Reviewerly/exHarmony huggingface dataset:
A Lightweight and Provable Secured Certificateless Signcryption Approach for Crowdsourced IIoT Applications

Industrial Internet of Things (IIoT) is a new type of Internet of Things (IoT), which enables sensors to merge with several smart devices to monitor machine status, environment, and collect data from industrial devices. On the other hand, cloud computing provides a good platform for storing crowdsourced data of IIoT. Due to the semi-trusted nature of cloud computing and communication through open channels, the IIoT environment needs security services such as confidentiality and authenticity. One such solution is provided by the identity-based signcryption. Unfortunately, the identity-based signcryption approach suffers from the key escrow problem. Certificateless signcryption is the alternative of identity-based signcryption that can resolve the key escrow problem. Here, we propose a lightweight certificateless signcryption approach for crowdsourced IIoT applications with the intention of enhancing security and decreasing the computational cost and communication overhead. The security and efficiency of the proposed approach are based on the hyper elliptic curve cryptosystem. The hyper elliptic curve is the advance version of the elliptic curve having small parameters and key size of 80 bits as compared to the elliptic curve which has 160-bits key size. Further, we validate the security requirements of our approach through automated validation of Internet security protocols and applications (AVISPA) tool with the help of high level protocol specification language (HLPSL). Moreover, our lightweight and secured scheme will attract low resource devices and will become a perk in the environment of IIoT.
POSTER

Deep neural networks (DNNs) perform well in the fields of image recognition, speech recognition, pattern analysis, and intrusion detection. However, DNNs are vulnerable to adversarial examples that add a small amount of noise to the original samples. These adversarial examples have mainly been studied in the field of images, but their effect on the audio field is currently of great interest. For example, adding small distortion that is difficult to identify by humans to the original sample can create audio adversarial examples that allow humans to hear without errors, but only to misunderstand the machine. Therefore, a defense method against audio adversarial examples is needed because it is a threat in this audio field. In this paper, we propose a method to detect audio adversarial examples. The key point of this method is to add a new low level distortion using audio modification, so that the classification result of the adversarial example changes sensitively. On the other hand, the original sample has little change in the classification result for low level distortion. Using this feature, we propose a method to detect audio adversarial examples. To verify the proposed method, we used the Mozilla Common Voice dataset and the DeepSpeech model as the target model. Based on the experimental results, it was found that the accuracy of the adversarial example decreased to 6.21% at approximately 12 dB. It can detect the audio adversarial example compared to the initial audio sample.
Slim graph

We propose Slim Graph: the first programming model and framework for practical lossy graph compression that facilitates high-performance approximate graph processing, storage, and analytics. Slim Graph enables the developer to express numerous compression schemes using small and programmable compression kernels that can access and modify local parts of input graphs. Such kernels are executed in parallel by the underlying engine, isolating developers from complexities of parallel programming. Our kernels implement novel graph compression schemes that preserve numerous graph properties, for example connected components, minimum spanning trees, or graph spectra. Finally, Slim Graph uses statistical divergences and other metrics to analyze the accuracy of lossy graph compression. We illustrate both theoretically and empirically that Slim Graph accelerates numerous graph algorithms, reduces storage used by graph datasets, and ensures high accuracy of results. Slim Graph may become the common ground for developing, executing, and analyzing emerging lossy graph compression schemes.
Legal Summarization for Multi-role Debate Dialogue via Controversy Focus Mining and Multi-task Learning

Multi-role court debate is a critical component in a civil trial where parties from different camps (plaintiff, defendant, witness, judge, etc.) actively involved. Unlike other types of dialogue, court debate can be lengthy, and important information, with respect to the controversy focus(es), often hides within the redundant and colloquial dialogue data. Summarizing court debate can be a novel but significant task to assist judge to effectively make the legal decision for the target trial. In this work, we propose an innovative end-to-end model to address this problem. Unlike prior summarization efforts, the proposed model projects the multi-role debate into the controversy focus space, which enables high-quality essential utterance(s) extraction in terms of legal knowledge and judicial factors. An extensive set of experiments with a large civil trial dataset shows that the proposed model can provide more accurate and readable summarization against several alternatives in the multi-role court debate scene.
Annealed gradient descent for deep learning

In this paper, we propose a novel annealed gradient descent (AGD) algorithm for deep learning. AGD optimizes a sequence of gradually improving smoother mosaic functions that approximate the original non-convex objective function according to an annealing schedule during optimization process. We present a theoretical analysis on AGD’s convergence properties and learning speed, as well as use some visualization methods to show its advantages. The proposed AGD algorithm is applied to learn both deep neural networks (DNNs) and Convolutional Neural Networks (CNNs) for variety of tasks includes image recognition and speech recognition. Experimental results on several widely-used databases, such as Switchboard, CIFAR-10 and Pascal VOC 2012, show that AGD yields better classification accuracy than SGD, and obviously accelerates the training speed of DNNs and CNNs.
oo7: Low-Overhead Defense Against Spectre Attacks via Program Analysis

