Reviewerly/exHarmony huggingface dataset:
Review and Arrange: Curriculum Learning for Natural Language Understanding

With the notable success of pretrained language models, the pretraining-fine-tuning paradigm has become a dominant solution for natural language understanding (NLU) tasks. Typically, the training instances of a target NLU task are introduced in a completely random order and treated equally at the fine-tuning stage. However, these instances can vary greatly in difficulty, and similar to human learning procedures, language models can benefit from an easy-to-difficult curriculum. Based on this concept, we propose a curriculum learning (CL) framework. Our framework consists of two stages, Review and Arrange, targeting the two main challenges in curriculum learning, i.e., how to define the difficulty of instances and how to arrange a curriculum based on the difficulty, respectively. In the first stage, we devise a cross-review (CR) method to train several teacher models first and then review the training set in a crossed manner to distinguish easy instances from difficult instances. In the second stage, two sampling algorithms, a coarse-grained arrangement (CGA) and a fine-grained arrangement (FGA), are proposed to arrange a curriculum for language models in which the learning materials start from the easiest instances, and more difficult instances are gradually added into the training procedure. Compared to previous heuristic CL methods, our framework can avoid the errors caused by a gap in difficulty between humans and machines and has strong generalization ability. We conduct comprehensive experiments, and the results show that our curriculum learning framework, without any manual model architecture design or use of external data, obtains significant and universal performance improvements on a wide range of NLU tasks in different languages.
Bypassing memory safety mechanisms through speculative control flow hijacks

The prevalence of memory corruption bugs in the past decades resulted in numerous defenses, such as stack canaries, control flow integrity (CFI), and memory-safe languages. These defenses can prevent entire classes of vulnerabilities, and help increase the security posture of a program. In this paper, we show that memory corruption defenses can be bypassed using speculative execution attacks. We study the cases of stack protectors, CFI, and bounds checks in Go, demonstrating under which conditions they can be bypassed by a form of speculative control flow hijack, relying on speculative or architectural overwrites of control flow data. Information is leaked by redirecting the speculative control flow of the victim to a gadget accessing secret data and acting as a side channel send. We also demonstrate, for the first time, that this can be achieved by stitching together multiple gadgets, in a speculative return-oriented programming attack. We discuss and implement software mitigations, showing moderate performance impact.
Perceptions of Anthropomorphism in a Chatbot Dialogue: The Role of Animacy and Intelligence

When interacting with embodied agents, users often rely on a variety of cues from the agent's embodiment to form perceptions of animacy and intelligence, including its appearance and behaviour. Due to chatbots' disembodiment, users' perceptions of a chatbot's animacy and intelligence are mostly dependent on the textual properties of the dialogue. The current study aims to investigate the mediating role of perceptions of chatbot's intelligence and animacy on users' perceptions of the chatbot's anthropomorphism. An online experiment was conducted with a chatbot and a web platform. Both systems asked users three basic questions for providing a restaurant recommendation. By communicating the same content via different modalities of communication (i.e., flowing dialogue and traditional web interface); this study compares the differences in these perceptions between chatbots to traditional web platforms. The results of a mediation analysis entail that the chatbot was perceived as more animate than the web platform, and accordingly, it was perceived as more anthropomorphic than the web platform as users' perceptions of animacy fully mediated this effect. Also, there is no evidence for differences in users' perceptions of intelligence between the chatbot and the web platform.
Enabling On-Device Self-Supervised Contrastive Learning with Selective Data Contrast

After a model is deployed on edge devices, it is desirable for these devices to learn from unlabeled data to continuously improve accuracy. Contrastive learning has demonstrated its great potential in learning from unlabeled data. However, the online input data are usually none independent and identically distributed (non-iid) and edge devices’ storages are usually too limited to store enough representative data from different data classes. We propose a framework to automatically select the most representative data from the unlabeled input stream, which only requires a small data buffer for dynamic learning. Experiments show that accuracy and learning speed are greatly improved.
Faster Lattice-Based KEMs via a Generic Fujisaki-Okamoto Transform Using Prefix Hashing

Constructing an efficient CCA-secure KEM is generally done by first constructing a passively-secure PKE scheme, and then applying the Fujisaki-Okamoto (FO) transformation. The original FO transformation was designed to offer security in a single user setting. A stronger notion, known as multi-user security, considers the attacker's advantage in breaking one of many user's ciphertexts. Bellare et al. (EUROCRYPT 2000) showed that standard single user security implies multi-user security with a multiplicative tightness gap equivalent to the number of users.
Joint Embedding of Deep Visual and Semantic Features for Medical Image Report Generation

