Reviewerly/exHarmony huggingface dataset:
Anomaly Detection Algorithm for Real-World Data and Evidence in Clinical Research: Implementation, Evaluation, and Validation Study

Background Statistical analysis, which has become an integral part of evidence-based medicine, relies heavily on data quality that is of critical importance in modern clinical research. Input data are not only at risk of being falsified or fabricated, but also at risk of being mishandled by investigators. Objective The urgent need to assure the highest data quality possible has led to the implementation of various auditing strategies designed to monitor clinical trials and detect errors of different origin that frequently occur in the field. The objective of this study was to describe a machine learning–based algorithm to detect anomalous patterns in data created as a consequence of carelessness, systematic error, or intentionally by entering fabricated values. Methods A particular electronic data capture (EDC) system, which is used for data management in clinical registries, is presented including its architecture and data structure. This EDC system features an algorithm based on machine learning designed to detect anomalous patterns in quantitative data. The detection algorithm combines clustering with a series of 7 distance metrics that serve to determine the strength of an anomaly. For the detection process, the thresholds and combinations of the metrics were used and the detection performance was evaluated and validated in the experiments involving simulated anomalous data and real-world data. Results Five different clinical registries related to neuroscience were presented—all of them running in the given EDC system. Two of the registries were selected for the evaluation experiments and served also to validate the detection performance on an independent data set. The best performing combination of the distance metrics was that of Canberra, Manhattan, and Mahalanobis, whereas Cosine and Chebyshev metrics had been excluded from further analysis due to the lowest performance when used as single distance metric–based classifiers. Conclusions The experimental results demonstrate that the algorithm is universal in nature, and as such may be implemented in other EDC systems, and is capable of anomalous data detection with a sensitivity exceeding 85%.
Implicit adversarial data augmentation and robustness with Noise-based Learning

We introduce a Noise-based Learning (NoL) approach for training neural networks that are intrinsically robust to adversarial attacks. We find that the learning of random noise introduced with the input with the same loss function used during posterior maximization, improves a model’s adversarial resistance. We show that the learnt noise performs implicit adversarial data augmentation boosting a model’s adversary generalization capability. We evaluate our approach’s efficacy and provide a simplistic visualization tool for understanding adversarial data, using Principal Component Analysis. We conduct comprehensive experiments on prevailing benchmarks such as MNIST, CIFAR10, CIFAR100, Tiny ImageNet and show that our approach performs remarkably well against a wide range of attacks. Furthermore, combining NoL with state-of-the-art defense mechanisms, such as adversarial training, consistently outperforms prior techniques in both white-box and black-box attacks.
Bounding Box Projection for Regression Uncertainty in Oriented Object Detection

Oriented object detection has recently attracted increasing attention for its importance in aerial image processing. Popular detection methods for oriented and densely packed objects usually utilize the rotation angle to reduce the overlap of bounding boxes over the horizontal line. However, those angle-based methods remain challenging due to the angular periodicity and regression inconsistent, which are uniformly summarized as regression uncertainty. Previous methods usually heal the regression uncertainty problem by adding complex limits on the loss function. In this paper, instead of expressing accurate oriented information by the rotation angle, we propose a novel projection-based method that employs six-parameter to describe rotated bounding box: two points position on the projected line(one center point and one chosen vertex), a projection ratio and a quadrant label, named as <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ProjBB</b> . ProjBB is angle-free and has three main advantages: i) More accurate angle distance without periodicity; ii) Projection ratio which is aspect ratio sensitive; iii) Theoretical uniqueness guarantee. Extensive experiments on two large-scale public aerial image datasets(i.e. DOTA and HRSC2016) and the scene text dataset ICDAR2015 show the competitive results of our proposed approach.
Learning to predict more accurate text instances for scene text detection

At present, multi-oriented text detection methods based on deep neural network have achieved promising performances on various benchmarks. Nevertheless, there are still some difficulties for arbitrary shape text detection, especially for a simple and proper representation of arbitrary shape text instances. In this paper, a pixel-based text detector is proposed to facilitate the representation and prediction of text instances with arbitrary shapes in a simple manner. Firstly, to alleviate the influence of the target vertex sorting and achieve the direct regression of arbitrary shape text instances, the starting-point-independent coordinates regression loss is proposed. Furthermore, to predict more accurate text instances, the text instance accuracy loss is proposed as an assistant task to refine the predicted coordinates under the guidance of IoU. To evaluate the effectiveness of our detector, extensive experiments have been carried on public benchmarks which contain arbitrary shape text instances and multi-oriented text instances. We obtain 84.8% of F-measure on Total-Text benchmark. The results show that our method can reach state-of-the-art performance.
RETRACTED ARTICLE: Information society and its impact on personality development

