Reviewerly/exHarmony huggingface dataset:
Performance of a Breast Cancer Detection AI Algorithm Using the Personal Performance in Mammographic Screening Scheme

Background The Personal Performance in Mammographic Screening (PERFORMS) scheme is used to assess reader performance. Whether this scheme can assess the performance of artificial intelligence (AI) algorithms is unknown. Purpose To compare the performance of human readers and a commercially available AI algorithm interpreting PERFORMS test sets. Materials and Methods In this retrospective study, two PERFORMS test sets, each consisting of 60 challenging cases, were evaluated by human readers between May 2018 and March 2021 and were evaluated by an AI algorithm in 2022. AI considered each breast separately, assigning a suspicion of malignancy score to features detected. Performance was assessed using the highest score per breast. Performance metrics, including sensitivity, specificity, and area under the receiver operating characteristic curve (AUC), were calculated for AI and humans. The study was powered to detect a medium-sized effect (odds ratio, 3.5 or 0.29) for sensitivity. Results A total of 552 human readers interpreted both PERFORMS test sets, consisting of 161 normal breasts, 70 malignant breasts, and nine benign breasts. No difference was observed at the breast level between the AUC for AI and the AUC for human readers (0.93% and 0.88%, respectively; P = .15). When using the developer's suggested recall score threshold, no difference was observed for AI versus human reader sensitivity (84% and 90%, respectively; P = .34), but the specificity of AI was higher (89%) than that of the human readers (76%, P = .003). However, it was not possible to demonstrate equivalence due to the size of the test sets. When using recall thresholds to match mean human reader performance (90% sensitivity, 76% specificity), AI showed no differences inperformance, with a sensitivity of 91% (P =. 73) and a specificity of 77% (P = .85). Conclusion Diagnostic performance of AI was comparable with that of the average human reader when evaluating cases from two enriched test sets from the PERFORMS scheme. © RSNA, 2023 See also the editorial by Philpotts in this issue.
ViHOS: Hate Speech Spans Detection for Vietnamese

The rise in hateful and offensive language directed at other users is one of the adverse side effects of the increased use of social networking platforms. This could make it difficult for human moderators to review tagged comments filtered by classification systems. To help address this issue, we present the ViHOS (Vietnamese Hate and Offensive Spans) dataset, the first human-annotated corpus containing 26k spans on 11k comments. We also provide definitions of hateful and offensive spans in Vietnamese comments as well as detailed annotation guidelines. Besides, we conduct experiments with various state-of-the-art models. Specifically, XLM-R_Large achieved the best F1-scores in Single span detection and All spans detection, while PhoBERT_Large obtained the highest in Multiple spans detection. Finally, our error analysis demonstrates the difficulties in detecting specific types of spans in our data for future research. Our dataset is released on GitHub.
How Does Generative Retrieval Scale to Millions of Passages?

The emerging paradigm of generative retrieval re-frames the classic information retrieval problem into a sequence-to-sequence modeling task, forgoing external indices and encoding an entire document corpus within a single Transformer. Although many different approaches have been proposed to improve the effectiveness of generative retrieval, they have only been evaluated on document corpora on the order of 100K in size. We conduct the first empirical study of generative retrieval techniques across various corpus scales, ultimately scaling up to the entire MS MARCO passage ranking task with a corpus of 8.8M passages and evaluating model sizes up to 11B parameters. We uncover several findings about scaling generative retrieval to millions of passages; notably, the central importance of using synthetic queries as document representations during indexing, the ineffectiveness of existing proposed architecture modifications when accounting for compute cost, and the limits of naively scaling model parameters with respect to retrieval performance. While we find that generative retrieval is competitive with state-of-the-art dual encoders on small corpora, scaling to millions of passages remains an important and unsolved challenge. We believe these findings will be valuable for the community to clarify the current state of generative retrieval, highlight the unique challenges, and inspire new research directions.
Advanced Statistical Methods for Eye Movement Analysis and Modelling: A Gentle Introduction

In this Chapter we show that by considering eye movements, and in particular, the resulting sequence of gaze shifts, a stochastic process, a wide variety of tools become available for analyses and modelling beyond conventional statistical methods. Such tools encompass random walk analyses and more complex techniques borrowed from the pattern recognition and machine learning fields. After a brief, though critical, probabilistic tour of current computational models of eye movements and visual attention, we lay down the basis for gaze shift pattern analysis. To this end, the concepts of Markov Processes, the Wiener process and related random walks within the Gaussian framework of the Central Limit Theorem will be introduced. Then, we will deliberately violate fundamental assumptions of the Central Limit Theorem to elicit a larger perspective, rooted in statistical physics, for analysing and modelling eye movements in terms of anomalous, non-Gaussian, random walks and modern foraging theory. Eventually, by resorting to machine learning techniques, we discuss how the analyses of movement patterns can develop into the inference of hidden patterns of the mind: inferring the observer's task, assessing cognitive impairments, classifying expertise.
A Set of Rules for Constructing Gender-Based Personality Types’ Composition for Software Programmer

