Miracle-dz/UniFAR-FTUs huggingface dataset:
Design challenges of smart meter-based applications

The smart grid is an interconnected electricity network. It integrates the electricity grid with powerful control and communications networks that can dynamically respond to customer demands and energy supply scenarios with increased reliability. One of the key components of the smart grid is the smart meter, which is the main sensor in the electricity distribution grid. As of today, the introduction of the smart meter has transformed manual electricity billing system to an automated meter reading system. In the future, the capabilities of smart meters will not only be limited to meter-readings but are expected to facilitate outage detections and demand side management, allowing the grid to respond dynamically to both customer demands and energy market pricing signals. However, these smart meters based applications face many challenges in implementing them in the network including provisioning adequate resources for smart metering traffic to guarantee the required quality of service (QoS) level, maintaining scalability of applications that require complex computations, ensuring the security of the smart metering data, and providing a platform to identify the effect of communications networks on smart meter applications. This thesis investigates approaches to overcome the challenges in implementing smart meter based applications to achieve a reliable and cost-efficient electricity network. In particular, this thesis examines efficient solutions to overcome three key challenges: mechanisms to guarantee QoS levels when smart meters use public communications networks to transport their data, approaches to guarantee the scalable deployment of the complex smart meter based applications, and a platform to efficiently simulate smart grid networks along with its control and communication operations in order to assess smart grid applications. For the smart meter communications network, the public telecommunications network is considered as a cost-effective solution as it does not involve any separate installation or maintenance costs. However, when sharing network resources with both public traffic and smart metering traffic, required QoS levels of essential broadband services along with those of smart meters should be satisfied. To this end, this thesis explores
Advanced chip package of a fiber-coupled superconducting single-photon detector for a closed-cycle cryostat

Superconducting single photon detectors are promising candidates for cutting-edge applications like space-to-ground communication and quantum-key-distribution. The challenge is to transfer the assembly technology from university research to industrial processes. Especially the positioning of the optical fiber with respect to the active area of the superconducting detector are open questions. We demonstrate the operation of a superconducting nanowire single photon detector (SNSPD) in a closed cycle cryostat. The thermal coupling between superconducting detector chip and closed-cycle cryostat is investigated. The possibility to mount the optical fiber in an RIE-ICP-etched hole in a silicon carrier wafer is demonstrated. Different illumination wavelength and intensities are used to validate the SNSPD assembly in the closed cycle cryostat. Further, an advanced package of the silicon carrier wafer and the SNSPD-chip is introduced. In this package, the SNSPD-chip is mounted via flip-chip technology on the silicon carrier wafer. The striven flip-chip position accuracy of ± 1 μm ensures the accurate coupling between optical fiber and active area of the SNSPD.
Novel high-isolation power divider integrated filtering response

ABSTRACT A bandpass-filtering-response power divider with good isolation is presented in this paper. The short-circuited-loaded resonators are employed to construct the presented compact power divider. The presented divider can be viewed as an integration of a second-order dual-mode bandpass filter and the quasi-Wilkinson power divider. The simulated and measured results of the proposed power divider demonstrate good performance, such as about 30-dB high isolation among the output ports, wide upper stopband, excellent amplitude and phase balancing, and reasonable bandpass-filtering response.
EMAIL CHAT DETECTION USING SEMANTIC IMPLEMENTED RELEGATED AUTO CONVERTER

As a profit of evident public broadcast, thermionic tyrannical has attain a ruthless embarrass afflicting children, adolescents and burgeoning adults. Machine order approaches make evident unmasking of hectoring messages in comprehensive cellular suited, and this could help to commenced a vigorous and safe informational news background. In this necessary exhort area, one cruel relevance press on and detrimental consequent portrayal horticulture of text messages. In this scan, we aim a new likeness behavior prepare to try this sadden. Our taste titled Semantic-Enhanced Marginalized DE circulate Auto-Encoder enters via linguistic develop of the here and there deep inspect come up with stacked cease auto encoder. The linguistic interruption consists of cure nonconformist rouse and impotence constraints, quality the grammatical disaster collide is bred stationed on department conclusion and vast embedding hone. Our prompted skyscraper spare retains the hidden mark forming of bossy info and gain a frightful and hostile report of text. Comprehensive experiments on two popular programmed imperious corpora (Twitter and Myspace) are conducted, and the results show that our asked approaches eclipse new norm text theory habits approaches.
A Realistic Model for Complex Networks

