Reviewerly/exHarmony huggingface dataset:
Detection of a Facemask Using Convolution Neural Network

Due to the COVID-19 pandemic, wearing the mask has become obligatory in public locations as it gives a most preventive impact in opposition to viral transmission. It has affected our day-to-day life to a greater extent. Though people had got vaccinated, mask wearing, social distance maintenance and sanitization need to be practiced probably till the pandemic gets vanished. Proposed work layout a real-time deep learning version to satisfy current demand for detection of facemask wearing position of someone earlier than he or she enters a public place. This paper provides a simplified method for achieving the intended goal in machine learning applications such as TensorFlow, Keras, OpenCV, and MobileNet. The proposed approach determines how the face mask is worn in real time; it leverages live image captures that provide accurate information about whether a person is wearing the mask appropriately. The parameters of the convolution neural network model are used to detect the presence of facial mask(s). The proposed approach attains the accuracy that is almost nearer to 99.75%.
SDR Implementation of WIFI Attacks Using GNURadio

IEEE 802.11 wireless networks have grown in popularity, offering users mobility and flexibility in accessing information. However, existing solutions for wireless LANs have been exposed to security vulnerabilities. In this paper, we target to implement passive and active attacks by exploiting some known security flaws in the IEEE 802.11 protocol, based on SDR technology. More specifically, a software radio implementation of the Wi-Fi protocol has been conducted using the USRP B210 hardware platform and software tools such as GNU Radio and Scapy. In addition, an experimental validation of some active and passive attacks has been carried out showing practical vulnerabilities of this protocol. It was concluded that the security issue of the Wi-Fi standard needs to be taken in consideration by fixing all these vulnerabilities that become possible when using SDR tools.
DLIME-Graphs: A DLIME Extension Based on Triple Embedding for Graphs

In the last years, several research works have been proposed for the Knowledge Graph Completion task. However, like most Machine Learning models, most Knowledge Graph Completion models are opaque and lack interpretability. In order to achieve transparency, several interpretable and explainable models have been proposed. The Deterministic Local Interpretable Model-Agnostic Explanations (DLIME) was proposed to solve the lack of stability of the Local Interpretable Model-Agnostic Explanations (LIME), one of the most popular surrogate models. However, using DLIME to explain Machine Learning models in graphs becomes an issue due to its experiments being published only with tabular data. Therefore, this work aims to propose an interpretable method for graphs as an extension of DLIME named DLIME-Graphs. As a triple representation, DLIME-Graphs uses triple embeddings computed by SBERT which in turn, are reduced by the UMAP technique. Instead of using Hierarchical Clustering as DLIME, DLIME-Graphs uses HDB-SCAN to get clusters. To explain a test triple, DLIME-Graphs proposes to train two interpretable models: logistic regression and decision tree plus getting the most similar triples by a k-NN algorithm. The demonstration through a study case showed that DLIME-Graphs is able to give explanations for 100% of the triples in the test dataset through the former models offering transparency and interpretability.
Towards Contrastive Context-Aware Conversational Emotion Recognition

Conversational Emotion Recognition (CER) aims at classifying the emotion of each utterance in a conversation. For a target utterance, its emotion is jointly determined by multiple factors, such as conversation topics, emotion labels and intra/inter-speaker influences, in the conversational context of it. Then an important research question arises: can the effects of these contextual factors be sufficiently captured by the current CER models? To answer this question, we carry out an empirical study on four representative CER models by a context-replacement methodology. The results suggest that these models either exhibit a label-copying effect, or rely heavily on the intra/inter-speaker dependency structure within the conversation, but do not make a good use of the semantics carried by the conversational context. Thus, there is a high risk that they overfit certain single factors, yet lacking a holistic understanding of the semantic context. To tackle the problem, we propose a semantic-guided contrastive context-aware CER method, namely C3ER, to augment/regularize a backbone CER model, which can be any neural CER framework. Specifically, C3ER takes the hidden states of utterances from the CER model as input, extracts the contrast pairs consisting of relevant and irrelevant utterances to the conversational context of a target utterance, and uses contrastive learning to establish a soft semantic constraint between the target utterance and its context. It is then jointly trained with the main CER model, forcing the model to gain a semantic understanding of the context. Extensive experimental results show that C3ER can significantly boost the accuracy and improve the robustness of the representative CER models.
Accelerating Hybrid Quantized Neural Networks on Multi-tenant Cloud FPGA

