Reviewerly/exHarmony huggingface dataset:
MPNet: Multi-scale Parallel Codec Net for Medical Image Segmentation

Medical image segmentation based on the deep learning codec network achieves highly accurate segmentation. However, the structure of the tandem decoder at different scales makes it difficult for the model to segment detailed regions well. Although such detailed areas are small, they are very important in clinical medicine, and therefore one current challenge is to improve detail segmentation. In this paper, we propose a method called the MPNet (Multi-scale Parallel Codec Net), which contains a structure with parallel encoder-decoder routes that respectively comprise a scale transformer and convolution. They extract characteristics at various scales, and the feature maps of each scale are fused through an intersection module to predict the output. The parallel multi-scale routes increase the width of the model, and thus a deep architecture is not needed to obtain precise segmentation. These shallow paths independently learn semantic features at different scales and make the gradient back-propagation faster and more stable. Moreover, a new time-varying loss function is also proposed to speed up network convergence further. Experimental results on four public datasets show that the proposed method performs better than some state-of-the-art methods.
SCAMS: A Novel Side-Channel Attack Mitigation System in IaaS Cloud

Infrastructure as a Service (IaaS) cloud enables tenants to access computing infrastructure easily. An underlying physical machine that provides physical resources can host multiple virtual machines (VMs) from different tenants. These co-resident VMs share the same physical resources such as CPU cache, memory, network interface card, among which cache-based side-channel attacks (CSCAs) pose severe threats to co-resident VMs. A malicious attacker can steal confidential information of a victim through CSCAs. The accessibility and sharing of the IaaS cloud provide a natural ground for CSCAs. In this paper, we propose SCAMS, a novel system to mitigate CSCAs in the multi-tenant IaaS cloud. SCAMS consists of three phases: (i) vulnerable cryptographic operations capturing; (ii) proactive event notification; (iii) cache anomaly monitoring. In the capturing phase, we modify the cryptographic library in the protected VM to allow sensitive cryptographic functions to trap into the handler functions in the hypervisor actively. In the notification phase, the hypervisor handler functions forward sensitive cryptographic events to the unified event notifier module, and the event notifier sends monitoring instructions to the cache monitoring module. In the monitoring phase, we apply hardware performance counters to monitor the number of cache hits or cache misses of co-resident VMs to analyze their cache behaviors. We implement SCAMS in a virtualization platform. Our experimental results show that it defends against CSCAs and introduces acceptable performance overhead.
An Enhanced Focused Web Crawler for Biomedical Topics Using Attention Enhanced Siamese Long Short Term Memory Networks

HIGHLIGHTS This paper proposes a new focused crawler for biomedical topics. This paper proposes a novel Attention Enhanced Siamese Long Short Term Memory Networks. The proposed model is trained using ADAM optimizer with Batch Normalization. This paper produces an average harvest rate of 0.39.
Evaluation of Arabic-Based Contextualized Word Embedding Models

The distributed representation of words, as in Word2Vec, FastText, and GloVe, results in the production of a single vector for each word type regardless of the polysemy or homonymy that many words may have. Context-sensitive representation as implemented in deep learning neural networks, on the other hand, produces different vectors for the multiple senses of a word. Several contextualized word embeddings have been produced for the Arabic language (e.g., AraBERT, QARiB, AraGPT, etc.). The majority of these were tested on a few NLP tasks but there was no direct comparison between them. As a result, we do not know which of these is most efficient and for which tasks. This paper is a first step in an endeavor to establish evaluation criteria for them. It describes 24 such embeddings, then conducts exploratory intrinsic and extrinsic evaluation of them. Afterwards, it tests relational knowledge in them, covering four semantic relations: colors of fruits, capitals of countries, causation, and general information. It also evaluates the utility of these models in Named Entity Recognition and Sentiment Analysis tasks. It has been demonstrated here that AraBERT-v02 and MARBERT are the best on both types of evaluation; therefore, both are recommended for fine-tuning Arabic NLP tasks. The ultimate conclusion is that it is feasible to test higher order reasoning relations in these embeddings.
Part of Speech (PoS) Tagging for Konkani Language Using HMM

For the first time, a part of speech (PoS) tagger tool using hidden Markov model (HMM) for Konkani the official language of the Indian state of Goa that is also spoken along the nearby coastal region has been built. For this study, over 100,000 PoS tagged Konkani sentences were used for the first time. The f-scores for HMM are 76.75% for training data and 71.29% for test data. To place our research in perspective, we provide a list of references of PoS tagging for Indian languages specifically using HMM.
Minimal Deviation from Mission Path After Automated Collision Avoidance for Small Fixed Wing UAVs

