Reviewerly/exHarmony huggingface dataset:
Information security assessment of wireless sensor networks based on bayesian attack graphs

Network security issues have become increasingly prominent, and information security risk assessment is an important part of network security protection. Security risk assessment based on methods such as attack trees, attack graphs, neural networks, and fuzzy logic has problems such as difficulty in data collection during the assessment process, excessive reliance on expert experience, failure to consider the actual network environment, or ineffective joint application. The qualitative and quantitative information security fuzzy comprehensive evaluation method uses the theory of fuzzy mathematics to better solve the above problems, so that the evaluation method is scientific, comprehensive and operable. To improve the accuracy of information security risk assessment in wireless sensor networks, we propose a fuzzy comprehensive evaluation method based on Bayesian attack graphs. This considers the impact of environmental factors of the assessed system on security risk and the spread of the effects on the Bayesian network. Therefore, this model can reflect possible situations due to network attacks in the wireless sensor network system. The results show that this quantitative evaluation method is applicable to assessing risk in wireless sensor network systems, and the results are more objective and accurate.
Attribute-Based Searchable Encryption Scheme Supporting Efficient Range Search in Cloud Computing

With the widespread application of cloud computing technology, data privacy security problem becomes more serious. The recent studies related to searchable encryption (SE) area have shown that the data owners can share their private data with efficient search function and high-strength security. However, the search method has yet to be perfected, compared with the plaintext search mechanism. In this paper, based LSSS matrix, we give a new searchable algorithm, which is suitable for many search method, such as exact search, Boolean search and range search. In order to improve the search efficiency, the 0, 1-coding theory is introduced in the process of ciphertext search. Meanwhile it is shown that multi-search mechanism can improve the efficiency of data sharing. Finally, the performance analysis is presented, which prove our scheme is secure, efficient, and human-friendly.
Improving Video Anomaly Detection Performance with Patch-level Loss and Segmentation Map

With the development of surveillance video analysis and the increase of security requirements, video anomaly detection has attracted more and more attention. In the literature of video anomaly detection, since the ambiguity of anomaly and the lack of abnormal data, the researchers focus on the unsupervised methods that have the foreground-background imbalance problem. There is a method that employs attention-driven loss to alleviate this problem, but this method fails to be extended to multiple scenarios. In this paper, we propose a patch-level loss and utilize a segmentation map to overcome this problem in multiple scenarios. The patch-level loss is based on Multi-Scale Structural Similarity (MS_SSIM), making the network focus on the boundaries of the foreground in the training process. The segmentation map is derived from FastFCN, which changes the errors between predicted frames and tested frames in the test phase. Specifically, it strengthens the errors generated by the foreground and weakens those generated by the background. The proposed method increases the class spacing of normal and anomaly. Experimental results on several public datasets show that the proposed method consistently improves the performance of video anomaly detection.
Enhancing Robustness of Graph Convolutional Networks via Dropping Graph Connections

Graph convolutional networks (GCNs) have emerged as one of the most popular neural networks for a variety of tasks over graphs. Despite their remarkable learning and inference ability, GCNs are still vulnerable to adversarial attacks that imperceptibly perturb graph structures and node features to degrade the performance of GCNs, which poses serious threats to the real-world applications. Inspired by the observations from recent studies suggesting that edge manipulations play a key role in graph adversarial attacks, in this paper, we take those attack behaviors into consideration and design a biased graph-sampling scheme to drop graph connections such that random, sparse and deformed subgraphs are constructed for training and inference. This method yields a significant regularization on graph learning, alleviates the sensitivity to edge manipulations, and thus enhances the robustness of GCNs. We evaluate the performance of our proposed method, while the experimental results validate its effectiveness against adversarial attacks.
Online pragmatic interpretations of scalar adjectives are affected by perceived speaker reliability

Linguistic communication requires understanding of words in relation to their context. Among various aspects of context, one that has received relatively little attention until recently is the speakers themselves. We asked whether comprehenders’ online language comprehension is affected by the perceived reliability with which a speaker formulates pragmatically well-formed utterances. In two eye-tracking experiments, we conceptually replicated and extended a seminal work by Grodner and Sedivy (2011). A between-participant manipulation was used to control reliability with which a speaker follows implicit pragmatic conventions (e.g., using a scalar adjective in accordance with contextual contrast). Experiment 1 replicated Grodner and Sedivy’s finding that contrastive inference in response to scalar adjectives was suspended when both the spoken input and the instructions provided evidence of the speaker’s (un)reliability: For speech from the reliable speaker, comprehenders exhibited the early fixations attributable to a contextually-situated, contrastive interpretation of a scalar adjective. In contrast, for speech from the unreliable speaker, comprehenders did not exhibit such early fixations. Experiment 2 provided novel evidence of the reliability effect in the absence of explicit instructions. In both experiments, the effects emerged in the earliest expected time window given the stimuli sentence structure. The results suggest that real-time interpretations of spoken language are optimized in the context of a speaker identity, characteristics of which are extrapolated across utterances.
Library Implementation and Performance Analysis of GlobalPlatform TEE Internal API for Intel SGX and RISC-V Keystone

