Reviewerly/exHarmony huggingface dataset:
Outsourcing LDA-Based Face Recognition to an Untrusted Cloud

Face recognition has been extensively employed in practice, such as attendance system and public security. Linear discriminant analysis (LDA) algorithm is one of the most significant ones in the field of face recognition, but it is very difficult for many clients to employ it in their resource-constrained devices (e.g., smartphones and notebook computers). Outsourcing computation provides a promising method for clients to perform heavy tasks with limited computing power. In this paper, we design a protocol of outsourcing LDA-based face recognition to an untrusted cloud, which can help the client to complete the operations of matrix inversion (MI), matrix multiplication (MM) and eigenvalue decomposition (ED) simultaneously. The proposed outsourcing protocol can hide the private data of the client from the cloud. More importantly, the client can verify whether the outsourcing results are correct or not with probability one and so it is impossible for the server to deceive the client. In addition, the proposed protocol greatly decreases the computational complexity of the client thus enabling the client to complete LDA algorithm efficiently. Finally, we implement the protocol and give a comprehensive evaluation. The experimental results demonstrate that the client obtain great computing savings and the face recognition accuracy in the proposed protocol is almost identical to the original LDA algorithm.
Cybersecurity Challenges in Small and Medium Enterprise (SMEs)

Over the past decade, digitalization has played a greater role in improving the business stature of small business by opening new venues, extending their reach, and thereby improving value producing opportunities. It also brought the small businesses additional responsibilities of having to deal with the security risks and threats which are ever-present on the digital platform. This chapter aims to review and analyse the cybersecurity risks in small businesses due to the adaption of new digital technologies. The chapter discusses the security risks and challenges dealt by small business, the key constraints in implementing a security program to comply with the legal and regulatory requirements, and how the cloud technology can answer many of those challenges.
How Different Explanations Impact Trust Calibration: The Case of Clinical Decision Support Systems

Machine learning has made rapid advances in safety-critical applications, such as traffic control, finance, and healthcare. With the criticality of decisions they support and the potential consequences of following their recommendations, it also became critical to provide users with explanations to interpret machine learning models in general, and black-box models in particular. However, despite the agreement on explainability as a necessity, there is little evidence on how recent advances in eXplainable Artificial Intelligence literature (XAI) can be applied in collaborative decision-making tasks, i.e., Human decision-maker and an AI system working together, to contribute to the process of trust calibration effectively. This research conducts an empirical study to evaluate four XAI classes for their impact on trust calibration. We take clinical decision support systems as a case study and adopt a within-subject design followed by semi-structured interviews. We gave participants clinical scenarios and XAI interfaces as a basis for decision-making and rating tasks. Our study involved 41 medical practitioners who use clinical decision support systems frequently. We found that users perceive the contribution of explanations to trust calibration differently according to the XAI class and to whether XAI interface design fits their job constraints and scope. We revealed additional requirements on how explanations shall be instantiated and designed to help a better trust calibration. Finally, we build on our findings and present guidelines for designing XAI interfaces.
A Formalization of Topological Spaces in Coq

Abstract It is a wish for Wu Wen-tsun to implement the mechanical proving of theorems in topology. Topological spaces constitute a fundamental concept of general topology, which is significant in understanding the essential content of general topology. Based on the machine proof system of axiomatic set theory, we presented a computer formalization of topological spaces in Coq. Basic examples of topological spaces are formalized, including indiscrete topological spaces and discrete topological spaces. Furthermore, the formal description of some well-known equivalent definitions of topological spaces are provided, and the machine proof of equivalent definitions based on neighborhood system and closure is presented. All the proof code has been verified in Coq, and the process of proof is standardized, rigorous and reliable.
FHC-NDS: Fuzzy Hierarchical Clustering of Multiple Nominal Data Streams

