Miracle-dz/UniFAR-FTUs huggingface dataset:
Defending Against Adversarial Attacks via Neural Dynamic System

Although deep neural networks (DNN) have achieved great success, their applications in safety-critical areas are hindered due to their vulnerability to adversarial attacks. Some recent works have accordingly proposed to enhance the robustness of DNN from a dynamic system perspective. Following this line of inquiry, and inspired by the asymptotic stability of the general nonautonomous dynamical system, we propose to make each clean instance be the asymptotically stable equilibrium points of a slowly time-varying system in order to defend against adversarial attacks. We present a theoretical guarantee that if a clean instance is an asymptotically stable equilibrium point and the adversarial instance is in the neighborhood of this point, the asymptotic stability will reduce the adversarial noise to bring the adversarial instance close to the clean instance. Motivated by our theoretical results, we go on to propose a nonautonomous neural ordinary differential equation (ASODE) and place constraints on its corresponding linear time-variant system to make all clean instances act as its asymptotically stable equilibrium points. Our analysis suggests that the constraints can be converted to regularizers in implementation. The experimental results show that ASODE improves robustness against adversarial attacks and outperforms the state-of-the-art methods.
Generalizing Consistent Multi-Class Classification with Rejection to be Compatible with Arbitrary Losses

Classification with rejection (CwR) refrains from making a prediction to avoid critical misclassification when encountering test samples that are difficult to classify. Though previous methods for CwR have been provided with theoretical guarantees, they are only compatible with certain loss functions, making them not flexible enough when the loss needs to be changed with the dataset in practice. In this paper, we derive a novel formulation for CwR that can be equipped with arbitrary loss functions while maintaining the theoretical guarantees. First, we show that K -class CwR is equivalent to a ( K +1) -class classification problem on the original data distribution with an augmented class, and propose an empirical risk minimization formulation to solve this problem with an estimation error bound. Then, we find necessary and sufficient conditions for the learning consistency of the surrogates constructed on our proposed formulation equipped with any classification-calibrated multi-class losses, where consistency means the surrogate risk minimization implies the target risk minimization for CwR. Finally, experimental results validate the effectiveness of our proposed method.
Impact of flux distribution on elementary heating events

This work used the COSMA Data Centric system at Durham University, operated by the Institute for Computational Cosmology on behalf of the STFC DiRAC HPC Facility (www.dirac.ac.uk. This equipment was funded by a BIS National E-infrastructure capital grant ST/K00042X/1, STFC capital grant ST/K00087X/1, DiRAC Operations grant ST/K003267/1 and Durham University. DiRAC is part of the National E-Infrastructure. I.D.M was funded by the Science and Technology Facilities Council (UK). The research leading to these results has also received funding from the European Research Council (ERC) under the European Union Horizon 2020 research and innovation programme (grant agreement No. 647214). J.O was funded by the Science and Technology Facilities Council (UK) by Doctoral Grant [ST/K502327/1].
22 GHz MONITORING OF SGR A * DURING THE G2 PERI-CENTER PASSING WITH THE JAPANESE VLBI NETWORK

We have been performing daily VLBI monitoring of the ux density of Sagittarius (Sgr) A* at 22GHz from February 2013 to August 2014 using a sub-array of the Japanese VLBI Network (JVN). Thepurpose of this monitoring is to explore the ux density variability at daily time resolution for a periodlonger than one year with the G2 cloud approaching. The ux density of Sgr A* is basically stable duringthe observational period, though there are some small variations. The average and scattering range areconsistent with the previously observed values. We have observed no strong are of Sgr A* although it isnear the expected peri-center passing.Key words: journals: individual: Sagittarius A*, VLBI1. INTRODUCTIONSgr A* is a compact radio source at the center of theMilky Way Galaxy, which contains a super massiveblack hole of 4 million solar masses (Ghez et al. (2008),Gillessen et al. (2009)). Gillessen et al. (2012) found asmall cloud, G2, near the black hole. The G2 cloud waspredicted to come closer to the peri-center distance of200 AU in the spring of 2014 (Gillessen et al. (2013)). There were some simulations which predicted that theemission of SgrA* might increase due to this event (e.g.Narayan et al. (2012), Saitoh et al. (2014)). Moni-toring observations of this event are very important todetermine what happens at the Galactic center. Wehave conducted daily monitoring of the ux density ofSgr A* at 22 GHz. The purpose of this monitoring is toexplore the ux density variability on the time resolu-tion of a day for the period long than one year with theG2 cloud approaches.2. OBSERVATIONSWe used a sub-array of the Japanese VLBI Network(JVN) for daily monitoring of the ux density of SgrA* at 22 GHz from February 25, 2013 to August 12,2014. The sub-array consists of the Mizusawa 10-m ra-http://pkas.kas. orgdio telescope, Takahagi /Hitachi 32-m radio telescopes,and Gifu 11-m radio telescope. The Tsukuba 32-m ra-dio telescope and Kashima 34-m radio telescope havejoined it on occasion. Because the sub-array has pro-jected baselines of 90-140 km on the line of site from SgrA*, we can measure the ux density of Sgr A* avoidingcontamination from the extended structure surround-ing Sgr A* and the ux density decrease from partiallyresolved-out ux arising from using the longer projectedbaselines. We used the K5/VSSP32 VLBI data acquisi-tion (DAQ) system (Kondo et al. (2006)), which storedthe VLBI data with 2 bit sampled 32 MHz bandwidth(128 M bps data rate). The observed data was trans-ported to JAXA over the internet, and was correlated bya software correlator in JAXA, Sagamihara after eachdaily observation. Three quasars, NRAO530, J1626-2951, and J1924-2914, were observed each day as uxcalibrators, and Sgr A* was observed twice each day.The on-source observational time of each source was 10minutes per day. The system temperature of each ra-dio telescope was between 100-200K except during thesummer season. The uncertainty of the correlated uxdensity over the whole period was about 0.3 Jy.649
Madinah Smart City System: Managing and Monitoring Traffic, Emergency Services, and Crowds in The Haram

