Reviewerly/exHarmony huggingface dataset:
A Dendritic Neuron Model with Adaptive Synapses Trained by Differential Evolution Algorithm

A dendritic neuron model with adaptive synapses (DMASs) based on differential evolution (DE) algorithm training is proposed. According to the signal transmission order, a DNM can be divided into four parts: the synaptic layer, dendritic layer, membrane layer, and somatic cell layer. It can be converted to a logic circuit that is easily implemented on hardware by removing useless synapses and dendrites after training. This logic circuit can be designed to solve complex nonlinear problems using only four basic logical devices: comparators, AND (conjunction), OR (disjunction), and NOT (negation). To obtain a faster and better solution, we adopt the most popular DE for DMAS training. We have chosen five classification datasets from the UCI Machine Learning Repository for an experiment. We analyze and discuss the experimental results in terms of the correct rate, convergence rate, ROC curve, and the cross-validation and then compare the results with a dendritic neuron model trained by the backpropagation algorithm (BP-DNM) and a neural network trained by the backpropagation algorithm (BPNN). The analysis results show that the DE-DMAS shows better performance in all aspects.
Earthquakes spatio–temporal distribution and fractal analysis in the Eurasian seismic belt

The Eurasian seismic belt is the second largest seismic zone in the world. It has numerous seismic activities which have enormous impact on human's life. It is of importance to study the spatio–temporal characteristics of the Eurasian seismic belt. As we can learn from previous studies, fractal and fractal dimension theories can be used to study seismic activities. In general, the temporal sequence and the spatio sequence of earthquakes both exhibit the fractal structures. When huge earthquakes occur, the fractal dimension of the temporal sequence is very low. As the days went by, the value of fractal dimension fluctuates. The seismic data from 1973 to 2014 in The Eurasian seismic belt are selected as the object of this study. Based on the single fractal model, the complex structure of the seismic spatio and temporal distribution in the seismic belt is quantitatively evaluated. Results show that over time the time interval of the seismic activity shortened, and the seismic activity on the Eurasian seismic belt has a nonlinear structure and self-similar characteristics. From the perspective of space, the fractal dimension of the Eurasian seismic belt tends to grow with time, and it also has a nonlinear structure and self-similar characteristics. When the temporal unit is set as 1 year, the accumulation and release of energy are probably periodic: the minor period might be about 8.5 years, and the major period might be about 13 years.
BioTouchPass2: Touchscreen Password Biometrics Using Time-Aligned Recurrent Neural Networks

Passwords are still used on a daily basis for all kind of applications. However, they are not secure enough by themselves in many cases. This work enhances password scenarios through two-factor authentication asking the users to draw each character of the password instead of typing them as usual. The main contributions of this study are as follows: i) We present the novel MobileTouchDB public database, acquired in an unsupervised mobile scenario with no restrictions in terms of position, posture, and devices. This database contains more than 64K on-line character samples performed by 217 users, with 94 different smartphone models, and up to 6 acquisition sessions. ii) We perform a complete analysis of the proposed approach considering both traditional authentication systems such as Dynamic Time Warping (DTW) and novel approaches based on Recurrent Neural Networks (RNNs). In addition, we present a novel approach named Time-Aligned Recurrent Neural Networks (TA-RNNs). This approach combines the potential of DTW and RNNs to train more robust systems against attacks. A complete analysis of the proposed approach is carried out using both MobileTouchDB and e-BioDigitDB databases. Our proposed TA-RNN system outperforms the state of the art, achieving a final 2.38% Equal Error Rate, using just a 4-digit password and one training sample per character. These results encourage the deployment of our proposed approach in comparison with traditional typed-based password systems where the attack would have 100% success rate under the same impostor scenario.
Analyzing bug fix for automatic bug cause classification

During the bug fixing process, developers usually need to analyze the source code to induce the bug cause, which is useful for bug understanding and localization. The bug fixes of historical bugs usually reflects the bug causes when fixing them. This paper aims at exploiting the corresponding relationship between bug causes and bug fixes to automatically classify bugs into their cause categories. First, we define the code-related bug classification criterion from the perspective of the cause of bugs. Then, we propose a new model to exploit the knowledge in the bug fix by constructing fix trees from the diff source code at Abstract Syntax Tree (AST) level, and representing each fix tree based on the encoding method of Tree-based Convolutional Neural Network (TBCNN). Finally, the corresponding relationship between bug causes and bug fixes is analyzed by automatically classifying bugs into their cause categories. We collected 2000 real-world bugs from two open source projects Mozilla and Radare2 to evaluate our approach. The experimental results show the existence of observational correlation between the bug fix and the cause of the historical bugs, and the proposed fix tree can effectively express the characteristics of the historical bugs for bug cause classification.
Measuring and visualizing cyber threat intelligence quality