The increasing adoption of Field-Programmable Gate Arrays (FPGA) into cloud and data center systems opens the way to the unprecedented acceleration of Machine Learning applications. Convolutional Neural Networks (CNN) have largely been adopted as algorithms for image classification and object detection. As we head towards FPGA multi-tenancy in the cloud, it becomes necessary to investigate architectures and mechanisms for the efficient deployment of CNN into multitenant FPGAs cloud Infrastructure. In this work, we propose an FPGA architecture and a design flow that support efficient integration of CNN applications into a cloud infrastructure that exposes multi-tenancy to cloud developers. We prototype the proposed approach on randomly allocated virtual regions to tenants. We study how space-sharing of a single device between multiple cloud tenants influence the design flow, the allocation of resources, and the performance in term of resource utilization and overall latency compared to single-tenant deployments. Prototyping results show a latency at most 8% lower than that of single-tenant deployment while achieving higher resource utilization. We also record a maximum frequency of up to 12% higher in multi-tenant implementations.
Android Mobile Application: Tsunami Alert System with an Escape Route for Evacuation in Municipal Disaster Risk Reduction and Management Office

The Tsunami alert system is an Android-based application that requires installation and is designed and developed for Abuyog residents. This system will deliver a real-time tsunami alert message and push notification to the registered netizen. It will also recommend a map of higher ground safe from the Tsunami. The Municipal Disaster Risk Reduction and Management Office (MDRRMO) spent a lot of time alarming the Abuyog Residents, aware of the possible incoming Tsunami. They started activities for the residents to prepare for how to survive before and during the disaster. This study aimed to develop a system that improves and provides efficiency for the residents of Abuyog and to support the preparations of the Municipal Disaster Risk Reduction and Management Office of Abuyog. Moreover, the project conducted was beneficial to the citizen of Abuyog that helped facilitate the dissemination of information to all netizens registered in the system. In this study, an Iterative Method was used to develop the system based on the client's perspective. The system was tested and revised until clients were satisfied with the result. And this system was evaluated by fifty (50) and exceeds end-users which concluded that the system is highly accepted with an average of 4.89 out of 5 as the highest for measuring the system's quality and performance proving that this has a great potential for disaster risk management.
TAGAN: Texture and Attention Guided Generative Adversarial Network for Image Super Resolution

Super Resolution (SR) methods based on Generative Adversarial Networks (GANs) accomplish predominant execution in visual perception and image quality. These methods are mainly generated by traditional Peak-Signal-to-Noise-Ratio (PSNR)oriented or perceptual-driven. As the reconstruction process usually loses high frequency information, various methods aim to preserve more details. To make the details of the generated image richer, the Gradient Weight (GW) loss is introduced in the proposed method, because the gradient can reflect the texture of the image to a certain extent. The GW loss function is helpful to improve the edge and detailed texture of the generated image. Furthermore, we introduce attention mechanism to the image reconstruction block via Squeeze and Excitation Net (SENet). Attention mechanism can effectively aggregate the global features obtained by the nonlinear mapping network, and improve the channel sensitivity of the model. With the help of GW and attention mechanism, the proposed method can achieve better performance and visual quality in image texture detail restoration. The performance comparison between the state-of-the-art methods and our proposed method verifies the feasibility and reliability of the proposed method.
Research on Fault Monitoring Device of Highway Bridge Expansion Joint Based on Internet of Things Technology

Among various large-scale engineering structures, highway bridge structures are characterized by a large number, long service period, and huge investment. Therefore, any construction quality, safety accidents, natural disasters or structural aging, insufficient bearing capacity, etc. may cause the bridge as a whole or Natural cumulative damage and accidental damage occurred in expansion joints of highway bridges, which affected the safety of bridge operation. Therefore, a research on a fault monitoring device for highway bridge expansion joints based on the Internet of Things technology is proposed. Design the fault signal acquisition equipment of the Internet of Things technology, first analyze the basic characteristics of the road bridge expansion joint fault, design a signal collector based on the Internet of Things technology, and design a signal extractor based on the Internet of Things technology. Design a data acquisition card for the failure monitoring of highway bridge expansion joints. Finally, through example analysis, it is proved that the detection accuracy of the highway bridge expansion joint failure monitoring device combined with the Internet of Things technology is very high, and the fault monitoring accuracy is good, which meets the basic requirements of highway bridge expansion joint failure monitoring.
ViSRE: A Unified Visual Analysis Dashboard for Proactive Cloud Outage Management