Trusted Execution Environment (TEE) becomes a popular security extension on current CPUs (e.g., Arm Trust-Zone, Intel SGX, and RISC-V Keystone), but each TEE has its original SDK and cannot keep software portability. GlobalPlatform (GP) defines the general APIs named “TEE Internal APIs”, and smartphones mainly use them. In addition, the implementation of GP API assumes a Trusted OS, and some TEEs cannot implement it directly. Furthermore, some TEEs offer Enclave Definition Language (EDL) for secure communication between a normal application and a trusted application, which is not assumed by GP APIs. In order to solve these problems, we propose a library implementation of GP TEE internal APIs on each TEE SDK. We selected GP APIs for architecture-dependent or independent (e.g., secure storage and time). The architecture-independent APIs require the help of Linux or can be implemented efficiently with CPU-specific instruction. They are implemented as same as possible on each architecture. The library is designed to fit for each EDL (i.e., “edger8r” on Intel SGX “keyedge” on RISC-V) and keeps the communication security. The library is implemented on Intel SGX and RISC-V Keystone. The performances are measured and compared with the OP-TEE which is a trusted OS style implementation on Arm TrustZone. The comparison shows the feature of each implementation.
Path Planning of UAVs Based on Improved Ant Colony System

Multi-UAV Cooperative Path Planning is important for improving the efficiency of UAVs in completing reconnaissance, surveillance, and search missions due to its parallelism and fault tolerance. This paper investigates the problem of path planning for multi-mission UAVs under complex constraints, and proposes a new method of UAV path optimization that minimizes task completion time. First, with models of separated regions and heterogeneous UAVs, the objective function and flight constraints are analyzed to fully search the best solutions for the path planning problem. Then, considering a dense target group model, a path planning method based on an Improved Ant Colony System algorithm (IACS) is proposed. The IACS algorithm is able to achieve an efficiency enhancement in time consumption on targets detection and minimize the total time of UAV mission execution by adopting a novel target exploring scheme called BI-directional simplified search strategy in the target selection process. Finally, the effectiveness of the proposed method is verified through simulation experiments with randomly generated targets.
KINEIN: A Making Guide on Indefinitely Deploying a Kinetic Display as a Research Product

Large-scale kinetic displays have been present for decades, both as mesmerising art installations and as information displays. In this paper we present KINEIN, a kinetic display whose large-scale and indefinite deployment time distinguish it from the prototypical level of HCI research artefacts. It consists of 625 “tiles” or “pixels” mounted on a 25 x 25 matrix and operates through mechanical movement. Instead of a motor rotating each tile individually, KINEIN has a vertical column system that moves tiles sequentially. Situated at the entrance of a learning environment, KINEIN is not only a playful artefact for children to physically interact with, but also the first encounter for the school's visitors.
Biomarker-Based Classification and Localization of Renal Lesions Using Learned Representations of Histology—A Machine Learning Approach to Histopathology

Several deep learning approaches have been proposed to address the challenges in computational pathology by learning structural details in an unbiased way. Transfer learning allows starting from a learned representation of a pretrained model to be directly used or fine-tuned for a new domain. However, in histopathology, the problem domain is tissue-specific and putting together a labelled data set is challenging. On the other hand, whole slide-level annotations, such as biomarker levels, are much easier to obtain. We compare two pretrained models, one histology-specific and one from ImageNet on various computational pathology tasks. We show that a domain-specific model (HistoNet) contains richer information for biomarker classification, localization of biomarker-relevant morphology within a slide, and the prediction of expert-graded features. We use a weakly supervised approach to discriminate slides based on biomarker level and simultaneously predict which regions contribute to that prediction. We employ multitask learning to show that learned representations correlate with morphological features graded by expert pathologists. All of these results are demonstrated in the context of renal toxicity in a mechanistic study of compound toxicity in rat models. Our results emphasize the importance of histology-specific models and their knowledge representations for solving a wide range of computational pathology tasks.
Augmenting Remote Interviews through Virtual Experience Prototypes

