Reviewerly/exHarmony huggingface dataset:
Robust Person Re-identification with Adversarial Examples Detection and Perturbation Extraction

Person re-identification (ReID) systems, those based on deep neural networks, have been shown their vulnerability to adversarial examples, i.e. images that only added slight perturbations. In previous defense methods, those input transformations based are easy to reduce the recognition accuracy, while those adversarial examples detection based only prevent the possible adversarial samples from entering the system rather than really improving the system’s robustness. In this paper, we aim to construct a robust person ReID model, which not only can defend against the adversarial attack, but also maintains the recognition accuracy. We combine the advantages of adversarial example detection and adversarial training in the model implementation. On the one hand, we propose a novel adversarial examples detection method based on perturbation information. This method not only has high detection accuracy, but also can purify the adversarial examples through a simple perturbation removal operation. On the other hand, we propose an adversarial example generation method for the matching problem, and use the adversarial examples generated by this method to extract the perturbation. That is, we train a perturbation extractor in a way like adversarial training. Experiments show the effectiveness of our method. For example, when facing Deep Mis-Ranking attack, the strongest attacker at present, our model’s accuracy is greatly improved from 36.68% to 73.97% compared to previous state-of-the-art defense model. In addition, our defense method can be deployed as a plug-and-play defense solution to protect ReID systems.
Part IB: Evaluation Complexity for Constrained Problems

We now extend our findings and analysis to the case where constraints are present. A priori, constrained optimization problems appear more complicated than unconstrained ones. Does this intuition translate to worst-case complexity bounds? The answer to that question depends on how expensive it is to compute and enforce the constraints (compared to evaluating the objective function and its derivatives).
Chapter 1: Introduction

On April 10, 2019, the world saw the first image of a black hole formed from data collected from the Event Horizon Telescope, an Earth-sized array of radio telescopes that each measured extraordinarily faint radio signals from the supermassive black hole at the center of Messier 87. This iconic image provided the first visual proof of the existence of what had previously only been a theoretical possibility. But perhaps of equal importance, the global event became a definitive example of the growing role that algorithms and computation play in scientific discovery.
Electromagnetic Cavities and Cavity Quantum Electrodynamics

Only for the sake of manipulating a single quantum system, one can realize it by applying intense electromagnetic waves such as lasers and/or microwaves, except for the photon itself as a quantum system. However, this is not the case when one wants to convert quantum states back and forth between a fixed quantum system and a photon, in a deterministic and efficient manner. For such a purpose, electromagnetic cavities, or electromagnetic resonators, are outstandingly significant components in quantum technologies for their ability to enhance the interaction between a single quantum system and a single photon. In this chapter, we will describe some properties of electromagnetic cavities and general schemes of cavity measurements. To put it concretely, We will introduce quality factor and lifetime of a cavity, finesse of a Fabry-Per ́ot cavity, internal loss and external coupling, and input-output theory with some applications. Succeeding sections are devoted to the introduction of cavity quantum electrodynamics where Jaynes–Cummings and Tavis–Cummings models are presented and weak, strong and strong dispersive regimes are analyzed.
Verifiable Private Information Retrieval

A computational PIR scheme allows a client to privately query a database hosted on a single server without downloading the entire database. We introduce the notion of verifiable PIR (vPIR) where the server can convince the client that the database satisfies certain properties without additional rounds and while keeping the communication sub-linear. For example, the server can prove that the number of rows in the database that satisfy a predicate P is exactly n. We define security by modeling vPIR as an ideal functionality and following the real-ideal paradigm. Starting from a standard PIR scheme, we construct a vPIR scheme for any database property that can be verified by a machine that reads the database once and maintains a bounded size state between rows. We also construct vPIR with public verification based on LWE or on DLIN. The main technical hurdle is to demonstrate a simulator that extracts a long input from an adversary that sends a single short message. Our vPIR constructions are based on the notion of batch argument for NP. As contribution of independent interest, we show that batch arguments are equivalent to quasi-arguments—a relaxation of SNARKs which is known to imply succinct argument for various sub-classes of NP.
Opinion mining for Arabic dialect in social media data fusion platforms: A systematic review

