Reviewerly/exHarmony huggingface dataset:
Fooling Neural Network Interpretations: Adversarial Noise to Attack Images

The accurate interpretation of neural network about how the network works is important. However, a manipulated explanation may have the potential to mislead human users not to trust a reliable network. Therefore, it is necessary to verify interpretation algorithms by designing effective attacks to simulate various possible threats in the real world. In this work, we mainly explore how to mislead interpretation. More specifically, we optimize the noise added to the input, which aims to highlight a certain area that we specify without changing the output category of network. With our proposed algorithm, we demonstrate that the state-of-the-art saliency maps based interpreters, e.g., Grad-CAM, Guided-Feature-Inversion, Grad-CAM++, Score-CAM and Full-Grad can be easily fooled. We propose two situations of fooling, Single-target attack and Multi-target attack, and show that the fooling can be transfered to different interpretation methods as well as generalized to the unseen samples with the universal noise. We also take image patches to fool Grad-CAM. Our results are proved in both qualitative and quantitative ways and we further propose a quantitative metric to measure the effectiveness of algorithm. We believe that our method can serve as an additional evaluation of robustness for future interpretation algorithms.
Either crop or pad the input volume: What is beneficial for Convolutional Neural Network?

Convolutional Neural Network (CNN) is the most popular method of deep learning in the machine learning field. Training a CNN has always been a demanding task compared to other machine learning paradigms, and this is due to its big space of hyper-parameters such as convolutional kernel size, number of strides, number of layers, pooling window size, etc. What makes the CNN’s huge hyper-parameters space optimization harder is that there is no universal robust theory supporting it, and any work flow proposed so far in literature is based on heuristics that are just rules of thumb and only depend on the dataset and problem at hand. In this work, it is empirically illustrated that the performance of a CNN is not linked only with the choice of the right hyper-parameters, but also linked to how some of the CNN operations are implemented. More specifically, the CNN performance is contrasted for two different implementations: cropping and padding the input volume. The results state that padding the input volume achieves higher accuracy and takes less time in training compared with cropping method.
Suggestions for the Development of Online Education at the College of Korean Medicine: Based on the Current Status of Online Education and Satisfaction Surveys due to COVID-19

This study was conducted to investigate the current status of online classes and evaluations during the COVID-19 pandemic and the satisfaction of students attending the College of Korean Medicine. A survey was conducted with students enrolled in Dongguk University
Detection of Anomalous Zigbee Transmissions Using Machine Learning

Effective spectrum awareness is critical to a large number of wireless communication systems. Malicious actors increasingly use the spectrum for their own purposes, such as to disrupt systems via jamming and/or spoofing. Radio anomaly detection approaches have been leveraged somewhat in wireless sensor networks, but most of these prior works have focused on detecting changes in sensor data (e.g., temperature and pressure), or in expert features rather than on anomalies occurring in the physical layer. This paper is focused on the detection of anomalous Zigbee transmissions using features extracted from the in-phase and quadrature components and network traffic data. We evaluated the performance of five supervised machine learning algorithms (i.e., Random Forest, J48, JRip, Naive Bayes, and PART) for anomalous RF detection and identified the best learner. Furthermore, we experimented with training sets of different sizes. The main findings include: (1) Adding network flow-based features improved the performance of most of the supervised machine learning algorithms for the detection of anomalous Zigbee transmissions; (2) Random Forest was the best performing learner with the highest F-score and G-score values when using feature-level fusion; and (3) The learners performed similarly across the different training set sizes for all supervised machine learning algorithms.
Automated Threat Modelling and Risk Analysis in E-Government Using Bpmn

In recent years, several extensions have been proposed to model security requirements in Business Process Model and Notation (BPMN). All existing strategies integrate security concepts directly into the BPMN formalism and, although there is widespread ambiguity about security terminology, all require the supervision of a security expert to avoid resulting misinterpretations or further security problems. In fact, business process professionals rarely have the required expertise to correctly interpret and include the necessary security-related concepts. In this work, we propose a new automated methodology that helps business process experts to identify security threats and automatically perform a risk analysis evaluation, in particular with eGovernment processes. The proposed approach requires a business specialist only to annotate the BPMN entities with high-level and non-security-related information, according to which the menaces that may affect the process can be automatically delineated. We based our methodology on the BPMN annotation mechanism and we leveraged the ENISA Threat Landscape knowledge-base for threat identification and the OWASP Risk rating methodology for risk Rating. To prove the effectiveness of the approach, we applied the proposed methodology to a simple case study, demonstrating how it can be adopted to grant GDPR (General Data Protection Regulation) compliance, respect to the mandatory Data Protection Impact Assessment (DPIA).
Cuckoo-Beetle Swarm Search for Nonlinear Optimization: A New Meta-Heuristic Algorithm