Medical image report generation (MeIRG) aims at generating associated diagnosis descriptions with natural language sentences from medical images, which is essential in the computer-aided diagnosis system. Nevertheless, this task remains challenging in that medical images and linguistic expressions should be understood jointly which however show great discrepancies in the modality. To fill this visual-to-semantic gap, we propose a novel framework that follows the encoder-decoder pipeline. Our framework is characterized by encoding both deep visual and semantic embeddings through a triple-branch network (TriNet) during the encoding phase. The visual attention branch captures attended visual embeddings from medical images with the soft-attention mechanism. The medical report (MeRP) embedding branch predicts semantic report embeddings. The embedding branch of medical subject headings (MeSH) obtains semantic embeddings of related medical tags as complementary information. Then, outputs of these branches are fused and fed into a decoder for the report generation. Experimental results on two benchmark datasets have demonstrated the excellent performance of our method. Related codes are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/yangyan22/Medical-Report-Generation-TriNet</uri> .
An Empirical Analysis of VM Startup Times in Public IaaS Clouds

VM startup time is an essential factor in designing elastic cloud applications. VM autoscaling can reduce the under-/over-provisioning period of VMs with a precise estimation of VM startup time, and in turn, it can guarantee the application's SLOs with improved cost-efficiency. Unfortunately, VM startup time has been little studied, and previous measurement results did not consider various configurations of VMs. This work performs a thorough analysis of VM startup times in two IaaS clouds (AWS, GCP). Specifically, we collected 300,000 data points from each provider by applying diverse VM configurations. i.e., different VM types, image sizes, location, purchase models. With extensive analysis, we found several important factors that can change VM startup time significantly. Moreover, by comparing with a previous study, we confirm that AWS made significant improvements for reducing VM startup times and currently has much quicker VM startup times than in the past.
Interplay among blockchain technology adoption strategy, e-supply chain management diffusion, entrepreneurial orientation and human resources information system in banking

Purpose To empirically study the direct and mediating relationships between blockchain technology adoption strategy (BCTAS), electronic supply chain management diffusion (eSCMD), entrepreneurial orientation (EO) and human resources information system (HRIS) in Egyptian banks. This paper aims to connect the dots and show the relationships linking these related constructs after the emergence of this breakthrough blockchain technology. Design/methodology/approach The authors have undertaken a thematic review of relevant multidisciplinary business management literature and then developed a conceptual model. This model was examined through adopting a mixed-methods approach, through which 300 quantitative questionnaires were filled by information technology (IT) staff at 12 banks in Egypt utilising a snowball sample. Besides, 20 qualitative interviews were carried out with international and Egyptian blockchain experts for exploratory and explanatory purposes. The suggested hypotheses were tested using structural equation modelling. Findings The results revealed that EO affects positively and significantly BCTAS and HRIS. BCTAS affects positively and significantly both HRIS and eSCMD. Concerning the linkage between external/supply chain and internal/organisational information diffusion, HRIS has a positive and significant effect on eSCMD. The direct EO–eSCMD relationship is not supported. Yet, indirectly, BCTAS mediates significantly EO–eSCMD and EO–HRIS relationships. Further, HRIS mediates significantly the indirect BCTAS–eSCMD relationship. Practical implications The findings of this research shed light on the benefits and challenges of adopting BCTAS within emerging markets in general and Egyptian banking in specific, which can support an effective and efficient decision-making process undertaken by strategic and functional banking managers with EO in similar emerging economies. Originality/value Conceptually and empirically, it is the first article that investigated direct and mediating EO–BCTAS–HRIS–eSCMD relationships in a promising banking industry of an emerging market. It solved an interdisciplinary research puzzle by piecing together the relevant contemporary literature on production, operations and SC management, entrepreneurship, HR management and strategic technology adoption.
Impulsive noise removal via a blind CNN enhanced by an iterative post-processing

In digital imaging, especially in the process of data acquisition and transmission, images are often affected by impulsive noise. Therefore, it is essential to remove impulsive noise from images before any further processing. Due to the remarkable performance of deep neural networks in different applications of image processing and computer vision, we present an end-to-end fully convolutional neural network to remove impulsive noise from images. To train our network, we generate a customized dataset with various noise densities in which the highly corrupted images are more frequent. Hence, our convolutional neural network is blind since the percentage of impulsive noise is not required as prior knowledge. Moreover, we define a multi-term loss function to train our network. In particular, we define a novel term to impose the sparsity nature of impulsive noise. Experimental results indicate that our deep learning approach significantly outperforms other state-of-the-art methods in terms of reconstruction quality and speed on a system equipped with GPU. Meanwhile, we introduce a fast iterative method, as a post-processing stage, to further improve the reconstruction quality of our neural network. The proposed post-processing algorithm improves the reconstruction quality in only a fraction of a second.
Research on Mobile Robot Path Planning Based on Improved A-star Algorithm

