Miracle-dz/UniFAR-FTUs huggingface dataset:
Thermally robust ring-shaped chromium perfect absorber of visible light

Abstract A number of light-absorbing devices based on plasmonic materials have been reported, and their device efficiencies (or absorption) are high enough to be used in real-life applications. Many light-absorbing applications such as thermophotovoltaics and energy-harvesting and energy-sensing devices usually require high-temperature durability; unfortunately, noble metals used for plasmonics are vulnerable to heat. As an alternative, refractory plasmonics has been introduced using refractory metals such as tungsten (3422°C) and transition metal nitrides such as titanium nitride (2930°C). However, some of these materials are not easy to handle for device fabrications owing to their ultra-high melting point. Here, we propose a light absorber based on chromium (Cr), which is heat tolerant due to its high melting temperature (1907°C) and is compatible with fabrication using conventional semiconductor manufacturing processes. The fabricated device has >95% average absorption of visible light (500–800 nm) independent of polarization states. To verify its tolerance of heat, the absorber was also characterized after annealing at 600°C. Because of its compactness, broadband operational wavelength, and heat tolerance, this Cr perfect absorber will have applications in high-temperature photonic devices such as solar thermophotovoltaics.
Evaluation of Plastic Recycling and Novel UV-Oriented Solution for Integration, Resilience, Inclusiveness, and Sustainability

At present, rapid urbanization has resulted in increased production of commodities, thereby enhancing the convenience of human lives. However, these products generate a lot of waste, which leads to severe environmental challenges damaging mother earth. Plastic has been an essential part of the world as society continues to develop. Bottles, toys, cars, and electronic products all have plastic components. With plastic becoming an increasingly ubiquitous presence in our daily lives, the production of plastic has also soared. A study estimated that 8.3 billion metric tons of plastic have been produced from the early 1950s to 2017 [1]. As production increases, disposal methods also need to improve. However, according to UNEP, less than 10% are recycled [2]. If plastic is not properly processed, it would remain in the world forever and create mass destruction to our environment. The oceans serve as a pertinent example, wherein a significant influx of plastic finds its way through waterways and beaches. As time moves on, plastic will start to degrade and become microplastic. According to the data, there is 51 trillion microplastic litter in the ocean [3]. They then affect our health entering the food chain, since a lot of sea animals see them as food. Overwhelmed plastics also affect human health. A research found that an apple has around 195,500 plastic particles per gram, ranking the highest among items they tested [4]. A report estimates that a person could eat a credit-card-size of plastics per week. Out of all types of plastics, including PET, HDPE, PVC, LDPE, PP, PS, and others [5]. PVC is estimated to be the most toxic to the human body. It contains a lot of phthalates, which can damage the liver, kidneys, lungs, and reproductive system. In this paper, we evaluate plastic pollution based on the framework of a closed feedback control loop: data acquisition, communication, decision-making, and action. Currently, there are multiple ways to collect data. For example, researchers can capture images from unmanned aerial vehicles and self-designed trucks. There are existing operational technologies that are actively engaged in the collection of plastics, particularly within the marine environment, such as 4ocean’s skimmer and Mr. Trash Wheel in Baltimore [6], [7]. There are also innovations such as turning used plastics into bricks [8]. In addition, We propose that effective smart plastic recycling should ideally interact with the other seven smart city subsystems proposed by UV: Smart Home, Smart Medicine and Healthcare, Smart ITS, Urban planning and Crowd management, Smart Energy Management, Smart City Infrastructure, Smart Response System for City Emergencies, and Smart Humanity.
Efficient control-oriented modelling of heterogeneous large-scale computer cooling systems

The power of modern computing equipment, from small devices such as laptops through a variety of cases up to entire data centres, makes cooling vital. Especially in large-scale systems, delivering the right cooling to the right place at the right time is crucial for both computing performance and energy efficiency. As such, modern cooling systems require a lot of controls. Given the many cases to face, designing and assessing such controls requires tools to rapidly and modularly build and manage computationally efficient simulation models, sometimes concentrating on the thermal policies aboard on a chip, sometimes on the cooling of a rack, sometimes on an entire date centre with its fluid conditioning and transport machinery, and so forth. Though technology exist to address many such cases individually, a holistic approach to embrace them all within a unified modelling methodology and workflow is still the subject of research. In this paper we distil our experience over the last years, and discuss how a solution based on joining purpose-specific chip modelling (using the 3D-ICE simulator) and Equation-Based Object-Oriented Modelling (employing the Modelica language) can help the joint design of a computing system and its cooling.
LETHE: Combined Time-to-Live Caching and Load Balancing on the Network Data Plane