Billions of Muslims across the globe consider Madinah in Saudi Arabia to be a holy city, thus it attracts millions of visitors every year. Therefore, cases of overcrowding, slow arrival of emergency services, and increased danger to life, can be regular occurrences if not managed efficiently. Consequently, this study aimed to address these abovementioned factors via the utilisation of many tools and mechanisms including cameras, image processing, tracking system, Artificial intelligence hubs, Network Data, Mobile- enabled internet of things sensors etc., as well as data from previous studies and Saudi government agencies. As a result, four main components were realised in the process of creating our proposed framework: control and analysis of traffic signals, control and analysis of human crowding, possessing smart emergency services in real time, and securing data through its collection, storage, and sharing. The combination of all four components fulfils the goals of this research and provides a more robust and efficient environment via improved coordination between different government departments in Madinah, effective emergency services, and improved speed of decision-making, which saves lives in cases of emergency situations where emergency services can attend to the points of concern efficiently and prevents deaths in events of stampedes via the appropriate control and management of crowds.
Determinants of local adaptation strategies to climate change at farm households level in the central region of Vietnam: a case study in flooding delta regions of Thua Thien Hue province

Global climate change poses risks to human and natural systems on a global scale, while Vietnam is considered one of the regions experiencing heavy impacts of climate change. Thus, this study aims to determine the different perceptions, barriers, and driving parameters of local households to adapt to climate change. A total of 350 respondents in local regions were considered for the survey. The Multi-Logis Regression (MLS) model was applied to determine the driving parameters affecting the choice methods. A majority of farm households realized the huge impacts of climate change and actively planned to adapt. The findings also indicated that farm households applied widening solutions (11 methods) to adapt to climate change; importantly, production diversification was reported as the most important solution. While there was still a minor percentage that did not apply, at least a choice (6%). The results from the MLS model indicated that the adaptation process is driven positively and significantly by several factors, including age of the household head, gender of the household head, typology of household, and perception of climate change. Based on the research findings, a number of short- and long-term recommendations were developed to improve the efficiency of climate change adaptation and reduce the negative impacts of climate change at farm level.
A New Technique for Elimination of Selective Harmonics using Cascade H - Bridge Multilevel Inverter

In this study, a new concept for reducing lower order harmonics (mainly third order) using a pulse width modulation technique known as selective harmonic elimination is introduced. The Raspberry Pi Pico is used to control the (five level) cascaded H-bridge inverter. Simulink /MATLAB is used to model the single-phase cascaded H-bridge five level(5_L) inverter's performance. To evaluate the system's performance, a hardware model is created. The results indicate a considerable improvement of the output waveform quality with reduction in total harmonic distortion.
A Novel Framework for Crime Anomaly Detection using Convolution Auto Encoder

Thousands of crimes are committed every day, and hundreds are taking place right now throughout the world. In this study, a Convolution Auto Encoder was employed, which was layered with ConvLSTM layers. The model is divided into three parts: encoder, latent space, and decoder. Three optimizers Adam, Adadelta and RMS Prop, were employed with the learned model. The accuracy, as well as a number of other metrics such as loss, precision, and F1-score, are compared to determine the best optimizer for the trained model utilized in this study. RMSProp optimizer presented the highest accuracy of 99.88% with the least loss value of 0.0045.
Comparative Analysis of LFSR and NLFSR based Lightweight Stream Ciphers for Use in IoT