This paper presents insights from a pilot study in which we used a Virtual Experience Prototype (VEP) to gather qualitative feedback in an online evaluation context. For our study, we created a VEP consisting of a non-immersive virtual reality simulation of an urban robot to inform the design of robotic expressions. Through the means of video conferencing software, we were able to collect qualitative data through the think-aloud protocol while participants interacted with the VEP, followed by subsequent in-depth interviews. By comparing our data to findings from previous in-the-wild deployments, we report on aspects which were comparable to in-person evaluations. Reflecting on our approach, we present a preliminary list of lessons learnt and examine how VEPs can support researchers and practitioners to gain rich feedback from participants in synchronous remote user testing. While bearing in mind the limitations over physical prototypes, we argue that VEPs can be used as a lightweight tool to engage participants in remote interviews through interactive spatial experiences.
Experiments in Text Classification: Analyzing the Sentiment of Electronic Product Reviews in Greek

Sentiment analysis, which deals with people’s sentiments as they appear in the growing amount of online social data, has been on the rise in the past few years. In its simplest form, sentiment analysis deals with the polarity of a given text, i.e., whether the opinion expressed in it is positive or negative. Sentiment analysis, or opinion mining applications on websites and the social media range from product reviews and brand reception to political issues and the stock market. The vast majority of the research in sentiment analysis has mostly dealt with English data, where there’s an abundance of readily available and annotated for sentiment corpora. With a few notable exceptions, the research in other minor languages such as Greek is lacking. This paper deals with sentiment analysis of electronic product reviews written in Greek. To this end, a small dataset of 480 positive and negative reviews is compiled and used, taken from the popular Greek e-commerce website, www.skroutz.gr. Different computational models for training and testing the dataset are evaluated, ranging from simple Naive Bayes with n-gram features to state-of-the-art BERT. The results look very promising for such a small corpus.
Analysis of Intensity, Duration, and Frequency Rain Daily of Java Island Using Mononobe Method

Abstract The importance of analyzing rainfall is expected to help in handling disaster-prone areas, so that the consequences can be minimized. This study aims to determine the intensity of rain in the short term. The data used in this study is the annual rainfall data in Java for 2005 - 2019 for each province. The method used in this study is the Mononobe Method to see the intensity of daily rain and its daily return period. The results of this study indicate that the intensity of rain during the duration of one hour to three hours decreases quite significantly, but at the duration of four to eight hours the decrease in rain intensity is not as big as the decrease in the duration of one hour to three hours. The highest rainfall intensity is in the one hour duration in West Java Province. However, the intensity for the five to eight hour duration tends to be the same for 6 provinces in Java. The rainfall threshold in Java is included in the category of heavy rain. High rainfall intensity lasts for a short duration, on the other hand, rainfall with low intensity tends to occur over a relatively long duration.
Design and Validation of BlockEval, A Blockchain Simulator

Blockchain technology is finding its application in a wide range of areas. Due to the highly decentralized and distributed nature of blockchain technology, it is essential to understand the behaviour of a system before its actual deployment. In this paper, we introduce the design and architecture of our blockchain simulator, BlockEval, which mimics the behaviour of concurrent operations that occur in a real-life blockchain system. We have established the correctness of our simulator by comparing it with an independent model that is built from the real Bitcoin-transactions data using deep-learning techniques. Unlike existing validation procedures which do not scale to large topologies, our method is scalable and efficient in terms of time and compute-infrastructure requirements. The observations made using our simulator are found to match with the results obtained from the model trained on real Bitcoin-transactions data.
Methods for Calculating Route Metrics in Data Transmission Networks

This article analyzes the characteristics of the routing protocols used in the data transmission network and their impact on network performance. Methods for calculating route metrics are given, which characterize the routing algorithm in detail.
A Tibetan Language Model That Considers the Relationship Between Suffixes and Functional Words

The complete semantic representation of a Tibetan sentence is mainly determined by the addition of a specific functional word. The choice of Tibetan functional words is mainly influenced (both explicitly and implicitly) by the sequence of Tibetan suffixes. In this article, we propose an RNN-based Tibetan radical suffix unit (TRSU) to consider this relationship. Specifically, for the Tibetan radical suffix unit-explicit (TRSU-E) method, the fixed suffix in Tibetan is used to determine the virtual functional words. For the Tibetan radical suffix unit-implicit (TRSU-I) method, the decision is assisted by adding a specific suffix. To test the method, we design a standard Tibetan corpus, which consists of different genres. Our experimental results show that the complexity of our method is reduced by up to 22.2% relative to the best baseline. Furthermore, with the hidden semantic information and implicit suffix, TRSU-I outperforms TRSU-E by reducing the perplexity (PPL) by 3%. Moreover, good results are achieved on the English Penn Treebank data set.
Symbolic Computation in Maude: Some Tapas

