Reviewerly/exHarmony huggingface dataset:
Efficient GPU Implementations of Post-Quantum Signature XMSS

The National Institute of Standards and Technology (NIST) approved XMSS as part of the post-quantum cryptography (PQC) development effort in 2018. XMSS is currently one of only two standardized PQC algorithms, but its performance limits its use. For example, the fastest record for some standardized parameters still takes more than a minute to generate a keypair. In this article, we present the first GPU implementation for XMSS and its variant XMSS <inline-formula><tex-math notation="LaTeX">$^{\mathsf {MT}}$</tex-math></inline-formula> . The high parallelism of GPUs is especially effective for reducing latency in key generation and improving throughput for signing and verifying. In order to meet various application scenarios, we provide three parallel XMSS schemes: <i>algorithmic parallelism</i> , <i>multi-keypair data parallelism</i> , and <i>single-keypair data parallelism</i> . For these schemes, we design custom parallel strategies that use more than 10,000 cores for all parameters provided by NIST. In addition, we analyze the availability of most previous serial optimizations and explore numerous techniques to fully exploit GPU performance. Our evaluations are made with the XMSSMT-SHA2_20/2_256 parameter set on a GeForce RTX 3090. The result shows the key generation latency is 3.20 ms, a speedup of 21,899× compared to the GPU ported version, which is also 54× speedup faster than the fastest work (174 ms). When 16384 tasks are executed, the throughput (task/s) for signing/verifying in the single-key and multi-key cases is 311,424/415,100 and 145,100/419,887, respectively. Compared to the throughput for signing/verifying (1695/4000) of the fastest work, we obtain a speedup of 184×/104× and 86×/105× in single-key and multi-key cases, respectively.
Insight Extraction From E-Health Bookings by Means of Hypergraph and Machine Learning

New technologies are transforming medicine, and this revolution starts with data. Usually, health services within public healthcare systems are accessed through a booking centre managed by local health authorities and controlled by the regional government. In this perspective, structuring e-health data through a Knowledge Graph (KG) approach can provide a feasible method to quickly and simply organize data and/or retrieve new information. Starting from raw health bookings data from the public healthcare system in Italy, a KG method is presented to support e-health services through the extraction of medical knowledge and novel insights. By exploiting graph embedding which arranges the various attributes of the entities into the same vector space, we are able to apply Machine Learning (ML) techniques to the embedded vectors. The findings suggest that KGs could be used to assess patients' medical booking patterns, either from unsupervised or supervised ML. In particular, the former can determine possible presence of hidden groups of entities that is not immediately available through the original legacy dataset structure. The latter, although the performance of the used algorithms is not very high, shows encouraging results in predicting a patient's likelihood to undergo a particular medical visit within a year. However, many technological advances remain to be made, especially in graph database technologies and graph embedding algorithms.
A hybrid attention mechanism for multi-target entity relation extraction using graph neural networks

Relation extraction is a cardinal natural language processing task employed to process a given corpus and infer hidden connections and relations between real-world objects. Most contemporary research works employ state-of-the-art language models and techniques for determining the type of relationship existing between a pair of entities in a given sentence but are computationally expensive and fail to identify or match the entities present in the sentence as a single task, rather they breakdown the problem into subtasks or rely on a multi-module framework requiring multiple propagations through the network. The paper presents a novel methodology for extracting relations between multiple pairs of entities present in the sentence and performs the relation classification task. The proposed methodology employs a graph neural network, and in contrast to the existing research, the proposed mechanism makes use of a hybrid attention mechanism to dynamically optimize the graph edges to capture the relevant details in the network graph to aid as an attention mechanism to aid in faster computation, compared to the more typical transformer-based networks that overwhelm CPU-based systems. The paper also studies the effect of the number of hop transformations on the graph and other hyper-parameters controlling the input sentence representation. The proposed model architecture achieves a macro avg. F1 score of 86.2 on the SemEval 2010 relation extraction dataset, with further room for improvement.
An efficient contradiction separation based automated deduction algorithm for enhancing reasoning capability