The Spectre vulnerability in modern processors has been widely reported. The key insight in this vulnerability is that speculative execution in processors can be misused to access the secrets. Subsequently, even though the speculatively executed instructions are squashed, the secret may linger in micro-architectural states such as cache, and can potentially be accessed by an attacker via side channels. In this paper, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">oo7</i> , a static analysis approach that can mitigate Spectre attacks by detecting potentially vulnerable code snippets in program binaries and protecting them against the attack by patching them. Our key contribution is to balance the concerns of effectiveness, analysis time and run-time overheads. We employ control flow extraction, taint analysis, and address analysis to detect tainted conditional branches and speculative memory accesses. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">oo7</i> can detect all fifteen purpose-built Spectre-vulnerable code patterns <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> , whereas Microsoft compiler with Spectre mitigation option can only detect two of them. We also report the results of a large-scale study on applying <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">oo7</i> to over 500 program binaries (average binary size 261 KB) from different real-world projects. We protect programs against Spectre attack by selectively inserting fences only at vulnerable conditional branches to prevent speculative execution. Our approach is experimentally observed to incur around 5.9 percent performance overheads on SPECint benchmarks.
Segmentation and Grade Prediction of Colon Cancer Digital Pathology Images Across Multiple Institutions

Distinguishing benign from malignant disease is a primary challenge for colon histopathologists. Current clinical methods rely on qualitative visual analysis of features such as glandular architecture and size that exist on a continuum from benign to malignant. Consequently, discordance between histopathologists is common. To provide more reliable analysis of colon specimens, we propose an end-to-end computational pathology pipeline that encompasses gland segmentation, cancer detection, and then further breaking down the malignant samples into different cancer grades. We propose a multi-step gland segmentation method, which models tissue components as ellipsoids. For cancer detection/grading, we encode cellular morphology, spatial architectural patterns of glands, and texture by extracting multi-scale features: (i) Gland-based: extracted from individual glands, (ii) local-patch-based: computed from randomly-selected image patches, and (iii) image-based: extracted from images, and employ a hierarchical ensemble-classification method. Using two datasets (Rawalpindi Medical College (RMC), n = 174 and gland segmentation (GlaS), n = 165) with three cancer grades, our method reliably delineated gland regions (RMC = 87.5%, GlaS = 88.4%), detected the presence of malignancy (RMC = 97.6%, GlaS = 98.3%), and predicted tumor grade (RMC = 98.6%, GlaS = 98.6%). Training the model using one dataset and testing it on the other showed strong concordance in cancer detection (Train RMC - Test GlaS = 94.5%, Train GlaS - Test RMC = 93.7%) and grading (Train RMC - Test GlaS = 95%, Train GlaS - Test RMC = 95%) suggesting that the model will be applicable across institutions. With further prospective validation, the techniques demonstrated here may provide a reproducible and easily accessible method to standardize analysis of colon cancer specimens.
Paradox Elimination in Dempster–Shafer Combination Rule with Novel Entropy Function: Application in Decision-Level Multi-Sensor Fusion

Multi-sensor data fusion technology in an important tool in building decision-making applications. Modified Dempster-Shafer (DS) evidence theory can handle conflicting sensor inputs and can be applied without any prior information. As a result, DS-based information fusion is very popular in decision-making applications, but original DS theory produces counterintuitive results when combining highly conflicting evidences from multiple sensors. An effective algorithm offering fusion of highly conflicting information in spatial domain is not widely reported in the literature. In this paper, a successful fusion algorithm is proposed which addresses these limitations of the original Dempster-Shafer (DS) framework. A novel entropy function is proposed based on Shannon entropy, which is better at capturing uncertainties compared to Shannon and Deng entropy. An 8-step algorithm has been developed which can eliminate the inherent paradoxes of classical DS theory. Multiple examples are presented to show that the proposed method is effective in handling conflicting information in spatial domain. Simulation results showed that the proposed algorithm has competitive convergence rate and accuracy compared to other methods presented in the literature.
Multiple-classifiers in software quality engineering: Combining predictors to improve software fault prediction ability