Automated collision avoidance for small unmanned vehicles operating at low altitude can be challenging as a system may need to autonomously make decisions about safe path deviation to avoid aerial obstacles. Historical avoidance alert systems, such as TCAS and ACAS, specific to manned aircraft were designed with human operation in mind and have proven reliability. These collision avoidance methods may not be appropriate for autonomous systems with control and decision delays since human intervention can be immediately required. Additionally, a single operator may be monitoring and controlling several unmanned aerial systems at the same time and may not have the same level of situational awareness as a manned aircraft's pilot. This effort examined a commonly used path planning algorithm to generate collision avoidance paths along with a method for path recovery. Since the autonomous vehicle may be performing aerial data collection at specific pre-defined locations or may need to operate in known locations, returning to the original mission path in a direct and efficient manner becomes essential. Clothoids that consider vehicle turn rate were used to identify a return trajectory after avoiding a collision to provide less mission path deviation when compared to picking a single waypoint and following a straight line segment in two dimensional simulations. Collisions were detected using forward facing cones projected on two separate fixed-wing aircraft representing possible future flight paths. Cross track error with and without clothoid recovery paths was computed to evaluate the collision avoidance process with recovery and identify improvements. Results indicate that the addition of a recovery planning phase in a collision avoidance system provides less cross track error resulting in less time spent off the original planned path and tighter coverage of mission areas requiring continuous monitoring.
Data-Driven Design and Evaluation of SMT Meta-Solving Strategies: Balancing Performance, Accuracy, and Cost

Many modern software engineering tools integrate SMT decision procedures and rely on the accuracy and performance of SMT solvers. We describe four basic patterns for integrating constraint solvers (earliest verdict, majority vote, feature-based solver selection, and verdict-based second attempt) that can be used for combining individual solvers into meta-decision procedures that balance accuracy, performance, and cost – or optimize for one of these metrics. In order to evaluate the effectiveness of meta-solving, we analyze and minimize 16 existing benchmark suites and benchmark seven state-of-the-art SMT solvers on 17k unique instances. From the obtained performance data, we can estimate the performance of different meta-solving strategies. We validate our results by implementing and analyzing two strategies. As additional results, we obtain (a) the first benchmark suite of unique SMT string problems with validated expected verdicts, (b) an extensive dataset containing data on benchmark instances as well as on the performance of individual decision procedures and several meta-solving strategies on these instances, and (c) a framework for generating data that can easily be used for similar analyses on different benchmark instances or for different decision procedures.
Attention-Based Graph Neural Network for Identifying Illicit Bitcoin Addresses

Soon after Bitcoin was proposed and became the most popular decentralized cryptocurrency, identification of illicit bitcoin addresses has become an important and popular study, which can help users avoid involvement in illicit activities. Most of the studies focus on digging the characteristic behavior of illicit addresses, or constructing features easier to distinguish illicit and licit addresses. The models used for identification in those studies include anomaly detection models and classification models. Although some of them have been proven effective, the following two insights are often neglected. First, the structure information of the Bitcoin transaction network, not only provides the direct transaction information but the indirect transaction information and trading preference group information based on the network graph. Second, the temporal information of transaction records, offers fine-grained and deep understanding of the address's trading behavior. These insights motivate us to propose the attention-based graph neural network in this paper. The Attention-based GNN(AT-GNN) part of the model recursively calculates the embeddings of address's neighbors with different distances based on the structure information of transaction network, and employs the convolution and attention mechanism to refine the address's embedding. The auto-encoder part of the model sufficiently capture the hidden temporal feature based on the temporal information of transaction records to enable more precise identification results. We conduct extensive experiments on the realistic dataset, demonstrating superiority over five state-of-the-art models. Further analysis verifies the importance of address's neighbor embeddings and hidden temporal feature for better identification, justifying the rationality and effectiveness of our model.
A class of linear sets in PG(1,q5)

Maximum scattered linear sets in PG(1,qn) have been completely classified for n≤4, see Csajbók and Zanella (2018) [9]; Lavrauw and Van de Voorde (2010) [10]. Here a wide class of linear sets in PG(1,q5) is studied which depends on two parameters. Conditions for the existence, in this class, of possible new maximum scattered linear sets in PG(1,q5) are exhibited.
Prevention of Phishing Attacks Using QR Code Safe Authentication

Phishing is a type of attack in which attackers obtain personal information such as usernames, passwords, credit card information, and network credentials. They deceive victims by impersonating a reputable individual or entity and conducting specific acts, such as clicking on a harmful connection or attachment or intentionally revealing sensitive information over the phone or through email. In general, phishing sites attempt to deceive victims by pretending they are on a legitimate website to steal their account credentials and other sensitive information. In this paper, we implemented a safe authentication system using secret-key sharing and QR codes. This authentication system has a dedicated mobile application for authentication, which will eliminate the process of entering the website's credentials and as a result, it will provide robustness for phishing.
On Residual-based Diagnosis of Physical Systems