It appeared recently that the classical random network model used to represent complex networks does not capture their main properties (clustering, degree distribution). Since then, various attempts have been made to provide network models having these properties. We propose here the first model which achieves the following challenges: it produces networks which have the three main wanted properties, it is based on some real-world observations, and it is sufficiently simple to make it possible to prove its main properties. We first give an overview of the field by presenting the main models introduced until now, then we discuss some remarks on some complex networks which lead us to the definition of our model. We then show that the model has the expected properties and that it can actually be seen as a general model for complex networks.
DGALab: an extensible software implementation for DGA

The development of a new dissolved gas analysis (DGA) method often requires a comparative study to assess the accuracy of the proposed technique. This is faced with the following challenges: (i) the time and effort required to implement and validate the implementation of existing DGA methods, adds to the comparative study cost; (ii) the output states of different DGA methods are not similar, which makes it difficult to put methods side by side in a comparative study; and (iii) the availability of test data is limited. In this study, a user-friendly graphical user interface software package, DGALab, is developed to overcome these challenges. DGALab implements a unified DGA diagnosis framework to map the output states of DGA methods to uniform specifications. DGALab includes a library implementing most common DGA techniques, and includes a repository for input datasets available in the literature and collected directly from laboratories. DGALab simplifies the addition of new DGA techniques written in virtually any programming language. As a result, the process of developing a new DGA technique is greatly simplified using DGALab. To evaluate the software package results, the datasets and methods implemented therein were used to regenerate the results published in earlier research papers.
Occualizer: Optimistic Concurrent Search Trees From Sequential Code

This paper presents Occualizer, a mechanical source code transformation for adding scalable optimistic synchronization to a sequential search tree implementation. Occualizer injects synchronization only to the update steps of tree operations, leaving traversal steps to execute unsynchronized, thereby maximizing parallelism. We use Occualizer to create concurrent versions of a sequential B+tree, trie, and red-black tree. Evaluation on a 28-core machine shows that Occualizer’s trees signiﬁcantly outperform prior mechanically-crafted trees on non-read-only workloads and are comparable (within 4%) on read-only workloads. Overall, Occualizer shrinks the performance gap between mechanically- and hand-crafted trees by up to 13 × . When using Occualizer’s B+tree as the index in the STO main-memory database, the system’s throughput degrades by less than 30% compared to the default Masstree index, and it scales better.
DIGITAL, SEMANTIC AND PHYSICAL ANALYSIS OF MEDIA INTEGRITY

ABSTRACT In this report, we present summary of the digital, physical and semantic image forensics and integrity methods developed by the DiSPARITY team, lead by the University of Southern California Information Sciences Institute, under DARPA's Media Forensics program between 2016 and 2020. The team also included University of Naples, Italy and University of Erlangen-Nuremberg, Germany. The DiSparity team has developed various state of the digital integrity methods (e.g. NoisePrint, GAN fingerprint and ManTra-Net), physical integrity (e.g. segmentation-free light direction estimation and analysis of incident light direction).
The interpretation and grammatical representation of animacy

Abstract:We are used to thinking about person, number, and gender as features to which the grammar is sensitive. But the place of animacy is less familiar, despite its robust syntactic activity in many languages. I investigate the pronominal system of Southeastern Sierra Zapotec, identifying an interpretive parallel between animacy and person. Third-person plural pronouns, which encode a four-way animacy distinction in the language, exhibit associativity, a cluster of interpretive properties that have been argued also to characterize first-and second-person plural pronouns. Building on Kratzer’s (2009) and Harbour’s (2016) theories of person, I propose a plurality-based semantics for animacy that captures their shared properties. The compositional mechanism underlying this semantics ties person and animacy features to a single syntactic position inside the noun phrase. This enables an understanding of these features’ shared relevance to syntactic operations, including those underlying pronoun cliticization. In these Zapotec varieties, it is constrained both by person (in the well-known person-case constraint) and by animacy.
Total Knee Arthroplasty After Genicular Nerve Radiofrequency Ablation: Reduction in Prolonged Opioid Use Without Increased Postsurgical Complications