Software development projects require a critical and costly testing phase to investigate efficiency of the resultant product. As the size and complexity of project increases, manual prediction of software defects becomes a time consuming and costly task. An alternative to manual defect prediction is the use of automated predictors to focus on faulty modules and let the software engineer to examine the defective part with more detail. In this aspect, improved fault predictors will always find a software quality application project to be applied on. There are many base predictors tested-designed for this purpose. However, base predictors might be combined with an ensemble strategy to further improve to increase their performance, particularly fault-detection abilities. The aim of this study is to demonstrate fault-prediction performance of ten ensemble predictors compared to baseline predictors empirically. In our experiments, we used 15 software projects from PROMISE repository and we evaluated the fault-detection performance of algorithms in terms of F-measure (FM) and Area under the Receiver Operating Characteristics (ROC) Curve (AUC). The results of experiments demonstrated that ensemble predictors might improve fault detection performance to some extent.
Assessing the accuracy of machine-assisted abstract screening with DistillerAI: a user study

Web applications that employ natural language processing technologies to support systematic reviewers during abstract screening have become more common. The goal of our project was to conduct a case study to explore a screening approach that temporarily replaces a human screener with a semi-automated screening tool.We evaluated the accuracy of the approach using DistillerAI as a semi-automated screening tool. A published comparative effectiveness review served as the reference standard. Five teams of professional systematic reviewers screened the same 2472 abstracts in parallel. Each team trained DistillerAI with 300 randomly selected abstracts that the team screened dually. For all remaining abstracts, DistillerAI replaced one human screener and provided predictions about the relevance of records. A single reviewer also screened all remaining abstracts. A second human screener resolved conflicts between the single reviewer and DistillerAI. We compared the decisions of the machine-assisted approach, single-reviewer screening, and screening with DistillerAI alone against the reference standard.The combined sensitivity of the machine-assisted screening approach across the five screening teams was 78% (95% confidence interval [CI], 66 to 90%), and the combined specificity was 95% (95% CI, 92 to 97%). By comparison, the sensitivity of single-reviewer screening was similar (78%; 95% CI, 66 to 89%); however, the sensitivity of DistillerAI alone was substantially worse (14%; 95% CI, 0 to 31%) than that of the machine-assisted screening approach. Specificities for single-reviewer screening and DistillerAI were 94% (95% CI, 91 to 97%) and 98% (95% CI, 97 to 100%), respectively. Machine-assisted screening and single-reviewer screening had similar areas under the curve (0.87 and 0.86, respectively); by contrast, the area under the curve for DistillerAI alone was just slightly better than chance (0.56). The interrater agreement between human screeners and DistillerAI with a prevalence-adjusted kappa was 0.85 (95% CI, 0.84 to 0.86%).The accuracy of DistillerAI is not yet adequate to replace a human screener temporarily during abstract screening for systematic reviews. Rapid reviews, which do not require detecting the totality of the relevant evidence, may find semi-automation tools to have greater utility than traditional systematic reviews.
Hand-crafted Features vs Residual Networks for Human Activities Recognition using Accelerometer

Inertial sensors combined with supervised machine learning techniques are largely employed for automatic Human Activity Recognition (HAR). Machine learning scientists made available to the community a plenty of labeled datasets that permit, especially in the recent years, to develop sophisticated techniques, such the ones based on deep learning. These techniques have recently become very popular because they are highly accurate. Nevertheless, some researchers still use the combination of traditional classifiers, such as SVM and k-NN, with handcrafted features or raw signals. The aim of this paper is to investigate the robustness of traditional classifiers combined with hand-crafted features compared with an end-to-end deep learning solution based on a Residual Network. Experiments on four public datasets are presented and discussed.
An Overview of Blockchain Applications and Attacks

In recent decades, Information Technology has contributed fundamentally to the development of financial markets, reforming the way in which financial institutions interact with each other. However, the established practices and norms of this sector may face an all-out overhaul as remarkable innovations such as Blockchain are maturing. The essence of Blockchain is that it is a public, shared and carefully designed record that allows mutually unknown individuals and institutions to share data in a reliable ledger and carry out all kinds of transactions. This ground-breaking technology is developed from cryptography and peer-to-peer network technologies. It is nearly immune to the majority of today's digital threats. Besides financial institutions, Blockchain based solutions have made it into other industries such as real estate, health care, the media as well as Government bodies. This paper will explain how Blockchain works, what it really is, types, its applications and threats and will offer a few ideas for prospective expansion of this technology.
Blockchain-based data transmission control for Tactical Data Link

