Reviewerly/exHarmony huggingface dataset:
Quantitative Evaluation of Machine Learning Explanations: A Human-Grounded Benchmark

Research in interpretable machine learning proposes different computational and human subject approaches to evaluate model saliency explanations. These approaches measure different qualities of explanations to achieve diverse goals in designing interpretable machine learning systems. In this paper, we propose a benchmark for image and text domains using multi-layer human attention masks aggregated from multiple human annotators. We then present an evaluation study to compare model saliency explanations obtained using Grad-cam and LIME techniques to human understanding and acceptance. We demonstrate our benchmark’s utility for quantitative evaluation of model explanations by comparing it with human subjective ratings and ground-truth single-layer segmentation masks evaluations. Our study results show that our threshold agnostic evaluation method with the human attention baseline is more effective than single-layer object segmentation masks to ground truth. Our experiments also reveal user biases in the subjective rating of model saliency explanations.
Text categorization with WEKA: A survey

Abstract This work shows the use of WEKA , a tool that implements the most common machine learning algorithms, to perform a Text Mining analysis on a set of documents. Applying these methods requires initial steps where the text is converted into a structured format. Both the processing phase and the analysis of the transformed dataset, using classification and clustering algorithms, can be carried out entirely with this tool, in a rigorous and simple way. The work describes the construction of two classification models starting from two different sets of documents. These models are not meant to be good or realistic, but just illustrate how WEKA can be used for a Text Mining analysis.
An incremental-learning model-based multiobjective estimation of distribution algorithm

Knowledge obtained from the properties of a Pareto-optimal set can guide an evolutionary search. Learning models for multiobjective estimation of distributions have led to improved search efficiency, but they incur a high computational cost owing to their use of a repetitive learning or iterative strategy. To overcome this drawback, we propose an algorithm for incremental-learning model-based multiobjective estimation of distributions. A learning mechanism based on an incremental Gaussian mixture model is embedded within the search procedure. In the proposed algorithm, all new solutions generated during the evolution are passed to a data stream, which is fed incrementally into the learning model to adaptively discover the structure of the Pareto-optimal set. The parameters of the model are updated continually as each newly generated datum is collected. Each datum is learned only once for the model, regardless of whether it has been preserved or deleted. Moreover, a sampling strategy based on the learned model is designed to balance the exploration/exploitation dilemma in the evolutionary search. The proposed algorithm is compared with six state-of-the-art algorithms for several benchmarks. The experimental results show that there is a significant improvement over the representative algorithms.
Streamline: a fast, flushless cache covert-channel attack by enabling asynchronous collusion

Covert-channel attacks exploit contention on shared hardware resources such as processor caches to transmit information between colluding processes on the same system. In recent years, covert channels leveraging cacheline-flush instructions, such as Flush+Reload and Flush+Flush, have emerged as the fastest cross-core attacks. However, current attacks are limited in their applicability and bit-rate not due to any fundamental hardware limitations, but due to their protocol design requiring flush instructions and tight synchronization between sender and receiver, where both processes synchronize every bit-period to maintain low error-rates.
Two-Way Feature Extraction Using Sequential and Multimodal Approach for Hateful Meme Classification

Millions of memes are created and shared every day on social media platforms. Memes are a great tool to spread humour. However, some people use it to target an individual or a group generating offensive content in a polite and sarcastic way. Lack of moderation of such memes spreads hatred and can lead to depression like psychological conditions. Many successful studies related to analysis of language such as sentiment analysis and analysis of images such as image classification have been performed. However, most of these studies rely only upon either one of these components. As classifying meme is one problem which cannot be solved by relying upon only any one of these aspects, the present work identifies, addresses, and ensembles both the aspects for analyzing such data. In this research, we propose a solution to the problems in which the classification depends on more than one model. This paper proposes two different approaches to solve the problem of identifying hate memes. The first approach uses sentiment analysis based on image captioning and text written on the meme. The second approach is to combine features from different modalities. These approaches utilize a combination of glove, encoder-decoder, and OCR with Adamax optimizer deep learning algorithms. Facebook Challenge Hateful Meme Dataset is utilized which contains approximately 8500 meme images. Both the approaches are implemented on the live challenge competition by Facebook and predicted quite acceptable results. Both approaches are tested on the validation dataset, and results are found to be promising for both models.
DynPTA: Combining Static and Dynamic Analysis for Practical Selective Data Protection

