Reviewerly/exHarmony huggingface dataset:
An efficient security data-driven approach for implementing risk assessment

Information security implementation in an organization regardless of its business processes will not be effective. Current approaches to risk assessment have moved towards business process-oriented ones. Thus, in new approaches, assets are got attention based on the business processes involved. But existing approaches that are based on business processes have their drawbacks. For example, we need to detail processes to know what security data is produced or used in tasks, and what are their importance from the organization’s point of view. Security data certainly has different meanings. How security data moves in an organization’s network environment is another important point. Therefore, the main task in information security would be protecting security data, from the point of creation location to storage. In this paper, several improvements over other solutions are presented. The business processes of the organization are categorized according to security concerns (called fear stories). In the next improvement, we have gone one step further in the business processes, which is extracting the organization’s security data. Therefore, security data plays a key role in our model. The next improvement is the introduction of the security data life cycle (creation, edit, display, process, transfer, store), and its adaptation to the asset layers (logical, physical, and human) through a series of predefined patterns. Thus, in this model, a multi-organization pyramid of security needs will be formed, each pyramid being a hierarchical multi-layer, involving security concerns, related business processes, extracted security data, assets involved, identified risks and optimal combination of security controls. At the end of the paper, we will show how the model presented in this paper will effectively improve the popular risk assessment methods such as CVSS (Common Vulnerability Scoring System) and OWASP (Open Web Application Security Project).
FileBounty: Fair Data Exchange

Digital contents are typically sold online through centralized and custodian marketplaces, which requires the trading partners to trust a central entity. We present FileBounty, a fair protocol which, assuming the cryptographic hash of the file of interest is known to the buyer, is trust-free and lets a buyer purchase data for a previously agreed monetary amount, while guaranteeing the integrity of the contents. To prevent misbehavior, FileBounty guarantees that any deviation from the expected participants’ behavior results in a negative financial payoff; i.e. we show that honest behavior corresponds to a subgame perfect Nash equilibrium. Our novel deposit refunding scheme is resistant to extortion attacks under rational adversaries. If buyer and seller behave honestly, FileBounty’s execution requires only three on-chain transactions, while the actual data is exchanged off-chain in an efficient and privacypreserving manner. We moreover show how FileBounty enables a flexible peer-to-peer setting where multiple parties fairly sell a file to a buyer.
In Search for a Cure

The whole globe has cranked up for coping with the COVID-19 situation. The hands-on tutorial targets at providing a comprehensive and pragmatic end-to-end walk-through for building an academic research paper recommender for the use case of COVID-19 related study, with the help of knowledge graph technology. The code examples that demonstrate the theories are reproducible and can hopefully provide value for researchers to build tools that support conducting research to find a cure to COVID-19.
Weighted Aggregator for the Open-World Knowledge Graph Completion

Open-world knowledge graph completion aims to find a set of missing triples through entity description, where entities can be either in or out of the graph. However, when aggregating entity description’s word embedding matrix to a single embedding, most existing models either use CNN and LSTM to make the model complex and ineffective, or use simple semantic averaging which neglects the unequal nature of the different words of an entity description. In this paper, an aggregator is proposed, adopting an attention network to get the weights of words in the entity description. This does not upset information in the word embedding, and make the single embedding of aggregation more efficient. Compared with state-of-the-art systems, experiments show that the model proposed performs well in the open-world KGC task.
Drone-Based Cattle Detection Using Deep Neural Networks

Cattle form an important source of farming in many countries. In literature, several attempts have been conducted to detect farm animals for different applications and purposes. However, these approaches have been based on detecting animals from images captured from ground level and most approaches use traditional machine learning approaches for their automated detection. In this modern era, Drones facilitate accessing images in challenging environments and scanning large-scale areas with minimum time, which enables many new applications to be established. Considering the fact that drones typically are flown at high altitude to facilitate coverage of large areas within a short time, the captured object size tend to be small and hence this significantly challenges the possible use of traditional machine learning algorithms for object detection. This research proposes a novel methodology to detect cattle in farms established in desert areas using Deep Neural Networks. We propose to detect animals based on a 'group-of-animals' concept and associated features in which different group sizes and animal density distribution are used. Two state-of-the-art Convolutional Neural Network (CNN) architectures, SSD-500 and YOLO V-3, are effectively configured, trained and used for the purpose and their performance efficiencies are compared. The results demonstrate the capability of the two generated CNN models to detect groups-of-animals in which the highest accuracy recorded was when using SSD-500 giving a F-score of 0.93, accuracy of 0.89 and mAP rate of 84.7.
Dealing with Robustness of Convolutional Neural Networks for Image Classification