Almost everything is linked to machines at this time because human existence is undergoing an information revolution that will make it easier for people to survive. One such technology is the Internet of Things (IOT), which allows its users to digitally link things nearby with mobile devices to create a smart world all around them. The number of IoT device users is growing exponentially over time, and as a result, there are more devices in practically every network on a regular basis. As the linked devices to the IoT infrastructure may be a source for data breaches or dangerous for the digital assets existing in the users' smartphones, protecting these devices is essential for the integrity of the user connected to a network. Since IoT devices lack the computational ability to protect the communication channel when data is transferred or transmitted inside a network of IoT infrastructure, lightweight cryptography was developed to preserve security among IoT devices. The research being done in the area of security received a very quick boost from the introduction of lightweight cryptography. There are now a number of lightweight cryptographic algorithms that provide a number of benefits when used. However, since there are so many options available, it may be difficult to select when, where, and which IoT device to utilize to establish security. This study will provide a platform for decision-making by providing an analytical comparison of all the most recent lightweight Stream cipher based on Linear Feedback Shift Register (LFSR) and Nonlinear Feedback Shift Register (NLFSR). In this work, we will examine the cipher's performance in constraints devices on both a software and hardware level, as well as the problems it faces. Any individual's performance may be considerably enhanced by making the right choice of cryptographic algorithm for the network.
Exploring the Impact of Big Data Analytics Capabilities on Indonesian Firm Performance - A Mediation Analysis of Business Process Agility and Process-oriented Dynamic Capability

Examining how influential Big Data Analytic Capabilities (BDAC) are in improving Firm Performance (FPER) in the Indonesian setting is the objectives of this study, which has seen a significant increase in Big Data expenditure in Asia but lacks research in this area. Additionally, the study proposes a theoretical model as a framework for developing BDAC. The IT Capability Framework and the mediation functions of both Process-oriented Dynamic Capability (PODC) and Business Process Agility (BPA serve as the foundation for this study model. A quantitative research method using purposive and snowball sampling techniques is applied to a sample of 53 Indonesian companies. The result is BPA is significant while the PODC is identified to be insignificant in mediating the BDAC and FPER relationship. This study provides a framework for understanding Big Data Analytics and empirical evidence of BDAC’s impact on the success of Indonesian firms.
Differential Privacy in Recommender System: Challenges and Oppertunities

Recommender system assist people in locating a selected object based totally on their preference from a wide range of products in online purchasing. The suggestions that can be carried out primarily depend on the user's hobbies, interpersonal habits, and interpersonal influence. To advise the product to user in more customized manner some social factors are involved. Social groups include users who have similar hobbies as consumer. E-trade web-sites are offering item suggestions to the customers to increase the sale of items, Recommendation is a statistics filtering technique. User's data that is used to provide recommendations needs to be secure. In this paper we have reviewed that differential privacy has been applied on Collaborative Filtering (CF), Gaussian mechanism, Clustering strategy to secure the data and also told the limitations of Differential privacy.
An AHP-TOPSIS based Decision Support Model for Selecting Biometric System

In this paper, a decision support model is proposed to help individuals in selecting appropriate biometric technology based on personalized preferences. The proposed model uses methods: (i) Analytic Hierarchy Process (AHP), and (ii) Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) under multiple criteria decision-making (MCDM) environment. AHP is applied to calculate the relative weights of biometric characteristics (decision criteria) and TOPSIS is applied to rank the biometric technologies for final selection. The model considers eight biometric technologies: face, ear, iris, fingerprint, voice, hand vein, hand geometry and signature, and selects best technology based on the seven traits (factors): universality, uniqueness, permanence, collectability, performance, acceptability and resistance to circumvention. To demonstrate the methodological process of decision-support model, an example of biometric technology selection is illustrated.
A Review of Segmentation and Recognition Techniques for Indian Sign Language using Machine Learning and Computer Vision

Indian Sign Language (ISL) is a complex and diverse language used by the deaf and hard-of-hearing community in India. ISL recognition and segmentation have been active areas of research in recent years due to the growing demand for assistive technologies for this population. In this review paper, we provide an overview of the various techniques used for ISL recognition and segmentation using machine learning and computer vision. We begin by discussing the challenges involved in ISL recognition and segmentation, such as the variations in sign language gestures, lighting conditions, and background noise. We then review the literature on various techniques used for ISL recognition. We analyze the strengths and limitations of each technique, comparing their accuracy, speed, and robustness against various factors. Finally, we present the recent advancements and future directions for ISL recognition and segmentation
An Analysis of Cyber security Risks and Authentication Systems