As control flow hijacking attacks become more challenging due to the deployment of various exploit mitigation technologies, the leakage of sensitive process data through the exploitation of memory disclosure vulnerabilities is becoming an increasingly important threat. To make matters worse, recently introduced transient execution attacks provide a new avenue for leaking confidential process data. As a response, various approaches for selectively protecting subsets of critical in-memory data have been proposed, which though either require a significant code refactoring effort, or do not scale for large applications.In this paper we present DynPTA, a selective data protection approach that combines static analysis with scoped dynamic data flow tracking (DFT) to keep a subset of manually annotated sensitive data always encrypted in memory. DynPTA ameliorates the inherent overapproximation of pointer analysis—a significant challenge that has prevented previous approaches from supporting large applications—by relying on lightweight label lookups to determine if potentially sensitive data is actually sensitive. Labeled objects are tracked only within the subset of value flows that may carry potentially sensitive data, requiring only a fraction of the program’s code to be instrumented for DFT. We experimentally evaluated DynPTA with real-world applications and demonstrate that it can prevent memory disclosure (Heartbleed) and transient execution (Spectre) attacks from leaking the protected data, while incurring a modest runtime overhead of up to 19.2% when protecting the private TLS key of Nginx with OpenSSL.
Efficient sensitivity orient blockchain encryption for improved data security in cloud

Data security in the cloud has become a dominant topic being discussed in recent times as the security of data in the cloud has been focused on by several researchers. However, the data security was enforced at the attribute level, the adversaries are capable of learning the method of data encryption even there are access restrictions are enforced at an attribute level. To challenge the adversaries with more sophisticated security measures, an efficient real-time service-centric feature sensitivity analysis (RSFSA) model is proposed in this paper. The RSFSA model analyses the sensitivity of different features being accessed by any service and at multiple levels. At each level, the method checks the set of features being accessed and the number of features the user has access grant to compute the FLAG value for the user according to the profile given. Based on the value of FLAG, the user has been granted or denied service access. On the other side, the method maintains different encryption schemes and keys for each level of features. As the features are organized in multiple levels, the method maintains a set of schemes and keys for each level dedicative. Based on the service level and data, the method selects an encryption scheme and key to perform data encryption. According to that, the service access data has been encrypted at the attribute level with a specific scheme and key. Data encrypted has been uploaded to the blockchain and the method modifies the reference part of the chain to connect only the blocks to which the user has access. The chain given to the user would do not contain any reference from a specific block to which the user has no access. The proposed method improves the performance of data security and access restriction greatly.
Using back-and-forth translation to create artificial augmented textual data for sentiment analysis models

Sentiment analysis classification models trained using neural networks require large amounts of data, but collecting these datasets requires significant time and resources. Although artificial data has been used successfully in computer vision, there are few effective and generalizable methods for creating artificial augmented text data. In this paper, a text based data augmentation method is proposed called back-and-forth translation that can be used to artificially increase the size of any natural language dataset. By creating augmented text data and adding it to the original dataset, it is demonstrated by empirical experiments that back-and-forth translation data augmentation can reduce the error rate in binary sentiment classification models by up to 3.4%. These results are shown to be statistically significant.
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:mi>n</mml:mi></mml:math>-HTLC: Neo hashed time-Lock commitment to defend against wormhole attack in payment channel networks

In today’s cryptocurrency, the Payment Channel Network (PCN) is noticed as one of the most gifted off-chain solutions for scalability issues. Besides this, it consumes lesser transaction fees and low transaction confirmation time. However, security and privacy issues need to be addressed appropriately to make the solution even more effective. Most of the existing HTLC (Hashed Time-Lock Contract) protocols revealed the sender’s information to the intermediate users in the payment route. In this work, we propose an effective secure and privacy-preserving Payment Channel Network protocol, named Neo Hashed Time-Lock Commitment (n-HTLC) protocol. (n-HTLC) does not require the sender to send any information to each intermediate user along the payment route, thus preserves the identity of the sender. But, (n-HTLC) is not compatible with Sphinx onion packet format. Therefore, a symmetric key encryption-based protocol called kTLC has been proposed. kTLC is compatible with the Sphinx onion packet format, which is used in the current Lightning network atop of the Bitcoin blockchain network. The security of both n-HTLC and kTLC are proved using the Universal Composability (UC) framework. It is observed that both ensure that no attacker can extract information on the payment route if at least one of the users in the path is honest. To analyze the performance of both n-HTLC and kTLC payment protocol, we conduct experiments using the snapshots of Ripple network1, Lightning network2, and synthetic network of Mazumdar and Ruj (2020). Our experimental results show that both n-HTLC and kTLC outperform state-of-the-art off-chain payment protocols in terms of computational and communication overhead.
Learning dual-margin model for visual tracking