Automated theorem prover (ATP) for first-order logic (FOL), as a significant inference engine, is one of the hot research areas in the field of knowledge representation and automated reasoning. E prover, as one of the leading ATPs, has made a significant contribution to the development of theorem provers for FOL, particularly equality handling, after more than two decades of development. However, there are still a large number of problems in the TPTP problem library, the benchmark problem library for ATPs, that E has yet to solve. The standard contradiction separation (S-CS) rule is an inference method introduced recently that can handle multiple clauses in a synergized way and has a few distinctive features which complements to the calculus of E. Binary clauses, on the other hand, are widely utilized in the automated deduction process for FOL because they have a minimal number of literals (typically only two literals), few symbols, and high manipulability. As a result, it is feasible to improve a prover's deduction capability by reusing binary clause. In this paper, a binary clause reusing algorithm based on the S-CS rule is firstly proposed, which is then incorporated into E with the objective to enhance E's performance, resulting in an extended E prover. According to experimental findings, the performance of the extended E prover not only outperforms E itself in a variety of aspects, but also solves 18 problems with rating of 1 in the TPTP library, meaning that none of the existing ATPs are able to resolve them.
Investigating acceptance behavior in software engineering—Theoretical perspectives

Software engineering research aims to establish software development practice on a scientific basis. However, the evidence of the efficacy of technology is insufficient to ensure its uptake in industry. In the absence of a theoretical frame of reference, we mainly rely on best practices and expert judgment from industry-academia collaboration and software process improvement research to improve the acceptance of the proposed technology. To identify acceptance models and theories and discuss their applicability in the research of acceptance behavior related to software development. We analyzed literature reviews within an interdisciplinary team to identify models and theories relevant to software engineering research. We further discuss acceptance behavior from the human information processing perspective of automatic and affect-driven processes ("fast" system 1 thinking) and rational and rule-governed processes ("slow" system 2 thinking). We identified 30 potentially relevant models and theories. Several of them have been used in researching acceptance behavior in contexts related to software development, but few have been validated in such contexts. They use constructs that capture aspects of (automatic) system 1 and (rational) system 2 oriented processes. However, their operationalizations focus on system 2 oriented processes indicating a rational view of behavior, thus overlooking important psychological processes underpinning behavior. Software engineering research may use acceptance behavior models and theories more extensively to understand and predict practice adoption in the industry. Such theoretical foundations will help improve the impact of software engineering research. However, more consideration should be given to their validation, overlap, construct operationalization, and employed data collection mechanisms when using these models and theories.
Linear dimensionality reduction method based on topological properties

Dimensionality reduction is an important data preprocessing technique that has been extensively studied in machine learning and data mining. Locality Preserving Projection (LPP) is a widely used linear unsupervised dimensionality reduction method, which maps high-dimensional data into low-dimensional subspace through linear transformation. Although various variants of LPP have been proposed to tackle different drawbacks of LPP, it is identified in this article that LPP does not possess the important topological property of translation invariance, that is, the linear transformation given by LPP is strongly related to the relative position between the data and the origin of the coordinate system. In this article, we theoretically analyze the reason why this drawback exists in LPP and propose to resolve it by introducing a kind of centralization to the model. Moreover, as topological properties are prominent information to characterize the structure of the data, this article proposes a further improvement of LPP to maintain topological connectivity of data after dimensionality reduction. Experiments on multiple synthetic and real-world datasets show that the new model incorporating topological properties outperforms not only the original LPP model but also several other classic linear or non-linear dimensionality reduction methods.
Identification of porcelain ewers in Tang, Song, and Yuan dynasties by digital shape characterization

As an important carrier of history, ancient ceramic ware is a crucial part of the traditional Chinese culture and heritage. The porcelain ewer is a typical example of ancient Chinese ceramic ware. The shapes of the porcelain ewers from the Tang, Song, and Yuan dynasties reflect their artistic characteristics. Different shapes and characteristics reflect the cultures of different dynasties and their production processes and lifestyle. Shapes are an important feature for identifying ancient ceramics. The complex structure of a porcelain ewer makes it difficult to reflect the evolution of its overall shape with partial shape changes. In this study, digital extraction methods of shape characteristics for porcelain ewers were discussed using samples of porcelain-handled ewers from the Tang dynasty to the Song and Yuan periods. Spatial correlation weights of same-side pixels and spatial similarity weights of symmetric pixels in the digital image were calculated by affinity measurement combined with the simple linear iterative cluster normalized cut (SLIC-Ncut) algorithm to segment porcelain ewers. The shape data of the main parts were extracted for analysis and preliminary classification. The position of the spout, the position of the handle, and the centre of gravity obtained from the shape data are crucial parameters for dating its age. The conclusions obtained are consistent with the traditional identification results, which indicate that the proposed method is an effective and feasible computer-aided method for identifying porcelain ewers.
Automatic recognition and classification of future work sentences from academic articles in a specific domain