This paper presents an analysis of cyber security risks and authentication systems. It provides an overview of the most commonly used authentication systems, their pros and cons, and the potential security risks associated with each one. It then discusses the importance of implementing an effective authentication system, the challenges involved in doing so, and the best practices to ensure a secure system. Finally, the paper concludes with recommendations for organizations to consider when implementing authentication systems. The paper provides an in-depth look at cyber security risks and authentication systems in order to help organizations make informed decisions on how to protect their data.
A Review on the use of Machine Learning Techniques in Music Recommendation System for Healthcare Management

Music is one of the most effective therapies for ailments and pushes patients to heal quickly. By making the patient mentally fit and assisting them in overcoming the ailment, music invokes the patient's thinking emotionally. This survey provides a study of various machine learning techniques and its types utilized in recommendation of music for health care management. The machine learning types include supervised, unsupervised techniques and their sub -categories are discussed. The research shows that music therapy improves people's quality of life while progressively diminishing the effects of sickness on the human body Music therapy through a good recommendation system acts as an effective remedy to eliminate the emotional and psychological health issues of individuals. The classification accuracy is considered as a key parameter to define the effectiveness of music recommendation systems. In this survey, machine learning techniques acts as a tool to recommend music for health care management of patients.
Future Directions of Artificial Intelligence and Machine Learning in Healthcare: A Systematic Analysis and Mapping Study

In order to organise previously conducted research on using machine learning in medical contexts, the authors of this paper employ a method known as systematic mapping. In order to do this, we looked through the abstracts of a variety of publications, including medical journals, healthcare periodicals, and conference proceedings, looking for the phrase “use of artificial intelligence and machine learning in healthcare. ” After doing a search on Google Scholar, which resulted in the retrieval of 500 papers, we classified these studies in accordance with their objectives, approaches, primary concerns, and illnesses. With the use of this technique, we were able to organise our findings into the five categories that are as follows: privacy and security; a framework for privacy and security; interpretable machine learning; medical image assessment; electronic health record processing; and transfer learning. In addition, we found that the evaluation of medical images is the topic that receives the most research, that interpretable machine learning and explainable artificial intelligence are becoming increasingly popular, and that most authors are primarily interested in cancer research. To restate our mission, we want to provide future generations of scholars with an accurate picture of where the field is now and where it is headed.
Predicting the Patient’s Severity Using Machine Learning Applied to Lungs MRI Images

Lung cancer has repeatedly shown itself to be one of the most fatal illnesses in the history of mankind. Additionally, it is among the most prevalent and deadly between all cancers. Lung cancer cases are rising quickly. In India, there are roughly 70,000 instances per year. Since the condition tends to be asymptomatic in its early stages, it is almost impossible to identify. Because of this, early cancer identification is crucial to preserving lives. A patient may have a better chance of recovery and cure with an early diagnosis. Effective cancer detection is greatly aided by technology. On the basis of their findings, numerous researchers have suggested various methodologies. Recently, various computer-aided diagnostic (CAD) methodologies and systems have been proposed, developed, and launched in an effort to employ computer technology to address this challenge. There are multiple ways using image processing-based methods to forecast the malignancy level for cancer, and those systems use a variety of machine learning techniques in addition to deep learning techniques, like ResNet 50 and DenseNet169 got accuracy 98.69 of ResNet 50 and 99.67 of DenseNet169. The purpose of this study is to list, debate, contrast, and analyse several methods of image segmentation, feature extraction, and other methodologies to classify and identify lung cancer in its early stages.
Deep Learning Based Methods in Image Analytics for Vehicle Detection: A Review

The literature review reveals a substantial body of work dedicated to the detection, categorization, and tracking of vehicles based on deep learning methods. There has been a lot of recent interest in the research of vehicle detection. Many multinational, collaborative academic-industrial research projects aim to better monitor and control traffic, making it safer and reducing the likelihood of accidents. Among the many sensors used in traffic management, detecting vehicles is essential (ITS). It’s also common knowledge that improving traveller security necessitates a more robust and sophisticated intelligent transportation system. Vehicle detection, tracking, and classification are the three primary operations of a vision-based traffic monitoring and surveillance system. The improved network prioritises the real time detection and also it takes detection accuracy into consideration, doing so through group normalisation to mitigate the effect of batch data size on model accuracy and the method of softening no maximum suppression to cut down on missed detections. The issue of real-time vehicle identification is being studied and researched as part of the bigger topic of intelligent transportation. Some research have offered a concept for the scheme, however there is a major problem: detecting vehicles in real time. In order to build a a scheme for vehicle tracking which is accurate and real time both in accordance with each actual circumstance, the YOLO study suggests a design base employing a deep learning algorithm as the system.