Existing trackers usually exploit robust features or online updating mechanisms to deal with target variations which is a key challenge in visual tracking. However, the features being robust to variations remain little spatial information, and existing online updating methods are prone to overfitting. In this paper, we propose a dual-margin model for robust and accurate visual tracking. The dual-margin model comprises an intra-object margin between different target appearances and an inter-object margin between the target and the background. The proposed method is able to not only distinguish the target from the background but also perceive the target changes, which tracks target appearance changing and facilitates accurate target state estimation. In addition, to exploit rich off-line video data and learn general rules of target appearance variations, we train the dual-margin model on a large off-line video dataset. We perform tracking under a Siamese framework using the constructed appearance set as templates. The proposed method achieves accurate and robust tracking performance on five public datasets while running in real-time. The favorable performance against the state-of-the-art methods demonstrates the effectiveness of the proposed algorithm.
Differentially private data fusion and deep learning Framework for Cyber–Physical–Social Systems: State-of-the-art and perspectives

The modern technological advancement influences the growth of the cyber–physical system and cyber–social system to a more advanced computing system cyber–physical–social system (CPSS). Therefore, CPSS leads the data science revolution by promoting tri-space information resource from a single space. The establishment of CPSSs increases the related privacy concerns. To provide privacy on CPSSs data, various privacy-preserving schemes have been introduced in the recent past. However, technological advancement in CPSSs requires the modifications of previous techniques to suit its dynamics. Meanwhile, differential privacy has emerged as an effective method to safeguard CPSSs data privacy. To completely comprehend the state-of-the-art developments and learn the field's research directions, this article provides a comprehensive review of differentially private data fusion and deep learning in CPSSs. Additionally, we present a novel differentially private data fusion and deep learning Framework for Cyber–Physical–Social Systems , and various future research directions for CPSSs.
Divide and conquer: Ill-light image enhancement via hybrid deep network

Intelligent system applications in computer vision suffer detection and identification problems in ill lighting conditions (i.e., non-uniform illumination), where under-exposed and over-exposed regions coexist in the captured images. Processing on these images results in over and under enhancement with colour and contrast distortions. The traditional methods design some handcrafted constraints and rely on image pairs and priors, whereas existing deep learning-based methods rely on large scale and even paired training data. But these method's capacity is limited to specific scenes (i.e., lighting conditions). In this paper, we present a deep-hybrid ill-light image enhancement method and propose a contrast enhancement strategy based on the decomposition of the input images into reflection J and illumination T. A Divide to Glitter network (D2G-Net) is designed to learn from the few-shots of training samples and do not require paired and large quantity training data. D2G-Net is comprised of a multilayer Division-Net for image division and a Glitter-Net to amplify the illumination map. We propose to regularize learning using a correlation consistency of decomposition extracted from the input data itself. Extensive experiments are organized under ill-lighting conditions, where a new test dataset is also proposed with robust lighting variation to evaluate the performance of the proposed method. Experimental results prove that our method has superior performance for preserving structural and texture details compared to state-of-the-art approaches, which suggests that our method is more practical in interactive computer vision and intelligent expert system applications.
A hybrid multiple-criteria decision portfolio with the resource constraints model of a smart healthcare management system for public medical centers

A Smart Healthcare Management System (SHMS) is an intelligent technology integration practice for public medical centers. During the implementation of the SHMS, information on strategy and decision-making problems play an important role in smart hospital development, which directly affects the effective workflow of the hospital operation and the service quality of patient care. Existing literature has explored the main factors for evaluation the SHMS, but a qualitative and quantitative analysis is rarely mentioned. To address the research gap, this study proposes a hybrid exploratory three-phased Multi-criteria Decision-making (MCDM) model that combines the Decision-Making Trial and Evaluation Laboratory Model (DEMATEL) approach, the Analytic Network Process (ANP), and Zero-One Goal Programming (ZOGP) for evaluating SHMS portfolios in a resource-limited environment. The results indicate that the Financial Subsidy Policy is the most important determinant for SHMS development, while the Medical Data Informational System (MDIS) and Medical Device and Drug Management System (MDMS) are the optimal SHMS portfolios that satisfy the goal of strategy weights and limited resources. The proposed hybrid decision model can improve the reliability of SHMS portfolios.
A Pragmatic Approach for Evaluating and Accrediting Digital Competence of Digital Profiles: A Case Study of Entrepreneurs and Remote Workers