Abstract The very raison d’être of cyber threat intelligence (CTI) is to provide meaningful knowledge about cyber security threats. The exchange and collaborative generation of CTI by the means of sharing platforms has proven to be an important aspect of practical application. It is evident to infer that inaccurate, incomplete, or outdated threat intelligence is a major problem as only high-quality CTI can be helpful to detect and defend against cyber attacks. Additionally, while the amount of available CTI is increasing it is not warranted that quality remains unaffected. In conjunction with the increasing number of available CTI, it is thus in the best interest of every stakeholder to be aware of the quality of a CTI artifact. This allows for informed decisions and permits detailed analyses. Our work makes a twofold contribution to the challenge of assessing threat intelligence quality. We first propose a series of relevant quality dimensions and configure metrics to assess the respective dimensions in the context of CTI. In a second step, we showcase the extension of an existing CTI analysis tool to make the quality assessment transparent to security analysts. Furthermore, analysts’ subjective perceptions are, where necessary, included in the quality assessment concept.
A Closer Look at Lightweight Graph Reordering

Graph analytics power a range of applications in areas as diverse as finance, networking and business logistics. A common property of graphs used in the domain of graph analytics is a power-law distribution of vertex connectivity, wherein a small number of vertices are responsible for a high fraction of all connections in the graph. These richly-connected (hot) vertices inherently exhibit high reuse. However, their sparse distribution in memory leads to a severe underutilization of on-chip cache capacity. Prior works have proposed lightweight skew-aware vertex reordering that places hot vertices adjacent to each other in memory, reducing the cache footprint of hot vertices and thus improving cache efficiency. However, in doing so, they may inadvertently destroy the inherent community structure within the graph, which may negate the performance gains achieved from the reduced footprint of hot vertices. In this work, we study existing reordering techniques and demonstrate the inherent tension between reducing the cache footprint of hot vertices and preserving original graph structure. We quantify the potential performance loss due to disruption in graph structure for different graph datasets. We further show that reordering techniques that employ fine-grain reordering significantly increase misses in the higher level caches, even when they reduce misses in the last level cache. To overcome the limitations of existing reordering techniques, we propose Degree-Based Grouping (DBG), a novel lightweight reordering technique that employs a coarse-grain reordering to largely preserve graph structure while reducing the cache footprint of hot vertices. Our evaluation on 40 combinations of various graph applications and datasets shows that, compared to a baseline with no reordering, DBG yields an average application speed-up of 16.8% vs 11.6% for the best-performing existing lightweight technique.
Strong quantum nonlocality without entanglement in multipartite quantum systems

In this paper, we generalize the concept of strong quantum nonlocality from two aspects. First, in a tripartite quantum system, we present a construction of strongly nonlocal quantum states containing $6{(d\ensuremath{-}1)}^{2}$ orthogonal product states in ${\mathbb{C}}^{d}\ensuremath{\bigotimes}{\mathbb{C}}^{d}\ensuremath{\bigotimes}{\mathbb{C}}^{d}$ and build $6{d}^{2}\ensuremath{-}8d+4$ product states in ${\mathbb{C}}^{d}\ensuremath{\bigotimes}{\mathbb{C}}^{d}\ensuremath{\bigotimes}{\mathbb{C}}^{d+1}$, which have been proved to be strongly nonlocal. Obviously, both results turn out to be one order of magnitude smaller than the number of basis states ${d}^{3}$ for $d\ensuremath{\ge}3$. Second, we give explicit forms of strongly nonlocal orthogonal product basis in ${\mathbb{C}}^{3}\ensuremath{\bigotimes}{\mathbb{C}}^{3}\ensuremath{\bigotimes}{\mathbb{C}}^{3}\ensuremath{\bigotimes}{\mathbb{C}}^{3}$ and ${\mathbb{C}}^{4}\ensuremath{\bigotimes}{\mathbb{C}}^{4}\ensuremath{\bigotimes}{\mathbb{C}}^{4}\ensuremath{\bigotimes}{\mathbb{C}}^{4}$ quantum systems, where four is the largest known number of subsystems in which there exists strong quantum nonlocality without entanglement up to now. All the results positively answer the open problems raised by Halder et al. [Phys. Rev. Lett. 122, 040403 (2019)]; that is, there do exist a small number of quantum states that can demonstrate strong quantum nonlocality without entanglement.
Natural Language Processing for Mimicking Clinical Trial Recruitment in Critical Care: A Semi-Automated Simulation Based on the LeoPARDS Trial