Efficient outage detection and remediation is crucial for effectively operating cloud computing systems. To remediate outages, system engineers must quickly identify the causal relationships between metrics and correlate events across multiple monitoring tools. In practice, this process largely remains reactive due to the complexity and general lack of interpretability within such monitoring environments. This work presents ViSRE: an integrated visual analytics system that integrates causal and predictive models with interactive visualizations to aid in proactive cloud outage management. We develop enhanced node representations for our causal graph representation to support system engineers in performing root cause analysis and reasoning about causality chains in multi-dimensional temporal data. We report the results of a quantitative assessment of the proposed predictive models, which show good performance guarantees. To evaluate and refine our system, we conduct a study with six cloud system engineers who verify that our proposed techniques can support proactive cloud maintenance by intuitively displaying temporal relationships between predicted and raw data. By correlating and presenting data from disparate sources, ViSRE also reduces context switching costs and reduces the time spent on manually correlating events during remediation of time-critical outages.
An Adaptive Weight Joint Loss Optimization for Dog Face Recognition

In recent years, the field of human face recognition has developed rapidly, and a large number of deep learning methods have proven their efficiency in human face recognition. However, these methods do not work well in the field of animal face recognition. There are two reasons. One is that the face recognition framework cannot fully extract the features of animal faces, and the other is that there are not enough animal datasets to fully train the model. In this paper, we collect a total of 11889 high-definition pictures containing 174 dog individuals, with an average of more than 60 samples per dog. On this dataset, we trained Swin Transformer as the backbone, and coupled with the triplet loss and cross-entropy loss function, it reaches an accuracy of 88.94%. Compared with the accuracy rate of 86.21% for using the TripletLoss alone and the accuracy rate of 86.60% for using the cross-entropy loss alone, this paper has a big improvement.
Automated Road Crossing System Using Real-Time Object Tracking

Urbanization and Industrialization have attracted more people into cities like Colombo, Mumbai and New Delhi. During morning and evening hours, most streets are crowded with people which leads to more pedestrian accidents. We have developed an automated road crossing system that assist pedestrians and blind people to cross the road, minimize pedestrian accidents and traffic jams. A CCTV is used to feed the real-time video into the system and then it detects the pedestrians after that the algorithm projects a countdown time which allows pedestrians to cross the road and at the same time it generates a specific tone to notify blind people to cross the road safety. The simulation results show the traffic light, waiting time, countdown time and the buzzer module tone for the pedestrians as well as for the drivers.
Supporting the identification of promising candidates in the recruitment for IT jobs

Nowadays, programmers are one of the most desirable professions in the labor market, and hiring the most skilled programmers is crucial for programming teams. Moreover, the costs of recruiting and training incompetent employees are high. Due to the constant need to recruit new programmers in companies, tools that support the recruitment process are desired by employers. The goal of this work is to support the recruitment process of programmers, involving automatic analysis of the source code submitted by the candidates. We focus on the identification of outstanding solutions to save the time, the recruitment team must spend on the assessment of the projects. We proposed a method that utilizes well known metrics to identify outstanding projects, probably delivered by promising candidates. In this work we discuss the results obtained for different sets of metrics as the requirements can be different for different positions. Obtained results justified that the proposed approach can be successfully applied in the recruitment process.
Quantum Resource Estimation for Quantum Chemistry Algorithms

Predicting the quantum resources for potential applications such as quantum chemistry plays a key role in the future development of quantum computers, and a systematic approach based on a reliable metric is needed for efficient resource estimation. In this work, we developed a Python software package QRE (Quantum Resource Estimator) for estimating quantum resources required to compute the properties of molecules. We define the key requirements for the quantum phase estimation (QPE) algorithm for molecules with different basis sets. We estimated the number of ancilla qubits, circuit depth, and time to solution. The input for QRE is the information about a molecule such as atom types, molecular geometries, charges, basis set, and level of theory. Also, we consider an error analysis scheme for QPE algorithm to calculate the number of ancilla qubits. As a part of the estimation, we calculate the number of rotation and CNOT gates for one Trotter step. Based on these results, the QRE program calculates costs for full QPE algorithms for the given gate time, fidelities of CNOT gates, and target success probability. It is expected that our work will contribute to a better understanding of the limitations of current and future quantum computers for quantum chemistry applications.
Experimental Validation of Origami’s Reservoir Computing Power and an Mechano-Intelligent Task of Payload Identification

Abstract Animals’ excellent capability to achieve complex locomotion and manipulation behaviors benefits from their unique body morphology, distributed sensory system, and direct connection with the environment, creating intelligence embodied directly in their bodies. This presents a significantly more flexible control strategy than most engineered robots that rely solely on a centralized control unit. A computing frame called physical reservoir computing is put forward to mimic the animal’s embodied intelligence, enabling many mechanical structures and materials to accomplish complex computation tasks. In this paper, we experimentally verify the computing capability of Miura-ori sheets using emulation tasks and find strategies to improve the computing performance by optimizing their physical and computational designs. Comprehensive parametric studies show that designing an unevenly distributed nodal mass and fine-tuning the origami folding designs are the most effective approach for improving computing performance. We also demonstrate the potential of using the origami reservoir to achieve an intelligent task: predicting the payload magnitude. In other words, the nonlinear dynamics of origami can be utilized to ‘feel’ the weight of objects on top of it. The results of this paper can pave the way to enable more advanced mechano-intelligence in origami-based structures and robots.
Mobile Application Design for Solo Traveler: Travemate