Future work sentences (FWS) are the particular sentences in academic papers that contain the author's description of their proposed follow-up research direction. This paper presents methods to automatically extract FWS from academic papers and classify them according to the different future directions embodied in the paper's content. FWS recognition methods will enable subsequent researchers to locate future work sentences more accurately and quickly and reduce the time and cost of acquiring the corpus. At the same time, changes in the content of future work will be illuminated, and a foundation will be laid for a more in-depth semantic analysis of future work sentences. The current work on automatic identification of future work sentences is relatively small, and the existing research cannot accurately identify FWS from academic papers, and thus cannot conduct data mining on a large scale. Furthermore, there are many aspects to the content of future work, and the subdivision of the content is conducive to the analysis of specific development directions. In this paper, Nature Language Processing (NLP) is used as a case study, and FWS are extracted from academic papers and classified into different types. We manually build an annotated corpus with six different types of FWS. Then, automatic recognition and classification of FWS are implemented using machine learning models, and the performance of these models is compared based on the evaluation metrics. The results show that the Bernoulli Bayesian model has the best performance in the automatic recognition task, with the Macro F1 reaching 90.73%, and the SCIBERT model has the best performance in the automatic classification task, with the weighted average F1 reaching 72.63%. Finally, we extract keywords from FWS and gain a deep understanding of the key content described in FWS, and we also demonstrate that content determination in FWS will be reflected in the subsequent research work by measuring the similarity between future work sentences and the abstracts.
Building a Resilient Cybersecurity Workforce: A Multidisciplinary Solution to the Problem of High Turnover of Cybersecurity Analysts

The high turnover of Security Operations Centre analysts, (SOC analysts), is a current issue being discussed globally, especially after the COVID-19 pandemic and the recent global widespread of cybersecurity breaches. The gaps in security operations, due to inconsistency in the availability of cyber security workforce exposes the organisation to the possibility of costly security breaches. Although there are diverse views on the factors responsible for the high turnover of SOC analysts, whatever the reasons, high turnover of SOC analysts, in many ways, constitute a serious risk to organisations. Employers have used financial incentives to address this problem, however, the problem still persists. This research therefore explores a multidisciplinary approach to finding comprehensive and sustainable solutions to this problem, through seeking the views of the stakeholders; the SOC analysts and cybersecurity leaders, including the views of experts from the fields of Human Resources (HR) and psychology. This research was conducted as a case study of the two types of Security Operations Centres (SOCs): in-house and Managed Security Service Providers (MSSP), between the months of March and May 2022. Semi-structured interview was used as the qualitative method of gathering the necessary data. The key findings from this research suggest that most SOC analysts consider other factors than money in deciding whether to leave or stay in an organisation. These factors are mainly informed by the characteristic needs and motivations of the young generation of SOC analysts; hence, cybersecurity leaders should be tactical in finding ways to engage and retain their critical workforce.
Orchidea: a comprehensive framework for target-based computer-assisted dynamic orchestration

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Blind Data Adversarial Bit-flip Attack against Deep Neural Networks

Because of their high accuracy, deep neural net-works (DNNs) have achieved amazing success in security-critical systems such as medical devices. It has recently been demon-strated that Adversarial Bit Flip Attacks (BFAs) against DNN hardware by flipping a very small number of bits can result in catastrophic accuracy loss. The reliance on test data, however, is a significant drawback of previous state-of-the-art bit-flip attack methods. This is frequently not possible with applications containing sensitive or proprietary data. In this paper, we propose Blind Data Adversarial Bit-flip Attack (BDFA), a novel technique to enable BFA against DNN hardware without any access to the training or testing data. This is achieved by optimizing for a synthetic dataset, which is engineered to match the statistics of batch normalization across different layers of the network and the targeted label. Experimental results show that BDFA could decrease the accuracy of ResNet50 significantly from 75.96% to 13.94% with only 4 bits flips.
Source Identification of 3D Printer Based on Layered Texture Encoders