SW-based systems depend more and more on AI also for critical tasks. For instance, the use of machine learning, especially for image recognition, is increasing ever more. As state-of-the-art, Convolutional Neural Networks (CNNs) are the most adopted techniques for image classification. Although they are proved to have optimal results, it is not clear what happens when unforeseen modifications during the image acquisition and elaboration occur. Thus, it is very important to assess the robustness of a CNN, especially when it is used in a safety critical system, as, e.g., in the medical domain or in automated driving systems. Most of the analyses made about the robustness of CNNs are focused on adversarial examples which are created by exploiting the CNN internal structure; however, these are not the only problems we can encounter with CNNs and, moreover, they may be unlikely in some fields. This is why, in this paper, we focus on the robustness analysis when plausible alterations caused by an error during the acquisition of the input images occur. We give a novel definition of robustness w.r.t. possible input alterations for a CNN and we propose a framework to compute it. Moreover, we analyse four methods (data augmentation, limited data augmentation, network parallelization, and limited network parallelization) which can be used to improve the robustness of a CNN for image classification. Analyses are conducted over a dataset of histologic images.
SILO: A Machine Learning Dataset of Synthetic Ground-Based Observations of LEO Satellites

Images of space objects may have their interpretability assessed with a Space-object National Imagery Interpretability Rating Scale (SNIIRS) score. The rules for such scores are specific, but the process of rating a large number of images can be time-consuming even for a skilled analyst. As this scale is subjective and based on interpretability of resolved features, it is also difficult to provide automated SNIIRS assessments with a simple algorithmic procedure. A Convolutional Neural Network (CNN) may be able to solve this problem, but such an effort requires a large labeled dataset of images. In this paper we will describe the effort to use wave-optics simulations to generate a dataset of SNIIRS-scored images of Low Earth Orbit (LEO) satellites observed from a ground-based optical observatory with varied turbulence conditions. This first iteration of the Scored Images of LEO Objects (SILO) dataset is intended to serve as a foundation for deep learning efforts, similar to how MNIST and ImageNet have been foundational datasets in other machine vision domains. This dataset is already being used in numerous machine learning efforts, including those pertaining to using CNNs to perform image interpretability assessment and to produce higher-resolution image recoveries from degraded image sets. In this paper we also describe some of the other potential uses for this dataset.
Using Machine Learning to Automatically Detect Anomalous Spacecraft Behavior from Telemetry Data

This paper demonstrates three important results: (1) the ability to accurately classify anomalous spacecraft behavior using only traditional machine-learning algorithms, (2) the ability to successfully deploy a machine-learning model trained on one specific component of a satellite in the service of detecting anomalous behavior in all other instances of that particular component on the bus, and (3) the ability of a machine-learning engine to detect impending failure of a satellite component without having been trained on examples of such failures. Automated anomaly classification is shown using telemetry data from the Suomi National Polar-orbiting Partnership (NPP) weather satellite. Both supervised and semi-supervised approaches yield exceptionally high accuracy (i.e. approximately 99%) in detecting whether a given 120-minute window of telemetry data from an instrument's scan drive mechanism contains an anomalous event. The most successful supervised algorithm was found to be a random-forest classifier; in the case of the semi-supervised approach, a Gaussian elliptic envelope drawn in feature space performed best. An exhaustive feature-selection study revealed that the most successful quantities for performing classification could be derived from statistical moments of the 120-minute telemetry windows under consideration. Applying trained models to previously unseen components and anticipating component failures are both demonstrated using telemetry streams from the reaction wheels aboard the Kepler space telescope. Two of the four reaction wheels suffered failures during that satellite's mission. Since only two examples of failure were available, an unsupervised method (i.e. a Gaussian elliptic envelope with non-negligible contamination) was used to learn typical reaction-wheel behavior. The model was trained using only a small fraction of the data from a few months of telemetry from only one of the four reaction wheels. When applied to the telemetry streams from the other three wheels, this model - in conjunction with a time-based clustering algorithm - was able to successfully warn of elevated friction levels three to six months prior to failure.
SoK

Wireless sensor networks play an important role in today's world: When measuring physical conditions, the quality of the sensor readings ultimately impacts the quality of various data analytical services. To maintain data correctness and quality, run-time measures such as anomaly detection techniques are gaining significance. In particular, the detection of threatening node anomalies caused by sensor node faults has become a crucial task.
A Dependently Typed Linear π-Calculus in Agda