The purpose of the study is to develop and test the original model which illustrates the development of personality in the information society. The study involved two Russian universities, higher educational institutions of the Arab Emirates and Kazakhstan. A total of 400 students participated in the research. The experiment consisted of three stages: introductory, experimental, and final. At the introductory stage of the study, the influences of the information society on the development of human personal qualities were analyzed. At the experimental stage, the original model which illustrates the development of personality in the information society was developed and implemented. At the final stage, the students were surveyed in accordance with the original methodology for the use of the capabilities and resources of the information society for personality development. After the implementation of the model, the number of students using the capabilities of the information society for personal development, the development of professional skills, and maintaining and strengthening health increased by 18.5%, 18.7%, and 10%, respectively. The practical value of the scientific research results relates to the possibility of using the questionnaires and models of personality development in the context of the information society in the higher education system of Russia, the Arab Emirates, Kazakhstan, and other countries. The model described in the study is relevant in the context of the transfer of the educational process in higher education to a distance format and the growing social isolation of an individual caused by the coronavirus pandemic.
Diverse Features Fusion Network for video-based action recognition

The two-stream convolutional network has been proved to be one milestone in the study of video-based action recognition. Lots of recent works modify internal structure of two-stream convolutional network directly and put top-level features into a 2D/3D convolution fusion module or a simpler one. However, these fusion methods cannot fully utilize features and the way fusing only top-level features lacks rich vital details. To tackle these issues, a novel network called Diverse Features Fusion Network (DFFN) is proposed. The fusion stream of DFFN contains two types of uniquely designed modules, the diverse compact bilinear fusion (DCBF) module and the channel-spatial attention (CSA) module, to distill and refine diverse compact spatiotemporal features. The DCBF modules use the diverse compact bilinear algorithm to fuse features extracted from multiple layers of the base network that are called diverse features in this paper. Further, the CSA module leverages channel attention and multi-size spatial attention to boost key information as well as restraining the noise of fusion features. We evaluate our three-stream network DFFN on three public challenging video action benchmarks: UCF101, HMDB51 and Something-Something V1. Experiment results indicate that our method achieves state-of-the-art performance.
Feasibility of quantum key distribution from high altitude platforms

Abstract This paper presents the feasibility study of deploying quantum key distribution (QKD) from high altitude platforms (HAPs), as a way of securing future communications applications and services. The paper provides a thorough review of the state of the art HAP technologies and summarises the benefits that HAPs can bring to the QKD services. A detailed link budget analysis is presented in the paper to evaluate the feasibility of delivering QKD from stratospheric HAPs flying at 20 km altitude. The results show a generous link budget under most operating conditions which brings the possibility of using diverged beams, thereby simplifying the pointing, acquisition and tracking of the optical system on the HAPs and ground, potentially widening the range of future use cases where QKD could be a viable solution.
Enhancing IoT Anomaly Detection Performance for Federated Learning

While federated learning (FL) has gained great attention for mobile and Internet of Things (IoT) computing with the benefits of scalable cooperative learning and privacy protection capabilities, there still exist a great deal of technical challenges to make it practically deployable. For instance, the distribution of the training process to a myriad of devices limits the classification performance of machine learning (ML) algorithms, often showing a significantly degraded accuracy compared to centralized learning. In this paper, we investigate the problem of performance limitation under FL and present the benefit of data augmentation with an application of anomaly detection using an IoT dataset. Our initial study reveals that one of the critical reasons for the performance degradation is that each device sees only a small fraction of data (that it generates), which limits the efficacy of the local ML model (constructed by the device). This becomes more critical if the data holds the class imbalance problem, observed not infrequently in practice (e.g., a small fraction of anomalies). Moreover, device heterogeneity with respect to data quantity is an open challenge in FL. Based on these observations, we examine the impact of data augmentation on detection performance in FL settings (both homogeneous and heterogeneous). Our experimental results show that even a simple random oversampling can improve detection performance with manageable learning complexity.
Breaking Neural Reasoning Architectures With Metamorphic Relation-Based Adversarial Examples