Clinical trials often fail to recruit an adequate number of appropriate patients. Identifying eligible trial participants is resource-intensive when relying on manual review of clinical notes, particularly in critical care settings where the time window is short. Automated review of electronic health records (EHR) may help, but much of the information is in free text rather than a computable form. We applied natural language processing (NLP) to free text EHR data using the CogStack platform to simulate recruitment into the LeoPARDS study, a clinical trial aiming to reduce organ dysfunction in septic shock. We applied an algorithm to identify eligible patients using a moving 1-hour time window, and compared patients identified by our approach with those actually screened and recruited for the trial, for the time period that data were available. We manually reviewed records of a random sample of patients identified by the algorithm but not screened in the original trial. Our method identified 376 patients, including 34 patients with EHR data available who were actually recruited to LeoPARDS in our centre. The sensitivity of CogStack for identifying patients screened was 90% (95% CI 85%, 93%). Of the 203 patients identified by both manual screening and CogStack, the index date matched in 95 (47%) and CogStack was earlier in 94 (47%). In conclusion, analysis of EHR data using NLP could effectively replicate recruitment in a critical care trial, and identify some eligible patients at an earlier stage, potentially improving trial recruitment if implemented in real time.
Thyroid Nodule Ultrasound Image Classification Through Hybrid Feature Cropping Network

With the increasing cases of thyroid malignant tumors, the diagnosis of thyroid nodule has attracted more and more attention. Deep learning has achieved promising results in computer-aided diagnosis due to the advantages of obtaining high-dimensional features. In this paper, we proposed a hybrid multi-branch convolutional neural network based on feature cropping method for feature extraction and classification of thyroid nodule ultrasound images. Firstly, we designed a backbone convolutional neural network to extract shared feature maps and a classification network as global branch. Next, we added a feature cropping branch in the network to perform multi-cropping on batch feature maps, to reduce the impact on classification caused by the similarity of local features between benign and malignant thyroid nodule images. Finally, based on softmax predictions of different branch feature maps, we utilize a weighted cross-entropy loss function to train our proposed binary-classification network. Experimental results show that our proposed method has achieved 96.13% accuracy, 93.24% precision, 97.18% recall, and 95.17% F1-measure in public dataset and local dataset, outperforming other models.
Block diagonal representation learning for robust subspace clustering

Subspace clustering groups a set of data into their underlying subspaces according to the low-dimensional subspace structure of data. The performance of spectral clustering-based approaches heavily depends on the learned block diagonal structure of the affinity matrix. However, this structure is fragile in the presence of noise within data. As such, the clustering performance is degraded significantly. On the other hand, in practice, we often do not have a prior knowledge of error distribution at all, which results in that we cannot model the error with suitable norms. To this end, in this paper, we propose a robust block diagonal representation learning for subspace clustering. Specifically, a non-convex regularizer is directly utilized to constrain the affinity matrix for exploiting the block diagonal structure. Furthermore, we use a penalty matrix to adaptively weight the reconstruction error so that we can handle noise without prior knowledge. We also devise an effective method to compute the parameters related to this matrix, reducing the complexity of the parameter trains. Experimental results show that our method outperformed the state-of-the-art methods on both synthetic data and real-world datasets.
Cascade U-ResNets for Simultaneous Liver and Lesion Segmentation

In recent years, several deep learning networks are proposed to segment 2D or 3D bio-medical images. However, in liver and lesion segmentation, the proportion of interested tissues and lesions are tiny when contrasting to the image background. That is, the objects to be segmented are highly imbalanced in terms of the frequency of occurrences. This makes existing deep learning networks prone to predict pixels of livers and lesions as background. To address this imbalance issue, several loss functions are proposed. Since no researches are having made a comparison among those proposed loss functions, we are curious about that which loss function is the best among them? At the same time, we also want to investigate whether the combination of several different loss functions is effective for liver and lesion segmentation. Firstly, we propose a novel deep learning network (cascade U-ResNets) to produce liver and lesion segmentation simultaneously. Then, we investigate the performance of 5 selected loss functions, WCE (Weighted Cross Entropy), DL (Dice Loss), WDL (Weighted Dice Loss), TL (Teverskry Loss), WTL (Weighted Teversky Loss), with our cascade U-ResNets. We further assemble all cascade U-ResNets trained with different loss functions together to segment livers and lesions jointly on the liver CT (Computed Tomography) volume. Experimental results on the LiTS dataset <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> showed our ensemble model can achieve much better results than every individual model for liver segmentation. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://competitions.codalab.org/competitions/17094#.
View-Dependent Dynamic Point Cloud Compression

