Reviewerly/exHarmony huggingface dataset:
Academic Research on the Role of Corporate Governance and IT Expertise in Addressing Cybersecurity Breaches: Implications for Practice, Policy, and Research

SUMMARY Frequent cyber-attacks on organizations in the last decade have caught the attention of practitioners and governance bodies, who have called for boards to take a more active role in managing and preventing future cyber risks. Governance surveys, however, find that boards are not sufficiently prepared to address cybersecurity risks due to a lack of IT expertise. Firms have begun appointing technology experts, creating board-level technology (IT) committees and delegating responsibilities to the audit committee as a means of managing cybersecurity risk. With the aim of understanding the current and future role of governance mechanisms in managing cybersecurity risks, this paper reviews the existing cybersecurity guidelines and regulations, and summarizes the empirical research related to corporate governance, security breaches, and IT expertise in overseeing cyber risks. Finally, we discuss implications for practice, policy, and researchers.
Ship detection from scratch in Synthetic Aperture Radar (SAR) images

Ship detection in Synthetic Aperture Radar (SAR) images has always been a hot topic for research. The development of Deep Neural Networks (DNNs) has strongly promoted the development of computer vision. DNNs are also increasingly applied to SAR ship detection. However, SAR ship detection still faces the following problems: (i) The network used for detection needs to be pre-trained on ImageNet, but there is a large bias between SAR images and ImageNet, which leads to training bias. (ii) The sizes of ship targets vary greatly, and many DNNs do not perform well on multi-scale and small-size SAR ship detection. Therefore, we have designed a SAR ship detector that does not require pre-training. We use DetNet as the backbone network, adopting stacked convolution instead of down-sampling to solve the problem of small object detection and adopt a feature reuse strategy to improve parameter efficiency. In addition, we introduce several branches in the proposal sub-network to provide multi-scale object detection. In the detection sub-network, we use position-sensitive region of interest pooling to improve the prediction accuracy. Experiments on SAR ship dataset prove that our method performs better than some pre-trained networks for small ship detection and complex background ship detection.
A fast learning algorithm for One-Class Slab Support Vector Machines

One-Class Slab Support Vector Machines (OCSSVM) have turned out to be better in terms of accuracy in certain types of classification problems than the traditional SVMs, One Class SVMs, and other one-class classifiers. This paper proposes a fast training method for the OCSSVM using a modified Sequential Minimal Optimization (SMO) algorithm, which would enhance its scalability without a significant compromise in precision. We compared our SMO-based algorithm, the regular OCSSVM, and other state-of-the-art one-class classifiers for time and accuracy on multiple benchmark datasets. The experimental results indicate that the proposed training method provides the best tradeoff between training time and accuracy among the compared methods. It achieves accuracies similar to the regular OCSSVM and better or comparable to existing state-of-the-art one-class classifiers. It provides better scalability than the regular OCSSVM and most other classifiers.
Privacy preserving distributed extremely randomized trees

Applying machine learning and data mining algorithms over data distributed in multiple sources is challenging. One complication is to perform data analysis without compromising personal information, which is a primary concern in healthcare applications. Another issue involves communication overhead incurred from the transfer of raw data from one party to others for conducting centralized data mining. In healthcare applications, we are particularly interested in running data mining algorithms over big data without disclosing sensitive information about data subjects due to privacy and legal concerns. In this paper, we consider the classification problem and show how the Extremely Randomized Trees (ERT) algorithm could be adapted for settings where (structured) data is distributed over multiple sources. We propose the Privacy-Preserving Distributed ERT approach for privacy-preserving utilization of the ERT algorithm in a distributed setting. To the best of our knowledge, this is the first application of the ERT algorithm in the distributed setting, with privacy consideration (without sharing the raw data or intermediate training values), without any loss in classification performance.
Group-Oriented Paper Recommendation With Probabilistic Matrix Factorization and Evidential Reasoning in Scientific Social Network

In recent years, the establishment of a substantial amount of academic groups on scientific social network has brought new opportunities for the collaboration among researchers. In this situation, conducting paper recommendation to these academic groups is of terrific necessity in that it can further facilitate group activities. However, when producing group recommendation, existing methods fail to make full use of the abundant group information, from which a great deal of valuable information can be inferred to facilitate the recommendation performance. In addition, those methods tend to assign an equal weight to each group member when aggregating their recommendations, which is unreasonable in practice. Although some improvements have been made to remedy this problem by assigning different weights to group members, they fail to take into account the reliabilities of group members. Therefore, a group-oriented paper recommendation method based on probabilistic matrix factorization and evidential reasoning (GPMF_ER) is proposed in this article to tackle these problems. More specifically, the group and paper content information are integrated into the probabilistic matrix factorization model to enhance the accuracy of individual recommendation. Afterward, evidential reasoning rule is introduced in the aggregation step to consider both the weights and reliabilities of group members. Extensive experiments have been conducted on the real world CiteULike dataset and the results demonstrate the effectiveness of the proposed method.
Research on the Construction of Online Learning Education Space Under the Background of Artificial Intelligence + Education