Programming in Maude is executable mathematical modeling. Your mathematical model is the code you execute. Both deterministic systems, specified equationally as so-called functional modules and concurrent ones, specified in rewriting logic as system modules, are mathematically modeled and programmed this way. But rewriting logic is also a logical framework in which many different logics can be naturally represented. And one would like not only to execute these models, but to reason about them at a high level. For this, symbolic methods that can automate much of the reasoning are crucial. Many of them are actually supported by Maude itself or by some of its tools. These methods are very general: they apply not just to Maude, but to many other logics, languages and tools. This paper presents some tapas about these Maude-based symbolic methods in an informal way to make it easy for many other people to learn about, and benefit from, them.
Moving vehicle detection from video sequences for Traffic Surveillance System

In the current scenario, Intelligent Transportation Systems play a significant role in smart city platform. Automatic moving vehicle detection from video sequences is the core component of the automated traffic management system. Humans can easily detect and recognize objects from complex scenes in a flash. Translating that thought process to a machine, however, requires us to learn the art of object detection using computer vision algorithms. This paper solves the traffic issues of the urban areas with an intelligent automatic transportation system. This paper includes automatic vehicle counting with the help of blob analysis, background subtraction with the use of a dynamic autoregressive moving average model, identify the moving objects with the help of a Boundary block detection algorithm, and tracking the vehicle. This paper analyses the procedure of a video-based traffic congestion system and divides it into greying, binarisation, de-nosing, and moving target detection. The investigational results show that the planned system can provide useful information for traffic surveillance.
NGM: Neural Gaussian Mirror for Controlled Feature Selection in Neural Networks

Deep neural networks (DNNs) have become increasingly popular and achieved outstanding performance in predictive tasks. However, the DNN framework itself cannot inform the user which features are more or less relevant for making the prediction, which limits its applicability in many scientific fields. We introduce neural Gaussian mirrors (NGMs), in which mirrored features are created, via a structured perturbation based on a kernel-based conditional dependence measure, to help evaluate feature importance. We design two modifications of the DNN architecture for incorporating mirrored features and providing mirror statistics to measure feature importance. As shown in simulated and real data examples, the proposed method controls the feature selection error rate at a predefined level and maintains a high selection power even with the presence of highly correlated features.
Active contour driven by adaptively weighted signed pressure force combined with Legendre polynomial for image segmentation

This paper proposes an active contour driven by adaptively weighted signed pressure force (SPF) combined with the Legendre polynomial method for image segmentation. First, an adaptively weighted global average intensity (GAI) term is defined wherein GAI differences are the weighted factors of the interior and exterior region-driving centers. Second, an adaptively weighted Legendre polynomial intensity (LPI) term is defined which adopts the Legendre polynomial intensity average differences as the weighted factors of the interior and exterior region-driving centers. Finally, the GAI and LPI terms are introduced into a novel SPF function and a coefficient is applied to weight their effect degrees; a new edge stopping function (ESF) is defined and combined with the region-based method to robustly converge the curve to the boundary of the object. Experiments demonstrate that this method is highly accurate and computationally efficient for images with inhomogeneous intensity, blurred edge, low contrast, and noise problems. Moreover, the segmentation results are independent of the initial contour.
Handling epistemic and aleatory uncertainties in probabilistic circuits

When collaborating with an AI system, we need to assess when to trust its recommendations. If we mistakenly trust it in regions where it is likely to err, catastrophic failures may occur, hence the need for Bayesian approaches for probabilistic reasoning in order to determine the confidence (or epistemic uncertainty) in the probabilities in light of the training data. We propose an approach to Bayesian inference of posterior distributions that overcomes the independence assumption behind most of the approaches dealing with a large class of probabilistic reasoning that includes Bayesian networks as well as several instances of probabilistic logic. We provide an algorithm for Bayesian inference of posterior distributions from sparse, albeit complete, observations, and for deriving inferences and their confidences keeping track of the dependencies between variables when they are manipulated within the unifying computational formalism provided by probabilistic circuits. Each leaf of such circuits is labelled with a beta-distributed random variable that provides us with an elegant framework for representing uncertain probabilities. We achieve better estimation of epistemic uncertainty than state-of-the-art approaches, including highly engineered ones, while being able to handle general circuits and with just a modest increase in the computational effort compared to using point probabilities.
Promoting Health Data Fluency Skills by Expanding Data and Informatics Work in Libraries: The Role of a Health Library Informaticist

(2021). Promoting Health Data Fluency Skills by Expanding Data and Informatics Work in Libraries: The Role of a Health Library Informaticist. Medical Reference Services Quarterly: Vol. 40, No. 1, pp. 130-138.