The huge text generated on social media in Arabic, especially the Arabic dialect becomes more attractive for Natural Language Processing (NLP) to extract useful and structured information that benefits many domains. The more challenging point is that this content is mostly written in an Arabic dialect with a big data fusion challenge, and the problem with these dialects it has no written rules like Modern Standard Arabic (MSA) or traditional Arabic, and it is changing slowly but unexpectedly. One of the ways to benefit from this huge data fusion is opinion mining, so we introduce this systematic review for opinion mining from Arabic text dialect for the years from 2016 until 2019. We have found that Saudi, Egyptian, Algerian, and Jordanian are the most studied dialects even if it is still under development and need a bit more effort, nevertheless, dialects like Mauritanian, Yemeni, Libyan, and somalin have not been studied in this period. Many data fusion models that show a good result is the last four years have been discussed.
Developing hierarchical anticipations via neural network-based event segmentation

Humans can make predictions on various time scales and hierarchical levels. Thereby, the learning of event encodings seems to play a crucial role. In this work we model the development of hierarchical predictions via autonomously learned latent event codes. We present a hierarchical recurrent neural network architecture, whose inductive learning biases foster the development of sparsely changing latent state that compress sensorimotor sequences. A higher level network learns to predict the situations in which the latent states tend to change. Using a simulated robotic manipulator, we demonstrate that the system (i) learns latent states that accurately reflect the event structure of the data, (ii) develops meaningful temporal abstract predictions on the higher level, and (iii) generates goal-anticipatory behavior similar to gaze behavior found in eye-tracking studies with infants. The architecture offers a step towards the autonomous learning of compressed hierarchical encodings of gathered experiences and the exploitation of these encodings to generate adaptive behavior.
Subsumption Cognitive Architecture

This chapter discusses subsumption cognitive architecture ideas, including approaches, potential methods, and examples, and then we finish with where the field is headed, plus other techniques. By the end of the chapter, you will have an understanding of the following:
The Indonesian Surface Weather Observation Monitoring System for Data Availability

Indonesian Meteorology, Climatology and Geophysics Agency (BMKG) is the only Meteorology, Climatology and Geophysics (MKG) data provider with a single data center. Data collection for MKG information services needs to be fast, precise, and accurate on both a national and international scale. By providing a single data provider, the BMKG database center facilitates and speeds up MKG data and information services. However, BMKG has no system for understanding the quantity of MKG data in the database or for obtaining information about MKG data periods that are not currently available. Accordingly, the BMKG database center intends on building an application or system that can detect how complete data is. Thus, the monitoring system of the availability of MKG data can assist the database center by completing incomplete data. It provides complete information on metadata based on stations and the availability of data in the database management system.
Demonstration of a photonic indistinguishability witness on a programmable quantum photonic processor

We experimentally demonstrate an efficient, semi-device-independent photonic indistinguishability witness by certifying three-photon indistinguishability on a programmable integrated quantum photonic processor.
Measurement of Temporal Characteristics of Kinesthetic Illusion Induced by Tendon Vibration

By applying vibration to muscle tendons, it is possible to induce illusory sensation (kinesthetic illusion) as if the body is moving, even without actual movement. However, with current knowledge, it is difficult to quantitatively control this illusion. In this study, we consider the relationship between temporal change of the vibration frequency and the motion illusion as a transfer function, and measure it through psychological experiments. In this paper, we conducted a preliminary experiment to confirm the feasibility of the measurement, but we could not obtain meaningful data at this time, and problems with the experimental method and system were surfaced.
Blockchain-based Smart Supply Chain Management

A promising solution to improve traditional supply chain security operation is by integrating blockchain technology. Combining the two technologies can result in processes with enhanced transparency, security, and automated administrative activities. This paper investigates the integration of blockchain technology to aid in supply chain management. A framework for tracking the time and location of items through the supply chain is developed using off-the-shelf low-cost components such as radio-frequency identification (RFID) and Raspberry PI 4B. Items can be scanned by RFID tags at any stage through the supply chain and recorded on the blockchain by triggering a smart contract developed on the Ethereum Kovan blockchain. The purpose of our work is to construct, test, and verify a feasible environment, in which the information from an RFID tag can be scanned, uploaded to the blockchain, and read easily by the system operators. Experimental analysis shows that the developed framework is functioning properly and is efficient for practical use.
Development of electric wheelchair with in-trident-configuration wheels on one-side

The purpose of this research is to develop and control an electric wheelchair with high adaptation to driving environment. For this purpose, we propose the concept of the wheelchair to be developed. We also design a control system of the wheelchair. Its power train consists of three wheels on one-side in trident configuration and planetary gear mechanism. An experimental verification of the power train has been performed.
Demo: Usage Control using Controlled Privacy Aware Face Recognition