Online learning education as a kind of developing trend in recent years not only improves the learners' learning enthusiasm, but also helps teachers understand the situation. If the education industry strengthens the building of e-learning education space, it can significantly improve the quality of learning, and lay a solid foundation for subsequent development.
Extraction of Binarized Neural Network Architecture and Secret Parameters Using Side-Channel Information

In recent years, neural networks have been applied to various applications. To speed up the evaluation, a method using binarized network weights has been introduced, facilitating extremely efficient hardware implementation. Using electromagnetic (EM) side-channel analysis techniques, this study presents a framework of model extraction from practical binarized neural network (BNN) hardware. The target BNN hardware is generated and synthesized using open-source and commercial high-level synthesis tools GUINNESS and Xilinx SDSoC, respectively. With the hardware implemented on an up-to-date FPGA chip, we demonstrate how the layers can be identified from a single EM trace measured during the network evaluation, and we also demonstrate how an attacker may use side-channel attacks to recover secret weights used in the network.
How viable is password cracking in digital forensic investigation? Analyzing the guessability of over 3.9 billion real-world accounts

Passwords have been and still remain the most common method of authentication in computer systems. These systems are therefore privileged targets of attackers, and the number of data breaches in the last few years attests to that. A detailed analysis of such data can provide insight on password trends and patterns users follow when they create a password. To this end, this paper presents the largest and most comprehensive analysis of real-world passwords to date – associated with over 3.9 billion accounts from Have I Been Pwned. This analysis includes statistics on use and most common patterns found in passwords and innovates with a breakdown of the constituent fragments that make each password. Furthermore, a classification of these fragments according to their semantic meaning, provides insight on the role of context in password selection. Finally, we provide an in-depth analysis on the guessability of these real-world passwords.
LEADOR: A Method for End-To-End Participatory Design of Autonomous Social Robots

Participatory design (PD) has been used to good success in human-robot interaction (HRI) but typically remains limited to the early phases of development, with subsequent robot behaviours then being hardcoded by engineers or utilised in Wizard-of-Oz (WoZ) systems that rarely achieve autonomy. In this article, we present LEADOR (Led-by-Experts Automation and Design Of Robots), an end-to-end PD methodology for domain expert co-design, automation, and evaluation of social robot behaviour. This method starts with typical PD, working with the domain expert(s) to co-design the interaction specifications and state and action space of the robot. It then replaces the traditional offline programming or WoZ phase by an in situ and online teaching phase where the domain expert can live-program or teach the robot how to behave whilst being embedded in the interaction context. We point out that this live teaching phase can be best achieved by adding a learning component to a WoZ setup, which captures implicit knowledge of experts, as they intuitively respond to the dynamics of the situation. The robot then progressively learns an appropriate, expert-approved policy, ultimately leading to full autonomy, even in sensitive and/or ill-defined environments. However, LEADOR is agnostic to the exact technical approach used to facilitate this learning process. The extensive inclusion of the domain expert(s) in robot design represents established responsible innovation practice, lending credibility to the system both during the teaching phase and when operating autonomously. The combination of this expert inclusion with the focus on in situ development also means that LEADOR supports a mutual shaping approach to social robotics. We draw on two previously published, foundational works from which this (generalisable) methodology has been derived to demonstrate the feasibility and worth of this approach, provide concrete examples in its application, and identify limitations and opportunities when applying this framework in new environments.
Deep-Hand: A Deep Inference Vision Approach of Recognizing a Hand Sign Language using American Alphabet

Sign-Language is to help people with hearing or speaking disabilities who are not able to communicate well with other people. Communicating with deaf people is a challenge for some speakers and people who do not know sign language. What the study proposed is to help people with such disabilities using the American Sign-Language with the corresponding hand gesture. Deaf individuals will be able to communicate or interact with other people conveniently. The study proposed hand gesture or hand sign language detection trained by using the YOLOv3 algorithm that aims to detect hand gestures or hand sign language that can recognize its equivalent letter alphabet. The study tools such as LabelImg for annotating the data set, categorizing each image of hand gestures based on their equivalent letter alphabet. In this study, Model 18 with 95.1804% training accuracy, 90.8242% validation accuracy, and mAP of 0.8275 is used for the final testing. As video with different hand gestures is presented, the results of every hand gesture detected range over 90%.
Worm computing: A blockchain-based resource sharing and cybersecurity framework