The need of fuzzy clustering arises in many real-world applications such as clumping the users based on their web browsing behavior where the behavior of a user can be similar to two different sets of users at the same instance. The aptness of fuzzy clustering for data streams is further intensified given their concept evolving nature. Data streams can be clustered either by following clustering-by-variable approach or clustering-by-example approach. Most of the existing fuzzy clustering-by-variable methods are applicable to numeric data streams only. In this article, a fuzzy hierarchical clustering method is proposed for clustering multiple nominal data streams using clustering-by-variable approach. The fuzzy affinity of data streams to different clusters is calculated using normalized cosine similarity to the cluster centroids. It handles the concept evolution by updating the hierarchical clustering structure by either merging and/or splitting the nodes depending on the extent to which the node entropy changes. The performance of the proposed method is analyzed and compared to hierarchical clustering for multiple nominal data streams (HCND), semifuzzy online divisive-agglomerative clustering, and nTreeClus on synthetic as well as real-world web-browsing dataset where it has outperformed all three in terms of cluster quality as quantified by Dunn index, modified Hubert <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\Gamma$</tex-math></inline-formula> statistic, and adjusted rand index. Furthermore, the experimental results show that the proposed method is highly promising with regard to capturing fuzzy clusters as indicated by Xie-Beni index, partition coefficient, and partition entropy.
Consensus Mechanism of Blockchain Based on PoR with Data Deduplication

As the basis of cloud computing, distributed storage technology mainly studies how data centers store, organize and manage data. Blockchain has become the most secure solution for cloud storage due to its decentralization and immutability. Consensus mechanism is one of the core technologies of blockchain, which affects the transaction processing capability, security and scalability of blockchain. The current mainstream consensus algorithms such as Proof of Work, Proof of Stake, and Delegated Proof of Stake all have the problem of wasting resources. And with the explosive growth of data, cloud storage nodes store a large amount of redundant data, which inevitably increases storage overhead and computing cost. To this end, we propose to use the Proof of Retrievability with deduplication algorithm as the consensus mechanism of the blockchain system and design a blockchain consensus protocol suitable for distributed storage. First, the data integrity verification protocol in the scheme guarantees that storage nodes correctly store the data they promise to store. Second, the deduplication algorithm in the protocol can optimize data auditing, greatly reduce the need for data storage space, and improve the scalability of data transmission. In addition, the scheme uses ring signatures in the audit process to ensure user anonymity and data unlinkability, while providing highly reliable data storage, and ensuring data storage security through blockchain. Finally, we demonstrate the security of the proposed scheme and evaluate its performance. The evaluation results show that our scheme is efficient and scalable.
Deep Investigation of the Recent Advances in Dialectal Arabic Speech Recognition

Speech recognition systems play an important role in human-machine interactions.Many systems exist for Arabic speech, however, there are limited systems for dialectal Arabic speech.The Arabic language comprises many properties, some of which are ideal for building automatic speech recognition systems such as syntax and phonology, while other properties are unsuitable for developing speech systems.Importantly, most data are in non-diacritized form, vary in dialect, and contain morphological complexity.Moreover, the Arabic dialects lack a standard structure.In this paper, we present an overview and summation of dialectal Arabic speech recognition systems using different approaches and techniques.The main goal of this paper is to compare and discuss different studies in dialectal Arabic speech systems including several criteria such as techniques, datasets, evaluation metrics, and dialect types.The study also includes a description of some techniques used in all steps of the dialectal Arabic speech system and introduces challenges and problems.Overall, more studies are required to obtain a more accurate speech system for dialectal Arabic.
Reducing Overconfidence Predictions in Autonomous Driving Perception

In state-of-the-art deep learning for object recognition, Softmax and Sigmoid layers are most commonly employed as the predictor outputs. Such layers often produce overconfidence predictions rather than proper probabilistic scores, which can thus harm the decision-making of ‘critical’ perception systems applied in autonomous driving and robotics. Given this, we propose a probabilistic approach based on distributions calculated out of the Logit layer scores of pre-trained networks which are then used to constitute new decision layers based on Maximum Likelihood (ML) and Maximum a-Posteriori (MAP) inference. We demonstrate that the hereafter called ML and MAP layers are more suitable for probabilistic interpretations than Softmax and Sigmoid-based predictions for object recognition.We explore distinct sensor modalities via RGB images and LiDARs (RV: range-view) data from the KITTI and Lyft Level-5 datasets, where our approach shows promising performance compared to the usual Softmax and Sigmoid layers, with the benefit of enabling interpretable probabilistic predictions. Another advantage of the approach introduced in this paper is that the so-called ML and MAP layers can be implemented in existing trained networks, that is, the approach benefits from the output of the Logit layer of pre-trained networks. Thus, there is no need to carry out a new training phase since the ML and MAP layers are used in the test/prediction phase. The Classification results are presented using reliability diagrams, while detection results are illustrated using precision-recall curves.
InvUNET: Involuted UNET for Breast Tumor Segmentation from Ultrasound