In this paper, we demonstrate an application of privacy-preserving face recognition combined with an Attribute-Based Access Control framework to regulate access from subjects to critical resources while preserving the subject's privacy. The demonstrator exploits a mechanism that dynamically computes the best trade-off between ensured privacy and data utility, based on image acquisition conditions, and a decision engine based on XACML policies to express complex and dynamic conditions. The demonstrator can handle the dynamic association of new identities, as well as modification of access conditions. Attendees of the demo session can interact with the demo in a variety of ways, including modifying the camera input, but also through the customization of rules as well as the privacy parameter.
Privacy-Preserving Distributed Transfer Learning and its Application in Intelligent Transportation

With the rapid development of intelligent transportation systems (ITS), more and more intelligent applications for ITS have received widespread attention, such as the vehicle detection, inference of typical routes, and traffic forecasting. In these applications, deep learning is widely used as a key artificial intelligence technology. However, most ITS providers fail to collect enough labeled traffic data for model training. As a complement to deep learning, transfer learning is an effective way to solve the scarcity of labeled data, which can transfer knowledge from labeled datasets to unlabeled datasets, thus improving the accuracy of prediction and classification. Nevertheless, when the labeled dataset and the unlabeled dataset are held by different entities, it is still unrealistic for two mutually distrustful entities to cooperate in transfer learning regarding data security and privacy preservation. Although some existing works provide privacy-preserving transfer learning methods, such methods fail to apply to traffic data with high sample dimensions due to their high computational cost and round complexity. To address this problem, we design an efficient privacy-preserving distributed transfer learning protocol, which is appropriate for traffic data. Compared to existing works, our protocol addresses the privacy-preserving problem of transfer learning for traffic data with high sample dimensions. In addition, our protocol has fewer interaction rounds and can be proved in the semi-honest model. Finally, we validate the effectiveness, efficiency and security of the proposed protocol via experiments. Furthermore, we show the application of the proposed protocol in intelligent transportation systems.
Augmented Reality and Education for Girls: An Inclusive Approach

Education has been shaken globally due to the Coronavirus disease 2019 (Covid-19) pandemic causing schools to close and thus constraining many learners with limited access to the traditional way of learning by attending classes physically. UNESCO revealed that school closures and learning loss across the globe from the pandemic reached 54 million in January 2022. Girls worldwide have limited access to education and the pandemic has made it extra difficult because of school closures. However, the application of virtual technologies like Augmented Reality (AR) in education can improve and sustain online learning to reach and include more girls in education. With the rise in the importance of AR, this paper is motivated to explore AR and education for girls with a focus on the inclusive approach. Literature review and content analysis are the methodologies used for a comparative study between India and Tanzania with findings indicating that there is a need to use and apply AR technologies particularly AR mobile technologies in education for girls to make the learning process fun, interactive and educative. The findings of this paper can assist education practitioners to use AR in education as an inclusive approach to widen the provision of education for girls.
Towards Efficient and Effective Self-supervised Learning of Visual Representations

Self-supervision has emerged as a propitious method for visual representation learning after the recent paradigm shift from handcrafted pretext tasks to instance-similarity based approaches. Most state-of-the-art methods enforce similarity between various augmentations of a given image, while some methods additionally use contrastive approaches to explicitly ensure diverse representations. While these approaches have indeed shown promising direction, they require a significantly larger number of training iterations when compared to the supervised counterparts. In this work, we explore reasons for the slow convergence of these methods, and further propose to strengthen them using well-posed auxiliary tasks that converge significantly faster, and are also useful for representation learning. The proposed method utilizes the task of rotation prediction to improve the efficiency of existing state-of-the-art methods. We demonstrate significant gains in performance using the proposed method on multiple datasets, specifically for lower training epochs.
AI Rover System Design and Analysis

Our goal is to develop an autonomous AI rover that can execute an exploratory mission in the Egyptian catacombs. Artificial intelligence (AI) systems such as the AI rover are complex and difficult to understand. Using the informal background story from Chapter 1 , we will “draw pictures” to better understand our complex problem. We will use Universal Modeling Language (UML) diagrams to understand the structure and behavior of the system. Models are simplifications of complex ideas. For example, Newton’s Second Law of Motion tries to explain the physics of momentum. It is a great approximation, but it is not 100% accurate. It leaves out gravity, friction, and so on. But it is helpful.
Network forensics analysis on email scam botnet

Using botnet to send spam is a common attack method of cyber blackmailers and extortionist. In recent years, with the widespread application of blockchain, a new type of extortion scam spam using bitcoin to achieve anonymous transfer have gradually emerged, which poses a great threat to cyber security. This paper aims to a university email system for spam botnet detection. We design a network forensics framework, which can identify extortion scam email and spam-sending botnet. Furthermore, this framework can also analyze the bitcoin money laundering network used by attacker. Experiment on real-world datasets shows that compared to some classic spam filtering models, our method has a higher recall rate on extortion scam email, while provides further analysis on botnet cluster and money laundering network.
Serum Cys C Assessment in Healthy Indian Population and Chronic Kidney Failure Individuals – A Case Control Study