Recently, the rapid growth of network resources and the cybersecurity issues become more and more rigorous due to the rapid progress of Internet of Things (IoTs). How to integrate the network resources and enhance the defense capabilities effectively is an urgent problem. In this paper, inspired by the spread of friendly worms in the network, we put forward a novel concept of worm computing which is a decentralized computing platform. The worm computing model is able to integrate the useful information for resource sharing and cooperative defense, which can achieve the resource utilization refreshing, computing capacity improvement and network security enhancement. We use a blockchain platform to build a fundamental decentralization system and store collaborative data by delivering a private chain, which solves the problem of data sharing and trust computing in the traditional collaboration model by providing decentralized and credible services to ensure the unchangeable modification of transactions. We conduct experiments on resource utilization and collaborative defense of malicious URLs and the results validate the effectiveness of the proposed worm computing model in improving resource utilization and network security.
Fast Implementation of 4-bit Convolutional Neural Networks for Mobile Devices

Quantized low-precision neural networks are very popular because they require less computational resources for inference and can provide high performance, which is vital for real-time and embedded recognition systems. However, their advantages are apparent for FPGA and ASIC devices, while general-purpose processor architectures are not always able to perform low-bit integer computations efficiently. The most frequently used low-precision neural network model for mobile central processors is an 8-bit quantized network. However, in a number of cases, it is possible to use fewer bits for weights and activations, and the only problem is the difficulty of efficient implementation. We introduce an efficient implementation of 4-bit matrix multiplication for quantized neural networks and perform time measurements on a mobile ARM processor. It shows 2.9 times speedup compared to standard floating-point multiplication and is 1.5 times faster than 8-bit quantized one. We also demonstrate a 4-bit quantized neural network for OCR recognition on the MIDV-500 dataset. 4-bit quantization gives 95.0% accuracy and 48% overall inference speedup, while an 8-bit quantized network gives 95.4% accuracy and 39% speedup. The results show that 4-bit quantization perfectly suits mobile devices, yielding good enough accuracy and low inference time.
On Efficient Large Maximal Biplex Discovery

Cohesive subgraph discovery is an important problem in bipartite graph mining. In this paper, we focus on one kind of cohesive structure, called k-biplex, where each vertex of one side is disconnected from at most k vertices of the other side. We consider the large maximal k-biplex enumeration problem which is to list all those maximal k-biplexes with the number of vertices at each side at least a non-negative integer . This formulation, we observe, has various applications and targets to find non-redundant results by excluding non-maximal ones. Existing approaches suffer from massive redundant computations and can only run on small and moderate datasets. Towards improving scalability, we propose an efficient tree-based algorithm with two advanced strategies and powerful pruning techniques. Experimental results on real and synthetic datasets show the superiority of our algorithm over existing approaches.
Spatial resolution enhancement method for Landsat imagery using a Generative Adversarial Network

Landsat and Sentinel-2 are two freely accessible satellite data that are relevant for global land cover monitoring. However, the uses of the latter data set are growing because of its higher spatial resolutions and the availability of benchmark data sets for deep learning applications. In this study, we integrate a style transfer (perceptual loss estimation from Sentinel 2 benchmark data) into a Generative Adversarial Network (GAN) to construct a single image super-resolution model. The proposed model upscales Landsat 8 images (using red, green, blue, and near-infrared bands at 30 m and Panchromatic band 15 m for high-resolution features exploiting) to 10 m (with Sentinel-2 as reference). Compared to pan-sharpening and other upscaling methods, the proposed method can produce more realistic, spatial convincing images at 10 m resolution and more similar to Sentinel-2 images than the other commonly used super-resolution imaging algorithms. As a result, the proposed method extends the usage of high-resolution benchmark data sets for lower resolution imagery to enrich supplement data sources for land cover classification.
Research on blockchain technology in cryptographic exploration

Building a traditional factory into a smart factory is one of the goals of “Industry 4.0”. As factories move towards smart development, the existing network security systems can no longer meet the needs of enterprises and users. Aiming at the hidden dangers of information leakage and illegal access to the data of cryptographic production facilities and products in the smart factory, the article combines the core technology of the Internet of Things radio frequency identification (RFID) technology and blockchain technology, and proposes a blockchain-based technology, the lightweight password security authentication mechanism of the smart factory RFID system, which has the characteristics of lightweight, anti-data leakage, and low management cost. It can ensure the safe and reliable access of industrial data while preventing the application of RFID in smart factories. Security issues such as replay attacks, man-in-the-middle attacks, and server spoofing attacks also provide new ideas for the research on data security protection for smart factories.
Preventing server-side request forgery attacks