The ability to read, reason, and infer lies at the heart of neural reasoning architectures. After all, the ability to perform logical reasoning over language remains a coveted goal of Artificial Intelligence. To this end, models such as the Turing-complete differentiable neural computer (DNC) boast of real logical reasoning capabilities, along with the ability to reason beyond simple surface-level matching. In this brief, we propose the first probe into DNC's logical reasoning capabilities with a focus on text-based question answering (QA). More concretely, we propose a conceptually simple but effective adversarial attack based on metamorphic relations. Our proposed adversarial attack reduces DNCs' state-of-the-art accuracy from 100% to 1.5% in the worst case, exposing weaknesses and susceptibilities in modern neural reasoning architectures. We further empirically explore possibilities to defend against such attacks and demonstrate the utility of our adversarial framework as a simple scalable method to improve model adversarial robustness.
A Graph-to-Sequence Learning Framework for Summarizing Opinionated Texts

There is a great need for effective summarization methods to absorb the key points of large amounts of opinions expressed on the Web. In this paper, we study the problem of opinionated text summarization, which aims to generate a coherent summary for a set of opinionated texts towards a specific topic (e.g., a movie or a controversial issue). The main characteristic of this problem is that the input set contains an arbitrary number of texts, which brings about redundant opinions and useless texts. Further, informative opinions to be summarized are scattered over different opinionated texts, thus it is vital to avoid focusing only on partial opinions. However, previous work can not tackle the above two issues effectively. To address such issues, we propose a two-stage graph-to-sequence learning framework for summarizing opinionated texts. The first stage selects summary-worthy texts from all input opinionated texts, and we construct an opinion relation graph to help estimate salience via exploiting the relationships among the input texts. Given the selected texts, the second stage generates an opinion summary via a maximal marginal relevance guided graph-to-sequence model, which gives consideration to both salient and non-redundant opinions. Experimental results on two benchmark datasets show that our framework outperforms the existing state-of-the-art methods. Human evaluation further verifies that our framework can generate more informative and compact opinion summaries than previous methods.
Zero Knowledge for Everything and Everyone: Fast ZK Processor with Cached ORAM for ANSI C Programs

We build a complete and efficient ZK toolchain that handles proof statements encoded as arbitrary ANSI C programs.Zero-Knowledge (ZK) proofs are foundational in cryptography. Recent ZK research has focused intensely on non-interactive proofs of small statements, useful in blockchain scenarios. We instead target large statements that are useful, e.g., in proving properties of programs.Recent work (Heath and Kolesnikov, CCS 2020 [HK20a]) designed an efficient proof-of-concept ZK machine (ZKM). Their machine executes arbitrary programs over a minimal instruction set, authenticating in ZK the program execution. In this work, we significantly extend this research thrust, both in terms of efficiency and generality. Our contributions include:• A rich and performance-oriented architecture for representing arbitrary ZK proofs as programs.• A complete compiler toolchain providing full support for ANSI C95 programs. We ran off-the-shelf buggy versions of the Linux programs sed and gzip, proving in ZK that each program has a bug. To our knowledge, this is the first ZK system capable of executing standard Linux programs.• Improved ZK oblivious RAM (ORAM). [HK20a] introduced an efficient ZK-specific ORAM BubbleRAM that consumes O(log <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> n) communication per access. We extend BubbleRAM with multi-level caching, decreasing communication to O(log n) per access. This introduces the possibility of a cache miss, which we handle cheaply. Our experiments show that cache misses are rare; in isolation, i.e., ignoring other processor costs, BubbleCache improves communication over BubbleRAM by more than 8×. Using BubbleCache improves our processor’s total communication (including costs of cache misses) by ≈ 25-30%.• Numerous low-level optimizations, resulting in a CPU that is both more expressive and ≈ 5.5× faster than [HK20a]’s.• Attention to user experience. Our engineer-facing ZK instrumentation and extensions are minimal and easy to use.Put together, our system is efficient and general, and can run many standard Linux programs. The resultant machine runs at up to 11KHz on a 1Gbps LAN and supports MBs of RAM.
Coupling compositional data analysis (CoDA) with hierarchical cluster analysis (HCA) for preliminary understanding of the dynamics of a complex water distribution system: the Naples (South Italy) case study

Providing safe tap water has been a global concern.
Smart Assistance of Elderly Individuals in Emergency Situations at Home

Health monitoring products can improve essential services for elderly patients, with personalized customer service and prescription prompts being two practical areas of assistance. The use of IoT in assistive devices can help to reduce the severity of diseases such as influenza. This therapeutic assistance can also provide precautionary information for infectious diseases such as tuberculosis, malaria, influenza, and HIV. Automatic speech recognition (ASR) can provide computer-generated assistance through IoT devices. For example, the possibility of survival from a sudden infarction is considerably better if an individual obtains assistance in a short period of time. For older individuals, IoT devices can monitor for signs of mental and physical deterioration, with gesture evaluation, interaction, recognition, and alerting methods. This chapter examines emergency assistance in cases of stroke, for which the appropriate therapeutic support can improve the outcome of patients.
The Impact of Data Merging on the Interpretation of Cross-Project Just-In-Time Defect Models