Introduction: Genicular nerve radiofrequency ablation (GNRFA) is an increasingly used nonsurgical treatment modality for patients with advanced knee osteoarthritis. Previous studies have demonstrated this to be an effective and safe method to decrease pain and increase functionality in this patient population. The purpose of this study was to compare 2-year postoperative complication rates and rates of prolonged postoperative opioid usage between patients undergoing total knee arthroplasty (TKA) after previous GNRFA and those undergoing TKA alone. Methods: Patients who underwent primary TKA after prior GNRFA (GNRFA-TKA) of the ipsilateral knee were identified in a national all-payer claims database from 2010 to 2019. Univariate and multivariable analyses were conducted comparing those with prior GNRFA and those without. Outcomes of interest included prolonged postoperative opioid usage, 2-year revision rates, and 90-day medical complications. Statistical analysis was conducted using R software provided by the PearlDiver Database. Results: In total, 675 patients in the GNRFA-TKA cohort were compared with a control cohort of 255,351 patients. Genicular nerve radiofrequency ablation-total knee arthroplasty patientshad lower odds of prolonged opioid use postoperatively (OR: 0.478; 95%: 0.409 to 0.559; P < 0.001). No notable difference was observed in the 2-year surgical outcomes between cohorts. Patients in the GNRFA-TKA cohort had lower odds of requiring a blood transfusion and having postoperative anemia, all arrhythmias, and urinary infections compared with primary TKA control patients. Conclusion: Preoperative GNRFA leads to a lower rate of prolonged postoperative opioid use in patients undergoing TKA, without an increased risk of complications. Future prospective studies are needed to validate the findings of this database study.
Drivers of Corporate Investment Slowdown in India: A Firm Level Analysis

: Majority of the existing literature has focused on examining the determinants of investment behaviour and the factors responsible for its slowdown at an aggregate level. There have only been a few studies analysing investment behaviour at firm level, and those are confined to the manufacturing sector using static panel models. In this context, we examine the sector specific heterogeneity of investment dynamics in India using firm level investment data spanning the period 2001-19. The study employs dynamic panel models on micro-level data to provide better clarity on the macroeconomic issue of investment slowdown in the country. This paper finds that a variety of factors contributed to the investment decline in India, including slower expansion of profitability and bank credit amounting to the twin balance sheet problem, debt sustainability, higher burden of indirect and corporate taxes, higher policy rates, rising real interest rates and increasing uncertainty related to economic policy.
Data-Efficient Communication Traffic Prediction With Deep Transfer Learning

Prediction of future traffic load is a crucial task to support the automatic Operations, Administration, and Management (OAM) of communication networks. Existing Machine Learning (ML) models require big data to accomplish this task. However, large data sets are not always available, due to the limited storage capacity and the high storage cost at Base Stations (BSs). To solve the problem, we leverage the spatial-temporal correlation among different BSs, which allows other BSs’ data to be used for the prediction of the target BS. One major challenge in realizing this idea is the imbalance of data amounts between neighbor BSs and a prediction target BS. If one simply aggregates the data from both neighbors and target, the target’s traffic features would be overwhelmed by the neighbors’ data. To address this challenge, we propose a Spatial-Temporal Transfer (STT) framework, which trains a base model with an aggregated data set from multiple BSs, and then carefully refines the base model to serve a target BS. To strike a perfect balance between general tendency and individual features, STT adopts an advanced transfer learning technique that exploits regularization on model parameters. Experiments show the efficiency of the proposed STT framework.
An Interaction-Aware Vehicle Behavior Prediction for Connected Automated Vehicles

Reliably anticipating the future behavior of surrounding vehicles is critical for the safe operation of the Connected Automated Vehicles (CAV) and improves traffic safety. This task requires processing the history and current behavior of a target vehicle and its surrounding vehicles. Nevertheless, this level of situational awareness is challenging due to the limited observability of the ego CAV’s mounted sensors, particularly in unsignalized intersections as an example of a complex scenario. In the current study, we propose an interaction-aware behavior prediction framework for CAVs which takes advantage of vehicular communication technologies to improve the prediction performance at the time of occlusion. With the help of Vehicle-to-Vehicle (V2V) communications, connected vehicles can gain an enriched understanding of the current behavior of the nearby vehicles, leading to an enhanced prediction. We benefit from graph convolutional networks to model the connection between the vehicles. We further analyze the proposed model over a large real-world dataset containing 14867 vehicle trajectories. The results indicate the higher performance of the introduced model against several benchmarks.
A Trust Management Framework for Connected Autonomous Vehicles Using Interaction Provenance

Connected autonomous vehicles (CAVs) have fostered the development of intelligent transportation systems that support critical safety information sharing with minimum latency and making driving decisions autonomously. However, the CAV environment is vulnerable to different external and internal attacks. Authorized but malicious entities which provide wrong information impose challenges in preventing internal attacks. An essential requirement for thwarting internal attacks is to identify the trustworthiness of the vehicles. This paper exploits interaction provenance to propose a trust management framework for CAVs that considers both in-vehicle and vehicular network security incidents, supports flexible security policies and ensures privacy. The framework contains an interaction provenance recording and trust management protocol that extracts events from interaction provenance and calculates trustworthiness using fuzzy policies based on the events. Simulation results show that the framework is effective and can be integrated with the CAV stack with minimal computation and communication overhead.
An Improved Linearity Ring Oscillator-Based Current-to-Digital Converter