Addressing the issue of mobile robots avoiding obstacles autonomously, the traditional A-star algorithm for route planning is improved. First, the full convolution interpolation processing is used to expand the search area of the traditional path planning A-star algorithm. The path inflection point of the improved A-star algorithm is reduced, which shortens the moving distance of the mobile robot. Secondly, through multiple naturalizations of adjacent inflection points, the node redundancy is effectively reduced, and the overall path planning length is greatly reduced. Finally, the improved algorithm is simulated, and the results show that the improved method is a simple, convenient and effective method, which enhances the route planning capability.
Genesis of the gold deposits in the Kunlun River area, East Kunlun, Qinghai Province: Constraints from geology, fluid inclusions and isotopes

The Kunlun River area, located in the central section of the East Kunlun orogenic belt and sandwiched between the Central and South Kunlun faults, includes a series of orogenic gold deposits (e.g., Heicigou, Dazaohuo, and Heihainan) that constitute a gold mineralization system closely related to the late Paleozoic to early Mesozoic collisional orogeny. These gold deposits are mainly hosted by the Ordovician and early-middle Triassic metamorphosed sedimentary rocks and controlled by NWW- to NW-trending secondary faults. The orebodies occur as auriferous quartz veins and surrounding altered rocks accompanied by silicification, sulfidation, carbonatization, sericitization and chloritization. Fluid inclusions, hydrogen and oxygen isotopes indicate that the ore-forming fluids are characterized by moderate temperature (mostly 200–250 ℃), low salinity (2.8–10.2% NaCleq), and CO2-bearing metamorphic water. Carbon, sulfur, and lead isotopes indicate that the ore-forming materials were derived from sedimentary rocks. Based on the ages of capture zircons in the auriferous quartz veins and the emplacement time of regional A-type granites, the gold mineralization time is confined between 239 and 200 Ma. Combined with regional tectonic evolution, these deposits were formed in the stage of tectonic system transformation from compression to extension in the middle-late Triassic. During this period, the release of tectonic stress resulted in the upward migration of ore-forming fluids along the regional faults and the gold precipitation in the secondary faults in the shallow crust by fluid immiscibility and fluid-rock interaction.
Sparse Pinball Twin Bounded Support Vector Clustering

Analyzing unlabeled data is of prime importance in machine learning. Creating groups and identifying an underlying clustering principle is essential to many fields, such as biomedical analysis and market research. Novel unsupervised machine learning algorithms, also called clustering algorithms, are developed and utilized for this task. Inspired by twin support vector machine (TWSVM) principles, a recently introduced plane-based clustering algorithm, the twin bounded support vector clustering (TBSVC), is used in widespread clustering problems. However, TBSVC is sensitive to noise and suffers from low resampling stability due to usage of hinge loss. Pinball loss is another type of loss function that is less sensitive toward noise in the datasets and is more stable for resampling of datasets. However, the use of pinball loss negatively affects the sparsity of the solution of the problem. In this article, we present a novel plane-based clustering method, the sparse TBSVC using pinball loss (pinSTBSVC). The proposed pinSTBSVC is the sparse version of our recently proposed TBSVC using pinball loss (pinTBSVC). Sparse solutions help create better-generalized solutions to clustering problems; hence, we attempt to use the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula> -insensitive pinball loss function to propose pinSTBSVC. The loss function used to propose pinSTBSVC provides sparsity to the solution of the problem and improves the aforementioned plane-based clustering algorithms. Experimental results performed on benchmark University of California, Irvine (UCI) datasets indicate that the proposed method outperforms other existing plane-based clustering algorithms. Additionally, we also give the application of our method in biomedical image clustering and marketing science. We show that the proposed method is more accurate on real-world datasets too. The code for the proposed algorithm is also provided on the author’s Github page: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/mtanveer1</uri> .
Data Augmentation of Thyroid Ultrasound Images Using Generative Adversarial Network

Ultrasound (US) has been investigated as a common method of computer aided diagnosis because of its low-cost, harmless and real-time scanning. Also the rapid development of deep learning segmentation and classification models alleviates the influence of low signal-to-noise ratio and artifacts of ultrasonic imaging. However, due to the privacy issues of medical data, it is not easy to acquire sufficient data for deep learning model training. In recent years, generative adversarial networks (GANs) are widely used in data augmentation. However, GANs suffer from the problem of mode collapse in the training process then generate images with a limited variety. On the other hand, variational auto-encoder (VAE) is free from mode collapse but it generates blurred images. In this work, we study an auto-encoding generative adversarial network combining the advantages of GAN and VAE to generate realistic images for medical thyroid ultrasound image augmentation. Experiment results show that the generated images can simulate realistic ultrasound features and thyroid tissues for augmentation and help training a U-Net model to get better segmentation results.
Byzantine-Robust Aggregation in Federated Learning Empowered Industrial IoT