Background Serum Cys C has been acknowledged as a simple and reliable biomarker for assessing renal function in research studies. Evolving evidence show its efficacy in improving the diagnostic approach when included in routine clinical practice. Objectives The study intended to evaluate and compare serum Cys C sCys C levels in healthy individuals with chronic kidney diseases.Materials and Methods A case control observational study was conducted at Padmashree Diagnostic Centre Vijayanagar Bengaluru. The sCys C test was assayed by latex enhanced immunoturbidometric method.Result Serum Cys C levels assessed in normal subjects was in the range of 1.03 plusmn 0.11 mgL comparable to the available reference interval for normal healthy population while sCys C values were between 3.55 and 4.71mgL with mean value of 4.3 mgL SDplusmn0.34 in chronic kidney patients. The results displayed significant increase in the values of serum Cys C in chronic kidney disease patients.Conclusion To conclude sCys C is a convenient diagnostic instrument for calculating the functional ability of the kidneys. Furthermore serum Cys C can be included in routine tests for clinical decision making and in the health care package for renal function.
Weak Dirichlet Processes

An important generalization of the notion of Dirichlet process is the one of weak Dirichlet process that is characterized as the sum of a local martingale and an orthogonal process in the sense that its covariation with any continuous local martingale vanishes. It is possible to show that a semimartingale, transformed via a space-time continuous function that is also continuously differentiable in space, becomes a weak Dirichlet process: this fact constitutes a sort of extended Itô chain rule. Volterra processes are typical examples of weak Dirichlet processes.
2022-RA-757-ESGO Bioimpedance screening for cervical cancer

<h3>Introduction/Background</h3> The pathological changes at different severity stages of cervical intraepithelial neoplasia (CIN) can give rise to the differences in electrical properties of the cervical tissues, which lay the foundation for screening of CIN using the non-invasive and cost-effective technique of electrical bioimpedance as well as paves the way of easy availability of CIN screening, especially in developing countries and thereby the reduction of the occurrence of cervical cancer. <h3>Methodology</h3> The complex impedance measurements collected by a multi-electrode probe from the clinically diagnosed 117 normal subjects, 18 subjects with the low-grade squamous intraepithelial lesion (LSIL), and 19 subjects with the high-grade squamous intraepithelial lesion (HSIL) have been fitted to a Cole-Cole model, which is used to characterize the electrical properties of biological tissues. The geometrical and electrical features have been extracted from the model and later exploited to classify the pathological conditions of cervical tissues using the receiver operating characteristic (ROC) curve based on three algorithms: logistic regression, support vector machine, and Naive Bayes. <h3>Results</h3> Regardless of the algorithms being used, the area under curve (AUC) obtained from the features of complex impedance is apparently higher than that of using the real part of impedance only, and the ROC areas between normal and LSIL are much more improved when complex features are exploited, as shown in figure 2. Furthermore, due to the significant discrepancy between the electrical properties of normal and HSIL cervix, greater AUC can be observed in the separation of normal and HSIL cervix, irrespective of the features and algorithms being used. <h3>Conclusion</h3> Compared with the utilization of only the real part of impedance measurements, our results present superior performance when using the combination of geometrical and electrical features from complex impedance in improving the effectiveness of classification during the screening of CIN according to the ROC analysis.
Topic Modeling on News Articles using Latent Dirichlet Allocation

Topic modeling is widely used to obtain the most vis-ible topics from a given text corpus. In this work, a demonstration of the most discussed topic modeling is presented from articles on the Reuters news website. These articles are collected and consequently processed with a Latent Dirichlet Allocation (LDA) unsupervised learning algorithm. The main goal is to build the best model(s) that accurately produces the most discussed topics. Such a model(s) can be used in real life to instantly get information about actual news to classify documents in a given dataset and extract dominated topics with their keywords. This helps to build, for example, correlations with user preferences and recommend interesting content. There are works which use different models to evaluate texts and obtain statistics about them, such as the most popular people's opinions about some question or to obtain popular and dominating subtopics of the specific topic dataset (e.g., medicine articles). As a result of the work, we were able to create a generic LDA model, trained on Wikipedia articles. The model successfully analyzes Reuters articles and extracted their topics as keyword sets. Then, they can be used to recommend content that is interesting to the target user, for example, based on the recommended content tags.