Many biosensors produce single-ended current outputs. Lab-on-chip applications demand parallel readout channels requiring low area current-to-digital converters. High HD2 has limited the current controlled ring oscillator’s (CCROs) adoption as a low area, single-ended converter. This work improves CCRO open loop linearity by 10 dB. A wide-bandwidth current buffer is also designed. A low area (0.0025 mm <sup>2</sup>), low power (<inline-formula> <tex-math notation="LaTeX">$357 ~\mu \mathrm {W}$ </tex-math></inline-formula>), single-ended, and 1 MHz bandwidth converter suitable for array readout is presented with the measured performance.
Evolution of resource generalism via generalized stress response confers increased reproductive thermal tolerance in a pest beetle

Generalism should be favoured evolutionarily when there is no genetic constraint or loss of fitness across alternative environments. However, evolution of generalism can require substantial evolutionary change, which can confer a general stress response to other aspects of the environment. We created generalist lineages from an ancestral, resource-specialized laboratory population of seed beetles (Callosobruchus maculatus) by rearing lines over 60 generations on a mixture of both ancestral and novel host species to test for costs associated with the evolution of generalism involving evolutionary changes in gene expression and correlated phenotypic responses during a shift to generalism. Evolved lines had higher fitness on the novel resource, with no loss of fitness on the ancestral resource, indicating that they overcame initial fitness trade-offs. This involved upregulation of major stress response (heat shock protein) genes and genes coding for metabolic enzymes, suggesting an underpinning metabolic and physiological cost. Resource generalist populations also evolved greater thermal tolerance breadth, highlighting that the evolution of resource generalism might pre-adapt species to respond favourably to other environmental stressors, following selection for generalized stress response gene upregulation. The rapid gain of novel hosts during a pest invasion might also confer greater thermal resilience to ongoing climate change.
Numerical analysis of all-optical silicon microring resonator-based cyclic redundancy check encoder

Abstract. Data are transmitted at a higher rate over long and short distances to fulfill the global requirement using all-optical (AO) technology. The reliability or accuracy of data transmission is also a key factor along with the higher data rate to achieve today’s desire perfectly. The cyclic redundancy check (CRC) code is a powerful, robust, and widely used error detecting code for data communication system and storage devices to find any unintentional changes during transmission. A (7, 4) CRC encoder has been analyzed numerically using AO silicon microring resonator (MRR) at a high-operational speed of 260 Gbps. CRC encoder is designed using MRR-based XOR gates and D flip flops. The proposed CRC encoder circuit is validated through MATLAB simulation. The optimization of essential parameters is accomplished through simulation against various metrics. These parameters could be utilized for practical execution of this design.
With or without him? Experimental Evidence on Gender-Sensitive Cash Grants and Trainings in Tunisia

Is it possible to stimulate women’s employment by relaxing their financial and human capital constraints? Does involving husbands help or hinder the effort? To examine these questions, we randomly allocated cash grants and financial training to 1,000 poor women in Tunisia. To encourage gender dialogue, a random subset of women could invite their male partner to the training. The cash grants and financial training positively impacted women’s income generating activities, but only for women who had to attend the training alone, suggesting that gender dialogue backfired. The program also reinforced traditional gender roles: it stimulated employment of other household members as well as investments in small-scale agriculture and livestock farming—two activities traditionally undertaken by women at home. Impacts on household living standards are overwhelmingly positive. JEL Classification: J16, L25, L26, O12
FIRP: Firefly Inspired Routing Protocol for Future Internet of Things

A network of smart sensors, usually called the Internet of Things (IoT), has lately attracted the attention of academia, industry, and government researchers. However, the IoT has faced many challenges and issues which clearly show that the dilemma of today's Internet architecture requires great effort. That’s why the Future Internet of Things (FIoT) has been frequently discussed. We distinguish two characteristics of the FIoT which make it unique: the interconnection of billions of smart objects and the limited resources of these smart objects. Routing Quality of Service (QoS) is a critical issue in this type of network, due to the devices’ characteristics. In this paper, we propose, implement, and evaluate a new bio-inspired routing protocol designed for the FIoT environments called FIRP using the Simple Additive Weight (SAW) Multi-Criteria Decision Making (MCDM) method. The idea of this algorithm was inspired from the behavior of fireflies that use their luminosity to find mates and food sources. The results of the simulation and the statistical tests show the efficiency of our routing algorithm, in particular, it improves the energy consumption, the routing overhead, and it minimizes the end-to-end delay.