During the last decades, digital competence has become essential at workplace. Nowadays, it is difficult to find a job where no ICT skills are required. At the same time, there is a lack of ecosystems for adult reskilling in digital competence. Moreover, most of them do not use of a common language and terminology, decreasing the possibilities of being used by a wider public. In addition, the assessment of digital competence cannot be done using simple self-assessment tests, but more complex tools such as simulations or other activities based on real scenarios. Considering this, we designed a performance-based evaluation system following a pragmatic approach based on DigComp framework. We carried out a needs analysis based on expert consultation (63 teleworkers and 82 entrepreneurs) to create an assessment syllabus and implement the assessment modules. Then, we conducted an analysis by experts (n = 21) of the relationship between the content of the tests and the construct it was intended to measure. After refinement, the system was piloted by end-users all over Europe (n = 525). Results confirmed that DigComp was the most appropriate reference when considering the transversality of digital competence, providing researchers with well-defined clear criteria.
An Innovative and Interactive Teaching Model for Cultivating Talent’s Digital Literacy in Decision Making, Sustainability, and Computational Thinking

In the modern era, talent cultivation plays an essential role in the transition process to sustainable development. The emerging direction of talent cultivation is intended to achieve global competence in computational thinking and digital literacy; however, there is still a gap in Taiwan. Scholars and educators have identified the contents of digital literacy, which include statistical concepts, data analysis skills, and critical thinking. To fill this gap, this study proposed an innovative educational teaching method that integrates data visualization techniques and critical thinking training to improve students’ decision-making literacy. An experiment on the curriculum with a one-group pre-test and post-test design was conducted for 79 participants. The data consisted of a geographical data map, visualized data diagrams, and a statistical test; the scores for critical thinking, academic self-confidence, and academic performance were separated into high, middle, and low achievement groups. For data analysis, the bootstrapping method (resampled 1000 times), paired sample t-test, and ANCOVA were applied for data analysis to compare the difference between each independent group. The results showed that the post-test scores (statistic test M = 9.59, infographic test M = 6.09, map test M = 5.02) were higher than the pre-test scores (statistic test M = 7.15, infographic test M = 3.46, map test M = 3.13). The most significant implication is the innovative teaching approach based on integrated data visualization and critical thinking. It plays a very significant role in enhancing decision-making abilities.
ChainsFL: Blockchain-driven Federated Learning from Design to Realization

Despite the advantages of Federated Learning (FL), such as devolving model training to intelligent devices and preserving data privacy, FL still faces the risk of the single point of failure and attack from malicious participants. Recently, blockchain is considered a promising solution that can transform FL training into a decentralized manner and improve security during training. However, traditional consensus mechanisms and architecture for blockchain can hardly handle the large-scale FL task due to the huge resource consumption, limited throughput, and high communication complexity. To this end, this paper proposes a two-layer blockchain-driven FL framework, called as ChainsFL, which is composed of multiple Raft-based shard networks (layer-l) and a Direct Acyclic Graph (DAG)-based main chain (layer-2) where layer-l limits the scale of each shard for a small range of information exchange, and layer-2 allows each shard to update and share the model in parallel and asynchronously. Furthermore, FL procedure in a blockchain manner is designed, and the refined DAG consensus mechanism to mitigate the effect of stale models is proposed. In order to provide a proof-of-concept implementation and evaluation, the shard blockchain base on Hyperledger Fabric is deployed on the self-made gateway as layer-l, and the self-developed DAG-based main chain is deployed on the personal computer as layer-2. The experimental results show that ChainsFL provides acceptable and sometimes better training efficiency and stronger robustness comparing with the typical existing FL systems.
An Introduction to Clustering Algorithms in Big Data

In big data, clustering is the process through which analysis is performed. Since the data is big, it is very difficult to perform clustering approach. Big data is mainly termed as petabytes and zeta bytes of data and high computation cost is needed for the implementation of clusters. In this chapter, the authors show how clustering can be performed on big data and what are the different types of clustering approach. The challenge during clustering approach is to find observations within the time limit. The chapter also covers the possible future path for more advanced clustering algorithms. The chapter will cover single machine clustering and multiple machines clustering, which also includes parallel clustering.