In this article we describe a novel diagnosis methodology for physical systems such as industrial production systems. The article consists of two parts: Part one analyzes the differences between using sensor values and using residual values for fault diagnosis. Residual values denote the health of a component by comparing sensor values to a predefined model of normal behaviour. We further analyse how faults propagate through components of a physical system and argue for the use of residual values for diagnosing physical systems. In part two we extend the theory of established consistency-based diagnosis algorithms to use residual values. We also illustrate how users of the presented diagnosis methodology are free to substitute the residual generating equations and the diagnosis algorithm to suit their specific needs. For diagnosis, we present the algorithm HySD, based on Satisfiability Modulo Linear Arithmetic. We present an implementation of HySD using threshold values and a symbolic diagnosis approach. However, the approach is also suitable to integrate modern machine learning methods for anomaly detection and combine them with a multitude of diagnosis approaches. Through experiments on the process-industry benchmark Tennessee Eastman Process and another benchmark consisting of multiple tank systems we show the feasibility of our approach. Overall we show how our novel diagnosis approach offers a practical methodology that allows industry to advance from current state of the art anomaly detection to automated fault diagnosis.
Distortion Map-Guided Feature Rectification for Efficient Video Semantic Segmentation

To leverage the strong cross-frame relations of videos, many video semantic segmentation methods tend to explore feature reuse and feature warping based on motion clues. However, since the video dynamics are too complex to model accurately, some warped feature values may be invalid. Moreover, the warping errors can accumulate across frames, thereby resulting in degraded segmentation performance. To tackle this problem, we present an efficient distortion map-guided feature rectification method for video semantic segmentation, specifically targeting the feature updating and correction on the distorted regions with unreliable optical flow. The updated features for the distorted regions are extracted from a light correction network (CoNet). A distortion map serves as the weighted attention to guide the feature rectification by aggregating the warped features and the updated features. The generation of the distortion map is simple yet effective in predicting the distorted areas in the warped features, i.e., moving boundaries, thin objects, and occlusions. In addition, we propose an auxiliary edge-semantics loss to implement the distorted region supervision with classes. Our network is trained in an end-to-end manner and highly modular. Comprehensive experiments on Cityscapes and CamVid datasets demonstrate that the proposed method has achieved state-of-the-art performance by weighing accuracy, inference speed, and temporal consistency on video semantic segmentation.
Designing metadata schema for a human library: a prototype

Purpose In the absence of a working model for describing, managing and archiving the human library resources, this study aims to attempt a practical approach that will provide all the necessary information to the library users, library professionals and researchers. Design/methodology/approach Initially, different metadata standards, archival projects and attributes of the human books were reviewed to identify appropriate metadata standards that accurately describe the resources of the human library. A free and open-source software; DSpace was considered for implementing newly defined metadata schema in this study. Thereafter, a set of new subject entries was incorporated to standardize the contents of the human library. Findings This study finds that the widely used metadata schema – Dublin Core (DC) is not appropriate to describe the contents of the human book. It shows that selected metadata elements from the types – person and event of schema.org can be used for describing, organizing and archiving the resources of the human library. It further highlights that existing subject entries are not sufficient to standardize the contents of these types of resources. Research limitations/implications Two metadata fields in DSpace are strongly recommended by the DSpace community to consider in the input-forms.xml file, that is why the study could not completely omit DC metadata elements in describing human books. Originality/value The study provides a roadmap to the library professionals on the inclusion of new metadata schemas in describing the uniquely featured resources of the library.
Multi-party deterministic joint RSP of arbitrary multi-qubit states via maximal slice states

In this paper, two efficient proposals are presented by using maximal slice states and orthogonal measurement basis to realize the joint remote state preparation (RSP) of two-qubit and three-qubit states, respectively. Furthermore, this kind of joint RSP schemes could be generalized to the case of arbitrary multi-qubit states, which can be transmitted from two senders to one receiver with the supervision of one manager. The successful probability of this joint RSP scheme is always equal to 100% in spite of that quantum channel is composed of non-maximally entangled states.
Attentive Mix: An Efficient Data Augmentation Method for Object Detection