Load balancers in distributed caching systems face a fundamental trade-off between networking and caching performance metrics. The first comprises how the network traffic of object requests and replies is balanced on the different links to/from the cache servers while the second denotes cache hit rates and response times. In a nutshell, the root of this trade-off lies in the combination of the skewed popularity and dynamic nature of incoming object requests as well as whether the load balancing function is agnostic to the caching application or not. In this paper, we present Lethe, a network data-plane load balancer for distributed Time-to-Live (TTL) caching. We con-sider TTL caching as it separates the object dynamics but still performs equivalently to many classical caching algorithms (e.g. LRU) under appropriate TTL parametrization. Lethe segregates the cache objects based on the pattern of the incoming requests and efficiently places objects in cache servers to balance the network traffic without sacrificing the caching system hit rate. We implement Lethe in P4 and experimentally show that it improves the average response time and cache hit rate as compared to application-agnostic load balancing even for a skewed and dynamically changing workload.
A fuel-efficient approach for platooning systems with ethical decision-making implications

Platooning system is a relevant solution for sustainable freight transportation due to its capability to substantially improve fuel efficiency, safety, and traffic flow. Most of the recent studies focus on the fuel-efficiency aspect while ignoring ethical aspects associated with the short intervehicle distance requirement. Unlike the literature, we address ethical implications that arise during platooning in addition to fuel-efficient improvement. We propose a decision-making algorithm based on a Markov Decision Process (MDP) that is able to switch between classical platooning controllers to improve fuel efficiency while accounting for a harm factor. Numerical results demonstrate the relevance of a joint design of ethical and fuel-efficiency aspects for suitable decisionmaking in platooning systems.
Leopard: A Black-Box Approach for Efficiently Verifying Various Isolation Levels

Isolation Levels (IL) act as correct contracts between applications and database management systems (DBMSs). The complex code logic and concurrent interactions among transactions make it a hard problem to expose violations of various ILs stated by DBMSs. With the recent proliferation of new DBMSs, especially the cloud ones, there is an urgent demand for a general way to verify various ILs. The core challenges come from the requirements of: (a) lightweight (verifying without modifying the application logic in workloads and the source code of DBMSs), (b) generality (verifying various ILs), and (c) efficiency (performing efficient verification on a long running workload). For lightweight, we propose to deduce transaction dependencies based on time intervals of operations collected from client-sides without touching the source code of DBMSs. For generality, based on a thorough analysis of existing concurrency control protocols, we summarize and abstract four mechanisms which can implement ILs in all commercial DBMSs we have investigated. For efficiency, we design a two-level pipeline to organize and sort massive time intervals in a time and memory conservative way; we propose a mechanism-mirrored verification to simulate the concurrency control protocols implemented in DBMSs for high throughputs. Leopard outperforms existing methods by up to 114× in verification time with a relative small memory usage. In practice, Leopard has a superpower to verify various ILs on any workload running on all commercial DBMSs. Moreover, it has successfully discovered 23 bugs that cannot be found by other existing methods.
Tensions in Inter-Organizational Data Sharing: Findings from Literature and Practice

Inter-organizational data sharing is a powerful engine required for driving the data economy. However, a major issue in research and practice remains overcoming barriers, such as a lack of trust preventing data sharing from penetrating all levels of business and fully exploiting its potential. Given the interplay of generating value from shared data and the barriers one needs to overcome, those wanting to share their data must balance what motivates them (incentives) and what hinders them (barriers) and solve the resulting conflicts (tensions). Solving these tensions enables organizations to make informed and sensible decisions on how to share data. Since sharing data has become more prevalent in research and practice, our paper tackles precisely that issue. We explore emerging tensions in inter-organizational data sharing using data generated from a systematic literature view and qualitative expert interviews. We complete our research with workshops to supplement the results and validate them.
Sequence-Oriented DBMS Fuzzing

The SQL specification consists of hundreds of statement types, which leads to difficulties in DBMS fuzzing: state-of-the-art works generally reuse the statements of predefined types; the limited types cannot cover the full input space and test the corresponding logic consequently. In this paper, we propose Lego, a fuzzer to generate SQL sequences with abundant types to improve DBMS fuzzing coverage. The key idea of sequence generation is type-affinity, which indicates the meaningful occurrence of SQL type pairs (e.g., INSERT and SELECT). During each fuzzing iteration, Lego first proactively explores SQL statements of different types and analyzes affinities with coverage feedback. Next, when a new affinity is discovered, Lego synthesizes new SQL sequences containing the types progressively.We evaluate Lego on PostgreSQL, MySQL, MariaDB, and Comdb2 against SQLancer, SQLsmith, and Squirrel. The sequence-oriented fuzzing helps Lego outperform other fuzzers on branch coverage by 44%–198%. More importantly, in the continuous fuzzing, Lego has discovered 102 new vulnerabilities confirmed by the corresponding vendors, including 6 bugs in PostgreSQL, 21 bugs in MySQL, 42 bugs in MariaDB, and 33 bugs in Comdb2. Among them, 22 CVEs have been assigned due to their severe security influences.
Reinforcement Learning based Tree Decomposition for Distance Querying in Road Networks