The current era has been declared as technological era where both profit and no-profit organisations rely solely on software to cope with myriad issues they typically face. The growing demand for software has equally placed challenging tasks on workplaces to produce quality and reliable software. Unfortunately, software development industries have drastically failed to produce software in due time or even if software is produced in time but it fails to yield the desired results. Keeping this problem in view, this study tried to address this problem by offering team composition model lucrative for software development. For instance, Personality types, especially Introvert (I) and Extrovert (E) traits, of team members of software development are explored with gender diversity with a key focus on the programmer role. Moreover, descriptive and predictive approaches were applied to gain the hidden facts from data. The data of this study was taken from both academia and industry to establish the generalizability in the findings. Additionally, different personality traits composition was set based on gender which was not studied in previous studies. The findings of this research suggest that male-programmer should be composed of E trait of personality and, whereas female-programmer should be I. The overall findings contribute to serve the cause of software development team and also contribute to the existing literature on software development and its team composition.
Red-Black Planning: A New Tractability Analysis and Heuristic Function

Red-black planning is a recent approach to partial delete relaxation, where red variables take the relaxed semantics (accumulating their values), while black variables take the regular semantics. Practical heuristic functions can be generated from tractable sub-classes of red-black planning. Prior work has identified such sub-classes based on the black causal graph, i.e., the projection of the causal graph onto the black variables. Here, we consider cross-dependencies between black and red variables instead. We show that, if no red variable relies on black preconditions, then red-black plan generation is tractable in the size of the black state space, i.e., the product of the black variables. We employ this insight to devise a new red-black plan heuristic in which variables are painted black starting from the causal graph leaves. We evaluate this heuristic on the planning competition benchmarks. Compared to a standard delete relaxation heuristic, while the increased runtime overhead often is detrimental, in some cases the search space reduction is strong enough to result in improved performance overall.
Full-length non-linear binary sequences with Zero Correlation Zone for multiuser communications

The research on new sets of sequences to be used asspreading codes in multiple user communications is still an activearea, despite the great amount of literature available since manyyears on this topic. In fact, new paradigms like dense anddecentralized wireless networks, where there is no centralcontroller to assign the resources to the nodes, are revamping theinterest on large sets of sequences providing adequate correlationproperties to support a big number of nodes, in potentially hostilechannels. This paper focuses on the Zero Correlation Zone (ZCZ)property exhibited by a family of non-linear binary sequencesfeaturing a great cardinality of their set and good securityrelatedfeatures, and provides evidence of their suitability tomultiuser communications, in channels affected by multipath.
Empirical Evaluation of the Automatic Generation of a Component-Based Software Architecture for Games

Component-based architecture is the software design of choice for most modern games and game engines, allowing for extensible and reusable software that easily adapts to changes in requirements. Nevertheless, designing a component-based architecture is not an easy achievement and some techniques have been previously proposed for the automatic generation of a component distribution to implement a given declarative model of the entities in the game. The goal of the work presented here is to empirically compare the quality of the component distributions designed by programmers, with those others automatically obtained with such techniques.
Big vs little core for energy-efficient Hadoop computing

Abstract Emerging big data applications require a significant amount of server computational power. However, the rapid growth in the data yields challenges to process them efficiently using current high-performance server architectures. Furthermore, physical design constraints, such as power and density, have become the dominant limiting factor for scaling out servers. Heterogeneous architectures that combine big Xeon cores with little Atom cores have emerged as a promising solution to enhance energy-efficiency by allowing each application to run on an architecture that matches resource needs more closely than a one-size-fits-all architecture. Therefore, the question of whether to map the application to big Xeon or little Atom in heterogeneous server architecture becomes important. In this paper, through a comprehensive system level analysis, we first characterize Hadoop-based MapReduce applications on big Xeon and little Atom-based server architectures to understand how the choice of big vs little cores is affected by various parameters at application, system and architecture levels and the interplay among these parameters. Second, we study how the choice between big and little core changes across various phases of MapReduce tasks. Furthermore, we show how the choice of most efficient core for a particular MapReduce phase changes in the presence of accelerators. The characterization analysis helps guiding scheduling decisions in future cloud-computing environment equipped with heterogeneous multicore architectures and accelerators. We have also evaluated the operational and the capital cost to understand how performance, power and area constraints for big data analytics affect the choice of big vs little core server as a more cost and energy efficient architecture.
Pre-treatment of soil X-ray powder diffraction data for cluster analysis