Tactical Data Link (TDL) is a communication system that utilizes a particular message format and a protocol to transmit data via wireless channels in an instant, automatic, and secure way. So far, TDL has shown its excellence in military applications. Current TDL adopts a distributed architecture to enhance anti-destruction capacity. However, It still faces a problem of data inconsistency and thus cannot well support cooperation across multiple militarily domains. To tackle this problem, we propose to leverage blockchain to build an automatic and adaptive data transmission control scheme for TDL. It achieves automatic data transmission and realizes information consistency among different TDL entities. Besides, applying smart contracts based on blockchain further enables adjusting data transmission policies automatically. Security analysis and experimental results based on simulations illustrate the effectiveness and efficiency of our proposed scheme.
High Intensity Image Encryption Scheme Based on Quantum Logistic Chaotic Map and Complex Hyperchaotic System

In this paper, a novel encryption algorithm based on quantum chaotic map and four-wing complex system is proposed. Dynamical performances of the two system are revealed by phase portraits, Lyapunov exponent spectrum, bifurcation diagram analyses and complexity entropy. Based on these analyses, an encryption algorithm is designed by the two chaotic system and DNA coding. Firstly, in process of permutation operation, quantum logistic map is utilized to generate chaotic sequences for disrupting the position of each pixel point combine with Arnold transformation. Then, using the complex hyperchaotic system, the value of each pixel is diffused through mathematical operation and DNA encoding. Finally, security performance is analyzed. Simulation results demonstrate that the algorithm has good robustness and high-quality performance, and it can effectively protect the subject image and resist conventional attack. This encryption scheme can provide a new realization method for security transmission and protection of image information.
Blockchain Compliance by Design: Regulatory Considerations for Blockchain in Clinical Research

As clinical research moves toward real-world data capture with increased data sharing, there is a growing need for patient-centered technologies that ensure data authenticity and promote researcher and patient access. Blockchain is one of an emerging set of distributed ledger technologies with the potential to offer both research data transparency and trust, while offering robust security measures. As blockchain-based systems are being developed for clinical research applications, these systems may be required to follow state and federal research regulations, such as ethical protections for human participants and data privacy. Blockchain developers and research organizations alike are struggling to identify and interpret these regulatory requirements. Further, regulatory agencies and policymakers have not yet provided blockchain stakeholders with clear guidelines to achieve compliance. This article provides an introduction to the clinical research and health information privacy regulations in the United States as well as data design standards and electronic signature laws. We also offer recommendations for blockchain developers, researchers, and research organizations for achieving compliant blockchain solutions in clinical research.
How to Communicate when Submitting Patches

Communication when submitting patches (CSP) is critical in software development, because ineffective communication wastes the time of developers and reviewers, and is even harmful to future product release and maintenance. In distributed software development, CSP is usually in the form of computer-mediated communication (CMC), which is a crucial topic concerned by the CSCW community. However, what to say and how to say in communication including CSP has been rarely studied. To bridge this knowledge gap and provide relevant guidance for developers to conduct CSP, in this study, we conducted an empirical study on the Linux kernel. We found four themes involving 17 expression elements that characterize what to express when submitting patches and three themes of contextual factors that determine how these elements are applied. Considering both expression elements and context, combined with an online survey, we obtained 17 practices for communication. Among them, four practices, such as "provide sufficient reasons" and "provide a trade-off" are the most important but difficult practices, for which we provide specific instructions. We also found that the "individual factors" plays a special role in communication, which may lead to potential problems even in accepted patches. Based on these findings, we discuss the recommendations for different practitioners, including patch submitters, reviewers, and tool designers, and the implications for open source software (OSS) communities and the CSCW researchers.
Feature Fusion Text Classification Model Combining CNN and BiGRU with Multi-Attention Mechanism

Convolutional neural networks (CNN) and long short-term memory (LSTM) have gained wide recognition in the field of natural language processing. However, due to the pre- and post-dependence of natural language structure, relying solely on CNN to implement text categorization will ignore the contextual meaning of words and bidirectional long short-term memory (BiLSTM). The feature fusion model is divided into a multiple attention (MATT) CNN model and a bi-directional gated recurrent unit (BiGRU) model. The CNN model inputs the word vector (word vector attention, part of speech attention, position attention) that has been labeled by the attention mechanism into our multi-attention mechanism CNN model. Obtaining the influence intensity of the target keyword on the sentiment polarity of the sentence, and forming the first dimension of the sentiment classification, the BiGRU model replaces the original BiLSTM and extracts the global semantic features of the sentence level to form the second dimension of sentiment classification. Then, using PCA to reduce the dimension of the two-dimensional fusion vector, we finally obtain a classification result combining two dimensions of keywords and sentences. The experimental results show that the proposed MATT-CNN+BiGRU fusion model has 5.94% and 11.01% higher classification accuracy on the MRD and SemEval2016 datasets, respectively, than the mainstream CNN+BiLSTM method.