Data augmentation is an import method to improve the model performance, many different data augmentation algorithms have been proposed in the field of machine learning, such as Mixup, Cutmix, Cutout, Mosaic, Attentive Cutmix, Dropout, DropBlock. The above algorithms are mainly applied in image classification tasks, but there is still lack of a data augmentation algorithm that can be used directly for object detection tasks. We propose an efficient data augmentation algorithm specifically applied in the field of object detection. Specifically, we select two images from dataset randomly, then use a trained model to generate the attention map from an image and paste it into another image to generate a new image. Finally, we train a new model on the original data with new generated data. The advantage is that it does not lead to discontinuities in the bounding box when applied to object detection and does not lead to overly complex target labels in the newly generated images. We conducted a series of experiments including training by taking a subset of the training set and testing on the full test set to compare the improvement in model robustness of different data augmentation method. Experimental results proof that proposed method achieves about 4% performance improvement on 10%, 50% and 80% fraction of the dataset, which demonstrates that our method can effectively improve the accuracy of the object detection model and enhance the generalization.
JSENet: A deep convolutional neural network for joint image super-resolution and enhancement

Super-resolution (SR) and image enhancement (IE) are two common and important tasks in image processing. Due to poor equipment, illumination, and photographic skills, many low-resolution and low-quality (in terms of color, and contrasts) photos are captured in our daily life. To make the photos more visually pleasing, we usually enlarge and retouch them in two separate steps. In this paper, we propose a new Joint Image Super-resolution and Enhancement Network (JSENet), which is arguably the first end-to-end method based on deep learning for joint SR and IE. Compared with simply conducting these two tasks in parallel or sequentially, JSENet employs the bilateral learning framework to integrate the two tasks together seamlessly. Meanwhile, two lightweight modules are designed for restoring details and generating color transformation coefficients respectively, which ensures that our JSENet can be deployed in real-world applications. Extensive experiments are conducted to demonstrate that JSENet outperforms the state-of-the-art SR-IE methods.
BERTDAN: Question-Answer Dual Attention Fusion Networks with Pre-trained Models for Answer Selection

Community question answering (CQA) becomes more and more popular in both academy and industry recently. However, a large number of answers often amass in question-answering communities. Hence, it is almost impossible for users to view item by item and select the most relevant one. As a result, answer selection becomes a very significant subtask of CQA. Hence, we propose question-answer dual attention fusion networks with the pre-trained model (BRETDAN) for the task of answer selection. Specifically, we apply BERT model, which has achieved a better result in GLUE leaderboard with deep transformer architectures as the encoder layer to do fine-tuning for question subjects, question bodies and answers, respectively, then the cross attention mechanism selecting out the most relevant answer for different questions. Finally, we apply dual attention fusion networks to filter the noise caused by introducing question and answer pairs. Specifically, the cross attention mechanism aims to extract interactive information between question subject and answer. In a similar way, the interactive information between question body and answer is also captured. Dual attention fusion aims to address the noise problem in the question and answer pairs. Experiments show that the BERTDAN model achieves significant performance on two datasets: SemEval-2015 and SemEval-2017, outperforming all baseline models.
Surrogate Measures of Safety

For about half a century, there has been interest in methods for road safety analysis that do not require to wait for crashes to happen. These methods should be faster, allow to estimate safety as well as or better than historical crash data, that is, provide surrogate measures of safety (SMoS), and give insights into the processes and factors that lead to crashes. They rely on the observation of traffic and events or interactions that are relevant to safety, in particular severe traffic conflicts that almost ended up as a crash. This chapter provides an introduction to the main concepts and definitions of SMoS, an overview of existing indicators to characterize safety-relevant interactions, how such data can be collected and analyzed for safety diagnosis. The chapter closes with perspectives on the rapid evolution of transport systems, with the ongoing vehicle automation, the increasing availability of sensors and the development of new forms of mobility: SMoS may be the best and sometimes only way to assess safety in such fast evolving conditions.
State-based confidence bounds for data-driven stochastic reachability using Hilbert space embeddings

In this paper, we compute finite sample bounds for data-driven approximations of the solution to stochastic reachability problems. Our approach uses a nonparametric technique known as kernel distribution embeddings, and provides probabilistic assurances of safety for stochastic systems in a model-free manner. By implicitly embedding the stochastic kernel of a Markov control process in a reproducing kernel Hilbert space, we can approximate the safety probabilities for stochastic systems with arbitrary stochastic disturbances as simple matrix operations and inner products. We present finite sample bounds for point-based approximations of the safety probabilities through construction of probabilistic confidence bounds that are state- and input-dependent. One advantage of this approach is that the bounds are responsive to non-uniformly sampled data, meaning that tighter bounds are feasible in regions of the state- and input-space with more observations. We numerically evaluate the approach, and demonstrate its efficacy on a neural network-controlled pendulum system.