Just-In-Time (JIT) defect models are classification models that identify the code commits that are likely to introduce defects. Cross-project JIT models have been introduced to address the suboptimal performance of JIT models when historical data is limited. However, many studies built cross-project JIT models using a pool of mixed data from multiple projects (i.e., data merging)—assuming that the properties of defect-introducing commits of a project are similar to that of the other projects, which is likely not true. In this paper, we set out to investigate the interpretation of JIT defect models that are built from individual project data and a pool of mixed project data with and without consideration of project-level variances. Through a case study of 20 datasets of open source projects, we found that (1) the interpretation of JIT models that are built from individual projects varies among projects; and (2) the project-level variances cannot be captured by a JIT model that is trained from a pool of mixed data from multiple projects without considering project-level variances (i.e., a global JIT model). On the other hand, a mixed-effect JIT model that considers project-level variances represents the different interpretations better, without sacrificing performance, especially when the contexts of projects are considered. The results hold for different mixed-effect learning algorithms. When the goal is to derive sound interpretation of cross-project JIT models, we suggest that practitioners and researchers should opt to use a mixed-effect modelling approach that considers individual projects and contexts.
A study on correlations between architectural smells and design patterns

Design patterns are recommended solutions for typical software design problems, with an extensively studied and documented impact on various quality factors. Flaws in design at a higher levels of abstraction are manifested in architectural smells. Some of those smells, similarly to code smells, can reduce the expected advantages of design patterns or even prevent their proper implementation. In this paper we study if and how design patterns and architectural smells are related, and how this knowledge could be exploited in practice. We present an empirical study with an analysis of 16 design patterns and 3 architectural smells in 60 open source Java systems. We analyze their diffuseness and correlation, and we extract association rules that describe their presence and dependencies. We demonstrate that there exist relationships between architectural smells and design patterns, both at the class and package levels. Some smells appear falsely positive, as they result from conscious decisions made by programmers, while the application of some patterns can be a cause of certain smells. Our results provide evidence that design patterns and architectural smells are related and affect each other. With knowledge about the relationships, programmers can avoid the side effects of applying some design patterns.
Tradeoffs in Permissioned vs Permissionless Blockchains: Trust and Performance

It is generally agreed that permissioned blockchains can improve on the operational cost and performance of permissionless blockchains, but it is usually assumed that this improvement comes at the cost of transaction security, especially in low-trust environments. We develop a model of transaction safety in permissioned and permissionless blockchains to study this tradeoff and find that in several settings there may be no tradeoff at all. With a minimal level of trust in the blockchain operators and the supporting institutions, well-designed permissioned blockchains can offer both higher operational efficiency and higher transaction security. While this minimal trust in the ``system'' is essential to the functioning of permissioned blockchains, it is also inherent in most business relationships, making permissioned blockchains well suited for enterprise applications of the technology. We explore the implications of our analysis for the design of permissioned blockchains, such as the reputation or bonding implications for their validators.
A robust and adaptive fuzzy logic based differential evolution algorithm using population diversity tuning for multi-objective optimization

This article presents an improved Multi-objective Differential Evolution based algorithm to solve multi-objective optimization problems. In the proposed algorithm named as Fuzzy Adaptive Multi-objective Differential Evolution with Diversity Control (FAMDE-DC), fuzzy system is used to control population diversity at decision variable space by self-adapting the crossover rate control parameter at various stages of evolution. Techniques such as non-dominated sorting, controlled elitism and dynamic crowding distance is used for selecting potential individuals. This control parameter adaptation and improved selection procedure results in controlling population diversity in decision space and identifying potential candidates in objective space, attaining true Pareto-optimal front with better convergence and diversity metrics. These properties make it robust and to be applied to varied problem domains without manual fine-tuning of parameters. The performance of FAMDE-DC algorithm is analysed using a set of benchmark test functions DTLZ and CEC2009 problems. Further the results are compared with other popular evolutionary based multi-objective algorithms. FAMDE-DC had a better Inverted Generational Distance (IGD) measure towards true Pareto-optimal front. The outcome of FAMDE-DC is also validated through nonparametric statistical tests Friedman and Wilcoxon signed rank test.