Breast cancer is one of the fatal health conditions across globe and early detection of such cancerous tumor is life saver. There are various diagnostic ways to detect the tumor, however, ultrasound is more helpful for certain scenarios such as young patient, lactating or pregnant women, radiation sickness and biopsy assistance. This work attempts to detect breast tumors from the ultrasound images using Involuted UNET (InvUNET). It is based on the hybrid combination of deep learning concepts such as UNET and involution layer. UNET has been a popular choice in medical segmentation and the lightweight involution kernels embed the location-specific and channel-agnostic representation learning. The proposed method is validated using Breast Ultrasound Images (BUSI) dataset and jaccard score of 0.7146 is obtained.
High-Precision Pixelwise SAR–Optical Image Registration via Flow Fusion Estimation Based on an Attention Mechanism

Due to the severe speckle noise and complex local deformation in synthetic aperture radar (SAR) images, the problem of high-precision pixelwise registration (dense registration) between SAR and optical images remains far from resolved. In this paper, an attention-mechanism-based optical flow fusion algorithm is proposed to achieve high-precision dense SAR-optical image registration. First, two descriptors, the scale-invariant feature transform (SIFT) and a descriptor based on phase congruency (PC), are used to describe SAR and optical images to eliminate their intensity differences. Then, the saliency feature map is extracted as a query matrix to weight the optical flow energy function. When extracting the saliency feature map, the Contour Robuste d'Ordre Non Entier (CRONE) and the ratio of exponentially weighted averages (ROEWA) operator are used to eliminate additive and multiplicative noise in the optical and SAR images, respectively. Finally, the optical flow fields based on SIFT and the PC-based descriptor are fused to compensate for registration ambiguity. Experimental results show that our method is feasible, effective and robust to noise, it enables high-precision registration under local deformation.
ClusterFormer: Neural Clustering Attention for Efficient and Effective Transformer

Recently, a lot of research has been carried out to improve the efficiency of Transformer.Among them, the sparse pattern-based method is an important branch of efficient Transformers.However, some existing sparse methods usually use fixed patterns to select words, without considering similarities between words.Other sparse methods use clustering patterns to select words, but the clustering process is separate from the training process of the target task, which causes a decrease in effectiveness.To address these limitations, we design a neural clustering method, which can be seamlessly integrated into the Self-Attention Mechanism in Transformer.The clustering task and the target task are jointly trained and optimized to benefit each other, leading to significant effectiveness improvement.In addition, our method groups the words with strong dependencies into the same cluster and performs the attention mechanism for each cluster independently, which improves the efficiency.We verified our method on machine translation, text classification, natural language inference, and text matching tasks.Experimental results show that our method outperforms two typical sparse attention methods, Reformer and Routing Transformer while having a comparable or even better time and memory efficiency.
Learning Disentangled Textual Representations via Statistical Measures of Similarity

When working with textual data, a natural application of disentangled representations is fair classification where the goal is to make predictions without being biased (or influenced) by sensitive attributes that may be present in the data (e.g., age, gender or race).Dominant approaches to disentangle a sensitive attribute from textual representations rely on learning simultaneously a penalization term that involves either an adversarial loss (e.g., a discriminator) or an information measure (e.g., mutual information).However, these methods require the training of a deep neural network with several parameter updates for each update of the representation model.As a matter of fact, the resulting nested optimization loop is both time consuming, adding complexity to the optimization dynamic, and requires a fine hyperparameter selection (e.g., learning rates, architecture).In this work, we introduce a family of regularizers for learning disentangled representations that do not require training.These regularizers are based on statistical measures of similarity between the conditional probability distributions with respect to the sensitive attributes.Our novel regularizers do not require additional training, are faster and do not involve additional tuning while achieving better results both when combined with pretrained and randomly initialized text encoders.
An Efficient Oblivious Random Data Access Scheme in Cloud Computing