Computing the shortest path distance between two vertices in a road network is a building block in numerous applications. To do so efficiently, the state-of-the-art proposals adopt a tree decomposition process with heuristic strategies to build 2-hop label indexes. However, these indexes suffer from large space overheads caused by either tree imbalance or a large tree height. Independently of this, reinforcement learning has recently show impressive performance at sequential decision making in spatial data management tasks. We observe that tree decomposition is naturally a sequential decision making problem that decides which vertex to process at each step. In this paper, we propose a reinforcement learning based tree decomposition (RLTD) approach that reduces the space overhead significantly. We model tree decomposition as a Markov Decision Process, exploiting features of both the network topological structure and the tree structure. We further optimize the tree decomposition process by taking the network density into account, which yields a great generalization of the model on large road networks. Extensive experiments with real-world data offer insights into the performance of the proposals, showing that they are able to reduce the space overhead by about 51% and achieve on average about 14% speedup for queries with almost the same preprocessing time when compared with the state-of-the-art proposals.
A Universal Robust Algorithm for Parameters Estimation of Induction Machines at Standstill Operation in Variable Frequency Drives

In this paper a comprehensive method is proposed for the electrical parameters estimation of three phase induction machines (IMs) at standstill condition. All the electrical parameters of IMs with high accuracy are extracted at standstill condition by employing the proposed estimation technique. This method is comprised of four steps. In the first and second steps, the stator resistance and the leakage reactance are estimated, respectively. The third step is related to rotor time constant estimation and the magnetizing inductance is estimated in the last step. The estimation process is based on the Γ-inverse model of IMs. Based on the estimated parameters, all of the parameters of the conventional d-q model of IMs are also calculated. The functionality of the proposed method is verified through multiple simulations executed for several IMs with different power ratings. Implementing this method can significantly improve the functionality of various IM drive systems, especially those based on the electrical model of machine.
Analysis, Design, and Comparison of Isolated DC-DC Converters for Traction Application

Solid-state transformers (SSTs) have recently been proposed in the literature as a promising solution for traction application. SSTs consist of a two-stage topology that includes an ac-dc and dc-dc stage. The optimization of the different stages involved in energy conversion (rectification, isolated dc-dc conversion, and inversion) is necessary for their efficient operation. The dc-dc conversion stage is an important stage as isolated dc-dc converters enable galvanic isolation offer higher power density and efficiency, support bidirectional power flow, and provide a wide voltage range. Therefore, with the aim of having a compact and more efficient railway traction systems, study of the isolated dc-dc converters with high efficiency, power density and improved thermal management is of utmost importance for creating compact and efficient railway traction systems based on SSTs. This paper presents a detailed analysis and design of various isolated bidirectional dc-dc converters suitable for traction applications in SST-based systems. The objective is to identify topologies that offer high efficiency and power density to improve the performance of SST-based systems. A 5kW prototype is designed and developed using SiC devices with a flexibility to test each of these topologies. An analysis is conducted to evaluate the efficiency, control complexity, component stress and soft switching range.
In-silico screening of Kamini plant (Murraya paniculata) against RNA Dependent RNA Polymerase (RDRP) of SARS-CoV-2

To identify potential inhibitors of the RNA-dependent RNA polymerase (RDRP) involved in coronavirus replication, a computational study was performed. 50 compounds from the Kamini plant (Murraya paniculata) were docked against the RDRP. The top five compounds had binding energies ranging from -7.4 kJ/mol to -6.8 kJ/mol. One compound (Trans-Muurola-4(14),5-Diene) was chosen after ADME study due to its favorable pharmacokinetic characteristics. Using molecular dynamics simulations, the stability of the protein-ligand complex was calculated in a solvent environment. This study suggests that further investigation of this compound is warranted in the wet lab, preclinical, and clinical trials.
IndiBits: Incremental Discovery of Relaxed Functional Dependencies using Bitwise Similarity

One of the main challenges in data profiling is to efficiently extract metadata from dynamic information sources, by avoiding the processing of the whole dataset from scratch upon modifications. In this paper, we present IndiBits, an algorithm for discovering relaxed functional dependencies (RFDs for short), which represent data relationships relying on approximate matching paradigms. IndiBits is able to dynamically infer and update the RFDs holding on a dataset upon modification operations performed on it. It exploits a binary representation of data similarities, a new validation method, and specific search methods, to dynamically update the set of RFDs, based on previously holding RFDs and the type of modifications performed over data. Experimental results demonstrate the effectiveness of IndiBits on real-world datasets, even in comparison with FD and RFD discovery algorithms in both static and dynamic scenarios.
Mathematical Model for Assessment of Production and Energy Utilization of Landfill Gas in the Pelagonija Region in Republic of N. Macedonia