Session types have consolidated as a formalism for the specification and static enforcement of communication protocols. Many different theories of dependent session types have been proposed, some enabling refined specifications on the content of messages, others allowing the structure of the protocols to depend on data exchanged in the protocol itself. In this work we continue a line of research studying the foundations of binary session types. In particular, we propose a variant of the linear π-calculus whose type structure encompasses virtually all dependent session types using just two type constructors: linear channel types and linear dependent pairs. We use Agda not only to formalize the metatheory of the calculus and obtain machine-checked proofs of type soundness, but also as host language in which we implement data-dependent protocols.
Long-Short Term Memory-Based Application on Adaptive Cross-Platform Decoder for Bit Patterned Magnetic Recording

Dynamic bit encoding and decoding of the magnetic recording process remain a challenge in that the process is restrained by the balance between reading and writing performance of the decoder's bit error rate (BER). Sequential neural networks offer data streamflow for processes to reproduce recoded bits from signal distribution, overcoming the limitation of codeword mapping designed for each specific bit-patterned magnetic recording (BPMR) channel. Here, we implement the vanilla long short-term memory (LSTM) for adaptive modulation decoders in various BPMR channel designs within a single network, which benefits multi-channel decoder calibration tools with the same standardization. Signal information from media readback, a two-dimensional (2D) equalizer, 2D Viterbi, and a 2D soft-output Viterbi algorithm (SOVA) detector is arranged as a tensor that enables sequence-to-sequence bit prediction even with a highly complex data arrangement. Our adaptive model can predict recorded bits from readback with accuracies of approximately 97% for rate 4/5 decoding and 75% for crossing platforms, using a recently proposed single-reader/two-track reading (SRTR) system at an areal density of 4 Tb/in <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> in a signal-to-noise ratio range of 1 to 8 dB. We conducted a BER simulation with the relevant results from conventional decoders and the LSTM model. Ultimately, our approach may demonstrate the limitation of supervised learning designed for BPMR systems and reveal a sequence data focus on LSTM that paves the way for sequential-type, unsupervised, mechanism-based, next-generation magnetic recordings.
Precision UAV Landing Control Based on Visual Detection

Precision landing is critical for rapid launching and recovering of unmanned aerial vehicles in ship-based scenarios and space-limited environments. The solution presented here uses a deep convolutional network based vision pipeline to detect the landing mark and estimate system state in real-time. The landing sequence is controlled by vehicle trajectory points using a linear predictive algorithm. The trajectory points are fed to a low level position controller and velocity controller. Both controllers demonstrate satisfactory performance and achieve <; 0.3-meter accuracy with the platform moving up to 80% of maximum UAV velocity.
FCC-Net: A Full-Coverage Collaborative Network for Weakly Supervised Remote Sensing Object Detection

With an ever-increasing resolution of optical remote-sensing images, how to extract information from these images efficiently and effectively has gradually become a challenging problem. As it is prohibitively expensive to label every object in these high-resolution images manually, there is only a small number of high-resolution images with detailed object labels available, highly insufficient for common machine learning-based object detection algorithms. Another challenge is the huge range of object sizes: it is difficult to locate large objects, such as buildings and small objects, such as vehicles, simultaneously. To tackle these problems, we propose a novel neural network based remote sensing object detector called full-coverage collaborative network (FCC-Net). The detector employs various tailored designs, such as hybrid dilated convolutions and multi-level pooling, to enhance multiscale feature extraction and improve its robustness in dealing with objects of different sizes. Moreover, by utilizing asynchronous iterative training alternating between strongly supervised and weakly supervised detectors, the proposed method only requires image-level ground truth labels for training. To evaluate the approach, we compare it against a few state-of-the-art techniques on two large-scale remote-sensing image benchmark sets. The experimental results show that FCC-Net significantly outperforms other weakly supervised methods in detection accuracy. Through a comprehensive ablation study, we also demonstrate the efficacy of the proposed dilated convolutions and multi-level pooling in increasing the scale invariance of an object detector.
Contelog: A declarative language for modeling and reasoning with contextual knowledge

Context-awareness is at the core of many modern-day applications in safety and secure-critical domains. In existing context-aware systems knowledge and context are not formally integrated, and consequently adaptation behaviors for safety-criticality cannot be formally reasoned. In modern day smart systems, such as healthcare and advanced manufacturing, context-awareness must be combined with contextual reasoning in order that new knowledge can be inferred and based on which strategic decisions can be made. Consequently, a rigorous approach is essential to represent contextual domain knowledge and inference rules in order to combine the logic of domain-based decision rules with contextual constraints for contextual reasoning, and decision making. In this paper we address this later issue and introduce a formal approach to achieve contextual reasoning. The framework that we create, called Contelog, conservatively extends the syntax and semantics of Datalog to reason with contextual knowledge. In this setting, contextual knowledge is reusable on its own. The significance is that by fixing the contextual knowledge, goal-specific analysis rules may be changed, and vice versa. By providing a theory of context, independent of the logic of the rules, we have developed a simple and sound context calculus using which it is possible to export knowledge reasoned in one context to another context. Query processing and implementation of Contelogprograms convince us that it has the capabilities to reason in systems where perception and cognition are formally combined for problem solving.
Communication-Efficient Gradient Coding for Straggler Mitigation in Distributed Learning