With the development of cloud computing and cloud storage techniques, much attention has been focused on the privacy protection of outsourced data. Existing searchable encryption solutions can ensure the confidentiality and availability of data stored on the cloud. However, searchable encryption is vulnerable to statistical inference attacks, which exploit the disclosure of access patterns on encrypted indexes and encrypted file sets, which has become a potential way to reveal user privacy. Oblivious random access memory (ORAM) is an important means of concealing access patterns, yet its direct use in searchable encryptions is expensive. This paper presents a scheme for efficient and oblivious access to encrypted databases through encrypted indexes. This scheme is a hybrid ORAM scheme, which utilizes semi-homomorphic encryption to perform calculations in the ciphertext domain, overcoming the limitations of the huge overhead associated with Path-ORAM. For excessive amounts of data, semi-homomorphic encryption can significantly reduce communication and storage overhead. Our scheme can achieve high-security encrypted search and update operations at the same time. Moreover, the execution speed of ODS-Tree is 2-8x faster than that of ORAM-based schemes. In addition, the proposed scheme reduces the data block transmission and storage costs compared to existing frameworks.
Improving Colon Carcinoma Grading by Advanced CNN Models

Cancer ranks as a leading cause of death and an important barrier to increasing life expectancy in every country of the world. For this reason, there is a great requirement for developing computer-aided approaches for accurate cancer diagnosis and grading that can overcome the problem of intra- and inter-observer inconsistency and thereby improve the accuracy and consistency in the cancer detection and treatment planning processes. In particular, the studies exploiting deep learning for automatic grading of colon carcinoma are still in infancy since the works in the literature did not exploit the most advanced models and methodologies of machine learning and systematic exploration of the most promising available convolutional networks is missing. To fill this gap, in this work, the most performing convolutional architectures in classification tasks have been exploited to improve colon carcinoma grading in histological images. The experimental proofs on the largest publicly available dataset demonstrated marked improvement with respect to the leading state of the art approaches.
Challenges and usage of cryptocurrencies in tourism

The subject of this research are challenges in the application of cryptocurrencies in tourism, with focus on the Republic of Serbia. The paper will present examination of the attitudes and opinions of the respondents in the tourist market of the Republic of Serbia about cryptocurrencies and their willingness to accept them in their work, business and travel. The methods used in the research are: hypothetical-deductive, structured (closed type) questions, dichotomic single choice questions; both nominal and interval measurement scales. Research should contribute to a better understanding of the functioning of digital currencies in the tourism sector and a closer understanding of the challenges and prospects for their application.
Malfunctioning Right in Our Backyards OR The Strangeness of Ecological Awareness

This chapter argues that ecological awareness requires some understanding of how things are constantly in processes of mediation. We set out to explore the aesthetic dimension of ecological awareness and environments by looking at how things in our world are constantly malfunctioning., We also maintain that learning and teaching about ecological awareness can begin right in our backyards. We use our actual backyards as examples of how educators might approach mediation, media, and the aesthetic dimensions of ecological awareness. Our path is one of alchemy, and we employ its symbols as provocations throughout the text. We argue that first and foremost it is important to understand that everything is constantly malfunctioning. Malfunctioning is a part of being. Using the multiplicitous relationships we have with the strange things we call ‘backyards’, Valerie and Jesse tell stories, recite poems, and mediate with the nonliving, living, and virtual things in/surrounding these strange places/objects. Ecological awareness is strange and pleasurable, and so are the pedagogical practices and elements that come from such awareness.
ReShape: A Decoder for Hypergraph Product Codes

The design of decoding algorithms is a significant technological component in the development of fault-tolerant quantum computers. Often design of quantum decoders is inspired by classical decoding algorithms, but there are no general principles for building quantum decoders from classical decoders. Given any pair of classical codes, we can build a quantum code using the hypergraph product, yielding a hypergraph product code. Here we show we can also lift the decoders for these classical codes. That is, given oracle access to a minimum weight decoder for the relevant classical codes, the corresponding <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$[[n,k,d]]$ </tex-math></inline-formula> quantum code can be efficiently decoded for any error of weight smaller than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(d-1)/2$ </tex-math></inline-formula> . The quantum decoder requires only <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(k)$ </tex-math></inline-formula> oracle calls to the classical decoder and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(n^{2})$ </tex-math></inline-formula> classical resources. The lift and the correctness proof of the decoder have a purely algebraic nature that draws on the discovery of some novel homological invariants of the hypergraph product codespace. While the decoder works perfectly for adversarial errors, that is errors of weight up to half the code distance, it is not suitable for more realistic stochastic noise models and therefore can not be used to establish an error correcting threshold.