Federated learning (FL) is a promising paradigm to empower on-device intelligence in Industrial Internet of Things (IIoT) due to its capability of training machine learning models across multiple IIoT devices while preserving the privacy of their local data. However, the distributed architecture of FL relies on aggregating the parameter list from the remote devices, which poses potential security risks caused by malicious devices. In this article, we propose a flexible and robust aggregation rule, called auto-weighted geometric median ( <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AutoGM</monospace> ), and analyze the robustness against outliers in the inputs. To obtain the value of <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AutoGM</monospace> , we design an algorithm based on the alternating optimization strategy. Using <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AutoGM</monospace> as aggregation rule, we propose two robust FL solutions <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AutoGM_FL</monospace> and <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AutoGM_PFL</monospace> . <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AutoGM_FL</monospace> learns a shared global model using the standard FL paradigm, and <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AutoGM_PFL</monospace> learns a personalized model for each device. We conduct extensive experiments on the FEMNIST and Bosch IIoT datasets. The experimental results show that our solutions are robust against both model poisoning and data poisoning attacks. In particular, our solutions sustain high performance even when 30% of the nodes perform model or 50% of the nodes perform data poisoning attacks.
The International Hierarchy of Money in Cross-Border Payment Systems: Developing Countries’ Regulation for Central Bank Digital Currencies and Facebook’s Stablecoin

Rich countries’ central bank digital currencies (CBDCs) and Facebook’s stablecoin constitute risks to developing countries. Foreign CBDCs risk facilitation of capital flight. Novi, Facebook’s wallet for WhatsApp risks countries’ domestic payment systems moving offshore. This article presents the analytical framework for assessing these risks by analyzing payment system dynamics in the framework of the theory of the monetary circuit applied to the international hierarchy of money. It quantifies the US and dollarized Ecuador’s pyramid of liabilities to demonstrate the impact of payment systems on the potential of banks’ money creation. While a domestic CBDC or a potent bank-money payment system can help to increase significantly the potential of money creation in a dollarized economy, the offshoring of domestic payment systems by substituting local currencies with foreign stablecoins such as Facebook’s Diem and its Novi wallet reduces the potential of money creation. It strongly encourages countries low in the hierarchy of money to engage in a diverse set of regulations to mitigate these risks preemptively and proactively regulate to promote a dynamic payment system without the risk of currency substitution.
Security and Cost Aware Scheduling of Real-Time IoT Workflows in a Mist Computing Environment

In this paper we propose a security and cost aware scheduling heuristic for real-time workflow jobs that process Internet of Things (IoT) data with various security requirements. The environment under study is a four-tier architecture, consisting of IoT, mist, fog and cloud layers. The resources in the mist, fog and cloud tiers are considered to be heterogeneous. The proposed scheduling approach is compared to a baseline strategy, which is security aware, but not cost aware. The performance evaluation of both heuristics is conducted via simulation, under different values of security level probabilities for the initial IoT input data of the entry tasks of the workflow jobs.
Quantum Secure Direct Communication with Mutual Authentication using a Single Basis

In this paper, we propose a new theoretical scheme for quantum secure direct communication (QSDC) with user authentication. Different from the previous QSDC protocols, the present protocol uses only one orthogonal basis of single-qubit states to encode the secret message. Moreover, this is a one-time and one-way communication protocol, which uses qubits prepared in a randomly chosen arbitrary basis, to transmit the secret message. We discuss the security of the proposed protocol against some common attacks and show that no eaves-dropper can get any information from the quantum and classical channels. We have also studied the performance of this protocol under realistic device noise. We have executed the protocol in IBMQ Armonk device and proposed a repetition code based protection scheme that requires minimal overhead.
Owicki-Gries Reasoning for C11 Programs with Relaxed Dependencies

Deductive verification techniques for C11 programs have advanced significantly in recent years with the development of operational semantics and associated logics for increasingly large fragments of C11. However, these semantics and logics have been developed in a restricted setting to avoid the thin-air-read problem. In this paper, we propose an operational semantics that leverages an intra-thread partial order (called semantic dependencies) induced by a recently developed denotational event-structure-based semantics. We prove that our operational semantics is sound and complete with respect to the denotational semantics. We present an associated logic that generalises a recent Owicki-Gries framework for RC11 (repaired C11), and demonstrate the use of this logic over several example proofs.