Dynamic point cloud (DPC) captures 3D object and scene with realistic appearance to mimic the natural reality. It inherently offers the six-degree-of-freedom (6DoF) for content consumption, which motivates us to facilitate the network-friendly view-dependent DPC streaming by leveraging the limited field of view of the human visual system (HVS) at specific moments, for significant network bandwidth reduction without the quality of experience (QoE) loss. Therefore, in this work, we propose the view-dependent DPC compression, noted as View-PCC, by which we can enable instantaneous and complete view reconstruction from partial streams, and 6DoF navigation by stream adaptation. These functionalities are offered by the hybrid global and local projection to effectively map 3D points to five perpendicular 2D image planes of a cube. Multi-view and multi-layer based global projection is first applied progressively and followed by the patch-based local projection with both intra-frame displaced arrangement and inter-frame temporal alignment, so as to maximize the number of effectively projected points and preserve the spatio-temporal coherency for better compression. Boundary padding is then augmented for each image plane that will be encoded using the successful 2D video coding standard - High-Efficiency Video Coding (HEVC). Our extensive simulations have shown the noticeable objective compression gains of View-PCC when compared with the typical octree-based approach and global-projection-based method. The subjective quality assessment suggests the better reconstruction quality of View-PCC, in comparison to the MPEG video-based point cloud compression (V-PCC).
Climate Crises and Crypto-Colonialism: Conjuring Value on the Blockchain Frontiers of the Global South

In this commentary we explore how international development, disaster relief and climate change mitigation credentials are being called upon to justify ‘crypto-colonialism’, whereby blockchain technology is used to extract economic benefits from those suffering the scars of colonial expansionism in the Global South. These benefits include land, labour and resources needed to facilitate local ‘crypto-utopian’ developments, or a ‘green economy’ elsewhere. As with past neoliberal development agendas imposing structural economic reforms, the contemporary crypto-colonial exercises discussed here are driven in pursuit of a common good – to protect the global commons and improve people’s lives. Within spaces where crypto-colonialism manifests, the governance frameworks of the associated technology is heavily entangled with social-spatial relations in multiple ways. We argue that despite being distributed, techno-ecological fixes are never placeless. How people engage with, resist or reconfigure a crypto-economy is geographically contingent. This commentary argues for more situated critical analysis of actually existing case-studies to reveal the inequitable terrain of project benefit distributions, and to expose the likely winners and losers within each. The success or failure of use-cases is less dependent on technical viability, but rather mediated through reactions to colonial contexts and historical experiences of various economic and climate crises.
Approximation by finite mixtures of continuous density functions that vanish at infinity

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Real time detection of acoustic anomalies in industrial processes using sequential autoencoders

Abstract Development of intelligent systems with the pursuit of detecting abnormal events in real world and in real time is challenging due to difficult environmental conditions, hardware limitations, and computational algorithmic restrictions. As a result, degradation of detection performance in dynamically changing environments is often encountered. However, in the next‐generation factories, an anomaly detection system based on acoustic signals is especially required to quickly detect and interfere with the abnormal events during the industrial processes due to the increased cost of complex equipment and facilities. In this study we propose a real time Acoustic Anomaly Detection (AAD) system with the use of sequence‐to‐sequence Autoencoder (AE) models in the industrial environments. The proposed processing pipeline makes use of the audio features extracted from the streaming audio signal captured by a single‐channel microphone. The reconstruction error generated by the AE model is calculated to measure the degree of abnormality of the sound event. The performance of Convolutional Long Short‐Term Memory AE (Conv‐LSTMAE) is evaluated and compared with sequential Convolutional AE (CAE) using sounds captured from various industrial manufacturing processes. In the experiments conducted with the real time AAD system, it is shown that the Conv‐LSTMAE‐based AAD demonstrates better detection performance than CAE model‐based AAD under different signal‐to‐noise ratio conditions of sound events such as explosion, fire and glass breaking.
Architecture-Centric Evaluation of Blockchain-Based Smart Contract E-Voting for National Elections

E-voting is one of the valid use cases of blockchain technology with many blockchain e-voting systems already proposed. But efforts that focus on critical analysis of blockchain e-voting architectures for national elections from stakeholders’ perspectives are mostly lacking in the literature. Therefore, government decision-makers and election stakeholders do not yet have a sufficient basis to understand the potential risks, challenges, and prospects that are associated with blockchain e-voting. This paper demonstrates how the use of the Architecture Trade-off Analysis Method (ATAM) can enable stakeholders in national elections to understand the risks, prospects, and challenges that could be associated with a blockchain e-voting system for national elections. By using a study context of South Africa, a proposed blockchain e-voting architecture was used as a basis to aid election stakeholders to reason on the concept of blockchain e-voting to get them to understand the potential risks, security threats, critical requirements attributes, and weaknesses that could be associated with using blockchain e-voting for national elections. The study found that blockchain e-voting can prevent many security attacks, internal vote manipulation, and promote transparency. However, voter validation and the security of the blockchain architecture are potential weaknesses that will need significant attention.