Distributed implementations of gradient-based methods, wherein a server distributes gradient computations across worker machines, need to overcome two limitations: delays caused by slow running machines called stragglers, and communication overheads. Recently, Ye and Abbe [ICML 2018] proposed a coding-theoretic paradigm to characterize a fundamental trade-off between computation load per worker, communication overhead per worker, and straggler tolerance. However, their proposed coding schemes suffer from heavy decoding complexity and poor numerical stability. In this paper, we develop a communication-efficient gradient coding framework to overcome these drawbacks. Our proposed framework enables using any linear code to design the encoding and decoding functions. When a particular code is used in this framework, its block-length determines the computation load, dimension determines the communication overhead, and minimum distance determines the straggler tolerance. The flexibility of choosing a code allows us to gracefully trade-off the straggler threshold and communication overhead for smaller decoding complexity and higher numerical stability. Further, we show that using a maximum distance separable (MDS) code generated by a random Gaussian matrix in our framework yields a gradient code that is optimal with respect to the trade-off and, in addition, satisfies stronger guarantees on numerical stability as compared to the previously proposed schemes. Finally, we evaluate our proposed framework on Amazon EC2 and demonstrate that it reduces the average iteration time by 16% as compared to prior gradient coding schemes.
Performance Evaluation of Background Modeling Methods for Object Detection and Tracking

Background modelling is a method of detecting moving objects from the difference between current frame and a reference frame. Benefits of background modelling and object detection are uncountable, and have enormous applications in the area of artificial intelligence, video survillance, medicine and some of security related applications. Popular background modelling methods are Gaussian Mixture Model(GMM), Adaptive Gaussian Mixture Model (AGMM), Kernel Density Estimator (KDE), Codebook, Visual Background Extractor(VIBE), Self-Organizing Background Subtraction (SOBS), all these models suffers from illumination changes and dynamic background. In this paper, GMM and SOBS algorithms are implemented using MATLAB software for object detection and tracking, and evaluated results using performance parameters such as recall, specificity, False positive Rate (FPR), False Negative Rate (FNR), precision, Percentage of Wrong Classification (PWC), F-measure. Obtained results shows that 9.71% improvement in specificity of GMM and 2.97% improvement in specificity of SOBS.
The future of the forensic science providers – Time to re-think our structures?

The organisation of the forensic chain, from scene of crime up to the court house, has in most countries hardly evolved with the societal needs as well as with the scientific developments. It can be expected that the forensic possibilities will be strongly enlarged in the coming years, based on the current scientific evolution. This combined with the reduction of the operating funds most laboratories are experiencing highlights the need to reflect on the way the forensic chain and its providers are organized. In this paper, we will present a model based on a cost-balanced approach and distributed competences. Specialized forensic disciplines or techniques are either made available in single points of operation, through buying in services from external providers or through international collaboration with other forensic laboratories. Different ways for implementing the international exchange are discussed. A comparison is made with the collaboration of the metrological laboratories over the world which could serve as a template for the implementation of the forensic structure.
High-precision location for occluded reference hole based on robust extraction algorithm

Abstract The reference hole is significant for pre-positioning in high-precision robot drilling. The visual and optical measurement of such holes is of great benefit in terms of improving location accuracy during robot drilling and subsequent rivet-in-hole assembly. However, the detection and location of the reference hole via visual sensors still presents signifcant challenges, owing to the occlusion of perforator clips prior to practical drilling. This study proposes a robust extraction algorithm, based on gray clustering and edge detection, for locating the occluded reference holes (ORH). Firstly, the boundary box of the ORH is segmented using a template matching and density clustering algorithm. Secondly, the discrete arcs of the ORH are extracted via a combination of K-means clustering, pixel-intensity, and concave points extraction based on edge concavity. Finally, the target arcs are filtered and obtained based on their ellipse geometric properties, followed by the center extraction of the ellipse fitting. The experimental results show that the location accuracy achieved in this way is within 0.03 mm, which is sufficiently accurate for robot drilling. Moreover, the ORH data can be extracted and located successfully even in challenging industrial environments, with varying levels of illumination.