This paper presents a simulation of biogas production in the projected landfill in the Pelagonija region in the Republic of N. Macedonia, using the Landfill Gas Emissions Model (LandGEM, version 3.03). The purpose of the analysis is to obtain the amount of produced biogas and the possibility of using it as energy, as well as the other products from anaerobic digestion which have a negative impact on the environment.
Automated Motorcycle Helmet Detection Using The Combination of YOLO AND CNN

Motorcycle accidents are a significant public concern worldwide. Accidents have been increasing rapidly which leads to the loss of several lives. One of the main contributing factors to the severity of injuries in these accidents is the lack of helmet use. Though the usage of helmets is necessary, it is also considered a piece of mandatory safety equipment among drivers, but they do not make use of it which causes unfortunate incidents to take place. Traffic police do try to bring awareness among the people but most of the population refuses to follow them. To avoid such incidents and to detect people from breaking the rules, this research was introduced. Here, we present a study based on an automated helmet detection system for motorcyclists using the combined techniques of YOLO and CNN. The combination of these image processing techniques such as You Only Look Once (YOLO) and Convolutional Neural Network (CNN) helps in the identification of helmets from image datasets. Because of this combination, the model can accurately identify helmets with a high degree of reliability due to the use of image datasets. And due to this combination, our system can accurately detect helmets with an accuracy of 94.29%.
Distributed MIMO Systems for 6G

This study focuses on Distributed MIMO (D-MIMO) systems and provides a discussion about their role in next generation networks. The paradigm shift to distributed networks offers great potential to address the 6G requirements, through macro diversity. As 6G scenarios and use cases continue to emerge, new challenges are likely to arise that may affect the widespread imp-lementation of D-MIMO. To address those, different deployment options have been proposed for roll-out considerations. They are composed of several sub-components that can be categorized as (i) wireless or wired fronthaul/backhaul, (ii) analog or digital signals, (iii) distributed or centralized processing, and (iv) coherent or non-coherent transmission. To facilitate standardization efforts, we provide 3GPP-aligned terminology for network nodes, multi-point transmission and reception schemes. In order to enable large-scale implementation of D-MIMO systems, it is important to determine the needed amount of distribution, develop practical solutions for high-frequency bands, and ways to convey data that meet the transport requirements. On this regard, we discuss key enablers and present simulation results for D-MIMO systems towards 6G. In particular, we present solutions for D-MIMO networks in dynamic scenarios related to channel estimation and layer-l mobility considering coherent and non-coherent joint transmission, and analog fronthaul implementation using analog-radio-over-fiber that are promising for high (upper mm-Wave and (sub-)THz) carrier frequencies, as well as integrated access and backhaul, network-controlled repeaters, and reconfigurable intelligent surfaces that are possible enablers for cost-efficient network densification at both low (cm-Wave, lower mm-Wave) and high carrier frequencies.
GALE: Active Adversarial Learning for Erroneous Node Detection in Graphs

We introduce GALE, an active adversarial learning framework to detect nodes with erroneous information in attributed graphs. GALE is empowered by a new adversarial active error detection framework, which interacts active learning with a graph generative adversarial model to best exploit limited labeled examples of erroneous nodes. It dynamically determines diversified query nodes in batches with bounded size in terms of node typicality to enrich a pool of examples, which in turn provides representative examples to best train an adversarial classifier to capture different types of errors. Moreover, GALE provides an annotation algorithm to suggest a context of possible correct attribute values and error types, to facilitate the labeling of query nodes. We show that using limited queries and examples, GALE significantly improves competing methods such as constraint-based detection, outlier detection, and Graph Neural Networks (e.g. GCNs), with 32%, 31%, and 17% gain in F-1 score on average, and is feasible in learning cost for large graphs.
Incorporating User’s Preference into Attributed Graph Clustering : Extended abstract

In contrast to global graph clustering, local graph clustering aims to find only one cluster that is concentrating on the given seed vertex (and also on the designated attributes for attributed graphs). Currently, very few methods can deal with this kind of task. To this end, we propose two quality measures for a local graph cluster: Graph Unimodality (GU) and Attribute Unimodality (AU). They measure the homogeneity/unimodality of the graph structure and the subspace that is composed of the designated attributes, respectively. We call their linear combination Compactness. Further, we propose LOCLU to optimize the Compactness score in order to find a good local graph cluster. The local graph cluster detected by LOCLU concentrates on the region of interest, provides efficient information flow in the graph, and exhibits a unimodal data distribution in the subspace of the designated attributes.