X-ray powder diffraction (XRPD) is widely applied for the qualitative and quantitative analysis of soil mineralogy. In recent years, high-throughput XRPD has resulted in soil XRPD datasets containing thousands of samples. The efforts required for conventional approaches of soil XRPD data analysis are currently restrictive for such large data sets, resulting in a need for computational methods that can aid in defining soil property – soil mineralogy relationships. Cluster analysis of soil XRPD data represents a rapid method for grouping data into discrete classes based on mineralogical similarities, and thus allows for sets of mineralogically distinct soils to be defined and investigated in greater detail. Effective cluster analysis requires minimisation of sample-independent variation and maximisation of sample-dependent variation, which entails pre-treatment of XRPD data in order to correct for common aberrations associated with data collection. A 24 factorial design was used to investigate the most effective data pre-treatment protocol for the cluster analysis of XRPD data from 12 African soils, each analysed once by five different personnel. Sample-independent effects of displacement error, noise and signal intensity variation were pre-treated using peak alignment, binning and scaling, respectively. The sample-dependent effect of strongly diffracting minerals overwhelming the signal of weakly diffracting minerals was pre-treated using a square-root transformation. Without pre-treatment, the 60 XRPD measurements failed to provide informative clusters. Pre-treatment via peak alignment, square-root transformation, and scaling each resulted in significantly improved partitioning of the groups (p < 0.05). Data pre-treatment via binning reduced the computational demands of cluster analysis, but did not significantly affect the partitioning (p > 0.1). Applying all four pre-treatments proved to be the most suitable protocol for both non-hierarchical and hierarchical cluster analysis. Deducing such a protocol is considered a prerequisite to the wider application of cluster analysis in exploring soil property – soil mineralogy relationships in larger datasets.
Variational Sparse Coding

Unsupervised discovery of interpretable features and controllable generation with highdimensional data are currently major challenges in machine learning, with applicationsin data visualisation, clustering and artificialdata synthesis. We propose a model basedon variational auto-encoders (VAEs) in whichinterpretation is induced through latent spacesparsity with a mixture of Spike and Slab distributions as prior. We derive an evidencelower bound for this model and propose a specific training method for recovering disentangled features as sparse elements in latent vectors. In our experiments, we demonstrate superior disentanglement performance to standardVAE approaches when an estimate of the number of true sources of variation is not availableand objects display different combinations ofattributes. Furthermore, the new model provides unique capabilities, such as recoveringfeature exploitation, synthesising samples thatshare attributes with a given input object andcontrolling both discrete and continuous features upon generation.
Governance, Risk, and Compliance in Cloud Scenarios

Cloud computing is changing the way organizations approach technology and its infrastructure. However, in spite of its attractiveness, cloud computing can be seen as a threat in terms of compliance. Given its intrinsic distributed nature, regulations and laws may differ and customers and cloud providers must find a way to balance increasing compliance pressures with cloud computing benefits. In this paper, the authors present a framework aimed to help organizations to cope with compliance aspects in their cloud-oriented environments. Built upon current literature on the topic and qualitative approaches, the framework has been implemented in two organizations. Results from its contribution are encouraging, leading to adopter organizations to less reported compliance violations and higher contribution of cloud computing to overall quality of service and organizational compliance management.
ARM2GC

We present ARM2GC, a novel secure computation framework based on Yao's Garbled Circuit (GC) protocol and the ARM processor. It allows users to develop privacy-preserving applications using standard high-level programming languages (e.g., C) and compile them using off-the-shelf ARM compilers, e.g., gcc-arm. The main enabler of this framework is the introduction of SkipGate, an algorithm that dynamically omits the communication and encryption cost of a gate when its output is independent of the private data. SkipGate greatly enhances the performance of ARM2GC by omitting costs of the gates associated with the instructions of the compiled binary, which is known by both parties involved in the computation. Our evaluation on benchmark functions demonstrates that ARM2GC outperforms the prior best solution by 156×.
Localization-delocalization transition in discrete-time quantum walks with long-range correlated disorder

We study the effects of spatially long-range correlated phase disorder on the Hadamard quantum walk on a line. The shift operator is built to exhibit an intrinsic disorder distribution featuring long-range correlations. To impose such, we resort to fractional Brownian motion with power-law spectrum $1/{k}^{2\ensuremath{\alpha}}$ with $\ensuremath{\alpha}\ensuremath{\ge}0$ being the exponent that controls the degree of correlations. We discuss the scaling behavior of the walker's wave packet and report a localization-delocalization transition controlled by $\ensuremath{\alpha}$. We unveil two intermediate dynamical regimes between exponential localization and full delocalization.
DC coefficient recovery for JPEG images in ubiquitous communication systems