The advancement and application of information technology have provided significant benefits to society. Complete and quality information is needed to achieve efficiency and make it easier for people to find the right and trusted vacation partner. One way that can be done is to create an information system to find vacation companions or travel companions to travel to the same destination at the same time. This study aims to design and build a mobile application. Travemate is a mobile application that helps travelers find the right and trustworthy friends or partners to travel to the same destination. The development of this information system is expected to help the community, especially solo travelers, reduce the time spent looking for the right partner or traveler who meets their criteria. This application is built using a mobile apps platform that can be installed on devices connected to the internet via smartphones and also with the features available on travemate to facilitate and optimize user activities in finding partners for their travels so that with this application, all activities will be easy, fun, and efficient. In the long term, the application programs we create can touch people all over the world, making it easier for people to find friends to travel with.
Identification in Search Models with Social Information

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Identification in Search Models with Social Information 44 Pages Posted: 7 Dec 2022 See all articles by Niccolò LomysNiccolò LomysCSEF - University of Naples Federico IIEmanuele TarantinoLuiss Guido Carli University; Einaudi Institute for Economics and Finance (EIEF) Date Written: November 28, 2022 Abstract We theoretically study how social information affects agents' search behavior and the resulting observable outcomes that identify search models. We generalize canonical empirical search models by allowing a share of agents in the population to observe some peers' choices. Social information changes optimal search. First, we show that neglecting social information leads to non-identification of search cost distributions under various standard datasets. Whether search costs are under or overestimated depends on the dataset. Second, we propose several remedies---such as data requirements, offline estimation techniques, exogenous variations, and partial identification approaches---that restore identification and consistent estimation. Keywords: Search and Learning, Social Information, Identification, Networks. JEL Classification: C1, C5, C8, D1, D6, D8. Suggested Citation: Suggested Citation Lomys, Niccolò and Tarantino, Emanuele, Identification in Search Models with Social Information (November 28, 2022). Available at SSRN: https://ssrn.com/abstract=4288045 Niccolò Lomys (Contact Author) CSEF - University of Naples Federico II ( email ) via Cinthia, 4Naples, Caserta 80126Italy Emanuele Tarantino Luiss Guido Carli University ( email ) Via O. Tommasini 1Rome, Roma 00100Italy Einaudi Institute for Economics and Finance (EIEF) ( email ) Via Due Macelli, 73Rome, 00187Italy Download This Paper Open PDF in Browser Do you have a job opening that you would like to promote on SSRN? Place Job Opening Paper statistics Downloads 6 Abstract Views 13 PlumX Metrics Feedback Feedback to SSRN Feedback (required) Email (required) Submit If you need immediate assistance, call 877-SSRNHelp (877 777 6435) in the United States, or +1 212 448 2500 outside of the United States, 8:30AM to 6:00PM U.S. Eastern, Monday - Friday. Submit a Paper Section 508 Text Only Pages SSRN Quick Links SSRN Solutions Research Paper Series Conference Papers Partners in Publishing Jobs & Announcements Newsletter Sign Up SSRN Rankings Top Papers Top Authors Top Organizations About SSRN SSRN Objectives Network Directors Presidential Letter Announcements Contact us FAQs Copyright Terms and Conditions Privacy Policy We use cookies to help provide and enhance our service and tailor content. To learn more, visit Cookie Settings. This page was processed by aws-apollo-4dc in 0.194 seconds
Multilevel Similarity-Aware Deep Metric Learning for Fine-Grained Image Retrieval

Fast and accurate image retrieval is an important and challenging task in massive image data scenarios. As the core technology of image retrieval tasks, deep metric learning aims at learning effective embedding representations that possess two properties among data points: positive concentrated and negative separated. In this work, we propose a multilevel similarity-aware method based on deep local descriptors for deep metric learning. We take the rich interclass similarity relationship based on the deep local invariant descriptors from the data into account to optimize sampling strategies for mining informative samples. The method dynamically adjusts the margin between data points to better match the true similarity relationship between classes. Specifically, for images in a batch, we first obtain deep local descriptors and calculate the similarity matrix of the channel, pixel, and spatial levels. Then, depending on the calculated comprehensive similarity matrix, we propose a multilevel similarity-aware loss function through the deviation between pairwise distance and violate margin to make full use of informative samples. The experimental results demonstrate that our proposed method outperforms other state-of-the-art methods in terms of fine-grained image retrieval and clustering tasks.