Conventional meta-heuristic algorithms have problems being trapped in local optima because of premature convergence and insufficient global search ability in solving nonlinear optimization. This brief proposes a new meta-heuristic algorithm, called cuckoo-beetle swarm search (CBSS) to handle this issue in nonlinear optimization. The proposed algorithm imitates the breeding habits of cuckoos and the food foraging characteristics of beetles. Levy flight is combined with particle search velocity to improve the global optimization of CBSS. Nonlinear benchmark functions are tested to verify the accuracy, search speed, and stability of the CBSS algorithm. The numerical verification proves that the proposed algorithm has better robustness and global search capability than its counterparts.
Human-in-the-Loop Based Named Entity Recognition

Named Entity Recognition (NER) is a challenging issue in Natural Language Processing (NLP) tasks, and has drawn much attention from industry and academia. At present, with the incessant evolution of Deep Neural Network (DNN) model, it has been widely used in NER tasks. However, DNN models heavily depend on a large amount of annotated training samples, and these models will show certain limitations when applied to domain-specific tasks. In this paper, a Human-in-the-loop based NER (H-NER) approach is proposed from the perspective of human-machine collaboration. In particular, interactive operations allow users to quickly annotate samples and verify the accuracy of annotated samples, and NER model will be updated iteratively based on progressively increased training samples. Experimental results show that this approach can effectively reduce the task load of annotation, and reach or even exceed the existing sample selection strategies in performance indicators such as F1-score (entity-level) and Accuracy (sentence-level).Furthermore, this approach will expand the serviceable range of NER and greatly improve its applicability.
Identification of the Optimal Number of Clusters in Textual Data

Clustering is an unsupervised process of grouping unlabeled data on the basis of similarity. To perform clustering, without knowing the number of clusters for any data set, is not pragmatic. The proposed method is used to find out the optimal number of clusters for unclassified textual data with the help of internal cluster validation techniques. The internal indexing used for validation in this work are Silhouette index, Davies Bouldin index, and Calinski Harabasz score along with K-means clustering algorithm. The result obtained through this method is further validated by the Elbow method of finding the number of clusters.
Investigating the Challenges Facing Composable/ Disaggregated Infrastructure Implementation: A Literature Review

There has been an exponential growth of IT technologies in the past few decades. Many technologies target the improvement of IT infrastructure as it plays a primary concern of any IT management. Composable / Disaggregated Infrastructure (CDI) is one of the newest technologies introducing a new concept to manage the IT infrastructure. However, many challenges arise at the beginning stages of any technology life cycle. Hence, this study aims to investigate the challenges involved in migrating or implementing CDI for an oil and gas organization in Oman. To achieve this aim, the research employs a systematic literature review for the studies conducted by academics and industrial institutions. To link the results with an existing organization, an interview was conducted with an IT governance expert from a leading oil and gas organization in Oman. This study provides a holistic view of the highlighted challenges faced with CDI technology. The results highlighted five main challenges, which are controlling end-to-end latency, supporting higher bandwidth and bandwidth density, scaling of the network, orchestration of resources, and power consumption. Key challenges such as end-to-end latency, supporting higher bandwidth, and density can be addressed by using Silicon Photonic (SiP) transceivers. For the oil and gas sector, if the large-sized organizations decide to proceed with CDI migration or implementation, the organizations are prone to face two main challenges: scaling the network and orchestrating resources, thus these challenges still need to be addressed.
An Analytical Evaluation of Cloud Computing Service model IaaS &amp; PaaS using Market Prospective

The Cloud sights an important assistance in internet computing. Which provide service in whole world- anything from anywhere. Nowadays, one of the most demandable technique is cloud computing which is not only useful for organization or IT industry but also beneficial for other computer users. Cloud computing is just not only for storage of data but it is much more than that such as it provide services, access to different applications/softwares and many more. Most of the IT companies depend upon it as it is easy to scale up and down. The best part of cloud computing is that it is pay-per-use service through its providers. Mainly cloud computing has three service models i.e. Infrastructure-as-a-Service, Platform-as-a-Service, Software-as-a-Service through which it gives all its usage facilities to its users with the help of high speed network connection. In this paper, the study of cloud computing is presented along with analytical comparative assessment of the two main service model i.e. IaaS and PaaS is carried out deprived of various parameters along with the description of all three models.
Interface Analysis Criteria for Mobile Devices: A Systematic Mapping