With the rapid growth in the three-dimensional (3D) printing content market, various unprecedented criminal cases and copyright protection issues have emerged. In response to this imminent and emergent difficulty, we propose a forensic technique for identifying the source of 3D printed products based only on surface inspection features. The surface texture of 3D printed objects exhibits, inevitably, extremely fine periodic features during the additive manufacturing process. We propose a two-stream texture encoder, referred to as CFTNet, combined with fast Fourier transform and positional encoding of the transformer encoder to leverage inherent periodic features occurring during the additive manufacturing. As benchmarks, we define detailed scenarios for six source identification problems and present detailed verification procedures with a large-scale benchmark dataset SI3DP++ for forensic real-world scenarios. A certain level of performance was achieved using six benchmarks, including printer and device-level identification. Moreover, we extended the baseline study based on the benchmark set to forensic test scenarios from multiple perspectives in preparation for real situations. We reveal both the dataset and detailed experimental design to provide an opportunity to facilitate future in-depth studies related to forensics and protection of intellectual property.
Survey of liver pathologists to assess attitudes towards digital pathology and artificial intelligence

Aims A survey of members of the UK Liver Pathology Group (UKLPG) was conducted, comprising consultant histopathologists from across the UK who report liver specimens and participate in the UK National Liver Pathology External Quality Assurance scheme. The aim of this study was to understand attitudes and priorities of liver pathologists towards digital pathology and artificial intelligence (AI). Methods The survey was distributed to all full consultant members of the UKLPG via email. This comprised 50 questions, with 48 multiple choice questions and 2 free-text questions at the end, covering a range of topics and concepts pertaining to the use of digital pathology and AI in liver disease. Results Forty-two consultant histopathologists completed the survey, representing 36% of fully registered members of the UKLPG (42/116). Questions examining digital pathology showed respondents agreed with the utility of digital pathology for primary diagnosis 83% (34/41), second opinions 90% (37/41), research 85% (35/41) and training and education 95% (39/41). Fatty liver diseases were an area of demand for AI tools with 80% in agreement (33/41), followed by neoplastic liver diseases with 59% in agreement (24/41). Participants were concerned about AI development without pathologist involvement 73% (30/41), however, 63% (26/41) disagreed when asked whether AI would replace pathologists. Conclusions This study outlines current interest, priorities for research and concerns around digital pathology and AI for liver pathologists. The majority of UK liver pathologists are in favour of the application of digital pathology and AI in clinical practice, research and education.
Modularized Mutuality Network for Emotion-Cause Pair Extraction

Emotion-cause pair extraction (ECPE) is an emerging task born out of Emotion cause extraction (ECE), which aims to extract the emotion clause and the corresponding cause clause simultaneously. Previous methods decompose ECPE into multiple sub-tasks, namely emotion clause extraction, cause clause extraction, and emotion-cause pair extraction, and employ different modules to address them separately. However, these methods fail to effectively capture the mutuality within the three sub-tasks, which may hinder the information interaction between emotion and cause. In this paper, we revisit and analyze the mutuality between emotion and cause clauses from a linguistic perspective and further propose a novel Modularized Mutuality Network (MMN) to capture the mutuality explicitly. Specifically, the mutuality can be divided into the following categories, including position bias, sentiment consistency, and natural duality. To this end, we design three modules wrapped with various simple but effective mechanisms to address the mutuality, respectively. Extensive experiments demonstrate that MMN achieves state-of-the-art performances on the ECPE task and detailed analyzed the effect of the three modules for capturing the mutuality within sub-tasks.
End-user development for interactive web-based virtual reality scenes

Recent advances in Virtual Reality (VR) technology and the increased availability of VR-equipped devices enable a wide range of consumer-oriented applications. For novice developers, however, creating interactive scenes for VR applications is a complex and cumbersome task that requires high technical knowledge which is often missing. This hinders the potential of enabling novices to create, modify, and execute their own interactive VR scenes. Although recent authoring tools for interactive VR scenes are promising, most of them focus on experts as the target group and neglect the novices with low programming knowledge. To lower the entry barrier, we provide an open-source web-based End-User Development (EUD) tool, called VREUD, that supports the rapid construction and execution of interactive VR scenes. To address the aspect of construction, VREUD enables the specification of the VR scene including interactions and tasks. Furthermore, our tool supports the execution and immersive experience of the created interactive VR scenes on VR head-mounted displays. Based on a user study, we have analyzed the effectiveness, efficiency, and user satisfaction of VREUD which shows promising results to empower novices in creating their interactive VR scenes.
More than just charts and graphs: What to teach in a data visualization course

Abstract Based on our experience developing and delivering a highly successful data visualization course within a Master of Science in Business Analytics program, we present a taxonomy for data visualization courses and recommend content and pedagogical features for each type of data visualization course therein. We also discuss the interdependence between data visualization and business communication as a critical consideration in course design.