effect of a magnet worn on the user’s hand on a nearby device’s magnetometer to enable the use of larger virtualized keyboards. While typing a certain key using a virtual standard keyboard, the hand and fingers of the user, with the attached magnet, move in a unique motion and thus a unique pattern is formed in the device’s embedded magnetometer sensor readings. To realize MagStroke, we address several challenges including handling background magnetic noise, keystroke detection and pattern segmentation, feature selection, and training overhead reduction. We implement a proof-of-concept prototype using off-the-shelf mobile devices and extensively evaluate the different modules in our system. Our results show that MagStroke can achieve a word recognition accuracy of 95% with a 46% reduction in training overhead, highlighting its promise as a ubiquitous off-the-shelf magnetic-based keyboard.
Direct Bonding of GaN to Diamond Substrate at Room Temperature

GaN-diamond integration is being paid far more attention to realize a better thermal management of GaN-HEMT device GaN-HEMT device with the increase of the requirements on high power density and high reliability. Although growth method has been widely investigated, novel bonding method bring new possibility to realize GaN-diamond integration without traditional problems. This paper reviewed the previous bonding researches for GaN-diamond integration.
New Study on a Necessary and Sufficient State-Feedback Stabilization Condition for Singular Discrete-Time System

This paper proposes a new necessary and sufficient condition for the state-feedback stabilization of discrete-time singular systems via a linear matrix inequality (LMI) approach. This paper derives an alternative form of admissibility criterion of the discrete-time singular systems as a necessary and sufficient condition. Then, for the closed-loop system obtained by using the state-feedback controller, the derived admissibility criterion is applied, which leads to the non-convex matrix inequalities. This paper specially chooses the block entries of the congruent transformation matrix so that the non-convex matrix inequalities are successfully converted into the convex one. Consequently, the state-feedback stabilization control for discrete-time singular systems is obtained as a necessary and sufficient condition in terms of LMIs. The feasibility of the proposed control is further described via a numerical example.