The COVID-19 pandemic has accelerated the digitization process. Interface design and human-computer interaction become political tools in this context, which can guarantee quality in the experience of users of mobile devices, reducing these social distances. In this study a systematic mapping was executed to clarify the outlines of criteria that are commonly evaluated on mobile device interfaces in the light of the literature, trying to emphasize more specific aspects, investigating the heuristics, metrics, and recommendations described in each study. This mapping identified 65 heuristics, which were divided into four groups: Usability, User Experience, Accessibility, and Aesthetics. Further research is needed to investigate how to structure the heuristics in a way that facilitates understanding, consultation, and the evaluation processes.
Lightweight Nontermination Inference with CHCs

Non-termination is an unwanted program property (considered a bug) for some software systems, and a safety property for other systems. In either case, automated discovery of preconditions for non-termination is of interest. We introduce NtHorn, a fast lightweight non-termination analyser, able to deduce non-trivial sufficient conditions for non-termination. Using Constrained Horn Clauses (CHCs) as a vehicle, we show how established techniques for CHC program transformation and abstract interpretation can be exploited for the purpose of non-termination analysis. NtHorn is comparable in power to the state-of-the-art non-termination analysis tools, as measured on standard competition benchmark suites (consisting of integer manipulating programs), while typically solving problems an order of magnitude faster.
Scaling the monitoring of approximate top-k queries in streaming windows

The continuous search for the k best results given a query (top-k) in a data stream is a problem that has gained a lot of attention in recent years, especially in social media and IoT contexts. In these contexts it is essential that the system is able to respond reactively. Therefore, in this study we address how to scale the monitoring of approximate continuous top-k queries to guarantee the reactiveness of the system.In this paper we introduce a scalable distributed version of the MinTopK+N algorithm that allows the system to remain reactive when the load increases. In addition, we present a scale- out/in algorithm that allows the system to add instances to cope with workload increments and remove instances to avoid over- provisioning. Furthermore, in a set of managed experiments, we demonstrated how the increase in the workload could affect the system performance and the impact of the instant of triggering scaling actions on the system performance. Finally, we present a study on the impact of the scaling action on the correctness of the result, showing that the scaling algorithm does not negatively impact the correctness of the results, and in certain conditions, it can even improve the quality of the approximation.
Deep Learning-Based Facial Recognition on Hybrid Architecture for Financial Services

The explosion of digital financial services during the last part of the 2010s in emerging markets needs a framework to confirm the customer identity for personalized service and to robust the compliance processes, especially, prevention of financial fraud and money laundering because sales of financial products and services use more frequently digital channels. Financial institutions need to deploy artificial intelligence technologies to evolve for the next step of this growing digital business, and they require a transformation of process and methods where models based on deep learning could be a tool to implement to prevent financial crime. However, those processes use large computational resources that require edge–cloud computing because to implement facial recognition, financial institutions need to connect IoT and mobile devices. Thus, this chapter aims to explore a mechanism for deep learning-based facial recognition to improve customer identification in financial institutions on a hybrid architecture. We propose a model that integrates traditional classifiers with convolutional neural networks (CNNs) to facial recognition involving visual sequences or different image sources as social media as a tool for financial institutions. In this chapter, we propose a hybrid architecture between the edge and cloud computing, which catalyzes the use of these face detection tools in personalized services. Our results show that the traditional models and deep learning integration achieve optimization of searching processes and facial recognition, but some methods work better than others, possibly by features of datasets and architecture infrastructure. In this case, we integrated machine learning architectures that facilitate the management of the facial recognition process to financial institutions and achieve better computational efficiency. We conclude that the combination of deep learning tools and face recognition systems not only needs tuning and training CNN parameters to enhance the tool’s accuracy and evolution, but also those tools require an architecture that optimizes its performance.
Data Security Technology and Scheme Design of Cloud Storage

With the development of cloud storage technology, more and more users choose to store data in the cloud. Cloud storage technology saves users’ local storage space, but users lose absolute control over data, which also leads to some cloud storage data security-related issues. Based on the cloud storage application architecture, this paper provides the complete solution for cloud storage data in three ways: first, access control, prevents the intrusion of illegal users or the damage caused by the careless operation of legitimate users; second, integrity audit, ensures that the data is truly and completely stored in the cloud; third, data encryption, protects the data in network transmission and cloud storage. In the actual application process, the three technologies need to be combined to achieve the ideal data security effect.
Formal Analysis of Kang et al.'s Authentication Protocol using Tamarin-Prover