In today's web, it is not uncommon for web applications to take a complete URL as input from users. Usually, once the web application receives a URL, the server opens a connection to it. However, if the URL points to an internal service and the server still makes the connection, the server becomes vulnerable to Server-Side Request Forgery (SSRF) attacks. These attacks can be highly destructive when they exploit internal services. They are equally destructive and need much less effort to succeed if the server is hosted in a cloud environment. Therefore, with the growing use of cloud computing, the threat of SSRF attacks is becoming more serious.
Generic Compiler for Publicly Verifiable Covert Multi-Party Computation

Covert security has been introduced as a compromise between semi-honest and malicious security. In a nutshell, covert security guarantees that malicious behavior can be detected by the honest parties with some probability, but in case detection fails all bets are off. While the security guarantee offered by covert security is weaker than full-fledged malicious security, it comes with significantly improved efficiency. An important extension of covert security introduced by Asharov and Orlandi (ASIACRYPT’12) is public verifiability, which allows the honest parties to create a publicly verifiable certificate of malicious behavior. Public verifiability significantly strengthen covert security as the certificate allows punishment via an external party, e.g., a judge. Most previous work on publicly verifiable covert (PVC) security focuses on the two-party case, and the multi-party case has mostly been neglected. In this work, we introduce a novel compiler for multi-party PVC secure protocols with no private inputs. The class of supported protocols includes the preprocessing of common multi-party computation protocols that are designed in the offline-online model. Our compiler leverages time-lock encryption to offer high probability of cheating detection (often also called deterrence factor) independent of the number of involved parties. Moreover, in contrast to the only earlier work that studies PVC in the multi-party setting (CRYPTO’20), we provide the first full formal security analysis.
Spoken Conversational Context Improves Query Auto-completion in Web Search

Web searches often originate from conversations in which people engage before they perform a search. Therefore, conversations can be a valuable source of context with which to support the search process. We investigate whether spoken input from conversations can be used as a context to improve query auto-completion. We model the temporal dynamics of the spoken conversational context preceding queries and use these models to re-rank the query auto-completion suggestions. Data were collected from a controlled experiment and comprised conversations among 12 participant pairs conversing about movies or traveling. Search query logs during the conversations were recorded and temporally associated with the conversations. We compared the effects of spoken conversational input in four conditions: a control condition without contextualization; an experimental condition with the model using search query logs; an experimental condition with the model using spoken conversational input; and an experimental condition with the model using both search query logs and spoken conversational input. We show the advantage of combining the spoken conversational context with the Web-search context for improved retrieval performance. Our results suggest that spoken conversations provide a rich context for supporting information searches beyond current user-modeling approaches.
WITHDRAWN: Effects of knowledge of cyber security on prevention of attacks

Our economy, infrastructure and societies rely to a large extent on information technology and computer networks solutions. Increasing dependency on information technologies has also multiplied the potential hazards of cyber-attacks. The prime goal of this study is to critically examine how the sufficient knowledge of cyber security threats plays a vital role in detection of any intrusion in simple networks and preventing the attacks. The study has evaluated various literatures and peer reviewed articles to examine the findings obtained by consolidating the outcomes of different studies and present the final findings into a simplified solution.
EdgeLD: Locally Distributed Deep Learning Inference on Edge Device Clusters

Deep Neural Networks (DNN) have been widely used in a large number of application scenarios. However, DNN models are generally both computation-intensive and memory-intensive, thus difficult to be deployed on resource-constrained edge devices. Most previous studies focus on local model compression or remote cloud offloading, but overlook the potential benefits brought by distributed DNN execution on multiple edge devices. In this paper, we propose EdgeLD, a new framework for locally distributed execution of DNN-based inference tasks on a cluster of edge devices. In EdgeLD, DNN models' time cost will be firstly profiled in terms of computing capability and network bandwidth. Guided by profiling, an efficient model partition scheme is designed in EdgeLD to balance the assigned workload and the inference runtime among different edge devices. We also propose to employ layer fusion to reduce communication overheads on exchanging intermediate data among devices. Experiment results show that our proposed partition scheme saves up to 15.82% of inference time with regard to the conventional solution. Besides, applying layer fusion can speedup the DNN inference by 1.11-1.13X. When combined, EdgeLD can accelerate the original inference time by 1.77-3.57X on a cluster of 2-4 edge devices.