With the development of data based technology, data transmission methods for ubiquitous computing and communication systems are more and more needed. There are various kinds of data processing techniques such as compression or coding to improve the transmission efficiency for heterogeneous networks with different requirements. However, the methods for improving fault tolerance in ubiquitous communication systems are still lacked especially for the multimedia data driven applications at the execution ends. In this paper, we propose an image content recovery method for JPEG images that can recover the image content by estimating the DC coefficients without any pre-know knowledge. This method can also be used to transmit the rough image content by reducing the data amount needed to be transmitted. Thus, fault tolerance can be achieved at the receivers’ ends for ubiquitous communication systems. The result analysis with different images compared with previous works proved the effectiveness of our method.
Multi-scale object detection by top-down and bottom-up feature pyramid network

While moving ahead with the object detection techno- logy, especially deep neural networks, many related tasks, such as medical application and industrial automation, have achieved great success.However, the detection of objects with multiple aspect ratios and scales is still a key problem.This paper proposes a top-down and bottom-up feature pyramid network (TDBU-FPN), which combines multi-scale feature representation and anchor generation at multiple aspect ratios.First, in order to build the multi-scale feature map, this paper puts a number of fully convolutional layers after the backbone.Second, to link neighboring feature maps, top-down and bottom-up flows are adopted to introduce context information via top-down flow and supplement suboriginal information via bottom-up flow.The top-down flow refers to the deconvolution procedure, and the bottom-up flow refers to the pooling procedure.Third, the problem of adapting different object aspect ratios is tackled via many anchor shapes with different aspect ratios on each multi-scale feature map.The proposed method is evaluated on the pattern analysis, statistical modeling and computational learning visual object classes (PASCAL VOC) dataset and reaches an accuracy of 79%, which exhibits a 1.8% improvement with a detection speed of 23 fps.
Privacy‐Preserving Public Auditing Scheme for Data Confidentiality and Accountability in Cloud Storage

Cloud data confidentiality need to be audited for the data owner's concern. Confidentiality auditing is usually based on logging schemes, whereas cloud data dynamics and sharing group dynamics result in massive logs, which makes confidentiality auditing a formidable task for user with limited resources. So we propose a public auditing scheme for data confidentiality, in which user resorts to a Third-party auditor (TPA) for auditing. Our scheme design a special log called attestation in which hash user pseudonym is used to preserve user privacy. Attestation-based data access identifying is presented in our scheme which brings no new vulnerabilities toward data confidentiality and no extra online burden for user. We further support accountability of responsible user for data leakage based on user pseudonym. Extensive security and performance analysis compare our scheme with existing auditing schemes. Results indicate that the proposed scheme is provably secure and highly efficient.
A Design-based Approach to Enhancing Technical Drawing Skills in Design and Engineering Education using VR and AR Tools

There are concerns from higher education (HE) institutions and industry about the decline in standards of technical drawings (TDs) due to the lack of understanding of basic geometric construction and the conventions of drafting skills that underpin the best practices. There is growing evidence that simulations/animations along with augmented and virtual reality (AR/VR) technologies can improve learners’ engagement, competence, and skills; especially when compared to traditional didactic methods. However, this approach to teaching and learning (TL and to develop an AR/VR education solution to address learning difficulties within the different critical TDs categories identified. This development is based on findings from an international study in three different categories covering the perception of TDs education, assessing of TDs knowledge and ability, and expectations of TDs education. This research project also covers the difficulties and good practices of multi-disciplinary teams for developing TDs and AR/VR contents where the approaches to T&L may differ between practices.
Guidelines for Architecting Android Apps: A Mixed-Method Empirical Study

For surviving in the highly competitive market of Android apps, it is fundamental for app developers to deliver apps of high quality and with short release times. A well-architected Android app is beneficial for developers, e.g. in terms of maintainability, testability, performance, and avoidance of resource leaks. However, how to properly architect Android apps is still debated and subject to conflicting opinions usually influenced by technological hypes rather than objective evidence. In this paper we present an empirical study on how developers architect Android apps, what architectural patterns and practices Android apps are based on, and their potential impact on quality. We apply a mixed-method empirical research design that combines (i) semi-structured interviews with Android practitioners in the field and (ii) a systematic analysis of both the grey (i.e., websites, Online blogs) and white literature (i.e., academic studies) on the architecture of Android apps. Based on the analysis of the state of the art and practice about architecting Android apps, we systematically extract a set of 42 evidence-based guidelines supporting developers when architecting their Android apps.