The increasing use of IoT leads to additional IoT cloud data and also security and privacy issues. Therefore, it is necessary to have an authentication protocol on the IoT cloud. One of the authentication protocols proposed for cloud IoT is an improved version of Amin et al.&#x0027;s protocol, Kang et al.&#x0027;s authentication protocol. The protocol combines the pseudo-identity and actual identity of the cloud server or user with the secret number owned by control server to prevent impersonation attacks effectively. However, no formal analysis has been carried out to prove the strength of the protocol. This research proposed a formal analysis using Tamarin-Prover, which is one of the tools to model a protocol symbolically and analyze the protocol formally. The research steps were performed based on the protocol analysis methodology. Verification using Tamarin-Prover is carried out on secrecy and authentication aspects, including aliveness, weak agreements, non-injective agreements, and injective agreements. The verification results show that Kang et al.&#x0027;s authentication protocol satisfies the secrecy aspect for each entity&#x0027;s nonce and session key, as well as the authentication aspect for the authentication process carried out by the control server to the user, the control server to the cloud server, the cloud server to the control server, and the user to the cloud server.
Causal Structure Learning Algorithm Based on Partial Rank Correlation under Additive Noise Model

Aiming at the structural learning problem of the additive noise model in causal discovery and the challenge of massive data processing in the era of artificial intelligence, this paper combines partial rank correlation coefficients and proposes two new Bayesian network causal structure learning algorithms: PRCB algorithm based on threshold selection and PRCS algorithm based on hypothesis testing. We mainly made three contributions. First, we proved that the partial rank correlation coefficient can be used as the standard of independence test, and explored the distribution of corresponding statistics. Second, the partial rank correlation coefficient is associated with the correlation, and a causal discovery algorithm PRCB based on partial rank correlation and an improved PRCS algorithm based on hypothesis testing are proposed. Finally, comparing with the existing technology on seven classic Bayesian networks, it proves the superiority of the algorithm in low-dimensional networks; the processing of millions of data on three high-dimensional Bayesian networks verifies the high-efficiency performance of the algorithm in high-dimensional large sample data; the application performance of the algorithm is tested by performing fault prediction on the real power plant equipment measurement point data set. Theoretical analysis and experimental results have proved the superiority of the algorithm.
Prostate Image Classification Using Pretrained Models: GoogLeNet and ResNet-50

Anatomic pathology errors often occur around 1&#x0025; to 43&#x0025; in diagnosis (depending on the disease). This error resulted in delayed treatment and an incorrect diagnosis. This study aims to detect the prostate cancer cells by using pretrained models of deep learning based on the value of performance metrics using a confusion matrix. Image data was taken from a light microscope at the Universitas Indonesia (UI) Hospital. In this study, 10-fold cross-validation was used as a validation of performance metrics on the model. In addition to the accuracy assessment, there is an assessment of precision, recall (sensitivity), specificity and F-score, as well as running time in the process. In this study, the accuracy value in each iteration has a relationship with other performance metric values, if the accuracy value decreases, the other performance metric values will also decrease, and vice versa. In training, ResNet-50 has an average accuracy value of 99.83&#x0025;, 0.2&#x0025; higher than GoogLeNet. Testing performances show that ResNet-50 has an average accuracy value of 98.02&#x0025;, slightly higher (0.28&#x0025;) than GoogLeNet. In F-score as well, GoogLeNet has a slightly smaller 0.54&#x0025; difference than ResNet-50. It can be concluded that the pretrained models has good performances in prostate images classification.
Panther: A Sponge Based Lightweight Authenticated Encryption Scheme

In the modern era, lots of resource-constrained devices have exploded, creating security issues that conventional cryptographic primitives cannot solve. These devices are connected to an unsecured network such as internet. These lightweight devices not only have limited resources, but also lead to the demand for new lightweight cryptographic primitives with low cost, high performance, low cost of deployment, and effective security outcomes. After reviewing various encryption schemes, designs, and security details, this paper provides a secure cipher Panther, which performs both encryption and authentication using the best components. The design of the Panther is based on a sponge structure using Topelitz matrix and NLFSR (Non-Linear Feedback Shift Register) as the main linear and non-linear components, respectively. Security analysis shows that it is not affected by advanced cryptographic analysis proposed in recent cryptographic literature.
Fuzzy Logic

In this book, fuzzy logic is used to incorporate the uncertainty due to vagueness into the methodology of the value of information to account for the vagueness of the data. Crisp versus fuzzy data is discussed. Membership functions, fuzzification, defuzzification, fuzzy rules and fuzzy inference systems are discussed in detail with an example.